






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
__snake_case    :    Tuple         =     False
__snake_case    :    Optional[int]         =     False







def 	_UpperCamelCase   (    UpperCamelCase_       :					Namespace		)  ->   Optional[Any]:

						"""simple docstring"""



						return TrainCommand(UpperCamelCase_		)


class     __SCREAMING_SNAKE_CASE					(   __lowercase):






						@staticmethod
						def       UpperCamelCase__      (	_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						parser.add_parser('train'     ,						help='CLI tool to train a model on a task.'   )

												train_parser.add_argument(
												    '--train_data'     ,						type=_UpperCamelCase     ,						required=_UpperCamelCase     ,						help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'     ,						)
												train_parser.add_argument(
												    '--column_label'     ,						type=_UpperCamelCase     ,						default=0     ,						help='Column of the dataset csv file with example labels.'   )
												train_parser.add_argument(
												    '--column_text'     ,						type=_UpperCamelCase     ,						default=1     ,						help='Column of the dataset csv file with example texts.'   )
												train_parser.add_argument(
												    '--column_id'     ,						type=_UpperCamelCase     ,						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=_UpperCamelCase     ,						default=''     ,						help='path to validation dataset.'   )
												train_parser.add_argument(
												    '--validation_split'     ,						type=_UpperCamelCase     ,						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=_UpperCamelCase     ,						default='./'     ,						help='path to saved the trained model.'   )

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






						def __init__(	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						logging.get_logger('transformers-cli/training'   )

												lowerCAmelCase__    						=						'tf' if is_tf_available() else 'torch'

												os.makedirs(args.output     ,						exist_ok=_UpperCamelCase   )
												lowerCAmelCase__    						=						args.output

												lowerCAmelCase__    						=						args.column_label
												lowerCAmelCase__    						=						args.column_text
												lowerCAmelCase__    						=						args.column_id

												self.logger.info(F"Loading {args.task} pipeline for {args.model}"   )
												if args.task == "text_classification":
																		lowerCAmelCase__    						=						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}"   )
												lowerCAmelCase__    						=						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     ,						)
												lowerCAmelCase__    						=						None
												if args.validation_data:
																		self.logger.info(F"Loading validation dataset from {args.validation_data}"   )
																		lowerCAmelCase__    						=						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     ,						)

												lowerCAmelCase__    						=						args.validation_split
												lowerCAmelCase__    						=						args.train_batch_size
												lowerCAmelCase__    						=						args.valid_batch_size
												lowerCAmelCase__    						=						args.learning_rate
												lowerCAmelCase__    						=						args.adam_epsilon






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												if self.framework == "tf":
																		return self.run_tf()
												return self.run_torch()






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												raise NotImplementedError







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												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   )













import numpy as np
from PIL import Image







def 	_UpperCamelCase   (    UpperCamelCase_       :					np.ndarray    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					int		)  ->   np.ndarray:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.array(UpperCamelCase_		)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('The input array is not a square matrix'		)
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0

						# compute the shape of the output matrix
						lowerCAmelCase__    						=						(arr.shape[0] - size) // stride + 1
						# initialize the output matrix with zeros of shape maxpool_shape
						lowerCAmelCase__    						=						np.zeros((maxpool_shape, maxpool_shape)		)

						while i < arr.shape[0]:
												if i + size > arr.shape[0]:
																		# if the end of the matrix is reached, break
																		break
												while j < arr.shape[1]:
																		# if the end of the matrix is reached, break
																		if j + size > arr.shape[1]:
																								break
																		# compute the maximum of the pooling matrix
																		lowerCAmelCase__    						=						np.max(arr[i : i + size, j : j + size]		)
																		# shift the pooling matrix by stride of column pixels
																		j += stride
																		mat_j += 1

												# shift the pooling matrix by stride of row pixels
												i += stride
												mat_i += 1

												# reset the column index to 0
												lowerCAmelCase__    						=						0
												lowerCAmelCase__    						=						0

						return updated_arr







def 	_UpperCamelCase   (    UpperCamelCase_       :					np.ndarray    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					int		)  ->   np.ndarray:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.array(UpperCamelCase_		)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('The input array is not a square matrix'		)
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0

						# compute the shape of the output matrix
						lowerCAmelCase__    						=						(arr.shape[0] - size) // stride + 1
						# initialize the output matrix with zeros of shape avgpool_shape
						lowerCAmelCase__    						=						np.zeros((avgpool_shape, avgpool_shape)		)

						while i < arr.shape[0]:
												# if the end of the matrix is reached, break
												if i + size > arr.shape[0]:
																		break
												while j < arr.shape[1]:
																		# if the end of the matrix is reached, break
																		if j + size > arr.shape[1]:
																								break
																		# compute the average of the pooling matrix
																		lowerCAmelCase__    						=						int(np.average(arr[i : i + size, j : j + size]		)		)
																		# shift the pooling matrix by stride of column pixels
																		j += stride
																		mat_j += 1

												# shift the pooling matrix by stride of row pixels
												i += stride
												mat_i += 1
												# reset the column index to 0
												lowerCAmelCase__    						=						0
												lowerCAmelCase__    						=						0

						return updated_arr


# Main Function
if __name__ == "__main__":
					from doctest import testmod

					testmod(name="""avgpooling""", verbose=True)

					# Loading the image
					__snake_case    :    List[str]         =     Image.open("""path_to_image""")

					# Converting the image to numpy array and maxpooling, displaying the result
					# Ensure that the image is a square matrix

					Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

					# Converting the image to numpy array and averagepooling, displaying the result
					# Ensure that the image is a square matrix

					Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()





