'''simple docstring'''




import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[str]    )    ->    str:


       """simple docstring"""







       _UpperCAmelCase     :  Tuple							      =    []
       for line in lines:
              _UpperCAmelCase     :  Optional[Any]							      =    re.sub(R"#.*"			,	""			,	_UpperCAmelCase    )  # remove comments
              if line:
                     filtered_lines.append(_UpperCAmelCase    )
       _UpperCAmelCase     :  Optional[int]							      =    "\n".join(_UpperCAmelCase    )

       # Make a hash from all this code
       _UpperCAmelCase     :  Optional[int]							      =    full_str.encode("utf-8"    )
       return shaaaa(_UpperCAmelCase    ).hexdigest()


# get importable module names and hash for caching
__SCREAMING_SNAKE_CASE       :     Optional[Any]     =  {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
__SCREAMING_SNAKE_CASE       :     Tuple     =  {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
__SCREAMING_SNAKE_CASE       :     str     =  {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
__SCREAMING_SNAKE_CASE       :     Dict[str, List[str]]     =  {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
  _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")


_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")


'''simple docstring'''




from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__SCREAMING_SNAKE_CASE       :     Optional[int]     =  {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE       :     Tuple     =  ["""GPTNeoXTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE       :     Dict     =  [
      """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTNeoXForCausalLM""",
      """GPTNeoXForQuestionAnswering""",
      """GPTNeoXForSequenceClassification""",
      """GPTNeoXForTokenClassification""",
      """GPTNeoXLayer""",
      """GPTNeoXModel""",
      """GPTNeoXPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neox import (
        GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoXForCausalLM,
        GPTNeoXForQuestionAnswering,
        GPTNeoXForSequenceClassification,
        GPTNeoXForTokenClassification,
        GPTNeoXLayer,
        GPTNeoXModel,
        GPTNeoXPreTrainedModel,
    )


else:
  import sys

  __SCREAMING_SNAKE_CASE       :     Any     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

