



"""simple docstring"""
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast





@require_vision
class 		__UpperCAmelCase				(	unittest.TestCase					):







 """simple docstring"""




 def  A  (				self					: Any	)->	Tuple:
   __UpperCamelCase  						=							tempfile.mkdtemp()

   __UpperCamelCase  						=							BlipImageProcessor()
   __UpperCamelCase  						=							BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"	)

   __UpperCamelCase  						=							BlipProcessor(lowerCAmelCase_						, lowerCAmelCase_	)

   processor.save_pretrained(self.tmpdirname	)





 def  A  (				self					: List[Any]						, **A_					: List[str]	)->	str:
   return AutoProcessor.from_pretrained(self.tmpdirname						, **lowerCAmelCase_	).tokenizer





 def  A  (				self					: int						, **A_					: Optional[int]	)->	Any:
   return AutoProcessor.from_pretrained(self.tmpdirname						, **lowerCAmelCase_	).image_processor





 def  A  (				self					: Optional[int]	)->	int:
   shutil.rmtree(self.tmpdirname	)





 def  A  (				self					: List[str]	)->	Any:
   __UpperCamelCase  						=							[np.random.randint(2_55						, size=(3, 30, 4_00)						, dtype=np.uinta	)]

   __UpperCamelCase  						=							[Image.fromarray(np.moveaxis(lowerCAmelCase_						, 0						, -1	)	) for x in image_inputs]

   return image_inputs





 def  A  (				self					: List[Any]	)->	int:
   __UpperCamelCase  						=							BlipProcessor(tokenizer=self.get_tokenizer()						, image_processor=self.get_image_processor()	)
   processor.save_pretrained(self.tmpdirname	)

   __UpperCamelCase  						=							self.get_tokenizer(bos_token="(BOS)"						, eos_token="(EOS)"	)
   __UpperCamelCase  						=							self.get_image_processor(do_normalize=lowerCAmelCase_						, padding_value=1.0	)

   __UpperCamelCase  						=							BlipProcessor.from_pretrained(
       self.tmpdirname						, bos_token="(BOS)"						, eos_token="(EOS)"						, do_normalize=lowerCAmelCase_						, padding_value=1.0	)

   self.assertEqual(processor.tokenizer.get_vocab()						, tokenizer_add_kwargs.get_vocab()	)
   self.assertIsInstance(processor.tokenizer						, lowerCAmelCase_	)

   self.assertEqual(processor.image_processor.to_json_string()						, image_processor_add_kwargs.to_json_string()	)
   self.assertIsInstance(processor.image_processor						, lowerCAmelCase_	)





 def  A  (				self					: Any	)->	Any:
   __UpperCamelCase  						=							self.get_image_processor()
   __UpperCamelCase  						=							self.get_tokenizer()

   __UpperCamelCase  						=							BlipProcessor(tokenizer=lowerCAmelCase_						, image_processor=lowerCAmelCase_	)

   __UpperCamelCase  						=							self.prepare_image_inputs()

   __UpperCamelCase  						=							image_processor(lowerCAmelCase_						, return_tensors="np"	)
   __UpperCamelCase  						=							processor(images=lowerCAmelCase_						, return_tensors="np"	)

   for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum()						, input_processor[key].sum()						, delta=1e-2	)





 def  A  (				self					: str	)->	Optional[int]:
   __UpperCamelCase  						=							self.get_image_processor()
   __UpperCamelCase  						=							self.get_tokenizer()

   __UpperCamelCase  						=							BlipProcessor(tokenizer=lowerCAmelCase_						, image_processor=lowerCAmelCase_	)

   __UpperCamelCase  						=							"lower newer"

   __UpperCamelCase  						=							processor(text=lowerCAmelCase_	)

   __UpperCamelCase  						=							tokenizer(lowerCAmelCase_						, return_token_type_ids=lowerCAmelCase_	)

   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]						, encoded_processor[key]	)





 def  A  (				self					: str	)->	Optional[int]:
   __UpperCamelCase  						=							self.get_image_processor()
   __UpperCamelCase  						=							self.get_tokenizer()

   __UpperCamelCase  						=							BlipProcessor(tokenizer=lowerCAmelCase_						, image_processor=lowerCAmelCase_	)

   __UpperCamelCase  						=							"lower newer"
   __UpperCamelCase  						=							self.prepare_image_inputs()

   __UpperCamelCase  						=							processor(text=lowerCAmelCase_						, images=lowerCAmelCase_	)

   self.assertListEqual(list(inputs.keys()	)						, ["pixel_values", "input_ids", "attention_mask"]	)

   # test if it raises when no input is passed
   with pytest.raises(lowerCAmelCase_	):
     processor()





 def  A  (				self					: List[str]	)->	str:
   __UpperCamelCase  						=							self.get_image_processor()
   __UpperCamelCase  						=							self.get_tokenizer()

   __UpperCamelCase  						=							BlipProcessor(tokenizer=lowerCAmelCase_						, image_processor=lowerCAmelCase_	)

   __UpperCamelCase  						=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   __UpperCamelCase  						=							processor.batch_decode(lowerCAmelCase_	)
   __UpperCamelCase  						=							tokenizer.batch_decode(lowerCAmelCase_	)

   self.assertListEqual(lowerCAmelCase_						, lowerCAmelCase_	)





 def  A  (				self					: Any	)->	Optional[Any]:
   __UpperCamelCase  						=							self.get_image_processor()
   __UpperCamelCase  						=							self.get_tokenizer()

   __UpperCamelCase  						=							BlipProcessor(tokenizer=lowerCAmelCase_						, image_processor=lowerCAmelCase_	)

   __UpperCamelCase  						=							"lower newer"
   __UpperCamelCase  						=							self.prepare_image_inputs()

   __UpperCamelCase  						=							processor(text=lowerCAmelCase_						, images=lowerCAmelCase_	)

   # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
   self.assertListEqual(list(inputs.keys()	)						, ["pixel_values", "input_ids", "attention_mask"]	)





'''simple docstring'''


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 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    PreTrainedTokenizer					,		UpperCamelCase			:    int					,		UpperCamelCase			:    Optional[int] = None					,		):





							'''simple docstring'''


							_a      	=							{}

							if train_file is not None:
														_a      	=							[train_file]
							if eval_file is not None:
														_a      	=							[eval_file]
							if test_file is not None:
														_a      	=							[test_file]

							_a      	=							datasets.load_dataset('''csv'''					,		data_files=UpperCamelCase )
							_a      	=							list(ds[list(files.keys() )[0]].features.keys() )
							_a      	=							features_name.pop(UpperCamelCase )
							_a      	=							list(set(ds[list(files.keys() )[0]][label_name] ) )
							_a      	=							{label: i for i, label in enumerate(UpperCamelCase )}
							_a      	=							tokenizer.model_input_names
							_a      	=							{}

							if len(UpperCamelCase ) == 1:
														for k in files.keys():
																					_a      	=							ds[k].map(
																					    lambda UpperCamelCase : tokenizer.batch_encode_plus(
																					        example[features_name[0]]					,		truncation=UpperCamelCase					,		max_length=UpperCamelCase					,		padding='''max_length''' )					,		batched=UpperCamelCase					,		)
							elif len(UpperCamelCase ) == 2:
														for k in files.keys():
																					_a      	=							ds[k].map(
																					    lambda UpperCamelCase : tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]])					,		truncation=UpperCamelCase					,		max_length=UpperCamelCase					,		padding='''max_length'''					,		)					,		batched=UpperCamelCase					,		)

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

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

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

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({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:
														_a      	=							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({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:
														_a      	=							val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({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:
														_a      	=							test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )

							return train_ds, val_ds, test_ds, labelaid


_snake_case : str  				=						logging.getLogger(__name__)

@dataclass
class      A       :
							lowercase_							     =							field(metadata={'help': 'Which column contains the label'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the training file'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the development file'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the test file'}       )
							lowercase_							     =							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.'
							        )
							    }	,)
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Overwrite the cached training and evaluation sets'}       )



@dataclass
class      A       :
							lowercase_							     =							field(
							    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}       )
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
							lowercase_							     =							field(default=_a	,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.
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,)



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
							_a	,				_a	,				_a      	=							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.

							_a      	=							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					,		)

							_a	,				_a	,				_a	,				_a      	=							get_tfds(
							    train_file=data_args.train_file					,		eval_file=data_args.dev_file					,		test_file=data_args.test_file					,		tokenizer=UpperCamelCase					,		label_column_id=data_args.label_column_id					,		max_seq_length=data_args.max_seq_length					,		)

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

							with training_args.strategy.scope():
														_a      	=							TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path					,		from_pt=bool('''.bin''' in model_args.model_name_or_path )					,		config=UpperCamelCase					,		cache_dir=model_args.cache_dir					,		)

							def compute_metrics(UpperCamelCase			:    EvalPrediction ) -> Dict:
														_a      	=							np.argmax(p.predictions					,		axis=1 )

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

							# Initialize our Trainer
							_a      	=							TFTrainer(
							    model=UpperCamelCase					,		args=UpperCamelCase					,		train_dataset=UpperCamelCase					,		eval_dataset=UpperCamelCase					,		compute_metrics=UpperCamelCase					,		)

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

							# Evaluation
							_a      	=							{}
							if training_args.do_eval:
														logger.info('''*** Evaluate ***''' )
														_a      	=							trainer.evaluate()
														_a      	=							os.path.join(training_args.output_dir					,		'''eval_results.txt''' )

														with open(UpperCamelCase					,		'''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(UpperCamelCase )

							return results


if __name__ == "__main__":
				main()

