
'''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		):
 __lowerCamelCase						:							Any         =					"all_checks"
 __lowerCamelCase						:							Dict         =					"basic_checks"
 __lowerCamelCase						:							Optional[Any]         =					"no_checks"







class    lowerCAmelCase_		(		__magic_name__		):
 pass







class    lowerCAmelCase_		(		__magic_name__		):
 pass







class    lowerCAmelCase_		(		__magic_name__		):
 pass







class    lowerCAmelCase_		(		__magic_name__		):
 pass







def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[dict]		,       SCREAMING_SNAKE_CASE_  :					dict		,       SCREAMING_SNAKE_CASE_  :					Dict=None    ):



    '''simple docstring'''





    if expected_checksums is None:
        logger.info("Unable to verify checksums."    )
        return
    if len(set(SCREAMING_SNAKE_CASE_    ) - set(SCREAMING_SNAKE_CASE_    )    ) > 0:
        raise ExpectedMoreDownloadedFiles(str(set(SCREAMING_SNAKE_CASE_    ) - set(SCREAMING_SNAKE_CASE_    )    )    )
    if len(set(SCREAMING_SNAKE_CASE_    ) - set(SCREAMING_SNAKE_CASE_    )    ) > 0:
        raise UnexpectedDownloadedFile(str(set(SCREAMING_SNAKE_CASE_    ) - set(SCREAMING_SNAKE_CASE_    )    )    )
    _lowerCAmelCase				=      [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
    _lowerCAmelCase				=      " for " + verification_name if verification_name is not None else ""
    if len(SCREAMING_SNAKE_CASE_    ) > 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_		(		__magic_name__		):
 pass







class    lowerCAmelCase_		(		__magic_name__		):
 pass







class    lowerCAmelCase_		(		__magic_name__		):
 pass







class    lowerCAmelCase_		(		__magic_name__		):
 pass







def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[dict]		,       SCREAMING_SNAKE_CASE_  :					dict    ):



    '''simple docstring'''





    if expected_splits is None:
        logger.info("Unable to verify splits sizes."    )
        return
    if len(set(SCREAMING_SNAKE_CASE_    ) - set(SCREAMING_SNAKE_CASE_    )    ) > 0:
        raise ExpectedMoreSplits(str(set(SCREAMING_SNAKE_CASE_    ) - set(SCREAMING_SNAKE_CASE_    )    )    )
    if len(set(SCREAMING_SNAKE_CASE_    ) - set(SCREAMING_SNAKE_CASE_    )    ) > 0:
        raise UnexpectedSplits(str(set(SCREAMING_SNAKE_CASE_    ) - set(SCREAMING_SNAKE_CASE_    )    )    )
    _lowerCAmelCase				=      [
        {"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(SCREAMING_SNAKE_CASE_    ) > 0:
        raise NonMatchingSplitsSizesError(str(SCREAMING_SNAKE_CASE_    )    )
    logger.info("All the splits matched successfully."    )


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					bool = True    ):



    '''simple docstring'''





    if record_checksum:
        _lowerCAmelCase				=      shaaaa()
        with open(SCREAMING_SNAKE_CASE_		,       "rb"    ) as f:
            for chunk in iter(lambda: f.read(1 << 20    )		,       B""    ):
                m.update(SCREAMING_SNAKE_CASE_    )
            _lowerCAmelCase				=      m.hexdigest()
    else:
        _lowerCAmelCase				=      None
    return {"num_bytes": os.path.getsize(SCREAMING_SNAKE_CASE_    ), "checksum": checksum}





def 	__a(SCREAMING_SNAKE_CASE_  :					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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE	 =							{"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							["XGLMTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							["XGLMTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XGLMForCausalLM",
           "XGLMModel",
           "XGLMPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "FlaxXGLMForCausalLM",
           "FlaxXGLMModel",
           "FlaxXGLMPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFXGLMForCausalLM",
           "TFXGLMModel",
           "TFXGLMPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm import XGLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm_fast import XGLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xglm import (
                  TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXGLMForCausalLM,
                  TFXGLMModel,
                  TFXGLMPreTrainedModel,
              )


else:
       import sys

       _SCREAMING_SNAKE_CASE	 =							_LazyModule(__name__, globals()["__file__"], _import_structure)


