




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

from .. import config
from .logging import get_logger


lowerCamelCase	:	Optional[int]				=   get_logger(__name__)




class 						__lowercase		(enum.Enum							):




      """simple docstring"""





      _snake_case												=						"""all_checks"""
      _snake_case												=						"""basic_checks"""
      _snake_case												=						"""no_checks"""




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""









class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""









class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""









class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""









def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase=None					)  ->       Any:
   if expected_checksums is None:
      logger.info("""Unable to verify checksums."""					)
      return
   if len(set(lowercase					) - set(lowercase					)					) > 0:
      raise ExpectedMoreDownloadedFiles(str(set(lowercase					) - set(lowercase					)					)					)
   if len(set(lowercase					) - set(lowercase					)					) > 0:
      raise UnexpectedDownloadedFile(str(set(lowercase					) - set(lowercase					)					)					)
   snake_case				:    Dict           =	[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
   snake_case				:    Union[str, Any]           =	""" for """ + verification_name if verification_name is not None else """"""
   if len(lowercase					) > 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 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""









class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""









class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""









class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""









def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       List[Any]:
   if expected_splits is None:
      logger.info("""Unable to verify splits sizes."""					)
      return
   if len(set(lowercase					) - set(lowercase					)					) > 0:
      raise ExpectedMoreSplits(str(set(lowercase					) - set(lowercase					)					)					)
   if len(set(lowercase					) - set(lowercase					)					) > 0:
      raise UnexpectedSplits(str(set(lowercase					) - set(lowercase					)					)					)
   snake_case				:    int           =	[
       {"""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(lowercase					) > 0:
      raise NonMatchingSplitsSizesError(str(lowercase					)					)
   logger.info("""All the splits matched successfully."""					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase = True					)  ->       dict:
   if record_checksum:
      snake_case				:    Dict           =	shaaaa()
      with open(lowercase						,"""rb"""					) as f:
         for chunk in iter(lambda: f.read(1 << 20					)						,b""""""					):
            m.update(lowercase					)
         snake_case				:    Optional[Any]           =	m.hexdigest()
   else:
      snake_case				:    int           =	None
   return {"num_bytes": os.path.getsize(lowercase					), "checksum": checksum}




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[Any]:
   if dataset_size and config.IN_MEMORY_MAX_SIZE:
      return dataset_size < config.IN_MEMORY_MAX_SIZE
   else:
      return False











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase	:	str				=   {
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	List[Any]				=   ['RoFormerTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RoFormerForCausalLM',
           'RoFormerForMaskedLM',
           'RoFormerForMultipleChoice',
           'RoFormerForQuestionAnswering',
           'RoFormerForSequenceClassification',
           'RoFormerForTokenClassification',
           'RoFormerLayer',
           'RoFormerModel',
           'RoFormerPreTrainedModel',
           'load_tf_weights_in_roformer',
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Any				=   [
           'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRoFormerForCausalLM',
           'TFRoFormerForMaskedLM',
           'TFRoFormerForMultipleChoice',
           'TFRoFormerForQuestionAnswering',
           'TFRoFormerForSequenceClassification',
           'TFRoFormerForTokenClassification',
           'TFRoFormerLayer',
           'TFRoFormerModel',
           'TFRoFormerPreTrainedModel',
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Union[str, Any]				=   [
           'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'FlaxRoFormerForMaskedLM',
           'FlaxRoFormerForMultipleChoice',
           'FlaxRoFormerForQuestionAnswering',
           'FlaxRoFormerForSequenceClassification',
           'FlaxRoFormerForTokenClassification',
           'FlaxRoFormerModel',
           'FlaxRoFormerPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase	:	Optional[int]				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





