



"""simple docstring"""






import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING






def        snake_case_  (      A_					:		Optional[Any]						):


       '''simple docstring'''





       return 1.0 / (1.0 + np.exp(-_outputs						))






def        snake_case_  (      A_					:		Union[str, Any]						):


       '''simple docstring'''





       _lowerCamelCase						:				Tuple    			= np.max(_outputs,				axis=-1,				keepdims=A_						)
       _lowerCamelCase						:				str    			= np.exp(_outputs - maxes						)
       return shifted_exp / shifted_exp.sum(axis=-1,				keepdims=A_						)






class        __snake_case       (	_lowercase):
  snake_case__				:   Tuple  			=						"sigmoid"
  snake_case__				:   str  			=						"softmax"
  snake_case__				:   Tuple  			=						"none"






@add_end_docstrings(
    _lowercase  ,     R"\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    "  ,     )
class        __snake_case       (	_lowercase):
  snake_case__				:   Dict  			=						False
  snake_case__				:   List[str]  			=						ClassificationFunction.NONE




  def __init__( self     :						List[str]						,					**__lowerCAmelCase     :						Optional[Any]		):






         """simple docstring"""






         super().__init__(**__lowerCAmelCase		)

         self.check_model_type(
             TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
             if self.framework == '''tf'''
             else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]						,					__lowerCAmelCase     :						Dict=None						,					__lowerCAmelCase     :						Union[str, Any]=None						,					__lowerCAmelCase     :						List[str]=""						,					**__lowerCAmelCase     :						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= tokenizer_kwargs

         _lowerCamelCase						:				List[Any]    			= {}
         if hasattr(self.model.config						,					'''return_all_scores'''		) and return_all_scores is None:
                _lowerCamelCase						:				str    			= self.model.config.return_all_scores

         if isinstance(__lowerCAmelCase						,					__lowerCAmelCase		) or top_k is None:
                _lowerCamelCase						:				Tuple    			= top_k
                _lowerCamelCase						:				List[str]    			= False
         elif return_all_scores is not None:
                warnings.warn(
                    '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
                    ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''						,					__lowerCAmelCase						,					)
                if return_all_scores:
                       _lowerCamelCase						:				Dict    			= None
                else:
                       _lowerCamelCase						:				Any    			= 1

         if isinstance(__lowerCAmelCase						,					__lowerCAmelCase		):
                _lowerCamelCase						:				str    			= ClassificationFunction[function_to_apply.upper()]

         if function_to_apply is not None:
                _lowerCamelCase						:				int    			= function_to_apply
         return preprocess_params, {}, postprocess_params




  def __call__( self     :						Tuple						,					*__lowerCAmelCase     :						int						,					**__lowerCAmelCase     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= super().__call__(*__lowerCAmelCase						,					**__lowerCAmelCase		)
         # TODO try and retrieve it in a nicer way from _sanitize_parameters.
         _lowerCamelCase						:				Dict    			= '''top_k''' not in kwargs
         if isinstance(args[0]						,					__lowerCAmelCase		) and _legacy:
                # This pipeline is odd, and return a list when single item is run
                return [result]
         else:
                return result




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple						,					__lowerCAmelCase     :						Optional[int]						,					**__lowerCAmelCase     :						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= self.framework
         if isinstance(__lowerCAmelCase						,					__lowerCAmelCase		):
                return self.tokenizer(**__lowerCAmelCase						,					return_tensors=__lowerCAmelCase						,					**__lowerCAmelCase		)
         elif isinstance(__lowerCAmelCase						,					__lowerCAmelCase		) and len(__lowerCAmelCase		) == 1 and isinstance(inputs[0]						,					__lowerCAmelCase		) and len(inputs[0]		) == 2:
                # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
                return self.tokenizer(
                    text=inputs[0][0]						,					text_pair=inputs[0][1]						,					return_tensors=__lowerCAmelCase						,					**__lowerCAmelCase		)
         elif isinstance(__lowerCAmelCase						,					__lowerCAmelCase		):
                # This is likely an invalid usage of the pipeline attempting to pass text pairs.
                raise ValueError(
                    '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
                    ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'''		)
         return self.tokenizer(__lowerCAmelCase						,					return_tensors=__lowerCAmelCase						,					**__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]						,					__lowerCAmelCase     :						Union[str, Any]		):






         """simple docstring"""






         return self.model(**__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						int						,					__lowerCAmelCase     :						Union[str, Any]						,					__lowerCAmelCase     :						Optional[Any]=None						,					__lowerCAmelCase     :						int=1						,					__lowerCAmelCase     :						Dict=True		):






         """simple docstring"""






         if function_to_apply is None:
                if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                       _lowerCamelCase						:				Optional[int]    			= ClassificationFunction.SIGMOID
                elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                       _lowerCamelCase						:				Optional[int]    			= ClassificationFunction.SOFTMAX
                elif hasattr(self.model.config						,					'''function_to_apply'''		) and function_to_apply is None:
                       _lowerCamelCase						:				List[str]    			= self.model.config.function_to_apply
                else:
                       _lowerCamelCase						:				List[Any]    			= ClassificationFunction.NONE

         _lowerCamelCase						:				Union[str, Any]    			= model_outputs['''logits'''][0]
         _lowerCamelCase						:				Dict    			= outputs.numpy()

         if function_to_apply == ClassificationFunction.SIGMOID:
                _lowerCamelCase						:				Dict    			= sigmoid(__lowerCAmelCase		)
         elif function_to_apply == ClassificationFunction.SOFTMAX:
                _lowerCamelCase						:				Tuple    			= softmax(__lowerCAmelCase		)
         elif function_to_apply == ClassificationFunction.NONE:
                _lowerCamelCase						:				str    			= outputs
         else:
                raise ValueError(f'''Unrecognized `function_to_apply` argument: {function_to_apply}'''		)

         if top_k == 1 and _legacy:
                return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

         _lowerCamelCase						:				Tuple    			= [
             {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(__lowerCAmelCase		)
         ]
         if not _legacy:
                dict_scores.sort(key=lambda __lowerCAmelCase		: x["score"]						,					reverse=__lowerCAmelCase		)
                if top_k is not None:
                       _lowerCamelCase						:				Tuple    			= dict_scores[:top_k]
         return dict_scores






'''simple docstring'''






import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
  from cookiecutter.main import cookiecutter

  __A					:  List[Any]											=      True
except ImportError:
  __A					:  int											=      False

__A					:  str											=      logging.get_logger(__name__)  # pylint: disable=invalid-name







def 				UpperCamelCase_			(    A__					:   Namespace					):
  '''simple docstring'''





  return AddNewModelCommand(args.testing							,    args.testing_file							,    path=args.path					)






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @staticmethod
    def __lowercase   (   lowerCamelCase    :  ArgumentParser		)   ->  int:
      lowerCAmelCase_			:      Optional[int]			=   parser.add_parser("""add-new-model"""		)
      add_new_model_parser.add_argument("""--testing"""    ,						action="""store_true"""    ,						help="""If in testing mode."""		)
      add_new_model_parser.add_argument("""--testing_file"""    ,						type=lowerCamelCase    ,						help="""Configuration file on which to run."""		)
      add_new_model_parser.add_argument(
          """--path"""    ,						type=lowerCamelCase    ,						help="""Path to cookiecutter. Should only be used for testing purposes."""		)
      add_new_model_parser.set_defaults(func=lowerCamelCase		)

    def __init__(   self    :  List[str]    ,						lowerCamelCase    :  bool    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  Any=None    ,						*lowerCamelCase    :  List[str]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      int			=   testing
      lowerCAmelCase_			:      Union[str, Any]			=   testing_file
      lowerCAmelCase_			:      Tuple			=   path





    def __lowercase   (   self    :  Tuple		)   ->  int:
      warnings.warn(
          """The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. """
          """It is not actively maintained anymore, so might give a result that won't pass all tests and quality """
          """checks, you should use `transformers-cli add-new-model-like` instead."""		)
      if not _has_cookiecutter:
        raise ImportError(
            """Model creation dependencies are required to use the `add_new_model` command. Install them by running """
            """the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n"""		)
      # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
      lowerCAmelCase_			:      int			=   [directory for directory in os.listdir() if """cookiecutter-template-""" == directory[:22]]
      if len(lowerCamelCase		) > 0:
        raise ValueError(
            """Several directories starting with `cookiecutter-template-` in current working directory. """
            """Please clean your directory by removing all folders starting with `cookiecutter-template-` or """
            """change your working directory."""		)

      lowerCAmelCase_			:      List[Any]			=   (
          Path(lowerCamelCase		).parent.parent.parent.parent if self._path is None else Path(self._path		).parent.parent
      )
      lowerCAmelCase_			:      Dict			=   path_to_transformer_root / """templates""" / """adding_a_new_model"""

      # Execute cookiecutter
      if not self._testing:
        cookiecutter(str(lowerCamelCase		)		)
      else:
        with open(self._testing_file    ,						"""r"""		) as configuration_file:
          lowerCAmelCase_			:      Tuple			=   json.load(lowerCamelCase		)

        cookiecutter(
            str(path_to_cookiecutter if self._path is None else self._path		)    ,						no_input=lowerCamelCase    ,						extra_context=lowerCamelCase    ,						)

      lowerCAmelCase_			:      List[str]			=   [directory for directory in os.listdir() if """cookiecutter-template-""" in directory[:22]][0]

      # Retrieve configuration
      with open(directory + """/configuration.json"""    ,						"""r"""		) as configuration_file:
        lowerCAmelCase_			:      Tuple			=   json.load(lowerCamelCase		)

      lowerCAmelCase_			:      str			=   configuration["""lowercase_modelname"""]
      lowerCAmelCase_			:      List[str]			=   configuration["""generate_tensorflow_pytorch_and_flax"""]
      os.remove(F'{directory}/configuration.json'		)

      lowerCAmelCase_			:      Dict			=   """PyTorch""" in generate_tensorflow_pytorch_and_flax
      lowerCAmelCase_			:      Optional[int]			=   """TensorFlow""" in generate_tensorflow_pytorch_and_flax
      lowerCAmelCase_			:      List[str]			=   """Flax""" in generate_tensorflow_pytorch_and_flax

      lowerCAmelCase_			:      Union[str, Any]			=   F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
      os.makedirs(lowerCamelCase    ,						exist_ok=lowerCamelCase		)
      os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}'    ,						exist_ok=lowerCamelCase		)

      # Tests require submodules as they have parent imports
      with open(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py'    ,						"""w"""		):
        pass

      shutil.move(
          F'{directory}/__init__.py'    ,						F'{model_dir}/__init__.py'    ,						)
      shutil.move(
          F'{directory}/configuration_{lowercase_model_name}.py'    ,						F'{model_dir}/configuration_{lowercase_model_name}.py'    ,						)

      def remove_copy_lines(lowerCamelCase    :  Any		):
        with open(lowerCamelCase    ,						"""r"""		) as f:
          lowerCAmelCase_			:      List[str]			=   f.readlines()
        with open(lowerCamelCase    ,						"""w"""		) as f:
          for line in lines:
            if "# Copied from transformers." not in line:
              f.write(lowerCamelCase		)

      if output_pytorch:
        if not self._testing:
          remove_copy_lines(F'{directory}/modeling_{lowercase_model_name}.py'		)

        shutil.move(
            F'{directory}/modeling_{lowercase_model_name}.py'    ,						F'{model_dir}/modeling_{lowercase_model_name}.py'    ,						)

        shutil.move(
            F'{directory}/test_modeling_{lowercase_model_name}.py'    ,						F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py'    ,						)
      else:
        os.remove(F'{directory}/modeling_{lowercase_model_name}.py'		)
        os.remove(F'{directory}/test_modeling_{lowercase_model_name}.py'		)

      if output_tensorflow:
        if not self._testing:
          remove_copy_lines(F'{directory}/modeling_tf_{lowercase_model_name}.py'		)

        shutil.move(
            F'{directory}/modeling_tf_{lowercase_model_name}.py'    ,						F'{model_dir}/modeling_tf_{lowercase_model_name}.py'    ,						)

        shutil.move(
            F'{directory}/test_modeling_tf_{lowercase_model_name}.py'    ,						F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py'    ,						)
      else:
        os.remove(F'{directory}/modeling_tf_{lowercase_model_name}.py'		)
        os.remove(F'{directory}/test_modeling_tf_{lowercase_model_name}.py'		)

      if output_flax:
        if not self._testing:
          remove_copy_lines(F'{directory}/modeling_flax_{lowercase_model_name}.py'		)

        shutil.move(
            F'{directory}/modeling_flax_{lowercase_model_name}.py'    ,						F'{model_dir}/modeling_flax_{lowercase_model_name}.py'    ,						)

        shutil.move(
            F'{directory}/test_modeling_flax_{lowercase_model_name}.py'    ,						F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py'    ,						)
      else:
        os.remove(F'{directory}/modeling_flax_{lowercase_model_name}.py'		)
        os.remove(F'{directory}/test_modeling_flax_{lowercase_model_name}.py'		)

      shutil.move(
          F'{directory}/{lowercase_model_name}.md'    ,						F'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md'    ,						)

      shutil.move(
          F'{directory}/tokenization_{lowercase_model_name}.py'    ,						F'{model_dir}/tokenization_{lowercase_model_name}.py'    ,						)

      shutil.move(
          F'{directory}/tokenization_fast_{lowercase_model_name}.py'    ,						F'{model_dir}/tokenization_{lowercase_model_name}_fast.py'    ,						)

      from os import fdopen, remove
      from shutil import copymode, move
      from tempfile import mkstemp

      def replace(lowerCamelCase    :  str    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  List[str]		):
        # Create temp file
        lowerCAmelCase_,							lowerCAmelCase_			:      int			=   mkstemp()
        lowerCAmelCase_			:      List[Any]			=   False
        with fdopen(lowerCamelCase    ,						"""w"""		) as new_file:
          with open(lowerCamelCase		) as old_file:
            for line in old_file:
              new_file.write(lowerCamelCase		)
              if line_to_copy_below in line:
                lowerCAmelCase_			:      List[str]			=   True
                for line_to_copy in lines_to_copy:
                  new_file.write(lowerCamelCase		)

        if not line_found:
          raise ValueError(F'Line {line_to_copy_below} was not found in file.'		)

        # Copy the file permissions from the old file to the new file
        copymode(lowerCamelCase    ,						lowerCamelCase		)
        # Remove original file
        remove(lowerCamelCase		)
        # Move new file
        move(lowerCamelCase    ,						lowerCamelCase		)

      def skip_units(lowerCamelCase    :  Optional[int]		):
        return (
            ("generating PyTorch" in line and not output_pytorch)
            or ("generating TensorFlow" in line and not output_tensorflow)
            or ("generating Flax" in line and not output_flax)
        )

      def replace_in_files(lowerCamelCase    :  Any		):
        with open(lowerCamelCase		) as datafile:
          lowerCAmelCase_			:      Dict			=   []
          lowerCAmelCase_			:      List[str]			=   False
          lowerCAmelCase_			:      str			=   False
          for line in datafile:
            if "# To replace in: " in line and "##" not in line:
              lowerCAmelCase_			:      Dict			=   line.split("""\""""		)[1]
              lowerCAmelCase_			:      int			=   skip_units(lowerCamelCase		)
            elif "# Below: " in line and "##" not in line:
              lowerCAmelCase_			:      Any			=   line.split("""\""""		)[1]
              lowerCAmelCase_			:      Tuple			=   skip_units(lowerCamelCase		)
            elif "# End." in line and "##" not in line:
              if not skip_file and not skip_snippet:
                replace(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		)

              lowerCAmelCase_			:      Dict			=   []
            elif "# Replace with" in line and "##" not in line:
              lowerCAmelCase_			:      int			=   []
            elif "##" not in line:
              lines_to_copy.append(lowerCamelCase		)

        remove(lowerCamelCase		)

      replace_in_files(F'{directory}/to_replace_{lowercase_model_name}.py'		)
      os.rmdir(lowerCamelCase		)



