
'''simple docstring'''




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			:				Tuple    		=			False
lowercase			:				str    		=			False







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    return TrainCommand(snake_case__						)







class  A				(							__snake_case						):





     @staticmethod
     def 				__lowerCAmelCase							(							SCREAMING_SNAKE_CASE	) ->				Optional[Any]:






         """simple docstring"""






         A       :   List[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						,			SCREAMING_SNAKE_CASE	) ->				Optional[Any]:






         """simple docstring"""






         A       :   Any					=						logging.get_logger('''transformers-cli/training'''	)

         A       :   List[Any]					=						'''tf''' if is_tf_available() else '''torch'''

         os.makedirs(args.output						,			exist_ok=SCREAMING_SNAKE_CASE	)
         A       :   List[str]					=						args.output

         A       :   Any					=						args.column_label
         A       :   Optional[Any]					=						args.column_text
         A       :   Optional[Any]					=						args.column_id

         self.logger.info(F'Loading {args.task} pipeline for {args.model}'	)
         if args.task == "text_classification":
             A       :   int					=						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       :   Any					=						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       :   Any					=						None
         if args.validation_data:
             self.logger.info(F'Loading validation dataset from {args.validation_data}'	)
             A       :   Tuple					=						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       :   Tuple					=						args.validation_split
         A       :   Optional[int]					=						args.train_batch_size
         A       :   Dict					=						args.valid_batch_size
         A       :   Any					=						args.learning_rate
         A       :   Any					=						args.adam_epsilon





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         raise NotImplementedError





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """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	)






'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


lowercase			:				Optional[Any]    		=			logging.get_logger(__name__)





class  A				(							__snake_case						):
     __magic_name__						=		['''pixel_values''']





     def __init__(							self						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = 1 / 255						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				None:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE	)
         A       :   str					=						size if size is not None else {'''shortest_edge''': 384}
         A       :   Tuple					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)

         A       :   str					=						do_resize
         A       :   List[Any]					=						size
         # Default value set here for backwards compatibility where the value in config is None
         A       :   List[Any]					=						crop_pct if crop_pct is not None else 224 / 256
         A       :   Optional[int]					=						resample
         A       :   Union[str, Any]					=						do_rescale
         A       :   List[str]					=						rescale_factor
         A       :   Union[str, Any]					=						do_normalize
         A       :   Any					=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         A       :   Optional[int]					=						image_std if image_std is not None else IMAGENET_STANDARD_STD





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				np.ndarray:






         """simple docstring"""






         A       :   str					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)
         if "shortest_edge" not in size:
             raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'	)
         A       :   Any					=						size['''shortest_edge''']

         if shortest_edge < 384:
             # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
             A       :   Dict					=						int(shortest_edge / crop_pct	)
             A       :   str					=						get_resize_output_image_size(SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)
             A       :   int					=						resize(image=SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			resample=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
             # then crop to (shortest_edge, shortest_edge)
             return center_crop(image=SCREAMING_SNAKE_CASE						,			size=(shortest_edge, shortest_edge)						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         else:
             # warping (no cropping) when evaluated at 384 or larger
             return resize(
                 SCREAMING_SNAKE_CASE						,			size=(shortest_edge, shortest_edge)						,			resample=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				List[str]:






         """simple docstring"""






         return rescale(SCREAMING_SNAKE_CASE						,			scale=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				np.ndarray:






         """simple docstring"""






         return normalize(SCREAMING_SNAKE_CASE						,			mean=SCREAMING_SNAKE_CASE						,			std=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,			**SCREAMING_SNAKE_CASE						,			) ->				PIL.Image.Image:






         """simple docstring"""






         A       :   int					=						do_resize if do_resize is not None else self.do_resize
         A       :   Tuple					=						crop_pct if crop_pct is not None else self.crop_pct
         A       :   Optional[Any]					=						resample if resample is not None else self.resample
         A       :   List[Any]					=						do_rescale if do_rescale is not None else self.do_rescale
         A       :   List[Any]					=						rescale_factor if rescale_factor is not None else self.rescale_factor
         A       :   Union[str, Any]					=						do_normalize if do_normalize is not None else self.do_normalize
         A       :   Union[str, Any]					=						image_mean if image_mean is not None else self.image_mean
         A       :   List[str]					=						image_std if image_std is not None else self.image_std

         A       :   Union[str, Any]					=						size if size is not None else self.size
         A       :   List[Any]					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)

         A       :   Any					=						make_list_of_images(SCREAMING_SNAKE_CASE	)

         if not valid_images(SCREAMING_SNAKE_CASE	):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''	)

         if do_resize and size is None or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize is True.'''	)

         if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
             raise ValueError('''crop_pct must be specified if size < 384.'''	)

         if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''	)

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.'''	)

         # All transformations expect numpy arrays.
         A       :   Optional[int]					=						[to_numpy_array(SCREAMING_SNAKE_CASE	) for image in images]

         if do_resize:
             A       :   Any					=						[self.resize(image=SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			crop_pct=SCREAMING_SNAKE_CASE						,			resample=SCREAMING_SNAKE_CASE	) for image in images]

         if do_rescale:
             A       :   str					=						[self.rescale(image=SCREAMING_SNAKE_CASE						,			scale=SCREAMING_SNAKE_CASE	) for image in images]

         if do_normalize:
             A       :   Dict					=						[self.normalize(image=SCREAMING_SNAKE_CASE						,			mean=SCREAMING_SNAKE_CASE						,			std=SCREAMING_SNAKE_CASE	) for image in images]

         A       :   Any					=						[to_channel_dimension_format(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) for image in images]

         A       :   Optional[int]					=						{'''pixel_values''': images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE						,			tensor_type=SCREAMING_SNAKE_CASE	)




