



"""simple docstring"""







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

from .. import config
from .logging import get_logger


UpperCamelCase     =							get_logger(__name__)






class       UpperCamelCase__			(   enum.Enum      ):

			"""simple docstring"""


			A__	:					Tuple							  =     "all_checks"
			A__	:					Optional[int]							  =     "basic_checks"
			A__	:					Any							  =     "no_checks"






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""








class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""








class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""








class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""





def       _lowerCamelCase				(							UpperCAmelCase_ :       Optional[dict],	UpperCAmelCase_ :       dict,	UpperCAmelCase_ :       Optional[Any]=None     )	-> str:






	"""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_     )     )     )
	A__											=					[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
	A__											=					" 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       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""








class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""








class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""








class       UpperCamelCase__			(   _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_     )     )     )
	A__											=					[
	    {"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:
		A__											=					shaaaa()
		with open(UpperCAmelCase_,	"rb"     ) as f:
			for chunk in iter(lambda: f.read(1 << 20     ),	b""     ):
				m.update(UpperCAmelCase_     )
			A__											=					m.hexdigest()
	else:
		A__											=					None
	return {"num_bytes": os.path.getsize(UpperCAmelCase_     ), "checksum": checksum}




def       _lowerCamelCase				(							UpperCAmelCase_ :       str     )	-> int:






	"""simple docstring"""





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









"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase     =							{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							["""PerceiverFeatureExtractor"""]
			UpperCamelCase     =							["""PerceiverImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """PerceiverForImageClassificationConvProcessing""",
			    """PerceiverForImageClassificationFourier""",
			    """PerceiverForImageClassificationLearned""",
			    """PerceiverForMaskedLM""",
			    """PerceiverForMultimodalAutoencoding""",
			    """PerceiverForOpticalFlow""",
			    """PerceiverForSequenceClassification""",
			    """PerceiverLayer""",
			    """PerceiverModel""",
			    """PerceiverPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
			from .tokenization_perceiver import PerceiverTokenizer

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_perceiver import PerceiverFeatureExtractor
						from .image_processing_perceiver import PerceiverImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_perceiver import (
						    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PerceiverForImageClassificationConvProcessing,
						    PerceiverForImageClassificationFourier,
						    PerceiverForImageClassificationLearned,
						    PerceiverForMaskedLM,
						    PerceiverForMultimodalAutoencoding,
						    PerceiverForOpticalFlow,
						    PerceiverForSequenceClassification,
						    PerceiverLayer,
						    PerceiverModel,
						    PerceiverPreTrainedModel,
						)

else:
			import sys

			UpperCamelCase     =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




