





'''simple docstring'''

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


class  a_					(    lowercase_	):

    '''simple docstring'''


    def __init__(		self			,		A			,		A = None			,		A = None			,		A = None			,		A = False			,		A = False			,		A = None			,		**A			,		)		->       List[str]:
        super().__init__(
            A			,		split=A			,		features=A			,		cache_dir=A			,		keep_in_memory=A			,		streaming=A			,		num_proc=A			,		**A			,		)
        _SCREAMING_SNAKE_CASE     =      path_or_paths if isinstance(A			,		A	) else {self.split: path_or_paths}
        _SCREAMING_SNAKE_CASE     =      Csv(
            cache_dir=A			,		data_files=A			,		features=A			,		**A			,		)





    def       snake_case_(		self	)		->       Optional[int]:







        if self.streaming:
            _SCREAMING_SNAKE_CASE     =      self.builder.as_streaming_dataset(split=self.split	)
        # Build regular (map-style) dataset
        else:
            _SCREAMING_SNAKE_CASE     =      None
            _SCREAMING_SNAKE_CASE     =      None
            _SCREAMING_SNAKE_CASE     =      None
            _SCREAMING_SNAKE_CASE     =      None

            self.builder.download_and_prepare(
                download_config=A			,		download_mode=A			,		verification_mode=A			,		base_path=A			,		num_proc=self.num_proc			,		)
            _SCREAMING_SNAKE_CASE     =      self.builder.as_dataset(
                split=self.split			,		verification_mode=A			,		in_memory=self.keep_in_memory	)
        return dataset




class  a_					:

    '''simple docstring'''


    def __init__(		self			,		A			,		A			,		A = None			,		A = None			,		**A			,		)		->       Union[str, Any]:







        if num_proc is not None and num_proc <= 0:
            raise ValueError(f'num_proc {num_proc} must be an integer > 0.'	)

        _SCREAMING_SNAKE_CASE     =      dataset
        _SCREAMING_SNAKE_CASE     =      path_or_buf
        _SCREAMING_SNAKE_CASE     =      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        _SCREAMING_SNAKE_CASE     =      num_proc
        _SCREAMING_SNAKE_CASE     =      """utf-8"""
        _SCREAMING_SNAKE_CASE     =      to_csv_kwargs


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.to_csv_kwargs.pop("""path_or_buf"""			,		A	)
        _SCREAMING_SNAKE_CASE     =      self.to_csv_kwargs.pop("""header"""			,		A	)
        _SCREAMING_SNAKE_CASE     =      self.to_csv_kwargs.pop("""index"""			,		A	)

        if isinstance(self.path_or_buf			,		(str, bytes, os.PathLike)	):
            with open(self.path_or_buf			,		"""wb+"""	) as buffer:
                _SCREAMING_SNAKE_CASE     =      self._write(file_obj=A			,		header=A			,		index=A			,		**self.to_csv_kwargs	)
        else:
            _SCREAMING_SNAKE_CASE     =      self._write(file_obj=self.path_or_buf			,		header=A			,		index=A			,		**self.to_csv_kwargs	)
        return written


    def       snake_case_(		self			,		A	)		->       int:
        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      args

        _SCREAMING_SNAKE_CASE     =      query_table(
            table=self.dataset.data			,		key=slice(A			,		offset + self.batch_size	)			,		indices=self.dataset._indices			,		)
        _SCREAMING_SNAKE_CASE     =      batch.to_pandas().to_csv(
            path_or_buf=A			,		header=header if (offset == 0) else False			,		index=A			,		**A	)
        return csv_str.encode(self.encoding	)





    def       snake_case_(		self			,		A			,		A			,		A			,		**A	)		->       List[str]:
        _SCREAMING_SNAKE_CASE     =      0

        if self.num_proc is None or self.num_proc == 1:
            for offset in logging.tqdm(
                range(0			,		len(self.dataset	)			,		self.batch_size	)			,		unit="""ba"""			,		disable=not logging.is_progress_bar_enabled()			,		desc="""Creating CSV from Arrow format"""			,		):
                _SCREAMING_SNAKE_CASE     =      self._batch_csv((offset, header, index, to_csv_kwargs)	)
                written += file_obj.write(A	)

        else:
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      len(self.dataset	), self.batch_size
            with multiprocessing.Pool(self.num_proc	) as pool:
                for csv_str in logging.tqdm(
                    pool.imap(
                        self._batch_csv			,		[(offset, header, index, to_csv_kwargs) for offset in range(0			,		A			,		A	)]			,		)			,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,		unit="""ba"""			,		disable=not logging.is_progress_bar_enabled()			,		desc="""Creating CSV from Arrow format"""			,		):
                    written += file_obj.write(A	)

        return written











'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase_         =			{
    """configuration_esm""": ["""ESM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """EsmConfig"""],
    """tokenization_esm""": ["""EsmTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """EsmForMaskedLM""",
          """EsmForSequenceClassification""",
          """EsmForTokenClassification""",
          """EsmModel""",
          """EsmPreTrainedModel""",
      ]
      lowercase_         =			["""EsmForProteinFolding""", """EsmFoldPreTrainedModel"""]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFEsmForMaskedLM""",
          """TFEsmForSequenceClassification""",
          """TFEsmForTokenClassification""",
          """TFEsmModel""",
          """TFEsmPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
      from .tokenization_esm import EsmTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_esm import (
                ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                EsmForMaskedLM,
                EsmForSequenceClassification,
                EsmForTokenClassification,
                EsmModel,
                EsmPreTrainedModel,
            )
            from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_esm import (
                TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEsmForMaskedLM,
                TFEsmForSequenceClassification,
                TFEsmForTokenClassification,
                TFEsmModel,
                TFEsmPreTrainedModel,
            )


else:
      import sys

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



