


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def 						a__			(				A__,      A__,      A__,      A__,      A__,      A__ = None,      ):
			SCREAMING_SNAKE_CASE_		:     int					=					{}

			if train_file is not None:
						SCREAMING_SNAKE_CASE_		:     Dict					=					[train_file]
			if eval_file is not None:
						SCREAMING_SNAKE_CASE_		:     str					=					[eval_file]
			if test_file is not None:
						SCREAMING_SNAKE_CASE_		:     str					=					[test_file]

			SCREAMING_SNAKE_CASE_		:     Tuple					=					datasets.load_dataset('csv',      data_files=A__				)
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					list(ds[list(files.keys()				)[0]].features.keys()				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					features_name.pop(A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					list(set(ds[list(files.keys()				)[0]][label_name]				)				)
			SCREAMING_SNAKE_CASE_		:     Any					=					{label: i for i, label in enumerate(A__				)}
			SCREAMING_SNAKE_CASE_		:     Dict					=					tokenizer.model_input_names
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					{}

			if len(A__				) == 1:
						for k in files.keys():
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					ds[k].map(
									    lambda A__				: tokenizer.batch_encode_plus(
									        example[features_name[0]],      truncation=A__,      max_length=A__,      padding='max_length'				),      batched=A__,      )
			elif len(A__				) == 2:
						for k in files.keys():
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					ds[k].map(
									    lambda A__				: tokenizer.batch_encode_plus(
									        (example[features_name[0]], example[features_name[1]]),      truncation=A__,      max_length=A__,      padding='max_length',      ),      batched=A__,      )

			def gen_train():
						for ex in transformed_ds[datasets.Split.TRAIN]:
									SCREAMING_SNAKE_CASE_		:     Tuple					=					{k: v for k, v in ex.items() if k in input_names}
									SCREAMING_SNAKE_CASE_		:     List[Any]					=					labelaid[ex[label_name]]
									yield (d, label)

			def gen_val():
						for ex in transformed_ds[datasets.Split.VALIDATION]:
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{k: v for k, v in ex.items() if k in input_names}
									SCREAMING_SNAKE_CASE_		:     List[Any]					=					labelaid[ex[label_name]]
									yield (d, label)

			def gen_test():
						for ex in transformed_ds[datasets.Split.TEST]:
									SCREAMING_SNAKE_CASE_		:     Dict					=					{k: v for k, v in ex.items() if k in input_names}
									SCREAMING_SNAKE_CASE_		:     Tuple					=					labelaid[ex[label_name]]
									yield (d, label)

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					(
			    tf.data.Dataset.from_generator(
			        A__,      ({k: tf.intaa for k in input_names}, tf.intaa),      ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				)),      )
			    if datasets.Split.TRAIN in transformed_ds
			    else None
			)

			if train_ds is not None:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

			SCREAMING_SNAKE_CASE_		:     int					=					(
			    tf.data.Dataset.from_generator(
			        A__,      ({k: tf.intaa for k in input_names}, tf.intaa),      ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				)),      )
			    if datasets.Split.VALIDATION in transformed_ds
			    else None
			)

			if val_ds is not None:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					(
			    tf.data.Dataset.from_generator(
			        A__,      ({k: tf.intaa for k in input_names}, tf.intaa),      ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				)),      )
			    if datasets.Split.TEST in transformed_ds
			    else None
			)

			if test_ds is not None:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

			return train_ds, val_ds, test_ds, labelaid


lowerCAmelCase__			:      Union[str, Any]								=logging.getLogger(__name__)







@dataclass
class 	__lowercase    :





							"""simple docstring"""







							_UpperCAmelCase      =						field(metadata={"""help""": """Which column contains the label"""}  )
							_UpperCAmelCase      =						field(default=__SCREAMING_SNAKE_CASE	,       metadata={"""help""": """The path of the training file"""}  )
							_UpperCAmelCase      =						field(default=__SCREAMING_SNAKE_CASE	,       metadata={"""help""": """The path of the development file"""}  )
							_UpperCAmelCase      =						field(default=__SCREAMING_SNAKE_CASE	,       metadata={"""help""": """The path of the test file"""}  )
							_UpperCAmelCase      =						field(
							    default=1_2_8	,       metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }	,       )
							_UpperCAmelCase      =						field(
							    default=__SCREAMING_SNAKE_CASE	,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )







@dataclass
class 	__lowercase    :





							"""simple docstring"""







							_UpperCAmelCase      =						field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
							_UpperCAmelCase      =						field(
							    default=__SCREAMING_SNAKE_CASE	,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
							_UpperCAmelCase      =						field(
							    default=__SCREAMING_SNAKE_CASE	,       metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
							_UpperCAmelCase      =						field(default=__SCREAMING_SNAKE_CASE	,       metadata={"""help""": """Set this flag to use fast tokenization."""}  )
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							_UpperCAmelCase      =						field(
							    default=__SCREAMING_SNAKE_CASE	,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}	,       )







def 						a__			(				):
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Any					=					parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir				)
			    and os.listdir(training_args.output_dir				)
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
						    ' --overwrite_output_dir to overcome.'				)

			# Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',      datefmt='%m/%d/%Y %H:%M:%S',      level=logging.INFO,      )
			logger.info(
			    F'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1				)}, '''
			    F'''16-bits training: {training_args.fpaa}'''				)
			logger.info(F'''Training/evaluation parameters {training_args}'''				)

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			SCREAMING_SNAKE_CASE_		:     Dict					=					AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,      cache_dir=model_args.cache_dir,      )

			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     str					=					get_tfds(
			    train_file=data_args.train_file,      eval_file=data_args.dev_file,      test_file=data_args.test_file,      tokenizer=A__,      label_column_id=data_args.label_column_id,      max_seq_length=data_args.max_seq_length,      )

			SCREAMING_SNAKE_CASE_		:     int					=					AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path,      num_labels=len(A__				),      labelaid=A__,      idalabel={id: label for label, id in labelaid.items()},      finetuning_task='text-classification',      cache_dir=model_args.cache_dir,      )

			with training_args.strategy.scope():
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					TFAutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path,      from_pt=bool('.bin' in model_args.model_name_or_path				),      config=A__,      cache_dir=model_args.cache_dir,      )

			def compute_metrics(A__				) -> Dict:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					np.argmax(p.predictions,      axis=1				)

						return {"acc": (preds == p.label_ids).mean()}

			# Initialize our Trainer
			SCREAMING_SNAKE_CASE_		:     int					=					TFTrainer(
			    model=A__,      args=A__,      train_dataset=A__,      eval_dataset=A__,      compute_metrics=A__,      )

			# Training
			if training_args.do_train:
						trainer.train()
						trainer.save_model()
						tokenizer.save_pretrained(training_args.output_dir				)

			# Evaluation
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					{}
			if training_args.do_eval:
						logger.info('*** Evaluate ***'				)
						SCREAMING_SNAKE_CASE_		:     Dict					=					trainer.evaluate()
						SCREAMING_SNAKE_CASE_		:     int					=					os.path.join(training_args.output_dir,      'eval_results.txt'				)

						with open(A__,      'w'				) as writer:
									logger.info('***** Eval results *****'				)

									for key, value in result.items():
												logger.info(F'''  {key} = {value}'''				)
												writer.write(F'''{key} = {value}\n'''				)

									results.update(A__				)

			return results


if __name__ == "__main__":
				main()









from typing import List, Optional, Union

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







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""image_processor""", """tokenizer"""]
							_UpperCAmelCase      =						"""Pix2StructImageProcessor"""
							_UpperCAmelCase      =						("""T5Tokenizer""", """T5TokenizerFast""")






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					False
										super().__init__(lowerCAmelCase__			,						lowerCAmelCase__       )






							def __call__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = 2_0_4_8			,						lowerCAmelCase__ = 0			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										if images is None and text is None:
													raise ValueError('You have to specify either images or text.'       )

										# Get only text
										if images is None and not self.image_processor.is_vqa:
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.tokenizer
													SCREAMING_SNAKE_CASE_		:     str					=					self.tokenizer(
													    text=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						padding=lowerCAmelCase__			,						truncation=lowerCAmelCase__			,						max_length=lowerCAmelCase__			,						stride=lowerCAmelCase__			,						pad_to_multiple_of=lowerCAmelCase__			,						return_attention_mask=lowerCAmelCase__			,						return_overflowing_tokens=lowerCAmelCase__			,						return_special_tokens_mask=lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__			,						return_length=lowerCAmelCase__			,						verbose=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__			,						)
													return text_encoding

										if not self.image_processor.is_vqa:
													# add pixel_values
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.image_processor(
													    lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						max_patches=lowerCAmelCase__			,						**lowerCAmelCase__       )
										else:
													# add pixel_values and bbox
													SCREAMING_SNAKE_CASE_		:     Any					=					self.image_processor(
													    lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						max_patches=lowerCAmelCase__			,						header_text=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if text is not None and not self.image_processor.is_vqa:
													SCREAMING_SNAKE_CASE_		:     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_attention_mask=lowerCAmelCase__			,						return_overflowing_tokens=lowerCAmelCase__			,						return_special_tokens_mask=lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__			,						return_length=lowerCAmelCase__			,						verbose=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__			,						)

													if "attention_mask" in text_encoding:
																SCREAMING_SNAKE_CASE_		:     Any					=					text_encoding.pop('attention_mask'       )
													if "input_ids" in text_encoding:
																SCREAMING_SNAKE_CASE_		:     List[str]					=					text_encoding.pop('input_ids'       )
										else:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					None

										if text_encoding is not None:
													encoding_image_processor.update(lowerCAmelCase__       )

										return encoding_image_processor






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase__			,						**lowerCAmelCase__       )






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.tokenizer.model_input_names
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





