# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.

from __future__ import annotations

from typing import List, Union, Optional
from typing_extensions import Literal

import httpx

from ..types import completion_create_params
from .._types import NOT_GIVEN, Body, Query, Headers, NotGiven
from .._utils import (
    maybe_transform,
    async_maybe_transform,
)
from .._compat import cached_property
from .._resource import SyncAPIResource, AsyncAPIResource
from .._response import (
    to_raw_response_wrapper,
    to_streamed_response_wrapper,
    async_to_raw_response_wrapper,
    async_to_streamed_response_wrapper,
)
from .._base_client import (
    make_request_options,
)
from ..types.completion_create_response import CompletionCreateResponse

__all__ = ["CompletionsResource", "AsyncCompletionsResource"]


class CompletionsResource(SyncAPIResource):
    @cached_property
    def with_raw_response(self) -> CompletionsResourceWithRawResponse:
        return CompletionsResourceWithRawResponse(self)

    @cached_property
    def with_streaming_response(self) -> CompletionsResourceWithStreamingResponse:
        return CompletionsResourceWithStreamingResponse(self)

    def create(
        self,
        *,
        model: Union[str, Literal["kilm-7b", "kilm-13b"]],
        prompt: object,
        frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
        logprobs: Optional[int] | NotGiven = NOT_GIVEN,
        max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
        presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
        seed: Optional[int] | NotGiven = NOT_GIVEN,
        stop: Union[Optional[str], List[str], None] | NotGiven = NOT_GIVEN,
        stream: Optional[bool] | NotGiven = NOT_GIVEN,
        stream_options: Optional[completion_create_params.StreamOptions] | NotGiven = NOT_GIVEN,
        temperature: Optional[float] | NotGiven = NOT_GIVEN,
        top_p: Optional[float] | NotGiven = NOT_GIVEN,
        # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
        # The extra values given here take precedence over values defined on the client or passed to this method.
        extra_headers: Headers | None = None,
        extra_query: Query | None = None,
        extra_body: Body | None = None,
        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
    ) -> CompletionCreateResponse:
        """
        Creates a completion for the provided prompt and parameters.

        Args:
          model: ID of the model to use. You can use the
              [List models](/docs/api-reference/models/list) API to see all of your available
              models, or see our [Model overview](/docs/models/overview) for descriptions of
              them.

          prompt: The prompt(s) to generate completions for, encoded as a string, array of
              strings, array of tokens, or array of token arrays.

              Note that <|endoftext|> is the document separator that the model sees during
              training, so if a prompt is not specified the model will generate as if from the
              beginning of a new document.

          frequency_penalty: Number between -2.0 and 2.0. 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.

              [See more information about frequency and presence penalties.](/docs/guides/text-generation/parameter-details)

          logprobs: Include the log probabilities on the `logprobs` most likely output tokens, as
              well the chosen tokens. For example, if `logprobs` is 5, the API will return a
              list of the 5 most likely tokens. The API will always return the `logprob` of
              the sampled token, so there may be up to `logprobs+1` elements in the response.

              The maximum value for `logprobs` is 5.

          max_tokens: The maximum number of [tokens](/tokenizer) that can be generated in the
              completion.

              The token count of your prompt plus `max_tokens` cannot exceed the model's
              context length.
              [Example Python code](https://cookbook.api-gateway.kilm.zalo.services/examples/how_to_count_tokens_with_tiktoken)
              for counting tokens.

          presence_penalty: Number between -2.0 and 2.0. 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.

              [See more information about frequency and presence penalties.](/docs/guides/text-generation/parameter-details)

          seed: If specified, our system will make a best effort to sample deterministically,
              such that repeated requests with the same `seed` and parameters should return
              the same result.

              Determinism is not guaranteed, and you should refer to the `system_fingerprint`
              response parameter to monitor changes in the backend.

          stop: Up to 4 sequences where the API will stop generating further tokens. The
              returned text will not contain the stop sequence.

          stream: Whether to stream back partial progress. If set, tokens will be sent as
              data-only
              [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
              as they become available, with the stream terminated by a `data: [DONE]`
              message.
              [Example Python code](https://cookbook.api-gateway.kilm.zalo.services/examples/how_to_stream_completions).

          stream_options: Options for streaming response. Only set this when you set `stream: true`.

          temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will
              make the output more random, while lower values like 0.2 will make it more
              focused and deterministic.

              We generally recommend altering this or `top_p` but not both.

          top_p: An alternative to sampling with temperature, called nucleus sampling, where the
              model considers the results of the tokens with top_p probability mass. So 0.1
              means only the tokens comprising the top 10% probability mass are considered.

              We generally recommend altering this or `temperature` but not both.

          extra_headers: Send extra headers

          extra_query: Add additional query parameters to the request

          extra_body: Add additional JSON properties to the request

          timeout: Override the client-level default timeout for this request, in seconds
        """
        return self._post(
            "/completions/",
            body=maybe_transform(
                {
                    "model": model,
                    "prompt": prompt,
                    "frequency_penalty": frequency_penalty,
                    "logprobs": logprobs,
                    "max_tokens": max_tokens,
                    "presence_penalty": presence_penalty,
                    "seed": seed,
                    "stop": stop,
                    "stream": stream,
                    "stream_options": stream_options,
                    "temperature": temperature,
                    "top_p": top_p,
                },
                completion_create_params.CompletionCreateParams,
            ),
            options=make_request_options(
                extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
            ),
            cast_to=CompletionCreateResponse,
        )


class AsyncCompletionsResource(AsyncAPIResource):
    @cached_property
    def with_raw_response(self) -> AsyncCompletionsResourceWithRawResponse:
        return AsyncCompletionsResourceWithRawResponse(self)

    @cached_property
    def with_streaming_response(self) -> AsyncCompletionsResourceWithStreamingResponse:
        return AsyncCompletionsResourceWithStreamingResponse(self)

    async def create(
        self,
        *,
        model: Union[str, Literal["kilm-7b", "kilm-13b"]],
        prompt: object,
        frequency_penalty: Optional[float] | NotGiven = NOT_GIVEN,
        logprobs: Optional[int] | NotGiven = NOT_GIVEN,
        max_tokens: Optional[int] | NotGiven = NOT_GIVEN,
        presence_penalty: Optional[float] | NotGiven = NOT_GIVEN,
        seed: Optional[int] | NotGiven = NOT_GIVEN,
        stop: Union[Optional[str], List[str], None] | NotGiven = NOT_GIVEN,
        stream: Optional[bool] | NotGiven = NOT_GIVEN,
        stream_options: Optional[completion_create_params.StreamOptions] | NotGiven = NOT_GIVEN,
        temperature: Optional[float] | NotGiven = NOT_GIVEN,
        top_p: Optional[float] | NotGiven = NOT_GIVEN,
        # Use the following arguments if you need to pass additional parameters to the API that aren't available via kwargs.
        # The extra values given here take precedence over values defined on the client or passed to this method.
        extra_headers: Headers | None = None,
        extra_query: Query | None = None,
        extra_body: Body | None = None,
        timeout: float | httpx.Timeout | None | NotGiven = NOT_GIVEN,
    ) -> CompletionCreateResponse:
        """
        Creates a completion for the provided prompt and parameters.

        Args:
          model: ID of the model to use. You can use the
              [List models](/docs/api-reference/models/list) API to see all of your available
              models, or see our [Model overview](/docs/models/overview) for descriptions of
              them.

          prompt: The prompt(s) to generate completions for, encoded as a string, array of
              strings, array of tokens, or array of token arrays.

              Note that <|endoftext|> is the document separator that the model sees during
              training, so if a prompt is not specified the model will generate as if from the
              beginning of a new document.

          frequency_penalty: Number between -2.0 and 2.0. 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.

              [See more information about frequency and presence penalties.](/docs/guides/text-generation/parameter-details)

          logprobs: Include the log probabilities on the `logprobs` most likely output tokens, as
              well the chosen tokens. For example, if `logprobs` is 5, the API will return a
              list of the 5 most likely tokens. The API will always return the `logprob` of
              the sampled token, so there may be up to `logprobs+1` elements in the response.

              The maximum value for `logprobs` is 5.

          max_tokens: The maximum number of [tokens](/tokenizer) that can be generated in the
              completion.

              The token count of your prompt plus `max_tokens` cannot exceed the model's
              context length.
              [Example Python code](https://cookbook.api-gateway.kilm.zalo.services/examples/how_to_count_tokens_with_tiktoken)
              for counting tokens.

          presence_penalty: Number between -2.0 and 2.0. 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.

              [See more information about frequency and presence penalties.](/docs/guides/text-generation/parameter-details)

          seed: If specified, our system will make a best effort to sample deterministically,
              such that repeated requests with the same `seed` and parameters should return
              the same result.

              Determinism is not guaranteed, and you should refer to the `system_fingerprint`
              response parameter to monitor changes in the backend.

          stop: Up to 4 sequences where the API will stop generating further tokens. The
              returned text will not contain the stop sequence.

          stream: Whether to stream back partial progress. If set, tokens will be sent as
              data-only
              [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format)
              as they become available, with the stream terminated by a `data: [DONE]`
              message.
              [Example Python code](https://cookbook.api-gateway.kilm.zalo.services/examples/how_to_stream_completions).

          stream_options: Options for streaming response. Only set this when you set `stream: true`.

          temperature: What sampling temperature to use, between 0 and 2. Higher values like 0.8 will
              make the output more random, while lower values like 0.2 will make it more
              focused and deterministic.

              We generally recommend altering this or `top_p` but not both.

          top_p: An alternative to sampling with temperature, called nucleus sampling, where the
              model considers the results of the tokens with top_p probability mass. So 0.1
              means only the tokens comprising the top 10% probability mass are considered.

              We generally recommend altering this or `temperature` but not both.

          extra_headers: Send extra headers

          extra_query: Add additional query parameters to the request

          extra_body: Add additional JSON properties to the request

          timeout: Override the client-level default timeout for this request, in seconds
        """
        return await self._post(
            "/completions/",
            body=await async_maybe_transform(
                {
                    "model": model,
                    "prompt": prompt,
                    "frequency_penalty": frequency_penalty,
                    "logprobs": logprobs,
                    "max_tokens": max_tokens,
                    "presence_penalty": presence_penalty,
                    "seed": seed,
                    "stop": stop,
                    "stream": stream,
                    "stream_options": stream_options,
                    "temperature": temperature,
                    "top_p": top_p,
                },
                completion_create_params.CompletionCreateParams,
            ),
            options=make_request_options(
                extra_headers=extra_headers, extra_query=extra_query, extra_body=extra_body, timeout=timeout
            ),
            cast_to=CompletionCreateResponse,
        )


class CompletionsResourceWithRawResponse:
    def __init__(self, completions: CompletionsResource) -> None:
        self._completions = completions

        self.create = to_raw_response_wrapper(
            completions.create,
        )


class AsyncCompletionsResourceWithRawResponse:
    def __init__(self, completions: AsyncCompletionsResource) -> None:
        self._completions = completions

        self.create = async_to_raw_response_wrapper(
            completions.create,
        )


class CompletionsResourceWithStreamingResponse:
    def __init__(self, completions: CompletionsResource) -> None:
        self._completions = completions

        self.create = to_streamed_response_wrapper(
            completions.create,
        )


class AsyncCompletionsResourceWithStreamingResponse:
    def __init__(self, completions: AsyncCompletionsResource) -> None:
        self._completions = completions

        self.create = async_to_streamed_response_wrapper(
            completions.create,
        )
