from __future__ import annotations
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
from typing import List, Optional, Union, Dict
from typing_extensions import TypedDict, Literal
from pydantic import BaseModel, Field
import sapiens_transformers.sapiens_optimizer as sapiens_optimizer
model_field = Field(description="The model to use for generating completions.", default=None)
max_tokens_field = Field(default=16, ge=1, description="The maximum number of tokens to generate.")
min_tokens_field = Field(default=0, ge=0, description="The minimum number of tokens to generate. It may return fewer tokens if another condition is met (e.g. max_tokens, stop).")
temperature_field = Field(default=0.8, description="Adjust the randomness of the generated text.\n\nTemperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run.")
top_p_field = Field(default=0.95, ge=0.0, le=1.0, description="Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P.\n\nTop-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top_p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text.")
min_p_field = Field(default=0.05, ge=0.0, le=1.0, description="Sets a minimum base probability threshold for token selection.\n\nThe Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter min_p represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with min_p=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out.")
stop_field = Field(default=None, description="A list of tokens at which to stop generation. If None, no stop tokens are used.")
stream_field = Field(default=False, description="Whether to stream the results as they are generated. Useful for chatbots.")
top_k_field = Field(default=40, ge=0, description="Limit the next token selection to the K most probable tokens.\n\nTop-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top_k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text.")
repeat_penalty_field = Field(default=1.1, ge=0.0, description="A penalty applied to each token that is already generated. This helps prevent the model from repeating itself.\n\nRepeat penalty is a hyperparameter used to penalize the repetition of token sequences during text generation. It helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient.")
presence_penalty_field = Field(default=0.0, ge=-2.0, le=2.0, description="Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.")
frequency_penalty_field = Field(default=0.0, ge=-2.0, le=2.0, description="Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.")
mirostat_mode_field = Field(default=0, ge=0, le=2, description="Enable Mirostat constant-perplexity algorithm of the specified version (1 or 2; 0 = disabled)")
mirostat_tau_field = Field(default=5.0, ge=0.0, le=10.0, description="Mirostat target entropy, i.e. the target perplexity - lower values produce focused and coherent text, larger values produce more diverse and less coherent text")
mirostat_eta_field = Field(default=0.1, ge=0.001, le=1.0, description="Mirostat learning rate")
grammar = Field(default=None, description="A CBNF grammar (as string) to be used for formatting the model's output.")
class CreateCompletionRequest(BaseModel):
    prompt: Union[str, List[str]] = Field(default="", description="The prompt to generate completions for.")
    suffix: Optional[str] = Field(default=None, description="A suffix to append to the generated text. If None, no suffix is appended. Useful for chatbots.")
    max_tokens: Optional[int] = Field(default=16, ge=0, description="The maximum number of tokens to generate.")
    min_tokens: int = min_tokens_field
    temperature: float = temperature_field
    top_p: float = top_p_field
    min_p: float = min_p_field
    echo: bool = Field(default=False, description="Whether to echo the prompt in the generated text. Useful for chatbots.")
    stop: Optional[Union[str, List[str]]] = stop_field
    stream: bool = stream_field
    logprobs: Optional[int] = Field(default=None, ge=0, description="The number of logprobs to generate. If None, no logprobs are generated.")
    presence_penalty: Optional[float] = presence_penalty_field
    frequency_penalty: Optional[float] = frequency_penalty_field
    logit_bias: Optional[Dict[str, float]] = Field(None)
    seed: Optional[int] = Field(None)
    model: Optional[str] = model_field
    n: Optional[int] = 1
    best_of: Optional[int] = 1
    user: Optional[str] = Field(default=None)
    top_k: int = top_k_field
    repeat_penalty: float = repeat_penalty_field
    logit_bias_type: Optional[Literal["input_ids", "tokens"]] = Field(None)
    mirostat_mode: int = mirostat_mode_field
    mirostat_tau: float = mirostat_tau_field
    mirostat_eta: float = mirostat_eta_field
    grammar: Optional[str] = None
    model_config = {"json_schema_extra": {"examples": [{"prompt": "\n\n### Instructions:\nWhat is the capital of France?\n\n### Response:\n", "stop": ["\n", "###"]}]}}
class CreateEmbeddingRequest(BaseModel):
    model: Optional[str] = model_field
    input: Union[str, List[str]] = Field(description="The input to embed.")
    user: Optional[str] = Field(default=None)
    model_config = {"json_schema_extra": {"examples": [{"input": "The food was delicious and the waiter..."}]}}
class ChatCompletionRequestMessage(BaseModel):
    role: Literal["system", "user", "assistant", "function"] = Field(default="user", description="The role of the message.")
    content: Optional[str] = Field(default="", description="The content of the message.")
class CreateChatCompletionRequest(BaseModel):
    messages: List[sapiens_optimizer.ChatCompletionRequestMessage] = Field(default=[], description="A list of messages to generate completions for.")
    functions: Optional[List[sapiens_optimizer.ChatCompletionFunction]] = Field(default=None, description="A list of functions to apply to the generated completions.")
    function_call: Optional[sapiens_optimizer.ChatCompletionRequestFunctionCall] = Field(default=None, description="A function to apply to the generated completions.")
    tools: Optional[List[sapiens_optimizer.ChatCompletionTool]] = Field(default=None, description="A list of tools to apply to the generated completions.")
    tool_choice: Optional[sapiens_optimizer.ChatCompletionToolChoiceOption] = Field(default=None, description="A tool to apply to the generated completions.")
    max_tokens: Optional[int] = Field(default=None, description="The maximum number of tokens to generate. Defaults to inf")
    min_tokens: int = min_tokens_field
    logprobs: Optional[bool] = Field(default=False, description="Whether to output the logprobs or not. Default is True")
    top_logprobs: Optional[int] = Field(default=None, ge=0, description="The number of logprobs to generate. If None, no logprobs are generated. logprobs need to set to True.")
    temperature: float = temperature_field
    top_p: float = top_p_field
    min_p: float = min_p_field
    stop: Optional[Union[str, List[str]]] = stop_field
    stream: bool = stream_field
    presence_penalty: Optional[float] = presence_penalty_field
    frequency_penalty: Optional[float] = frequency_penalty_field
    logit_bias: Optional[Dict[str, float]] = Field(None)
    seed: Optional[int] = Field(None)
    response_format: Optional[sapiens_optimizer.ChatCompletionRequestResponseFormat] = Field(default=None)
    model: Optional[str] = model_field
    n: Optional[int] = 1
    user: Optional[str] = Field(None)
    top_k: int = top_k_field
    repeat_penalty: float = repeat_penalty_field
    logit_bias_type: Optional[Literal["input_ids", "tokens"]] = Field(None)
    mirostat_mode: int = mirostat_mode_field
    mirostat_tau: float = mirostat_tau_field
    mirostat_eta: float = mirostat_eta_field
    grammar: Optional[str] = None
    model_config = {"json_schema_extra": {"examples": [{"messages": [ChatCompletionRequestMessage(role="system", content="You are a helpful assistant.").model_dump(),
    ChatCompletionRequestMessage(role="user", content="What is the capital of France?").model_dump()]}]}}
class ModelData(TypedDict):
    id: str
    object: Literal["model"]
    owned_by: str
    permissions: List[str]
class ModelList(TypedDict):
    object: Literal["list"]
    data: List[ModelData]
class TokenizeInputRequest(BaseModel):
    model: Optional[str] = model_field
    input: str = Field(description="The input to tokenize.")
    model_config = {"json_schema_extra": {"examples": [{"input": "How many tokens in this query?"}]}}
class TokenizeInputResponse(BaseModel):
    tokens: List[int] = Field(description="A list of tokens.")
    model_config = {"json_schema_extra": {"example": {"tokens": [123, 321, 222]}}}
class TokenizeInputCountResponse(BaseModel):
    count: int = Field(description="The number of tokens in the input.")
    model_config = {"json_schema_extra": {"example": {"count": 5}}}
class DetokenizeInputRequest(BaseModel):
    model: Optional[str] = model_field
    tokens: List[int] = Field(description="A list of toekns to detokenize.")
    model_config = {"json_schema_extra": {"example": [{"tokens": [123, 321, 222]}]}}
class DetokenizeInputResponse(BaseModel):
    text: str = Field(description="The detokenized text.")
    model_config = {"json_schema_extra": {"example": {"text": "How many tokens in this query?"}}}
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
