"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


UpperCAmelCase__  		=						logging.get_logger(__name__)


class        a				(		SCREAMING_SNAKE_CASE__   ):


    def __init__(					self	:  Dict   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Dict   ,				**__lowerCAmelCase	:  Union[str, Any]					):
      _UpperCAmelCase						 =      feature_size
      _UpperCAmelCase						 =      sampling_rate
      _UpperCAmelCase						 =      padding_value

      _UpperCAmelCase						 =      kwargs.pop("""padding_side"""   ,				"""right"""					)
      _UpperCAmelCase						 =      kwargs.pop("""return_attention_mask"""   ,				_lowercase					)

      super().__init__(**_lowercase					)


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Any = True   ,				__lowerCAmelCase	:  Any = None   ,				__lowerCAmelCase	:  List[Any] = False   ,				__lowerCAmelCase	:  List[Any] = None   ,				__lowerCAmelCase	:  Tuple = None   ,				__lowerCAmelCase	:  Tuple = None   ,				):

      if isinstance(_lowercase   ,				(list, tuple)					) and isinstance(processed_features[0]   ,				(dict, BatchFeature)					):
        _UpperCAmelCase						 =      {
            key: [example[key] for example in processed_features] for key in processed_features[0].keys()
        }

      # The model's main input name, usually `input_values`, has be passed for padding
      if self.model_input_names[0] not in processed_features:
        raise ValueError(
            """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
            f''' to this method that includes {self.model_input_names[0]}, but you provided'''
            f''' {list(processed_features.keys()					)}'''					)

      _UpperCAmelCase						 =      processed_features[self.model_input_names[0]]
      _UpperCAmelCase						 =      (
          return_attention_mask if return_attention_mask is not None else self.return_attention_mask
      )

      if len(_lowercase					) == 0:
        if return_attention_mask:
          _UpperCAmelCase						 =      []
        return processed_features

      # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
      # and rebuild them afterwards if no return_tensors is specified
      # Note that we lose the specific device the tensor may be on for PyTorch

      _UpperCAmelCase						 =      required_input[0]
      if isinstance(_lowercase   ,				(list, tuple)					):
        # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
        _UpperCAmelCase						 =      0
        while len(required_input[index]					) == 0:
          index += 1
        if index < len(_lowercase					):
          _UpperCAmelCase						 =      required_input[index][0]

      if return_tensors is None:
        if is_tf_tensor(_lowercase					):
          _UpperCAmelCase						 =      """tf"""
        elif is_torch_tensor(_lowercase					):
          _UpperCAmelCase						 =      """pt"""
        elif isinstance(_lowercase   ,				(int, float, list, tuple, np.ndarray)					):
          _UpperCAmelCase						 =      """np"""
        else:
          raise ValueError(
              f'''type of {first_element} unknown: {type(_lowercase					)}. '''
              """Should be one of a python, numpy, pytorch or tensorflow object."""					)

      for key, value in processed_features.items():
        if isinstance(value[0]   ,				(int, float)					):
          _UpperCAmelCase						 =      to_numpy(_lowercase					)
        else:
          _UpperCAmelCase						 =      [to_numpy(_lowercase					) for v in value]

        # Convert padding_strategy in PaddingStrategy
      _UpperCAmelCase						 =      self._get_padding_strategies(padding=_lowercase   ,				max_length=_lowercase					)

      _UpperCAmelCase						 =      processed_features[self.model_input_names[0]]

      _UpperCAmelCase						 =      len(_lowercase					)
      if not all(len(_lowercase					) == batch_size for v in processed_features.values()					):
        raise ValueError("""Some items in the output dictionary have a different batch size than others."""					)

      _UpperCAmelCase						 =      []
      for i in range(_lowercase					):
        _UpperCAmelCase						 =      {k: v[i] for k, v in processed_features.items()}
        # truncation
        _UpperCAmelCase						 =      self._truncate(
            _lowercase   ,				max_length=_lowercase   ,				pad_to_multiple_of=_lowercase   ,				truncation=_lowercase   ,				)
        truncated_inputs.append(_lowercase					)

      if padding_strategy == PaddingStrategy.LONGEST:
        # make sure that `max_length` cannot be longer than the longest truncated length
        _UpperCAmelCase						 =      max(len(input_slice[self.model_input_names[0]]					) for input_slice in truncated_inputs					)
        _UpperCAmelCase						 =      PaddingStrategy.MAX_LENGTH

      _UpperCAmelCase						 =      {}
      for i in range(_lowercase					):
        # padding
        _UpperCAmelCase						 =      self._pad(
            truncated_inputs[i]   ,				max_length=_lowercase   ,				padding_strategy=_lowercase   ,				pad_to_multiple_of=_lowercase   ,				return_attention_mask=_lowercase   ,				)

        for key, value in outputs.items():
          if key not in batch_outputs:
            _UpperCAmelCase						 =      []
          if value.dtype is np.dtype(np.floataa					):
            _UpperCAmelCase						 =      value.astype(np.floataa					)
          batch_outputs[key].append(_lowercase					)

      return BatchFeature(_lowercase   ,				tensor_type=_lowercase					)


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Tuple = None   ,				__lowerCAmelCase	:  List[Any] = PaddingStrategy.DO_NOT_PAD   ,				__lowerCAmelCase	:  int = None   ,				__lowerCAmelCase	:  int = None   ,				):
      _UpperCAmelCase						 =      processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
        _UpperCAmelCase						 =      len(_lowercase					)

      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
        _UpperCAmelCase						 =      ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      _UpperCAmelCase						 =      padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_lowercase					) < max_length

      if return_attention_mask and "attention_mask" not in processed_features:
        _UpperCAmelCase						 =      np.ones(len(_lowercase					)   ,				dtype=np.intaa					)

      if needs_to_be_padded:
        _UpperCAmelCase						 =      max_length - len(_lowercase					)
        if self.padding_side == "right":
          if return_attention_mask:
            _UpperCAmelCase						 =      np.pad(
                processed_features["""attention_mask"""]   ,				(0, difference)					)
          _UpperCAmelCase						 =      ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
          _UpperCAmelCase						 =      np.pad(
              _lowercase   ,				_lowercase   ,				"""constant"""   ,				constant_values=self.padding_value					)
        elif self.padding_side == "left":
          if return_attention_mask:
            _UpperCAmelCase						 =      np.pad(
                processed_features["""attention_mask"""]   ,				(difference, 0)					)
          _UpperCAmelCase						 =      ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
          _UpperCAmelCase						 =      np.pad(
              _lowercase   ,				_lowercase   ,				"""constant"""   ,				constant_values=self.padding_value					)
        else:
          raise ValueError("""Invalid padding strategy:""" + str(self.padding_side					)					)

      return processed_features


    def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Union[str, Any] = None   ,				__lowerCAmelCase	:  List[str] = None   ,				__lowerCAmelCase	:  Any = None   ,				):

      if not truncation:
        return processed_features
      elif truncation and max_length is None:
        raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""					)

      _UpperCAmelCase						 =      processed_features[self.model_input_names[0]]

      # find `max_length` that fits `pad_to_multiple_of`
      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
        _UpperCAmelCase						 =      ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      _UpperCAmelCase						 =      len(_lowercase					) > max_length

      if needs_to_be_truncated:
        _UpperCAmelCase						 =      processed_features[self.model_input_names[0]][:max_length]
        if "attention_mask" in processed_features:
          _UpperCAmelCase						 =      processed_features["""attention_mask"""][:max_length]

      return processed_features


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Tuple=False   ,				__lowerCAmelCase	:  Optional[int]=None					):

      if padding is not False:
        if padding is True:
          _UpperCAmelCase						 =      PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
        elif not isinstance(_lowercase   ,				_lowercase					):
          _UpperCAmelCase						 =      PaddingStrategy(_lowercase					)
        elif isinstance(_lowercase   ,				_lowercase					):
          _UpperCAmelCase						 =      padding
      else:
        _UpperCAmelCase						 =      PaddingStrategy.DO_NOT_PAD

      # Set max length if needed
      if max_length is None:
        if padding_strategy == PaddingStrategy.MAX_LENGTH:
          raise ValueError(
              f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''					)

        # Test if we have a padding value
      if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
        raise ValueError(
            """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
            """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""					)

      return padding_strategy




"""simple docstring"""


from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional

from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


try:
    from fastapi import Body, FastAPI, HTTPException
    from fastapi.routing import APIRoute
    from pydantic import BaseModel
    from starlette.responses import JSONResponse
    from uvicorn import run

    UpperCAmelCase__  		=						True
except (ImportError, AttributeError):
    UpperCAmelCase__  		=						object






    def     __UpperCAmelCase					(			*lowercase   ,**lowercase		):







      """simple docstring"""
      pass

    UpperCAmelCase__  		=						False


UpperCAmelCase__  		=						logging.get_logger("""transformers-cli/serving""")






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      pipeline(
      task=args.task   ,model=args.model if args.model else None   ,config=args.config   ,tokenizer=args.tokenizer   ,device=args.device   ,)
  return ServeCommand(lowercase   ,args.host   ,args.port   ,args.workers		)


class        a				(		lowerCAmelCase_   ):
    _snake_case     :  dict



class        a				(		lowerCAmelCase_   ):
    _snake_case     :  List[str]
    _snake_case     :  Optional[List[int]]



class        a				(		lowerCAmelCase_   ):
    _snake_case     :  str



class        a				(		lowerCAmelCase_   ):
    _snake_case     :  Any



class        a				(		lowerCAmelCase_   ):


    @staticmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  ArgumentParser					):
      _UpperCAmelCase						 =      parser.add_parser(
          """serve"""   ,				help="""CLI tool to run inference requests through REST and GraphQL endpoints."""					)
      serve_parser.add_argument(
          """--task"""   ,				type=__lowerCAmelCase   ,				choices=get_supported_tasks()   ,				help="""The task to run the pipeline on"""   ,				)
      serve_parser.add_argument("""--host"""   ,				type=__lowerCAmelCase   ,				default="""localhost"""   ,				help="""Interface the server will listen on."""					)
      serve_parser.add_argument("""--port"""   ,				type=__lowerCAmelCase   ,				default=8888   ,				help="""Port the serving will listen to."""					)
      serve_parser.add_argument("""--workers"""   ,				type=__lowerCAmelCase   ,				default=1   ,				help="""Number of http workers"""					)
      serve_parser.add_argument("""--model"""   ,				type=__lowerCAmelCase   ,				help="""Model's name or path to stored model."""					)
      serve_parser.add_argument("""--config"""   ,				type=__lowerCAmelCase   ,				help="""Model's config name or path to stored model."""					)
      serve_parser.add_argument("""--tokenizer"""   ,				type=__lowerCAmelCase   ,				help="""Tokenizer name to use."""					)
      serve_parser.add_argument(
          """--device"""   ,				type=__lowerCAmelCase   ,				default=-1   ,				help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""   ,				)
      serve_parser.set_defaults(func=__lowerCAmelCase					)


    def __init__(					self	:  Tuple   ,				__lowerCAmelCase	:  Pipeline   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  int					):
      _UpperCAmelCase						 =      pipeline

      _UpperCAmelCase						 =      host
      _UpperCAmelCase						 =      port
      _UpperCAmelCase						 =      workers

      if not _serve_dependencies_installed:
        raise RuntimeError(
            """Using serve command requires FastAPI and uvicorn. """
            """Please install transformers with [serving]: pip install \"transformers[serving]\"."""
            """Or install FastAPI and uvicorn separately."""					)
      else:
        logger.info(f'''Serving model over {host}:{port}'''					)
        _UpperCAmelCase						 =      FastAPI(
            routes=[
                APIRoute(
                    """/"""   ,				self.model_info   ,				response_model=__lowerCAmelCase   ,				response_class=__lowerCAmelCase   ,				methods=["""GET"""]   ,				),
                APIRoute(
                    """/tokenize"""   ,				self.tokenize   ,				response_model=__lowerCAmelCase   ,				response_class=__lowerCAmelCase   ,				methods=["""POST"""]   ,				),
                APIRoute(
                    """/detokenize"""   ,				self.detokenize   ,				response_model=__lowerCAmelCase   ,				response_class=__lowerCAmelCase   ,				methods=["""POST"""]   ,				),
                APIRoute(
                    """/forward"""   ,				self.forward   ,				response_model=__lowerCAmelCase   ,				response_class=__lowerCAmelCase   ,				methods=["""POST"""]   ,				),
            ]   ,				timeout=600   ,				)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      run(self._app   ,				host=self.host   ,				port=self.port   ,				workers=self.workers					)


    def      lowerCAmelCase_  (					self	:  str					):
      return ServeModelInfoResult(infos=vars(self._pipeline.model.config					)					)


    def      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  str = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)   ,				__lowerCAmelCase	:  bool = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)					):
      try:
        _UpperCAmelCase						 =      self._pipeline.tokenizer.tokenize(__lowerCAmelCase					)

        if return_ids:
          _UpperCAmelCase						 =      self._pipeline.tokenizer.convert_tokens_to_ids(__lowerCAmelCase					)
          return ServeTokenizeResult(tokens=__lowerCAmelCase   ,				tokens_ids=__lowerCAmelCase					)
        else:
          return ServeTokenizeResult(tokens=__lowerCAmelCase					)

      except Exception as e:
        raise HTTPException(status_code=500   ,				detail={"""model""": """""", """error""": str(__lowerCAmelCase					)}					)


    def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  List[int] = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)   ,				__lowerCAmelCase	:  bool = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)   ,				__lowerCAmelCase	:  bool = Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)   ,				):
      try:
        _UpperCAmelCase						 =      self._pipeline.tokenizer.decode(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)
        return ServeDeTokenizeResult(model=""""""   ,				text=__lowerCAmelCase					)
      except Exception as e:
        raise HTTPException(status_code=500   ,				detail={"""model""": """""", """error""": str(__lowerCAmelCase					)}					)


    async def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  List[Any]=Body(__lowerCAmelCase   ,				embed=__lowerCAmelCase					)					):

      # Check we don't have empty string
      if len(__lowerCAmelCase					) == 0:
        return ServeForwardResult(output=[]   ,				attention=[]					)

      try:
        # Forward through the model
        _UpperCAmelCase						 =      self._pipeline(__lowerCAmelCase					)
        return ServeForwardResult(output=__lowerCAmelCase					)
      except Exception as e:
        raise HTTPException(500   ,				{"""error""": str(__lowerCAmelCase					)}					)
