from __future__ import annotations as _annotations

import os

import httpx
from openai import AsyncOpenAI

from pydantic_ai import ModelProfile
from pydantic_ai.exceptions import UserError
from pydantic_ai.models import cached_async_http_client
from pydantic_ai.profiles.cohere import cohere_model_profile
from pydantic_ai.profiles.deepseek import deepseek_model_profile
from pydantic_ai.profiles.google import google_model_profile
from pydantic_ai.profiles.harmony import harmony_model_profile
from pydantic_ai.profiles.meta import meta_model_profile
from pydantic_ai.profiles.mistral import mistral_model_profile
from pydantic_ai.profiles.openai import OpenAIJsonSchemaTransformer, OpenAIModelProfile
from pydantic_ai.profiles.qwen import qwen_model_profile
from pydantic_ai.providers import Provider

try:
    from openai import AsyncOpenAI
except ImportError as _import_error:  # pragma: no cover
    raise ImportError(
        'Please install the `openai` package to use the Ollama provider, '
        'you can use the `openai` optional group — `pip install "pydantic-ai-slim[openai]"`'
    ) from _import_error


class OllamaProvider(Provider[AsyncOpenAI]):
    """Provider for local or remote Ollama API."""

    @property
    def name(self) -> str:
        return 'ollama'

    @property
    def base_url(self) -> str:
        return str(self.client.base_url)

    @property
    def client(self) -> AsyncOpenAI:
        return self._client

    def model_profile(self, model_name: str) -> ModelProfile | None:
        prefix_to_profile = {
            'llama': meta_model_profile,
            'gemma': google_model_profile,
            'qwen': qwen_model_profile,
            'qwq': qwen_model_profile,
            'deepseek': deepseek_model_profile,
            'mistral': mistral_model_profile,
            'command': cohere_model_profile,
            'gpt-oss': harmony_model_profile,
        }

        profile = None
        for prefix, profile_func in prefix_to_profile.items():
            model_name = model_name.lower()
            if model_name.startswith(prefix):
                profile = profile_func(model_name)

        # As OllamaProvider is always used with OpenAIChatModel, which used to unconditionally use OpenAIJsonSchemaTransformer,
        # we need to maintain that behavior unless json_schema_transformer is set explicitly
        return OpenAIModelProfile(json_schema_transformer=OpenAIJsonSchemaTransformer).update(profile)

    def __init__(
        self,
        base_url: str | None = None,
        api_key: str | None = None,
        openai_client: AsyncOpenAI | None = None,
        http_client: httpx.AsyncClient | None = None,
    ) -> None:
        """Create a new Ollama provider.

        Args:
            base_url: The base url for the Ollama requests. If not provided, the `OLLAMA_BASE_URL` environment variable
                will be used if available.
            api_key: The API key to use for authentication, if not provided, the `OLLAMA_API_KEY` environment variable
                will be used if available.
            openai_client: An existing
                [`AsyncOpenAI`](https://github.com/openai/openai-python?tab=readme-ov-file#async-usage)
                client to use. If provided, `base_url`, `api_key`, and `http_client` must be `None`.
            http_client: An existing `httpx.AsyncClient` to use for making HTTP requests.
        """
        if openai_client is not None:
            assert base_url is None, 'Cannot provide both `openai_client` and `base_url`'
            assert http_client is None, 'Cannot provide both `openai_client` and `http_client`'
            assert api_key is None, 'Cannot provide both `openai_client` and `api_key`'
            self._client = openai_client
        else:
            base_url = base_url or os.getenv('OLLAMA_BASE_URL')
            if not base_url:
                raise UserError(
                    'Set the `OLLAMA_BASE_URL` environment variable or pass it via `OllamaProvider(base_url=...)`'
                    'to use the Ollama provider.'
                )

            # This is a workaround for the OpenAI client requiring an API key, whilst locally served,
            # openai compatible models do not always need an API key, but a placeholder (non-empty) key is required.
            api_key = api_key or os.getenv('OLLAMA_API_KEY') or 'api-key-not-set'

            if http_client is not None:
                self._client = AsyncOpenAI(base_url=base_url, api_key=api_key, http_client=http_client)
            else:
                http_client = cached_async_http_client(provider='ollama')
                self._client = AsyncOpenAI(base_url=base_url, api_key=api_key, http_client=http_client)