




"""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__			=		False
lowercase__			=		False







def 	_snake_case     (							lowercase__						):
				return TrainCommand(lowercase__						)






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			@staticmethod
			def    A_       (    lowercase    ):
							_lowerCamelCase	:     List[Any]       =       parser.add_parser('train'    , help='CLI tool to train a model on a task.'    )

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

							train_parser.add_argument(
							    '--task'    , type=lowercase    , default='text_classification'    , help='Task to train the model on.'    )
							train_parser.add_argument(
							    '--model'    , type=lowercase    , default='bert-base-uncased'    , help='Model\'s name or path to stored model.'    )
							train_parser.add_argument('--train_batch_size'    , type=lowercase    , default=32    , help='Batch size for training.'    )
							train_parser.add_argument('--valid_batch_size'    , type=lowercase    , default=64    , help='Batch size for validation.'    )
							train_parser.add_argument('--learning_rate'    , type=lowercase    , default=3E-5    , help='Learning rate.'    )
							train_parser.add_argument('--adam_epsilon'    , type=lowercase    , default=1E-08    , help='Epsilon for Adam optimizer.'    )
							train_parser.set_defaults(func=lowercase    )
			def __init__(    self    , lowercase    ):
							_lowerCamelCase	:     Optional[int]       =       logging.get_logger('transformers-cli/training'    )

							_lowerCamelCase	:     Union[str, Any]       =       'tf' if is_tf_available() else 'torch'

							os.makedirs(args.output    , exist_ok=lowercase    )
							_lowerCamelCase	:     Dict       =       args.output

							_lowerCamelCase	:     List[str]       =       args.column_label
							_lowerCamelCase	:     List[Any]       =       args.column_text
							_lowerCamelCase	:     List[Any]       =       args.column_id

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

							_lowerCamelCase	:     Any       =       args.validation_split
							_lowerCamelCase	:     Any       =       args.train_batch_size
							_lowerCamelCase	:     Optional[Any]       =       args.valid_batch_size
							_lowerCamelCase	:     int       =       args.learning_rate
							_lowerCamelCase	:     Any       =       args.adam_epsilon
			def    A_       (    self    ):
							if self.framework == "tf":
											return self.run_tf()
							return self.run_torch()
			def    A_       (    self    ):
							raise NotImplementedError






			def    A_       (    self    ):
							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 collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

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


if TYPE_CHECKING:
			from ...processing_utils import ProcessorMixin
			from ...utils import TensorType


lowercase__			=		logging.get_logger(__name__)

lowercase__			=		{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     """layoutlmv3"""
			def __init__(    self    , lowercase=50265    , lowercase=768    , lowercase=12    , lowercase=12    , lowercase=3072    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=2    , lowercase=0.02    , lowercase=1E-5    , lowercase=1    , lowercase=0    , lowercase=2    , lowercase=1024    , lowercase=128    , lowercase=128    , lowercase=True    , lowercase=32    , lowercase=128    , lowercase=64    , lowercase=256    , lowercase=True    , lowercase=True    , lowercase=True    , lowercase=224    , lowercase=3    , lowercase=16    , lowercase=None    , **lowercase    , ):
							super().__init__(
							    vocab_size=lowercase    , hidden_size=lowercase    , num_hidden_layers=lowercase    , num_attention_heads=lowercase    , intermediate_size=lowercase    , hidden_act=lowercase    , hidden_dropout_prob=lowercase    , attention_probs_dropout_prob=lowercase    , max_position_embeddings=lowercase    , type_vocab_size=lowercase    , initializer_range=lowercase    , layer_norm_eps=lowercase    , pad_token_id=lowercase    , bos_token_id=lowercase    , eos_token_id=lowercase    , **lowercase    , )
							_lowerCamelCase	:     List[str]       =       max_ad_position_embeddings
							_lowerCamelCase	:     Optional[Any]       =       coordinate_size
							_lowerCamelCase	:     int       =       shape_size
							_lowerCamelCase	:     Optional[Any]       =       has_relative_attention_bias
							_lowerCamelCase	:     Dict       =       rel_pos_bins
							_lowerCamelCase	:     Tuple       =       max_rel_pos
							_lowerCamelCase	:     int       =       has_spatial_attention_bias
							_lowerCamelCase	:     Optional[int]       =       rel_ad_pos_bins
							_lowerCamelCase	:     List[Any]       =       max_rel_ad_pos
							_lowerCamelCase	:     Any       =       text_embed
							_lowerCamelCase	:     List[Any]       =       visual_embed
							_lowerCamelCase	:     str       =       input_size
							_lowerCamelCase	:     Optional[Any]       =       num_channels
							_lowerCamelCase	:     Tuple       =       patch_size
							_lowerCamelCase	:     Optional[int]       =       classifier_dropout






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     version.parse("""1.12"""		)
			@property
			def    A_       (    self    ):
							# The order of inputs is different for question answering and sequence classification
							if self.task in ["question-answering", "sequence-classification"]:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]    )
							else:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
											    ]    )
			@property
			def    A_       (    self    ):
							return 1E-5
			@property
			def    A_       (    self    ):
							return 12






			def    A_       (    self    , lowercase    , lowercase = -1    , lowercase = -1    , lowercase = False    , lowercase = None    , lowercase = 3    , lowercase = 40    , lowercase = 40    , ):
							setattr(processor.image_processor    , 'apply_ocr'    , lowercase    )

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							_lowerCamelCase	:     Any       =       compute_effective_axis_dimension(
							    lowercase    , fixed_dimension=OnnxConfig.default_fixed_batch    , num_token_to_add=0    )
							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							_lowerCamelCase	:     Tuple       =       processor.tokenizer.num_special_tokens_to_add(lowercase    )
							_lowerCamelCase	:     Any       =       compute_effective_axis_dimension(
							    lowercase    , fixed_dimension=OnnxConfig.default_fixed_sequence    , num_token_to_add=lowercase    )
							# Generate dummy inputs according to compute batch and sequence
							_lowerCamelCase	:     List[Any]       =       [[' '.join([processor.tokenizer.unk_token]    ) * seq_length]] * batch_size

							# Generate dummy bounding boxes
							_lowerCamelCase	:     List[str]       =       [[[48, 84, 73, 128]]] * batch_size

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
							_lowerCamelCase	:     Tuple       =       self._generate_dummy_images(lowercase    , lowercase    , lowercase    , lowercase    )

							_lowerCamelCase	:     str       =       dict(
							    processor(
							        lowercase    , text=lowercase    , boxes=lowercase    , return_tensors=lowercase    , )    )

							return inputs