
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


__lowerCAmelCase												=	get_logger(__name__)


class       _lowerCAmelCase   (   enum.Enum				):







       '''simple docstring'''

       lowerCAmelCase_ 						=						"all_checks"
       lowerCAmelCase_ 						=						"basic_checks"
       lowerCAmelCase_ 						=						"no_checks"


class       _lowerCAmelCase   (   UpperCamelCase__				):







       '''simple docstring'''

       pass


class       _lowerCAmelCase   (   UpperCamelCase__				):







       '''simple docstring'''

       pass


class       _lowerCAmelCase   (   UpperCamelCase__				):







       '''simple docstring'''

       pass


class       _lowerCAmelCase   (   UpperCamelCase__				):







       '''simple docstring'''

       pass






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=None				):
       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   (   UpperCamelCase__				):







       '''simple docstring'''

       pass


class       _lowerCAmelCase   (   UpperCamelCase__				):







       '''simple docstring'''

       pass


class       _lowerCAmelCase   (   UpperCamelCase__				):







       '''simple docstring'''

       pass


class       _lowerCAmelCase   (   UpperCamelCase__				):







       '''simple docstring'''

       pass






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
       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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE = True				):
       if record_checksum:
              _snake_case       =  shaaaa()
              with open(UpperCamelCase__							, """rb"""				) as f:
                     for chunk in iter(lambda: f.read(1 << 20				)							, b""""""				):
                            m.update(UpperCamelCase__				)
                     _snake_case       =  m.hexdigest()
       else:
              _snake_case       =  None
       return {"num_bytes": os.path.getsize(UpperCamelCase__				), "checksum": checksum}






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
       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_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase												=	{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ResNetForImageClassification',
				    'ResNetModel',
				    'ResNetPreTrainedModel',
				    'ResNetBackbone',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFResNetForImageClassification',
				    'TFResNetModel',
				    'TFResNetPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'FlaxResNetForImageClassification',
				    'FlaxResNetModel',
				    'FlaxResNetPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_resnet import (
								    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ResNetBackbone,
								    ResNetForImageClassification,
								    ResNetModel,
								    ResNetPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_resnet import (
								    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFResNetForImageClassification,
								    TFResNetModel,
								    TFResNetPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
				import sys

				__lowerCAmelCase												=	_LazyModule(__name__, globals()['__file__'], _import_structure)