"""Tests for litellm/llms/azure/azure.py AzureChatCompletion handler behaviour."""

import asyncio
import json
import os
import time
from collections.abc import Mapping
from typing import Final
from unittest.mock import ANY, MagicMock, patch

import httpx
import pytest
import respx
from httpx import Client, Headers
from openai import AsyncAzureOpenAI, AzureOpenAI

import litellm
from litellm import completion
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.azure.azure import AzureChatCompletion
from litellm.llms.azure.common_utils import process_azure_headers


class _FakeRawResponse:
    headers: Final = {"x-ms-is-spilled-over": "true"}

    def parse(self):
        return iter(())


class _FakeRawCompletions:
    def create(self, **kwargs):
        return _FakeRawResponse()


def test_sync_streaming_stamps_response_headers_on_the_logging_obj() -> None:
    """Sync streaming must mirror async_streaming and record the provider response
    headers on model_call_details, or downstream consumers (spillover-aware cost
    calculation) cannot see them."""
    client = AzureOpenAI(api_key="fake", api_version="2024-02-01", azure_endpoint="https://fake.openai.azure.com")
    client.chat.completions.with_raw_response = _FakeRawCompletions()

    logging_obj = LiteLLMLoggingObj(
        model="azure/gpt-4o-spill-test",
        messages=[{"role": "user", "content": "Hi"}],
        stream=True,
        call_type="completion",
        start_time=time.time(),
        litellm_call_id="spill-sync-1",
        function_id="f",
    )

    AzureChatCompletion().streaming(
        logging_obj=logging_obj,
        api_base="https://fake.openai.azure.com",
        api_key="fake",
        api_version="2024-02-01",
        dynamic_params=False,
        data={"messages": [{"role": "user", "content": "Hi"}], "stream": True},
        model="gpt-4o-spill-test",
        timeout=30.0,
        max_retries=0,
        client=client,
    )

    assert logging_obj.model_call_details["response_headers"] == {"x-ms-is-spilled-over": "true"}


class _CancelledRawCompletions:
    async def create(self, **kwargs):
        raise asyncio.CancelledError()


@pytest.mark.asyncio
async def test_acompletion_propagates_cancelled_error() -> None:
    client = AsyncAzureOpenAI(
        api_key="fake-key",
        api_version="2024-02-01",
        azure_endpoint="https://fake-resource.openai.azure.com",
    )
    client.chat.completions.with_raw_response = _CancelledRawCompletions()

    with pytest.raises(asyncio.CancelledError):
        await litellm.acompletion(
            model="azure/fake-deployment",
            messages=[{"role": "user", "content": "hi"}],
            client=client,
        )


def test_process_azure_headers_empty():
    result = process_azure_headers({})
    assert result == {}, "Expected empty dictionary for no input"


def test_process_azure_headers_with_all_headers():
    input_headers = Headers(
        {
            "x-ratelimit-limit-requests": "100",
            "x-ratelimit-remaining-requests": "90",
            "x-ratelimit-limit-tokens": "10000",
            "x-ratelimit-remaining-tokens": "9000",
            "other-header": "value",
        }
    )

    expected_output = {
        "x-ratelimit-limit-requests": "100",
        "x-ratelimit-remaining-requests": "90",
        "x-ratelimit-limit-tokens": "10000",
        "x-ratelimit-remaining-tokens": "9000",
        "llm_provider-x-ratelimit-limit-requests": "100",
        "llm_provider-x-ratelimit-remaining-requests": "90",
        "llm_provider-x-ratelimit-limit-tokens": "10000",
        "llm_provider-x-ratelimit-remaining-tokens": "9000",
        "llm_provider-other-header": "value",
    }

    result = process_azure_headers(input_headers)
    assert result == expected_output, "Unexpected output for all Azure headers"


def test_process_azure_headers_with_partial_headers():
    input_headers = Headers(
        {
            "x-ratelimit-limit-requests": "100",
            "x-ratelimit-remaining-tokens": "9000",
            "other-header": "value",
        }
    )

    expected_output = {
        "x-ratelimit-limit-requests": "100",
        "x-ratelimit-remaining-tokens": "9000",
        "llm_provider-x-ratelimit-limit-requests": "100",
        "llm_provider-x-ratelimit-remaining-tokens": "9000",
        "llm_provider-other-header": "value",
    }

    result = process_azure_headers(input_headers)
    assert result == expected_output, "Unexpected output for partial Azure headers"


def test_process_azure_headers_with_no_matching_headers():
    input_headers = Headers({"unrelated-header-1": "value1", "unrelated-header-2": "value2"})

    expected_output = {
        "llm_provider-unrelated-header-1": "value1",
        "llm_provider-unrelated-header-2": "value2",
    }

    result = process_azure_headers(input_headers)
    assert result == expected_output, "Unexpected output for non-matching headers"


def test_process_azure_headers_with_dict_input():
    input_headers = {
        "x-ratelimit-limit-requests": "100",
        "x-ratelimit-remaining-requests": "90",
        "other-header": "value",
    }

    expected_output = {
        "x-ratelimit-limit-requests": "100",
        "x-ratelimit-remaining-requests": "90",
        "llm_provider-x-ratelimit-limit-requests": "100",
        "llm_provider-x-ratelimit-remaining-requests": "90",
        "llm_provider-other-header": "value",
    }

    result = process_azure_headers(input_headers)
    assert result == expected_output, "Unexpected output for dict input"


@pytest.mark.parametrize(
    "input, call_type",
    [
        ({"messages": [{"role": "user", "content": "Hello world"}]}, "completion"),
        ({"input": "Hello world"}, "embedding"),
        ({"prompt": "Hello world"}, "image_generation"),
    ],
)
@pytest.mark.parametrize(
    "header_value",
    [
        "headers",
        "extra_headers",
    ],
)
def test_azure_extra_headers(input, call_type, header_value):
    from litellm import embedding, image_generation

    # Clear the LLM clients cache to ensure the new http_client is used
    litellm.in_memory_llm_clients_cache.flush_cache()

    http_client = Client()

    messages = [{"role": "user", "content": "Hello world"}]
    with patch.object(http_client, "send", new=MagicMock()) as mock_client:
        litellm.client_session = http_client
        try:
            if call_type == "completion":
                func = completion
            elif call_type == "embedding":
                func = embedding
            elif call_type == "image_generation":
                func = image_generation

            data = {
                "model": "azure/gpt-4.1-mini",
                "api_base": "https://openai-gpt-4-test-v-1.openai.azure.com",
                "api_version": "2023-07-01-preview",
                "api_key": "my-azure-api-key",
                header_value: {
                    "Authorization": "my-bad-key",
                    "Ocp-Apim-Subscription-Key": "hello-world-testing",
                },
                **input,
            }
            response = func(**data)
            print(response)

        except Exception as e:
            print(e)

        mock_client.assert_called()

        print(f"mock_client.call_args: {mock_client.call_args}")
        request = mock_client.call_args[0][0]
        print(request.method)  # This will print 'POST'
        print(request.url)  # This will print the full URL
        print(request.headers)  # This will print the full URL
        auth_header = request.headers.get("Authorization")
        apim_key = request.headers.get("Ocp-Apim-Subscription-Key")
        print(auth_header)
        assert auth_header == "my-bad-key"
        assert apim_key == "hello-world-testing"


@pytest.mark.parametrize(
    "api_base, model, expected_endpoint",
    [
        (
            "https://fake-azure-endpoint.invalid",
            "dall-e-3-test",
            "https://fake-azure-endpoint.invalid/openai/deployments/dall-e-3-test/images/generations?api-version=2023-12-01-preview",
        ),
        (
            "https://fake-azure-endpoint.invalid/openai/deployments/my-custom-deployment",
            "dall-e-3",
            "https://fake-azure-endpoint.invalid/openai/deployments/my-custom-deployment/images/generations?api-version=2023-12-01-preview",
        ),
    ],
)
def test_process_azure_endpoint_url(api_base, model, expected_endpoint):
    from litellm.llms.azure.azure import AzureChatCompletion

    azure_chat_completion = AzureChatCompletion()
    input_args = {
        "azure_client_params": {
            "api_version": "2023-12-01-preview",
            "azure_endpoint": api_base,
            "azure_deployment": model,
            "max_retries": 2,
            "timeout": 600,
            "api_key": "sk-test-mock-key-505",
        },
        "model": model,
    }
    result = azure_chat_completion.create_azure_base_url(**input_args)
    assert result == expected_endpoint, "Unexpected endpoint"


@patch("azure.identity.UsernamePasswordCredential")
@patch("azure.identity.get_bearer_token_provider")
def test_get_azure_ad_token_from_username_password(mock_get_bearer_token_provider, mock_credential):
    from litellm.llms.azure.common_utils import (
        get_azure_ad_token_from_username_password,
    )

    client_id = "test-client-id"
    username = "test-username"
    password = "test-password"

    mock_token_provider = lambda: "mock-token"
    mock_get_bearer_token_provider.return_value = mock_token_provider

    result = get_azure_ad_token_from_username_password(
        client_id=client_id, azure_username=username, azure_password=password
    )

    mock_credential.assert_called_once_with(client_id=client_id, username=username, password=password)

    mock_get_bearer_token_provider.assert_called_once_with(ANY, "https://cognitiveservices.azure.com/.default")

    assert result() == "mock-token"


def test_azure_openai_gpt_4o_naming(monkeypatch):
    from pydantic import BaseModel, Field

    monkeypatch.setenv("AZURE_API_VERSION", "2024-10-21")

    client = AzureOpenAI(
        api_key="test-api-key",
        base_url="https://fake-azure-endpoint.invalid",
        api_version="2023-12-01-preview",
    )

    class ResponseFormat(BaseModel):
        number: str = Field(description="total number of days in a week")
        days: list[str] = Field(description="name of days in a week")

    with patch.object(client, "post") as mock_post:
        try:
            completion(
                model="azure/gpt4o",
                messages=[{"role": "user", "content": "Hello world"}],
                response_format=ResponseFormat,
                client=client,
            )
        except Exception as e:
            print(e)

        mock_post.assert_called_once()

        print(mock_post.call_args.kwargs["body"])

        assert "tool_calls" not in mock_post.call_args.kwargs["body"]


@pytest.mark.parametrize(
    "api_version",
    [
        "2024-10-21",
        # "2024-02-15-preview",
    ],
)
def test_azure_gpt_4o_with_tool_call_and_response_format(api_version):
    from litellm import completion
    from typing import Optional
    from pydantic import BaseModel
    import litellm


    client = AzureOpenAI(
        api_key="fake-key",
        base_url="https://fake-azure.openai.azure.com",
        api_version=api_version,
    )

    class InvestigationOutput(BaseModel):
        alert_explanation: Optional[str] = None
        investigation: Optional[str] = None
        conclusions_and_possible_root_causes: Optional[str] = None
        next_steps: Optional[str] = None
        related_logs: Optional[str] = None
        app_or_infra: Optional[str] = None
        external_links: Optional[str] = None

    tools = [
        {
            "type": "function",
            "function": {
                "name": "get_current_time",
                "description": "Returns the current date and time",
                "strict": True,
                "parameters": {
                    "properties": {
                        "timezone": {
                            "type": "string",
                            "description": "The timezone to get the current time for (e.g., 'UTC', 'America/New_York')",
                        }
                    },
                    "required": ["timezone"],
                    "type": "object",
                    "additionalProperties": False,
                },
            },
        }
    ]

    with patch.object(client, "post") as mock_post:
        mock_post.return_value.headers = {}
        mock_post.return_value.parse.return_value = litellm.ModelResponse(
            choices=[{"message": {"role": "assistant", "content": InvestigationOutput().model_dump_json()}}]
        )
        response = litellm.completion(
            model="azure/gpt-4.1-mini",
            messages=[
                {
                    "role": "system",
                    "content": "You are a tool-calling AI assist provided with common devops and IT tools that you can use to troubleshoot problems or answer questions.\nWhenever possible you MUST first use tools to investigate then answer the question.",
                },
                {
                    "role": "user",
                    "content": "What is the current date and time in NYC?",
                },
            ],
            drop_params=True,
            temperature=0.00000001,
            tools=tools,
            tool_choice="auto",
            response_format=InvestigationOutput,  # commenting this line will cause the output to be correct
            api_version=api_version,
            client=client,
        )

        mock_post.assert_called_once()

        if api_version == "2024-10-21":
            assert "response_format" in mock_post.call_args.kwargs["body"]
        else:
            assert "response_format" not in mock_post.call_args.kwargs["body"]
        assert response.choices[0].message.content == InvestigationOutput().model_dump_json()


def test_map_openai_params():
    """
    Ensure response_format does not override tools
    """
    from litellm.llms.azure.chat.gpt_transformation import AzureOpenAIConfig

    azure_openai_config = AzureOpenAIConfig()
    tools = [
        {
            "type": "function",
            "function": {
                "name": "get_current_time",
                "description": "Returns the current date and time",
                "strict": True,
                "parameters": {
                    "properties": {
                        "timezone": {
                            "type": "string",
                            "description": "The timezone to get the current time for (e.g., 'UTC', 'America/New_York')",
                        }
                    },
                    "required": ["timezone"],
                    "type": "object",
                    "additionalProperties": False,
                },
            },
        }
    ]
    received_args = {
        "non_default_params": {
            "temperature": 1e-08,
            "response_format": {
                "type": "json_schema",
                "json_schema": {
                    "schema": {
                        "properties": {
                            "alert_explanation": {
                                "anyOf": [{"type": "string"}, {"type": "null"}],
                                "title": "Alert Explanation",
                            },
                            "investigation": {
                                "anyOf": [{"type": "string"}, {"type": "null"}],
                                "title": "Investigation",
                            },
                            "conclusions_and_possible_root_causes": {
                                "anyOf": [{"type": "string"}, {"type": "null"}],
                                "title": "Conclusions And Possible Root Causes",
                            },
                            "next_steps": {
                                "anyOf": [{"type": "string"}, {"type": "null"}],
                                "title": "Next Steps",
                            },
                            "related_logs": {
                                "anyOf": [{"type": "string"}, {"type": "null"}],
                                "title": "Related Logs",
                            },
                            "app_or_infra": {
                                "anyOf": [{"type": "string"}, {"type": "null"}],
                                "title": "App Or Infra",
                            },
                            "external_links": {
                                "anyOf": [{"type": "string"}, {"type": "null"}],
                                "title": "External Links",
                            },
                        },
                        "title": "InvestigationOutput",
                        "type": "object",
                        "additionalProperties": False,
                        "required": [
                            "alert_explanation",
                            "investigation",
                            "conclusions_and_possible_root_causes",
                            "next_steps",
                            "related_logs",
                            "app_or_infra",
                            "external_links",
                        ],
                    },
                    "name": "InvestigationOutput",
                    "strict": True,
                },
            },
            "tools": tools,
            "tool_choice": "auto",
        },
        "optional_params": {},
        "model": "gpt-4o",
        "drop_params": True,
        "api_version": "2024-02-15-preview",
    }
    optional_params = azure_openai_config.map_openai_params(**received_args)
    assert "tools" in optional_params
    assert len(optional_params["tools"]) > 1


@pytest.mark.usefixtures("fake_provider_credentials")
@pytest.mark.parametrize("max_retries", [0, 4])
@pytest.mark.parametrize("stream", [True, False])
@patch(
    "litellm.main.azure_chat_completions.make_sync_azure_openai_chat_completion_request"
)
def test_azure_max_retries_0(
    mock_make_sync_azure_openai_chat_completion_request, max_retries, stream
):
    import litellm
    from litellm import completion

    # Clear the LLM clients cache to ensure max_retries is set correctly
    litellm.in_memory_llm_clients_cache.flush_cache()

    try:
        completion(
            model="azure/gpt-4.1-mini",
            messages=[{"role": "user", "content": "Hello world"}],
            max_retries=max_retries,
            stream=stream,
        )
    except Exception as e:
        print(e)

    mock_make_sync_azure_openai_chat_completion_request.assert_called_once()
    assert (
        mock_make_sync_azure_openai_chat_completion_request.call_args.kwargs[
            "azure_client"
        ].max_retries
        == max_retries
    )


@pytest.mark.usefixtures("fake_provider_credentials")
@pytest.mark.parametrize("max_retries", [0, 4])
@pytest.mark.parametrize("stream", [True, False])
@patch("litellm.main.azure_chat_completions.make_azure_openai_chat_completion_request")
@pytest.mark.asyncio
async def test_async_azure_max_retries_0(
    make_azure_openai_chat_completion_request, max_retries, stream
):
    import litellm
    from litellm import acompletion

    # Clear the LLM clients cache to ensure max_retries is set correctly
    litellm.in_memory_llm_clients_cache.flush_cache()

    try:
        await acompletion(
            model="azure/gpt-4.1-mini",
            messages=[{"role": "user", "content": "Hello world"}],
            max_retries=max_retries,
            stream=stream,
        )
    except Exception as e:
        print(e)

    make_azure_openai_chat_completion_request.assert_called_once()
    assert (
        make_azure_openai_chat_completion_request.call_args.kwargs[
            "azure_client"
        ].max_retries
        == max_retries
    )


def test_azure_openai_responses_bridge():
    from litellm import completion
    import litellm

    litellm.turn_on_debug()

    with patch.object(litellm, "responses") as mock_responses:
        try:
            response = completion(
                model="azure/responses/test-azure-computer-use-preview",
                messages=[{"role": "user", "content": "Hello world"}],
                api_base=os.getenv("AZURE_COMPUTER_USE_API_BASE"),
                api_version="2025-04-01-preview",
                api_key=os.getenv("AZURE_COMPUTER_USE_API_KEY"),
            )
        except Exception as e:
            print(e)

        mock_responses.assert_called_once()
        assert (
            mock_responses.call_args.kwargs["model"]
            == "azure/test-azure-computer-use-preview"
        )
        assert mock_responses.call_args.kwargs["custom_llm_provider"] == "azure"


def test_azure_with_content_safety_error():
    """
    Verify user can access innererror from the Azure OpenAI exception
    """
    from litellm import completion
    from litellm.exceptions import ContentPolicyViolationError
    from litellm.litellm_core_utils.exception_mapping_utils import exception_type
    from unittest.mock import MagicMock

    mock_exception = Exception(
        "The response was filtered due to the prompt triggering Azure OpenAI's content management policy"
    )
    mock_exception.body = {
        "innererror": {
            "code": "ResponsibleAIPolicyViolation",
            "content_filter_result": {
                "hate": {"filtered": False, "severity": "safe"},
                "jailbreak": {"filtered": False, "detected": False},
                "self_harm": {"filtered": False, "severity": "safe"},
                "sexual": {"filtered": False, "severity": "safe"},
                "violence": {"filtered": True, "severity": "high"},
            },
        }
    }

    mock_response = MagicMock()
    mock_response.status_code = 400
    mock_exception.response = mock_response

    with pytest.raises(ContentPolicyViolationError) as exc_info:
        exception_type(
            model="azure/gpt-4o-new-test",
            original_exception=mock_exception,
            custom_llm_provider="azure",
        )

    e = exc_info.value
    print("got exception=", e)
    assert e.provider_specific_fields is not None
    print("got provider_specific_fields=", e.provider_specific_fields)
    assert e.provider_specific_fields.get("innererror") is not None
    assert (
        e.provider_specific_fields["innererror"]["code"]
        == "ResponsibleAIPolicyViolation"
    )
    assert (
        e.provider_specific_fields["innererror"]["content_filter_result"]["violence"][
            "filtered"
        ]
        is True
    )
    assert (
        e.provider_specific_fields["innererror"]["content_filter_result"]["violence"][
            "severity"
        ]
        == "high"
    )


def _azure_sse_body(chunks: tuple[Mapping[str, object], ...]) -> str:
    return "".join(f"data: {json.dumps(chunk)}\n\n" for chunk in chunks) + "data: [DONE]\n\n"


@respx.mock
def test_azure_chat_stream_preserves_text_and_special_characters():
    response_chunks: Final = (
        {
            "id": "chatcmpl-azure",
            "object": "chat.completion.chunk",
            "created": 1,
            "model": "gpt-4.1-mini",
            "choices": [{"index": 0, "delta": {"content": "<xml>café ☃</xml>"}, "finish_reason": None}],
        },
        {
            "id": "chatcmpl-azure",
            "object": "chat.completion.chunk",
            "created": 1,
            "model": "gpt-4.1-mini",
            "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}],
        },
    )
    route: Final = respx.post(
        url__regex=r"https://azure-stream\.openai\.azure\.com/.*/chat/completions.*"
    ).mock(
        return_value=httpx.Response(
            200,
            text=_azure_sse_body(response_chunks),
            headers={"content-type": "text/event-stream"},
        )
    )

    stream: Final = litellm.completion(
        model="azure/gpt-4.1-mini",
        messages=[{"role": "user", "content": "Respond with the <xml> tag only"}],
        api_base="https://azure-stream.openai.azure.com",
        api_version="2024-02-15-preview",
        api_key="test-azure-key",
        max_retries=0,
        stream=True,
    )
    chunks: Final = list(stream)

    assert route.call_count == 1
    assert [chunk._hidden_params["custom_llm_provider"] for chunk in chunks] == ["azure"] * len(chunks)
    assert "".join(chunk.choices[0].delta.content or "" for chunk in chunks) == "<xml>café ☃</xml>"
    assert [chunk.choices[0].finish_reason for chunk in chunks if chunk.choices[0].finish_reason] == ["stop"]


@respx.mock
def test_azure_chat_stream_reassembles_tool_call_arguments():
    response_chunks: Final = (
        {
            "id": "chatcmpl-azure-tools",
            "object": "chat.completion.chunk",
            "created": 1,
            "model": "gpt-4.1-mini",
            "choices": [
                {
                    "index": 0,
                    "delta": {
                        "tool_calls": [
                            {
                                "index": 0,
                                "id": "call-weather",
                                "type": "function",
                                "function": {"name": "get_current_weather", "arguments": '{"city":'},
                            }
                        ]
                    },
                    "finish_reason": None,
                }
            ],
        },
        {
            "id": "chatcmpl-azure-tools",
            "object": "chat.completion.chunk",
            "created": 1,
            "model": "gpt-4.1-mini",
            "choices": [
                {
                    "index": 0,
                    "delta": {"tool_calls": [{"index": 0, "function": {"arguments": '"Boston"}'}}]},
                    "finish_reason": "tool_calls",
                }
            ],
        },
    )
    route: Final = respx.post(
        url__regex=r"https://azure-tool-stream\.openai\.azure\.com/.*/chat/completions.*"
    ).mock(
        return_value=httpx.Response(
            200,
            text=_azure_sse_body(response_chunks),
            headers={"content-type": "text/event-stream"},
        )
    )
    messages: Final = [{"role": "user", "content": "What is the weather in Boston?"}]
    stream: Final = litellm.completion(
        model="azure/gpt-4.1-mini",
        messages=messages,
        tools=[
            {
                "type": "function",
                "function": {
                    "name": "get_current_weather",
                    "parameters": {
                        "type": "object",
                        "properties": {"city": {"type": "string"}},
                        "required": ["city"],
                    },
                },
            }
        ],
        tool_choice="auto",
        api_base="https://azure-tool-stream.openai.azure.com",
        api_version="2024-02-15-preview",
        api_key="test-azure-key",
        max_retries=0,
        stream=True,
    )
    chunks: Final = list(stream)
    response: Final = litellm.stream_chunk_builder(chunks, messages=messages)
    tool_call: Final = response.choices[0].message.tool_calls[0]

    assert route.call_count == 1
    assert tool_call.function.name == "get_current_weather"
    assert json.loads(tool_call.function.arguments) == {"city": "Boston"}
    assert response.choices[0].finish_reason == "tool_calls"