import json
import time
import uuid
import json
import time
import uuid
from enum import Enum
from typing import Dict, List, Literal, Optional, Tuple, Union

from typing import Dict, List, Literal, Optional, Tuple, Union

from openai._models import BaseModel as OpenAIObject
from pydantic import ConfigDict
from typing_extensions import Dict, Required, TypedDict, override

from ..litellm_core_utils.core_helpers import map_finish_reason
from .llms.openai import ChatCompletionToolCallChunk, ChatCompletionUsageBlock
from typing_extensions import Dict, Required, TypedDict, override

from ..litellm_core_utils.core_helpers import map_finish_reason
from .llms.openai import ChatCompletionToolCallChunk, ChatCompletionUsageBlock


def _generate_id():  # private helper function
    return "chatcmpl-" + str(uuid.uuid4())


class LiteLLMCommonStrings(Enum):
    redacted_by_litellm = "redacted by litellm. 'litellm.turn_off_message_logging=True'"


SupportedCacheControls = ["ttl", "s-maxage", "no-cache", "no-store"]


class CostPerToken(TypedDict):
    input_cost_per_token: float
    output_cost_per_token: float


class ProviderField(TypedDict):
    field_name: str
    field_type: Literal["string"]
    field_description: str
    field_value: str


class ModelInfo(TypedDict, total=False):
    """
    Model info for a given model, this is information found in litellm.model_prices_and_context_window.json
    """

    max_tokens: Required[Optional[int]]
    max_input_tokens: Required[Optional[int]]
    max_output_tokens: Required[Optional[int]]
    input_cost_per_token: Required[float]
    input_cost_per_token_above_128k_tokens: Optional[float]
    input_cost_per_image: Optional[float]
    input_cost_per_audio_per_second: Optional[float]
    input_cost_per_video_per_second: Optional[float]
    output_cost_per_token: Required[float]
    output_cost_per_token_above_128k_tokens: Optional[float]
    output_cost_per_image: Optional[float]
    output_cost_per_video_per_second: Optional[float]
    output_cost_per_audio_per_second: Optional[float]
    litellm_provider: Required[str]
    mode: Required[
        Literal[
            "completion", "embedding", "image_generation", "chat", "audio_transcription"
        ]
    ]
    supported_openai_params: Required[Optional[List[str]]]
    supports_system_messages: Optional[bool]


class GenericStreamingChunk(TypedDict):
    text: Required[str]
    tool_use: Optional[ChatCompletionToolCallChunk]
    is_finished: Required[bool]
    finish_reason: Required[str]
    usage: Optional[ChatCompletionUsageBlock]
    index: int


from enum import Enum


class CallTypes(Enum):
    embedding = "embedding"
    aembedding = "aembedding"
    completion = "completion"
    acompletion = "acompletion"
    atext_completion = "atext_completion"
    text_completion = "text_completion"
    image_generation = "image_generation"
    aimage_generation = "aimage_generation"
    moderation = "moderation"
    amoderation = "amoderation"
    atranscription = "atranscription"
    transcription = "transcription"
    aspeech = "aspeech"
    speech = "speech"


class TopLogprob(OpenAIObject):
    token: str
    """The token."""

    bytes: Optional[List[int]] = None
    """A list of integers representing the UTF-8 bytes representation of the token.

    Useful in instances where characters are represented by multiple tokens and
    their byte representations must be combined to generate the correct text
    representation. Can be `null` if there is no bytes representation for the token.
    """

    logprob: float
    """The log probability of this token, if it is within the top 20 most likely
    tokens.

    Otherwise, the value `-9999.0` is used to signify that the token is very
    unlikely.
    """


class ChatCompletionTokenLogprob(OpenAIObject):
    token: str
    """The token."""

    bytes: Optional[List[int]] = None
    """A list of integers representing the UTF-8 bytes representation of the token.

    Useful in instances where characters are represented by multiple tokens and
    their byte representations must be combined to generate the correct text
    representation. Can be `null` if there is no bytes representation for the token.
    """

    logprob: float
    """The log probability of this token, if it is within the top 20 most likely
    tokens.

    Otherwise, the value `-9999.0` is used to signify that the token is very
    unlikely.
    """

    top_logprobs: List[TopLogprob]
    """List of the most likely tokens and their log probability, at this token
    position.

    In rare cases, there may be fewer than the number of requested `top_logprobs`
    returned.
    """


class ChoiceLogprobs(OpenAIObject):
    content: Optional[List[ChatCompletionTokenLogprob]] = None
    """A list of message content tokens with log probability information."""


class FunctionCall(OpenAIObject):
    arguments: str
    name: Optional[str] = None


class Function(OpenAIObject):
    arguments: str
    name: Optional[str] = None

    def __init__(
        self,
        arguments: Union[Dict, str],
        name: Optional[str] = None,
        **params,
    ):
        if isinstance(arguments, Dict):
            arguments = json.dumps(arguments)
        else:
            arguments = arguments

        name = name

        # Build a dictionary with the structure your BaseModel expects
        data = {"arguments": arguments, "name": name, **params}

        super(Function, self).__init__(**data)

    def __contains__(self, key):
        # Define custom behavior for the 'in' operator
        return hasattr(self, key)

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)


class ChatCompletionDeltaToolCall(OpenAIObject):
    id: Optional[str] = None
    function: Function
    type: Optional[str] = None
    index: int


class HiddenParams(OpenAIObject):
    original_response: Optional[str] = None
    model_id: Optional[str] = None  # used in Router for individual deployments
    api_base: Optional[str] = None  # returns api base used for making completion call

    model_config = ConfigDict(extra="allow", protected_namespaces=())

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)

    def json(self, **kwargs):
        try:
            return self.model_dump()  # noqa
        except:
            # if using pydantic v1
            return self.dict()


class ChatCompletionMessageToolCall(OpenAIObject):
    def __init__(
        self,
        function: Union[Dict, Function],
        id: Optional[str] = None,
        type: Optional[str] = None,
        **params,
    ):
        super(ChatCompletionMessageToolCall, self).__init__(**params)
        if isinstance(function, Dict):
            self.function = Function(**function)
        else:
            self.function = function

        if id is not None:
            self.id = id
        else:
            self.id = f"{uuid.uuid4()}"

        if type is not None:
            self.type = type
        else:
            self.type = "function"

    def __contains__(self, key):
        # Define custom behavior for the 'in' operator
        return hasattr(self, key)

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)


class Message(OpenAIObject):
    def __init__(
        self,
        content: Optional[str] = "default",
        role="assistant",
        logprobs=None,
        function_call=None,
        tool_calls=None,
        **params,
    ):
        super(Message, self).__init__(**params)
        self.content = content
        self.role = role
        if function_call is not None:
            self.function_call = FunctionCall(**function_call)

        if tool_calls is not None:
            self.tool_calls = []
            for tool_call in tool_calls:
                self.tool_calls.append(ChatCompletionMessageToolCall(**tool_call))

        if logprobs is not None:
            self._logprobs = ChoiceLogprobs(**logprobs)

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)

    def json(self, **kwargs):
        try:
            return self.model_dump()  # noqa
        except:
            # if using pydantic v1
            return self.dict()


class Delta(OpenAIObject):
    def __init__(
        self,
        content=None,
        role=None,
        function_call=None,
        tool_calls=None,
        **params,
    ):
        super(Delta, self).__init__(**params)
        self.content = content
        self.role = role

        if function_call is not None and isinstance(function_call, dict):
            self.function_call = FunctionCall(**function_call)
        else:
            self.function_call = function_call
        if tool_calls is not None and isinstance(tool_calls, list):
            self.tool_calls = []
            for tool_call in tool_calls:
                if isinstance(tool_call, dict):
                    if tool_call.get("index", None) is None:
                        tool_call["index"] = 0
                    self.tool_calls.append(ChatCompletionDeltaToolCall(**tool_call))
                elif isinstance(tool_call, ChatCompletionDeltaToolCall):
                    self.tool_calls.append(tool_call)
        else:
            self.tool_calls = tool_calls

    def __contains__(self, key):
        # Define custom behavior for the 'in' operator
        return hasattr(self, key)

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)


class Choices(OpenAIObject):
    def __init__(
        self,
        finish_reason=None,
        index=0,
        message: Optional[Union[Message, dict]] = None,
        logprobs=None,
        enhancements=None,
        **params,
    ):
        super(Choices, self).__init__(**params)
        if finish_reason is not None:
            self.finish_reason = map_finish_reason(
                finish_reason
            )  # set finish_reason for all responses
        else:
            self.finish_reason = "stop"
        self.index = index
        if message is None:
            self.message = Message()
        else:
            if isinstance(message, Message):
                self.message = message
            elif isinstance(message, dict):
                self.message = Message(**message)
        if logprobs is not None:
            self.logprobs = logprobs
        if enhancements is not None:
            self.enhancements = enhancements

    def __contains__(self, key):
        # Define custom behavior for the 'in' operator
        return hasattr(self, key)

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)


class Usage(OpenAIObject):
    def __init__(
        self, prompt_tokens=None, completion_tokens=None, total_tokens=None, **params
    ):
        super(Usage, self).__init__(**params)
        if prompt_tokens:
            self.prompt_tokens = prompt_tokens
        if completion_tokens:
            self.completion_tokens = completion_tokens
        if total_tokens:
            self.total_tokens = total_tokens

    def __contains__(self, key):
        # Define custom behavior for the 'in' operator
        return hasattr(self, key)

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)


class StreamingChoices(OpenAIObject):
    def __init__(
        self,
        finish_reason=None,
        index=0,
        delta: Optional[Delta] = None,
        logprobs=None,
        enhancements=None,
        **params,
    ):
        super(StreamingChoices, self).__init__(**params)
        if finish_reason:
            self.finish_reason = finish_reason
        else:
            self.finish_reason = None
        self.index = index
        if delta is not None:
            if isinstance(delta, Delta):
                self.delta = delta
            elif isinstance(delta, dict):
                self.delta = Delta(**delta)
        else:
            self.delta = Delta()
        if enhancements is not None:
            self.enhancements = enhancements

        if logprobs is not None and isinstance(logprobs, dict):
            self.logprobs = ChoiceLogprobs(**logprobs)
        else:
            self.logprobs = logprobs  # type: ignore

    def __contains__(self, key):
        # Define custom behavior for the 'in' operator
        return hasattr(self, key)

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)


class ModelResponse(OpenAIObject):
    id: str
    """A unique identifier for the completion."""

    choices: List[Union[Choices, StreamingChoices]]
    """The list of completion choices the model generated for the input prompt."""

    created: int
    """The Unix timestamp (in seconds) of when the completion was created."""

    model: Optional[str] = None
    """The model used for completion."""

    object: str
    """The object type, which is always "text_completion" """

    system_fingerprint: Optional[str] = None
    """This fingerprint represents the backend configuration that the model runs with.

    Can be used in conjunction with the `seed` request parameter to understand when
    backend changes have been made that might impact determinism.
    """

    _hidden_params: dict = {}

    def __init__(
        self,
        id=None,
        choices=None,
        created=None,
        model=None,
        object=None,
        system_fingerprint=None,
        usage=None,
        stream=None,
        stream_options=None,
        response_ms=None,
        hidden_params=None,
        **params,
    ):
        if stream is not None and stream is True:
            object = "chat.completion.chunk"
            if choices is not None and isinstance(choices, list):
                new_choices = []
                for choice in choices:
                    if isinstance(choice, StreamingChoices):
                        _new_choice = choice
                    elif isinstance(choice, dict):
                        _new_choice = StreamingChoices(**choice)
                    new_choices.append(_new_choice)
                choices = new_choices
            else:
                choices = [StreamingChoices()]
        else:
            object = "chat.completion"
            if choices is not None and isinstance(choices, list):
                new_choices = []
                for choice in choices:
                    if isinstance(choice, Choices):
                        _new_choice = choice
                    elif isinstance(choice, dict):
                        _new_choice = Choices(**choice)
                    new_choices.append(_new_choice)
                choices = new_choices
            else:
                choices = [Choices()]
        if id is None:
            id = _generate_id()
        else:
            id = id
        if created is None:
            created = int(time.time())
        else:
            created = created
        model = model
        if usage is not None:
            if isinstance(usage, dict):
                usage = Usage(**usage)
            else:
                usage = usage
        elif stream is None or stream is False:
            usage = Usage()
        if hidden_params:
            self._hidden_params = hidden_params

        init_values = {
            "id": id,
            "choices": choices,
            "created": created,
            "model": model,
            "object": object,
            "system_fingerprint": system_fingerprint,
        }

        if usage is not None:
            init_values["usage"] = usage

        super().__init__(
            **init_values,
            **params,
        )

    def __contains__(self, key):
        # Define custom behavior for the 'in' operator
        return hasattr(self, key)

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)

    def json(self, **kwargs):
        try:
            return self.model_dump()  # noqa
        except:
            # if using pydantic v1
            return self.dict()


class Embedding(OpenAIObject):
    embedding: Union[list, str] = []
    index: int
    object: str

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)


class EmbeddingResponse(OpenAIObject):
    model: Optional[str] = None
    """The model used for embedding."""

    data: Optional[List] = None
    """The actual embedding value"""

    object: str
    """The object type, which is always "embedding" """

    usage: Optional[Usage] = None
    """Usage statistics for the embedding request."""

    _hidden_params: dict = {}

    def __init__(
        self,
        model=None,
        usage=None,
        stream=False,
        response_ms=None,
        data=None,
        **params,
    ):
        object = "list"
        if response_ms:
            _response_ms = response_ms
        else:
            _response_ms = None
        if data:
            data = data
        else:
            data = None

        if usage:
            usage = usage
        else:
            usage = Usage()

        model = model
        super().__init__(model=model, object=object, data=data, usage=usage)

    def __contains__(self, key):
        # Define custom behavior for the 'in' operator
        return hasattr(self, key)

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)

    def json(self, **kwargs):
        try:
            return self.model_dump()  # noqa
        except:
            # if using pydantic v1
            return self.dict()


class Logprobs(OpenAIObject):
    text_offset: List[int]
    token_logprobs: List[float]
    tokens: List[str]
    top_logprobs: List[Dict[str, float]]


class TextChoices(OpenAIObject):
    def __init__(self, finish_reason=None, index=0, text=None, logprobs=None, **params):
        super(TextChoices, self).__init__(**params)
        if finish_reason:
            self.finish_reason = map_finish_reason(finish_reason)
        else:
            self.finish_reason = None
        self.index = index
        if text is not None:
            self.text = text
        else:
            self.text = None
        if logprobs is None:
            self.logprobs = None
        else:
            if isinstance(logprobs, dict):
                self.logprobs = Logprobs(**logprobs)
            else:
                self.logprobs = logprobs

    def __contains__(self, key):
        # Define custom behavior for the 'in' operator
        return hasattr(self, key)

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)

    def json(self, **kwargs):
        try:
            return self.model_dump()  # noqa
        except:
            # if using pydantic v1
            return self.dict()


class TextCompletionResponse(OpenAIObject):
    """
    {
        "id": response["id"],
        "object": "text_completion",
        "created": response["created"],
        "model": response["model"],
        "choices": [
        {
            "text": response["choices"][0]["message"]["content"],
            "index": response["choices"][0]["index"],
            "logprobs": transformed_logprobs,
            "finish_reason": response["choices"][0]["finish_reason"]
        }
        ],
        "usage": response["usage"]
    }
    """

    id: str
    object: str
    created: int
    model: Optional[str]
    choices: List[TextChoices]
    usage: Optional[Usage]
    _response_ms: Optional[int] = None
    _hidden_params: HiddenParams

    def __init__(
        self,
        id=None,
        choices=None,
        created=None,
        model=None,
        usage=None,
        stream=False,
        response_ms=None,
        object=None,
        **params,
    ):
        if stream:
            object = "text_completion.chunk"
            choices = [TextChoices()]
        else:
            object = "text_completion"
            if choices is not None and isinstance(choices, list):
                new_choices = []
                for choice in choices:
                    if isinstance(choice, TextChoices):
                        _new_choice = choice
                    elif isinstance(choice, dict):
                        _new_choice = TextChoices(**choice)
                    new_choices.append(_new_choice)
                choices = new_choices
            else:
                choices = [TextChoices()]
        if object is not None:
            object = object
        if id is None:
            id = _generate_id()
        else:
            id = id
        if created is None:
            created = int(time.time())
        else:
            created = created

        model = model
        if usage:
            usage = usage
        else:
            usage = Usage()

        super(TextCompletionResponse, self).__init__(
            id=id,
            object=object,
            created=created,
            model=model,
            choices=choices,
            usage=usage,
            **params,
        )

        if response_ms:
            self._response_ms = response_ms
        else:
            self._response_ms = None
        self._hidden_params = HiddenParams()

    def __contains__(self, key):
        # Define custom behavior for the 'in' operator
        return hasattr(self, key)

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)


class ImageObject(OpenAIObject):
    """
    Represents the url or the content of an image generated by the OpenAI API.

    Attributes:
    b64_json: The base64-encoded JSON of the generated image, if response_format is b64_json.
    url: The URL of the generated image, if response_format is url (default).
    revised_prompt: The prompt that was used to generate the image, if there was any revision to the prompt.

    https://platform.openai.com/docs/api-reference/images/object
    """

    b64_json: Optional[str] = None
    url: Optional[str] = None
    revised_prompt: Optional[str] = None

    def __init__(self, b64_json=None, url=None, revised_prompt=None):
        super().__init__(b64_json=b64_json, url=url, revised_prompt=revised_prompt)

    def __contains__(self, key):
        # Define custom behavior for the 'in' operator
        return hasattr(self, key)

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)

    def json(self, **kwargs):
        try:
            return self.model_dump()  # noqa
        except:
            # if using pydantic v1
            return self.dict()


class ImageResponse(OpenAIObject):
    created: Optional[int] = None

    data: Optional[List[ImageObject]] = None

    usage: Optional[dict] = None

    _hidden_params: dict = {}

    def __init__(self, created=None, data=None, response_ms=None):
        if response_ms:
            _response_ms = response_ms
        else:
            _response_ms = None
        if data:
            data = data
        else:
            data = None

        if created:
            created = created
        else:
            created = None

        super().__init__(data=data, created=created)
        self.usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}

    def __contains__(self, key):
        # Define custom behavior for the 'in' operator
        return hasattr(self, key)

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)

    def json(self, **kwargs):
        try:
            return self.model_dump()  # noqa
        except:
            # if using pydantic v1
            return self.dict()


class TranscriptionResponse(OpenAIObject):
    text: Optional[str] = None

    _hidden_params: dict = {}

    def __init__(self, text=None):
        super().__init__(text=text)

    def __contains__(self, key):
        # Define custom behavior for the 'in' operator
        return hasattr(self, key)

    def get(self, key, default=None):
        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
        return getattr(self, key, default)

    def __getitem__(self, key):
        # Allow dictionary-style access to attributes
        return getattr(self, key)

    def __setitem__(self, key, value):
        # Allow dictionary-style assignment of attributes
        setattr(self, key, value)

    def json(self, **kwargs):
        try:
            return self.model_dump()  # noqa
        except:
            # if using pydantic v1
            return self.dict()
