





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
_a 						=      False
_a 						=      False





def lowerCAmelCase__(__snake_case							)				->      List[str]:







	'''simple docstring'''







	return TrainCommand(__snake_case							)






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			@staticmethod
			def 		__lowerCamelCase				(    __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		parser.add_parser('''train'''       ,  help='''CLI tool to train a model on a task.'''							)

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

				train_parser.add_argument(
				    '''--task'''       ,  type=__lowerCAmelCase       ,  default='''text_classification'''       ,  help='''Task to train the model on.'''							)
				train_parser.add_argument(
				    '''--model'''       ,  type=__lowerCAmelCase       ,  default='''bert-base-uncased'''       ,  help='''Model\'s name or path to stored model.'''							)
				train_parser.add_argument('''--train_batch_size'''       ,  type=__lowerCAmelCase       ,  default=3_2       ,  help='''Batch size for training.'''							)
				train_parser.add_argument('''--valid_batch_size'''       ,  type=__lowerCAmelCase       ,  default=6_4       ,  help='''Batch size for validation.'''							)
				train_parser.add_argument('''--learning_rate'''       ,  type=__lowerCAmelCase       ,  default=3E-5       ,  help='''Learning rate.'''							)
				train_parser.add_argument('''--adam_epsilon'''       ,  type=__lowerCAmelCase       ,  default=1E-08       ,  help='''Epsilon for Adam optimizer.'''							)
				train_parser.set_defaults(func=__lowerCAmelCase							)





			def __init__(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		logging.get_logger('''transformers-cli/training'''							)

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

				os.makedirs(args.output       ,  exist_ok=__lowerCAmelCase							)
				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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				raise NotImplementedError





			def 		__lowerCamelCase				(    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							)










from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a 						=      logging.get_logger(__name__)

_a 						=      {
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""yolos"""





			def __init__(    self       ,  __lowerCAmelCase=7_6_8       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=3_0_7_2       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=1E-12       ,  __lowerCAmelCase=[5_1_2, 8_6_4]       ,  __lowerCAmelCase=1_6       ,  __lowerCAmelCase=3       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=1_0_0       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=5       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=0.1       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				super().__init__(**__lowerCAmelCase							)

				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		hidden_act
				lowerCamelCase__								=		hidden_dropout_prob
				lowerCamelCase__								=		attention_probs_dropout_prob
				lowerCamelCase__								=		initializer_range
				lowerCamelCase__								=		layer_norm_eps
				lowerCamelCase__								=		image_size
				lowerCamelCase__								=		patch_size
				lowerCamelCase__								=		num_channels
				lowerCamelCase__								=		qkv_bias
				lowerCamelCase__								=		num_detection_tokens
				lowerCamelCase__								=		use_mid_position_embeddings
				lowerCamelCase__								=		auxiliary_loss
				# Hungarian matcher
				lowerCamelCase__								=		class_cost
				lowerCamelCase__								=		bbox_cost
				lowerCamelCase__								=		giou_cost
				# Loss coefficients
				lowerCamelCase__								=		bbox_loss_coefficient
				lowerCamelCase__								=		giou_loss_coefficient
				lowerCamelCase__								=		eos_coefficient






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						version.parse("""1.11""" )





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]							)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 1E-4





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 1_2



