



'''simple docstring'''






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


UpperCamelCase__:  Tuple				      =     logging.get_logger(__name__)

@dataclass
class 							SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	lowerCamelCase__     					=			field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()     )}     )
	lowerCamelCase__     					=			field(
	    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}     )
	lowerCamelCase__     					=			field(
	    default=128				,						metadata={
	        """help""": (
	            """The maximum total input sequence length after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded."""
	        )
	    }				,						)
	lowerCamelCase__     					=			field(
	    default=__A				,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )







	def 	A      (		self     :	str						)     ->						Tuple:
								UpperCAmelCase					:			int									=						self.task_name.lower()

class 							SCREAMING_SNAKE_CASE(				__A     ):







	"""simple docstring"""






	lowerCamelCase__     					=			"""train"""
	lowerCamelCase__     					=			"""dev"""
	lowerCamelCase__     					=			"""test"""

class 							SCREAMING_SNAKE_CASE(				__A     ):







	"""simple docstring"""






	lowerCamelCase__     					=			42
	lowerCamelCase__     					=			42
	lowerCamelCase__     					=			42







	def __init__(		self     :	Optional[Any]						,     __snake_case     :	GlueDataTrainingArguments						,     __snake_case     :	PreTrainedTokenizerBase						,     __snake_case     :	Optional[int] = None						,     __snake_case     :	Union[str, Split] = Split.train						,     __snake_case     :	Optional[str] = None						,     )     ->						int:
								warnings.warn(
								    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
								    '''library. You can have a look at this example script for pointers: '''
								    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''						,     lowerCAmelCase_						,     )
								UpperCAmelCase					:			Dict									=						args
								UpperCAmelCase					:			Any									=						glue_processors[args.task_name]()
								UpperCAmelCase					:			Any									=						glue_output_modes[args.task_name]
								if isinstance(lowerCAmelCase_						,     lowerCAmelCase_						):
															try:
																						UpperCAmelCase					:			Union[str, Any]									=						Split[mode]
															except KeyError:
																						raise KeyError('''mode is not a valid split name'''						)
        # Load data features from cache or dataset file
								UpperCAmelCase					:			Optional[int]									=						os.path.join(
								    cache_dir if cache_dir is not None else args.data_dir						,     F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""						,     )
								UpperCAmelCase					:			Optional[Any]									=						self.processor.get_labels()
								if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
								    "RobertaTokenizer",
								    "RobertaTokenizerFast",
								    "XLMRobertaTokenizer",
								    "BartTokenizer",
								    "BartTokenizerFast",
								):
															# HACK(label indices are swapped in RoBERTa pretrained model)
															UpperCAmelCase					:			Optional[Any]									=						label_list[2], label_list[1]
								UpperCAmelCase					:			Any									=						label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								UpperCAmelCase					:			Any									=						cached_features_file + ".lock"
								with FileLock(lowerCAmelCase_						):
															if os.path.exists(lowerCAmelCase_						) and not args.overwrite_cache:
																						UpperCAmelCase					:			List[Any]									=						time.time()
																						UpperCAmelCase					:			Tuple									=						torch.load(lowerCAmelCase_						)
																						logger.info(
																						    F"""Loading features from cached file {cached_features_file} [took %.3f s]"""						,     time.time() - start						)
															else:
																						logger.info(F"""Creating features from dataset file at {args.data_dir}"""						)

																						if mode == Split.dev:
																													UpperCAmelCase					:			Dict									=						self.processor.get_dev_examples(args.data_dir						)
																						elif mode == Split.test:
																													UpperCAmelCase					:			Union[str, Any]									=						self.processor.get_test_examples(args.data_dir						)
																						else:
																													UpperCAmelCase					:			Any									=						self.processor.get_train_examples(args.data_dir						)
																						if limit_length is not None:
																													UpperCAmelCase					:			Any									=						examples[:limit_length]
																						UpperCAmelCase					:			List[str]									=						glue_convert_examples_to_features(
																						    lowerCAmelCase_						,     lowerCAmelCase_						,     max_length=args.max_seq_length						,     label_list=lowerCAmelCase_						,     output_mode=self.output_mode						,     )
																						UpperCAmelCase					:			Optional[Any]									=						time.time()
																						torch.save(self.features						,     lowerCAmelCase_						)
																						# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																						logger.info(
																						    F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""						)







	def __len__(		self     :	List[str]						)     ->						int:
								return len(self.features						)







	def __getitem__(		self     :	Union[str, Any]						,     __snake_case     :	Union[str, Any]						)     ->						InputFeatures:
								return self.features[i]







	def 	A      (		self     :	str						)     ->						Dict:
								return self.label_list













"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class      UpperCamelCase_			(__A	):
	__magic_name__       =							['''image_processor''', '''tokenizer''']
	__magic_name__       =							'''FlavaImageProcessor'''
	__magic_name__       =							('''BertTokenizer''', '''BertTokenizerFast''')





	def __init__( self   :					Optional[int]      ,      lowerCAmelCase_   :					Tuple=None      ,      lowerCAmelCase_   :					List[str]=None      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     List[Any]:
				UpperCAmelCase_	:					Any     	=   None
				if "feature_extractor" in kwargs:
							warnings.warn(
							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
							    " instead."      ,      lowerCAmelCase_      ,      )
							UpperCAmelCase_	:					Tuple     	=   kwargs.pop("feature_extractor"		)

				UpperCAmelCase_	:					Optional[Any]     	=   image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
							raise ValueError("You need to specify an `image_processor`."		)
				if tokenizer is None:
							raise ValueError("You need to specify a `tokenizer`."		)

				super().__init__(lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   self.image_processor





	def __call__( self   :					List[Any]      ,      lowerCAmelCase_   :					Optional[ImageInput] = None      ,      lowerCAmelCase_   :					Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Union[bool, str, PaddingStrategy] = False      ,      lowerCAmelCase_   :					Union[bool, str, TruncationStrategy] = False      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					Optional[bool] = None      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = False      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					Optional[Union[str, TensorType]] = None      ,      **lowerCAmelCase_   :					Optional[int]      ,      )		->     List[Any]:

				if text is None and images is None:
							raise ValueError("You have to specify either text or images. Both cannot be none."		)

				if text is not None:
							UpperCAmelCase_	:					Union[str, Any]     	=   self.tokenizer(
							    text=lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      truncation=lowerCAmelCase_      ,      max_length=lowerCAmelCase_      ,      stride=lowerCAmelCase_      ,      pad_to_multiple_of=lowerCAmelCase_      ,      return_token_type_ids=lowerCAmelCase_      ,      return_attention_mask=lowerCAmelCase_      ,      return_overflowing_tokens=lowerCAmelCase_      ,      return_special_tokens_mask=lowerCAmelCase_      ,      return_offsets_mapping=lowerCAmelCase_      ,      return_length=lowerCAmelCase_      ,      verbose=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )
				if images is not None:
							UpperCAmelCase_	:					Optional[Any]     	=   self.image_processor(
							    lowerCAmelCase_      ,      return_image_mask=lowerCAmelCase_      ,      return_codebook_pixels=lowerCAmelCase_      ,      return_tensors=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				if text is not None and images is not None:
							encoding.update(lowerCAmelCase_		)
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowerCAmelCase_		)      ,      tensor_type=lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      *lowerCAmelCase_   :					int      ,      **lowerCAmelCase_   :					List[str]		)		->     Any:
				return self.tokenizer.batch_decode(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      *lowerCAmelCase_   :					List[str]      ,      **lowerCAmelCase_   :					Optional[int]		)		->     List[Any]:
				return self.tokenizer.decode(*lowerCAmelCase_      ,      **lowerCAmelCase_		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   self.tokenizer.model_input_names
				UpperCAmelCase_	:					Any     	=   self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[Any]:
				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,      lowerCAmelCase_      ,      )
				return self.image_processor_class





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				warnings.warn(
				    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,      lowerCAmelCase_      ,      )
				return self.image_processor



