"""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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast



@require_vision
class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			tempfile.mkdtemp()

										snake_case								=			BlipImageProcessor()
										snake_case								=			GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'    )

										snake_case								=			BlipaProcessor(lowerCAmelCase		,    lowerCAmelCase    )

										processor.save_pretrained(self.tmpdirname    )

				def 		snake_case (				self		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return AutoProcessor.from_pretrained(self.tmpdirname		,    **lowerCAmelCase    ).tokenizer

				def 		snake_case (				self		,    **lowerCAmelCase    ):






										"""simple docstring"""

										return AutoProcessor.from_pretrained(self.tmpdirname		,    **lowerCAmelCase    ).image_processor

				def 		snake_case (				self    ):






										"""simple docstring"""

										shutil.rmtree(self.tmpdirname    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			[np.random.randint(2_55		,    size=(3, 30, 4_00)		,    dtype=np.uinta    )]

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

										return image_inputs

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			BlipaProcessor(tokenizer=self.get_tokenizer()		,    image_processor=self.get_image_processor()    )
										processor.save_pretrained(self.tmpdirname    )

										snake_case								=			self.get_tokenizer(bos_token='(BOS)'		,    eos_token='(EOS)'    )
										snake_case								=			self.get_image_processor(do_normalize=lowerCAmelCase		,    padding_value=1.0    )

										snake_case								=			BlipaProcessor.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 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_image_processor()
										snake_case								=			self.get_tokenizer()

										snake_case								=			BlipaProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

										snake_case								=			self.prepare_image_inputs()

										snake_case								=			image_processor(lowerCAmelCase		,    return_tensors='np'    )
										snake_case								=			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 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_image_processor()
										snake_case								=			self.get_tokenizer()

										snake_case								=			BlipaProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

										snake_case								=			'lower newer'

										snake_case								=			processor(text=lowerCAmelCase    )

										snake_case								=			tokenizer(lowerCAmelCase		,    return_token_type_ids=lowerCAmelCase    )

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

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_image_processor()
										snake_case								=			self.get_tokenizer()

										snake_case								=			BlipaProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

										snake_case								=			'lower newer'
										snake_case								=			self.prepare_image_inputs()

										snake_case								=			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 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_image_processor()
										snake_case								=			self.get_tokenizer()

										snake_case								=			BlipaProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

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

										snake_case								=			processor.batch_decode(lowerCAmelCase    )
										snake_case								=			tokenizer.batch_decode(lowerCAmelCase    )

										self.assertListEqual(lowerCAmelCase		,    lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_image_processor()
										snake_case								=			self.get_tokenizer()

										snake_case								=			BlipaProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

										snake_case								=			'lower newer'
										snake_case								=			self.prepare_image_inputs()

										snake_case								=			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 enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


SCREAMING_SNAKE_CASE__      						=  get_logger(__name__)



class   lowerCAmelCase_    (			enum.Enum					):



				"""simple docstring"""
				_lowerCAmelCase     :    Union[str, Any]			  =						"""all_checks"""
				_lowerCAmelCase     :    Optional[Any]			  =						"""basic_checks"""
				_lowerCAmelCase     :    Optional[int]			  =						"""no_checks"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[dict] ,			_UpperCamelCase       :				dict ,			_UpperCamelCase       :				str=None					)					->				Dict:





						"""simple docstring"""






						if expected_checksums is None:
												logger.info('Unable to verify checksums.'					)
												return
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise ExpectedMoreDownloadedFiles(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise UnexpectedDownloadedFile(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						snake_case								=			[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
						snake_case								=			' for ' + verification_name if verification_name is not None else ''
						if len(_UpperCamelCase					) > 0:
												raise NonMatchingChecksumError(
												    f"""Checksums didn't match{for_verification_name}:\n"""
												    f"""{bad_urls}\n"""
												    'Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'					)
						logger.info('All the checksums matched successfully' + for_verification_name					)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[dict] ,			_UpperCamelCase       :				dict					)					->				Tuple:





						"""simple docstring"""






						if expected_splits is None:
												logger.info('Unable to verify splits sizes.'					)
												return
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise ExpectedMoreSplits(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise UnexpectedSplits(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						snake_case								=			[
						    {'expected': expected_splits[name], 'recorded': recorded_splits[name]}
						    for name in expected_splits
						    if expected_splits[name].num_examples != recorded_splits[name].num_examples
						]
						if len(_UpperCamelCase					) > 0:
												raise NonMatchingSplitsSizesError(str(_UpperCamelCase					)					)
						logger.info('All the splits matched successfully.'					)





def    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				bool = True					)					->				dict:





						"""simple docstring"""






						if record_checksum:
												snake_case								=			shaaaa()
												with open(_UpperCamelCase ,			'rb'					) as f:
																		for chunk in iter(lambda: f.read(1 << 2_0					) ,			B''					):
																								m.update(_UpperCamelCase					)
																		snake_case								=			m.hexdigest()
						else:
												snake_case								=			None
						return {"num_bytes": os.path.getsize(_UpperCamelCase					), "checksum": checksum}





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[int]					)					->				int:





						"""simple docstring"""






						if dataset_size and config.IN_MEMORY_MAX_SIZE:
												return dataset_size < config.IN_MEMORY_MAX_SIZE
						else:
												return False


