


from typing import TYPE_CHECKING

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


UpperCAmelCase__        =				{
    '''configuration_openai''': ['''OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OpenAIGPTConfig'''],
    '''tokenization_openai''': ['''OpenAIGPTTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				['''OpenAIGPTTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''OpenAIGPTDoubleHeadsModel''',
      '''OpenAIGPTForSequenceClassification''',
      '''OpenAIGPTLMHeadModel''',
      '''OpenAIGPTModel''',
      '''OpenAIGPTPreTrainedModel''',
      '''load_tf_weights_in_openai_gpt''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFOpenAIGPTDoubleHeadsModel''',
      '''TFOpenAIGPTForSequenceClassification''',
      '''TFOpenAIGPTLMHeadModel''',
      '''TFOpenAIGPTMainLayer''',
      '''TFOpenAIGPTModel''',
      '''TFOpenAIGPTPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
  from .tokenization_openai import OpenAIGPTTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_openai_fast import OpenAIGPTTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_openai import (
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OpenAIGPTDoubleHeadsModel,
        OpenAIGPTForSequenceClassification,
        OpenAIGPTLMHeadModel,
        OpenAIGPTModel,
        OpenAIGPTPreTrainedModel,
        load_tf_weights_in_openai_gpt,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_openai import (
        TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFOpenAIGPTDoubleHeadsModel,
        TFOpenAIGPTForSequenceClassification,
        TFOpenAIGPTLMHeadModel,
        TFOpenAIGPTMainLayer,
        TFOpenAIGPTModel,
        TFOpenAIGPTPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__        =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





from __future__ import annotations
def 							UpperCAmelCase_   (				__snake_case		)	->     list[int]:







 """simple docstring"""


 if not list_of_ints:
  return []

 if min(__snake_case		) < 0:
  raise ValueError('''All numbers must be positive'''		)

 _lowercase				 =max(len(bin(__snake_case		)[2:]		) for x in list_of_ints		)
 return _msd_radix_sort(__snake_case	,		__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case		)	->     list[int]:







 """simple docstring"""


 if bit_position == 0 or len(__snake_case		) in [0, 1]:
  return list_of_ints

 _lowercase				 =[]
 _lowercase				 =[]
 # Split numbers based on bit at bit_position from the right
 for number in list_of_ints:
  if (number >> (bit_position - 1)) & 1:
   # number has a one at bit bit_position
   ones.append(__snake_case		)
  else:
   # number has a zero at bit bit_position
   zeros.append(__snake_case		)

    # recursively split both lists further
 _lowercase				 =_msd_radix_sort(__snake_case	,		bit_position - 1		)
 _lowercase				 =_msd_radix_sort(__snake_case	,		bit_position - 1		)

 # recombine lists
 _lowercase				 =zeros
 res.extend(__snake_case		)

 return res
def 							UpperCAmelCase_   (				__snake_case		)	->     List[str]:







 """simple docstring"""


 _lowercase				 =len(__snake_case		)
 if not list_of_ints or length == 1:
  return

 if min(__snake_case		) < 0:
  raise ValueError('''All numbers must be positive'''		)

 _lowercase				 =max(len(bin(__snake_case		)[2:]		) for x in list_of_ints		)
 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		0	,		__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)	->     Any:







 """simple docstring"""


 if bit_position == 0 or end_index - begin_index <= 1:
  return

 bit_position -= 1

 _lowercase				 =begin_index
 _lowercase				 =end_index - 1
 while i <= j:
  _lowercase				 =False
  if not (list_of_ints[i] >> bit_position) & 1:
   # found zero at the beginning
   i += 1
   _lowercase				 =True
  if (list_of_ints[j] >> bit_position) & 1:
   # found one at the end
   j -= 1
   _lowercase				 =True

  if changed:
   continue

  _lowercase ,       _lowercase				 =list_of_ints[j], list_of_ints[i]
  j -= 1
  if j != i:
   i += 1

 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)
 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

