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

    _lowercase							:      Dict      							=True
except ImportError:
    _lowercase							:      Union[str, Any]      							=False

_lowercase							:      Any      							=logging.get_logger(__name__)  # pylint: disable=invalid-name



def A__  (			lowercase:     Namespace					)				->		Optional[Any]:
       return AddNewModelCommand(args.testing,		args.testing_file,		path=args.path					)



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  @staticmethod
  def 		SCREAMING_SNAKE_CASE_		(				SCREAMING_SNAKE_CASE__    :					ArgumentParser     )    ->				Optional[int]:
         A						:     Tuple         =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=SCREAMING_SNAKE_CASE__      ,	help='Configuration file on which to run.'     )
         add_new_model_parser.add_argument(
             '--path'      ,	type=SCREAMING_SNAKE_CASE__      ,	help='Path to cookiecutter. Should only be used for testing purposes.'     )
         add_new_model_parser.set_defaults(func=SCREAMING_SNAKE_CASE__     )







  def __init__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					bool      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					List[str]=None      ,	*SCREAMING_SNAKE_CASE__    :					Dict     )    ->				Union[str, Any]:
         A						:     Union[str, Any]         =testing
         A						:     str         =testing_file
         A						:     List[Any]         =path







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				List[str]:
         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
         A						:     List[str]         =[directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:22]]
         if len(SCREAMING_SNAKE_CASE__     ) > 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.'     )

         A						:     Optional[int]         =(
             Path(SCREAMING_SNAKE_CASE__     ).parent.parent.parent.parent if self._path is None else Path(self._path     ).parent.parent
         )
         A						:     int         =path_to_transformer_root / 'templates' / 'adding_a_new_model'

         # Execute cookiecutter
         if not self._testing:
                cookiecutter(str(SCREAMING_SNAKE_CASE__     )     )
         else:
                with open(self._testing_file      ,	'r'     ) as configuration_file:
                       A						:     List[str]         =json.load(SCREAMING_SNAKE_CASE__     )

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

         A						:     Optional[Any]         =[directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:22]][0]

         # Retrieve configuration
         with open(directory + '/configuration.json'      ,	'r'     ) as configuration_file:
                A						:     Union[str, Any]         =json.load(SCREAMING_SNAKE_CASE__     )

         A						:     List[str]         =configuration['lowercase_modelname']
         A						:     Tuple         =configuration['generate_tensorflow_pytorch_and_flax']
         os.remove(f'{directory}/configuration.json'     )

         A						:     Union[str, Any]         ='PyTorch' in generate_tensorflow_pytorch_and_flax
         A						:     List[Any]         ='TensorFlow' in generate_tensorflow_pytorch_and_flax
         A						:     str         ='Flax' in generate_tensorflow_pytorch_and_flax

         A						:     Tuple         =f'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
         os.makedirs(SCREAMING_SNAKE_CASE__      ,	exist_ok=SCREAMING_SNAKE_CASE__     )
         os.makedirs(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}'      ,	exist_ok=SCREAMING_SNAKE_CASE__     )

         # 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(SCREAMING_SNAKE_CASE__    :					Union[str, Any]     ):
                with open(SCREAMING_SNAKE_CASE__      ,	'r'     ) as f:
                       A						:     Union[str, Any]         =f.readlines()
                with open(SCREAMING_SNAKE_CASE__      ,	'w'     ) as f:
                       for line in lines:
                              if "# Copied from transformers." not in line:
                                     f.write(SCREAMING_SNAKE_CASE__     )

         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(SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					List[str]     ):
                # Create temp file
                A  ,  A						:     Optional[int]         =mkstemp()
                A						:     Union[str, Any]         =False
                with fdopen(SCREAMING_SNAKE_CASE__      ,	'w'     ) as new_file:
                       with open(SCREAMING_SNAKE_CASE__     ) as old_file:
                              for line in old_file:
                                     new_file.write(SCREAMING_SNAKE_CASE__     )
                                     if line_to_copy_below in line:
                                            A						:     List[Any]         =True
                                            for line_to_copy in lines_to_copy:
                                                   new_file.write(SCREAMING_SNAKE_CASE__     )

                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(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
                # Remove original file
                remove(SCREAMING_SNAKE_CASE__     )
                # Move new file
                move(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

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

                                     A						:     Any         =[]
                              elif "# Replace with" in line and "##" not in line:
                                     A						:     Optional[int]         =[]
                              elif "##" not in line:
                                     lines_to_copy.append(SCREAMING_SNAKE_CASE__     )

                remove(SCREAMING_SNAKE_CASE__     )

         replace_in_files(f'{directory}/to_replace_{lowercase_model_name}.py'     )
         os.rmdir(SCREAMING_SNAKE_CASE__     )







import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
    raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
_lowercase							:      str      							=False
_lowercase							:      Optional[Any]      							=False



def A__  (			lowercase:     Namespace					)				->		Optional[int]:
       return TrainCommand(lowercase					)



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  @staticmethod
  def 		SCREAMING_SNAKE_CASE_		(				SCREAMING_SNAKE_CASE__    :					ArgumentParser     )    ->				Dict:
         A						:     Optional[Any]         =parser.add_parser('train'      ,	help='CLI tool to train a model on a task.'     )

         train_parser.add_argument(
             '--train_data'      ,	type=SCREAMING_SNAKE_CASE__      ,	required=SCREAMING_SNAKE_CASE__      ,	help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'      ,	)
         train_parser.add_argument(
             '--column_label'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=0      ,	help='Column of the dataset csv file with example labels.'     )
         train_parser.add_argument(
             '--column_text'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=1      ,	help='Column of the dataset csv file with example texts.'     )
         train_parser.add_argument(
             '--column_id'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=2      ,	help='Column of the dataset csv file with example ids.'     )
         train_parser.add_argument(
             '--skip_first_row'      ,	action='store_true'      ,	help='Skip the first row of the csv file (headers).'     )

         train_parser.add_argument('--validation_data'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=''      ,	help='path to validation dataset.'     )
         train_parser.add_argument(
             '--validation_split'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=0.1      ,	help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'      ,	)

         train_parser.add_argument('--output'      ,	type=SCREAMING_SNAKE_CASE__      ,	default='./'      ,	help='path to saved the trained model.'     )

         train_parser.add_argument(
             '--task'      ,	type=SCREAMING_SNAKE_CASE__      ,	default='text_classification'      ,	help='Task to train the model on.'     )
         train_parser.add_argument(
             '--model'      ,	type=SCREAMING_SNAKE_CASE__      ,	default='bert-base-uncased'      ,	help='Model\'s name or path to stored model.'     )
         train_parser.add_argument('--train_batch_size'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=32      ,	help='Batch size for training.'     )
         train_parser.add_argument('--valid_batch_size'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=64      ,	help='Batch size for validation.'     )
         train_parser.add_argument('--learning_rate'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=3e-5      ,	help='Learning rate.'     )
         train_parser.add_argument('--adam_epsilon'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=1e-08      ,	help='Epsilon for Adam optimizer.'     )
         train_parser.set_defaults(func=SCREAMING_SNAKE_CASE__     )







  def __init__(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Namespace     )    ->				List[Any]:
         A						:     Optional[int]         =logging.get_logger('transformers-cli/training'     )

         A						:     Dict         ='tf' if is_tf_available() else 'torch'

         os.makedirs(args.output      ,	exist_ok=SCREAMING_SNAKE_CASE__     )
         A						:     Optional[Any]         =args.output

         A						:     List[str]         =args.column_label
         A						:     int         =args.column_text
         A						:     Union[str, Any]         =args.column_id

         self.logger.info(f'Loading {args.task} pipeline for {args.model}'     )
         if args.task == "text_classification":
                A						:     Optional[Any]         =TextClassificationPipeline.from_pretrained(args.model     )
         elif args.task == "token_classification":
                raise NotImplementedError
         elif args.task == "question_answering":
                raise NotImplementedError

         self.logger.info(f'Loading dataset from {args.train_data}'     )
         A						:     Tuple         =Processor.create_from_csv(
             args.train_data      ,	column_label=args.column_label      ,	column_text=args.column_text      ,	column_id=args.column_id      ,	skip_first_row=args.skip_first_row      ,	)
         A						:     Dict         =None
         if args.validation_data:
                self.logger.info(f'Loading validation dataset from {args.validation_data}'     )
                A						:     List[Any]         =Processor.create_from_csv(
                    args.validation_data      ,	column_label=args.column_label      ,	column_text=args.column_text      ,	column_id=args.column_id      ,	skip_first_row=args.skip_first_row      ,	)

         A						:     Optional[Any]         =args.validation_split
         A						:     str         =args.train_batch_size
         A						:     Any         =args.valid_batch_size
         A						:     Dict         =args.learning_rate
         A						:     List[str]         =args.adam_epsilon







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Union[str, Any]:
         if self.framework == "tf":
                return self.run_tf()
         return self.run_torch()







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				List[str]:
         raise NotImplementedError







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				str:
         self.pipeline.fit(
             self.train_dataset      ,	validation_data=self.valid_dataset      ,	validation_split=self.validation_split      ,	learning_rate=self.learning_rate      ,	adam_epsilon=self.adam_epsilon      ,	train_batch_size=self.train_batch_size      ,	valid_batch_size=self.valid_batch_size      ,	)

         # Save trained pipeline
         self.pipeline.save_pretrained(self.output     )






