import warnings
from typing import Any, Callable, Dict, Optional, Sequence

from llama_index.legacy.bridge.pydantic import Field, PrivateAttr
from llama_index.legacy.callbacks import CallbackManager
from llama_index.legacy.core.llms.types import (
    ChatMessage,
    ChatResponse,
    ChatResponseAsyncGen,
    ChatResponseGen,
    CompletionResponse,
    CompletionResponseAsyncGen,
    CompletionResponseGen,
    LLMMetadata,
    MessageRole,
)
from llama_index.legacy.llms.base import (
    llm_chat_callback,
    llm_completion_callback,
)
from llama_index.legacy.llms.cohere_utils import (
    CHAT_MODELS,
    acompletion_with_retry,
    cohere_modelname_to_contextsize,
    completion_with_retry,
    messages_to_cohere_history,
)
from llama_index.legacy.llms.llm import LLM
from llama_index.legacy.types import BaseOutputParser, PydanticProgramMode


class Cohere(LLM):
    model: str = Field(description="The cohere model to use.")
    temperature: float = Field(description="The temperature to use for sampling.")
    max_retries: int = Field(
        default=10, description="The maximum number of API retries."
    )
    additional_kwargs: Dict[str, Any] = Field(
        default_factory=dict, description="Additional kwargs for the Cohere API."
    )
    max_tokens: int = Field(description="The maximum number of tokens to generate.")

    _client: Any = PrivateAttr()
    _aclient: Any = PrivateAttr()

    def __init__(
        self,
        model: str = "command",
        temperature: float = 0.5,
        max_tokens: int = 512,
        timeout: Optional[float] = None,
        max_retries: int = 10,
        api_key: Optional[str] = None,
        additional_kwargs: Optional[Dict[str, Any]] = None,
        callback_manager: Optional[CallbackManager] = None,
        system_prompt: Optional[str] = None,
        messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
        completion_to_prompt: Optional[Callable[[str], str]] = None,
        pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
        output_parser: Optional[BaseOutputParser] = None,
    ) -> None:
        try:
            import cohere
        except ImportError as e:
            raise ImportError(
                "You must install the `cohere` package to use Cohere."
                "Please `pip install cohere`"
            ) from e
        additional_kwargs = additional_kwargs or {}
        callback_manager = callback_manager or CallbackManager([])

        self._client = cohere.Client(api_key, client_name="llama_index")
        self._aclient = cohere.AsyncClient(api_key, client_name="llama_index")

        super().__init__(
            temperature=temperature,
            additional_kwargs=additional_kwargs,
            timeout=timeout,
            max_retries=max_retries,
            model=model,
            callback_manager=callback_manager,
            max_tokens=max_tokens,
            system_prompt=system_prompt,
            messages_to_prompt=messages_to_prompt,
            completion_to_prompt=completion_to_prompt,
            pydantic_program_mode=pydantic_program_mode,
            output_parser=output_parser,
        )

    @classmethod
    def class_name(cls) -> str:
        """Get class name."""
        return "Cohere_LLM"

    @property
    def metadata(self) -> LLMMetadata:
        return LLMMetadata(
            context_window=cohere_modelname_to_contextsize(self.model),
            num_output=self.max_tokens,
            is_chat_model=True,
            model_name=self.model,
            system_role=MessageRole.CHATBOT,
        )

    @property
    def _model_kwargs(self) -> Dict[str, Any]:
        base_kwargs = {
            "model": self.model,
            "temperature": self.temperature,
        }
        return {
            **base_kwargs,
            **self.additional_kwargs,
        }

    def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
        return {
            **self._model_kwargs,
            **kwargs,
        }

    @llm_chat_callback()
    def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
        history = messages_to_cohere_history(messages[:-1])
        prompt = messages[-1].content
        all_kwargs = self._get_all_kwargs(**kwargs)
        if all_kwargs["model"] not in CHAT_MODELS:
            raise ValueError(f"{all_kwargs['model']} not supported for chat")

        if "stream" in all_kwargs:
            warnings.warn(
                "Parameter `stream` is not supported by the `chat` method."
                "Use the `stream_chat` method instead"
            )
        response = completion_with_retry(
            client=self._client,
            max_retries=self.max_retries,
            chat=True,
            message=prompt,
            chat_history=history,
            **all_kwargs,
        )
        return ChatResponse(
            message=ChatMessage(role=MessageRole.ASSISTANT, content=response.text),
            raw=response.__dict__,
        )

    @llm_completion_callback()
    def complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponse:
        all_kwargs = self._get_all_kwargs(**kwargs)
        if "stream" in all_kwargs:
            warnings.warn(
                "Parameter `stream` is not supported by the `chat` method."
                "Use the `stream_chat` method instead"
            )

        response = completion_with_retry(
            client=self._client,
            max_retries=self.max_retries,
            chat=False,
            prompt=prompt,
            **all_kwargs,
        )

        return CompletionResponse(
            text=response.generations[0].text,
            raw=response.__dict__,
        )

    @llm_chat_callback()
    def stream_chat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponseGen:
        history = messages_to_cohere_history(messages[:-1])
        prompt = messages[-1].content
        all_kwargs = self._get_all_kwargs(**kwargs)
        all_kwargs["stream"] = True
        if all_kwargs["model"] not in CHAT_MODELS:
            raise ValueError(f"{all_kwargs['model']} not supported for chat")
        response = completion_with_retry(
            client=self._client,
            max_retries=self.max_retries,
            chat=True,
            message=prompt,
            chat_history=history,
            **all_kwargs,
        )

        def gen() -> ChatResponseGen:
            content = ""
            role = MessageRole.ASSISTANT
            for r in response:
                if "text" in r.__dict__:
                    content_delta = r.text
                else:
                    content_delta = ""
                content += content_delta
                yield ChatResponse(
                    message=ChatMessage(role=role, content=content),
                    delta=content_delta,
                    raw=r.__dict__,
                )

        return gen()

    @llm_completion_callback()
    def stream_complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponseGen:
        all_kwargs = self._get_all_kwargs(**kwargs)
        all_kwargs["stream"] = True

        response = completion_with_retry(
            client=self._client,
            max_retries=self.max_retries,
            chat=False,
            prompt=prompt,
            **all_kwargs,
        )

        def gen() -> CompletionResponseGen:
            content = ""
            for r in response:
                content_delta = r.text
                content += content_delta
                yield CompletionResponse(
                    text=content, delta=content_delta, raw=r._asdict()
                )

        return gen()

    @llm_chat_callback()
    async def achat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponse:
        history = messages_to_cohere_history(messages[:-1])
        prompt = messages[-1].content
        all_kwargs = self._get_all_kwargs(**kwargs)
        if all_kwargs["model"] not in CHAT_MODELS:
            raise ValueError(f"{all_kwargs['model']} not supported for chat")
        if "stream" in all_kwargs:
            warnings.warn(
                "Parameter `stream` is not supported by the `chat` method."
                "Use the `stream_chat` method instead"
            )

        response = await acompletion_with_retry(
            aclient=self._aclient,
            max_retries=self.max_retries,
            chat=True,
            message=prompt,
            chat_history=history,
            **all_kwargs,
        )

        return ChatResponse(
            message=ChatMessage(role=MessageRole.ASSISTANT, content=response.text),
            raw=response.__dict__,
        )

    @llm_completion_callback()
    async def acomplete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponse:
        all_kwargs = self._get_all_kwargs(**kwargs)
        if "stream" in all_kwargs:
            warnings.warn(
                "Parameter `stream` is not supported by the `chat` method."
                "Use the `stream_chat` method instead"
            )

        response = await acompletion_with_retry(
            aclient=self._aclient,
            max_retries=self.max_retries,
            chat=False,
            prompt=prompt,
            **all_kwargs,
        )

        return CompletionResponse(
            text=response.generations[0].text,
            raw=response.__dict__,
        )

    @llm_chat_callback()
    async def astream_chat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponseAsyncGen:
        history = messages_to_cohere_history(messages[:-1])
        prompt = messages[-1].content
        all_kwargs = self._get_all_kwargs(**kwargs)
        all_kwargs["stream"] = True
        if all_kwargs["model"] not in CHAT_MODELS:
            raise ValueError(f"{all_kwargs['model']} not supported for chat")
        response = await acompletion_with_retry(
            aclient=self._aclient,
            max_retries=self.max_retries,
            chat=True,
            message=prompt,
            chat_history=history,
            **all_kwargs,
        )

        async def gen() -> ChatResponseAsyncGen:
            content = ""
            role = MessageRole.ASSISTANT
            async for r in response:
                if "text" in r.__dict__:
                    content_delta = r.text
                else:
                    content_delta = ""
                content += content_delta
                yield ChatResponse(
                    message=ChatMessage(role=role, content=content),
                    delta=content_delta,
                    raw=r.__dict__,
                )

        return gen()

    @llm_completion_callback()
    async def astream_complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponseAsyncGen:
        all_kwargs = self._get_all_kwargs(**kwargs)
        all_kwargs["stream"] = True

        response = await acompletion_with_retry(
            aclient=self._aclient,
            max_retries=self.max_retries,
            chat=False,
            prompt=prompt,
            **all_kwargs,
        )

        async def gen() -> CompletionResponseAsyncGen:
            content = ""
            async for r in response:
                content_delta = r.text
                content += content_delta
                yield CompletionResponse(
                    text=content, delta=content_delta, raw=r._asdict()
                )

        return gen()