


'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


UpperCAmelCase_ :     Optional[int]								=      datasets.utils.logging.get_logger(__name__)

UpperCAmelCase_ :     List[str]								=      ['names', 'prefix']
UpperCAmelCase_ :     int								=      ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
UpperCAmelCase_ :     Dict								=      ['encoding_errors', 'on_bad_lines']
UpperCAmelCase_ :     Optional[int]								=      ['date_format']

@dataclass
class 						_lowerCamelCase					(    datasets.BuilderConfig				):




 '''simple docstring'''
 __lowercase				:		Optional[Any]    							=						''','''
 __lowercase				:		str    							=						None
 __lowercase				:		Union[str, Any]    							=						'''infer'''
 __lowercase				:		Tuple    							=						None
 __lowercase				:		Optional[Any]    							=						None
 __lowercase				:		Tuple    							=						None
 __lowercase				:		int    							=						None
 __lowercase				:		str    							=						None
 __lowercase				:		Optional[int]    							=						True
 __lowercase				:		int    							=						None
 __lowercase				:		Any    							=						None
 __lowercase				:		Any    							=						None
 __lowercase				:		int    							=						None
 __lowercase				:		List[str]    							=						False
 __lowercase				:		Union[str, Any]    							=						None
 __lowercase				:		int    							=						None
 __lowercase				:		List[Any]    							=						None
 __lowercase				:		Dict    							=						True
 __lowercase				:		Optional[Any]    							=						True
 __lowercase				:		List[str]    							=						False
 __lowercase				:		str    							=						True
 __lowercase				:		Tuple    							=						None
 __lowercase				:		str    							=						'''.'''
 __lowercase				:		Dict    							=						None
 __lowercase				:		Optional[Any]    							=						'''"'''
 __lowercase				:		Dict    							=						0
 __lowercase				:		Any    							=						None
 __lowercase				:		str    							=						None
 __lowercase				:		int    							=						None
 __lowercase				:		Dict    							=						None
 __lowercase				:		Optional[int]    							=						True
 __lowercase				:		Optional[Any]    							=						True
 __lowercase				:		Optional[int]    							=						0
 __lowercase				:		str    							=						True
 __lowercase				:		Optional[Any]    							=						False
 __lowercase				:		List[str]    							=						None
 __lowercase				:		Any    							=						1_0_0_0_0
 __lowercase				:		Any    							=						None
 __lowercase				:		Union[str, Any]    							=						'''strict'''
 __lowercase				:		List[Any]    							=						'''error'''
 __lowercase				:		List[str]    							=						None


 def  snake_case__   (      self						):

  """simple docstring"""







  if self.delimiter is not None:
   __A  :		int       =			self.delimiter
  if self.column_names is not None:
   __A  :		Dict       =			self.column_names







 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			{
      """sep""": self.sep,
      """header""": self.header,
      """names""": self.names,
      """index_col""": self.index_col,
      """usecols""": self.usecols,
      """prefix""": self.prefix,
      """mangle_dupe_cols""": self.mangle_dupe_cols,
      """engine""": self.engine,
      """converters""": self.converters,
      """true_values""": self.true_values,
      """false_values""": self.false_values,
      """skipinitialspace""": self.skipinitialspace,
      """skiprows""": self.skiprows,
      """nrows""": self.nrows,
      """na_values""": self.na_values,
      """keep_default_na""": self.keep_default_na,
      """na_filter""": self.na_filter,
      """verbose""": self.verbose,
      """skip_blank_lines""": self.skip_blank_lines,
      """thousands""": self.thousands,
      """decimal""": self.decimal,
      """lineterminator""": self.lineterminator,
      """quotechar""": self.quotechar,
      """quoting""": self.quoting,
      """escapechar""": self.escapechar,
      """comment""": self.comment,
      """encoding""": self.encoding,
      """dialect""": self.dialect,
      """error_bad_lines""": self.error_bad_lines,
      """warn_bad_lines""": self.warn_bad_lines,
      """skipfooter""": self.skipfooter,
      """doublequote""": self.doublequote,
      """memory_map""": self.memory_map,
      """float_precision""": self.float_precision,
      """chunksize""": self.chunksize,
      """encoding_errors""": self.encoding_errors,
      """on_bad_lines""": self.on_bad_lines,
      """date_format""": self.date_format,
  }

  # some kwargs must not be passed if they don't have a default value
  # some others are deprecated and we can also not pass them if they are the default value
  for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
   if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()      ,      a_						):
    del pd_read_csv_kwargs[pd_read_csv_parameter]

     # Remove 2.0 new arguments
  if not (datasets.config.PANDAS_VERSION.major >= 2):
   for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
    del pd_read_csv_kwargs[pd_read_csv_parameter]

     # Remove 1.3 new arguments
  if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
   for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
    del pd_read_csv_kwargs[pd_read_csv_parameter]

  return pd_read_csv_kwargs


class 						_lowerCamelCase					(    datasets.ArrowBasedBuilder				):




 '''simple docstring'''
 __lowercase				:		Any    							=						CsvConfig


 def  snake_case__   (      self						):

  """simple docstring"""







  return datasets.DatasetInfo(features=self.config.features						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  if not self.config.data_files:
   raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""						)
  __A  :		Optional[Any]       =			dl_manager.download_and_extract(self.config.data_files						)
  if isinstance(a_      ,      (str, list, tuple)						):
   __A  :		Any       =			data_files
   if isinstance(a_      ,      a_						):
    __A  :		Optional[int]       =			[files]
   __A  :		Optional[Any]       =			[dl_manager.iter_files(a_						) for file in files]
   return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,      gen_kwargs={'files': files}						)]
  __A  :		List[Any]       =			[]
  for split_name, files in data_files.items():
   if isinstance(a_      ,      a_						):
    __A  :		Tuple       =			[files]
   __A  :		List[Any]       =			[dl_manager.iter_files(a_						) for file in files]
   splits.append(datasets.SplitGenerator(name=a_      ,      gen_kwargs={'files': files}						)						)
  return splits


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  if self.config.features is not None:
   __A  :		Dict       =			self.config.features.arrow_schema
   if all(not require_storage_cast(a_						) for feature in self.config.features.values()						):
    # cheaper cast
    __A  :		Dict       =			pa.Table.from_arrays([pa_table[field.name] for field in schema]      ,      schema=a_						)
   else:
    # more expensive cast; allows str <-> int/float or str to Audio for example
    __A  :		Optional[int]       =			table_cast(a_      ,      a_						)
  return pa_table







 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		List[Any]       =			self.config.features.arrow_schema if self.config.features else None
  # dtype allows reading an int column as str
  __A  :		Any       =			(
      {
          name: dtype.to_pandas_dtype() if not require_storage_cast(a_						) else object
          for name, dtype, feature in zip(schema.names      ,      schema.types      ,      self.config.features.values()						)
      }
      if schema is not None
      else None
  )
  for file_idx, file in enumerate(itertools.chain.from_iterable(a_						)						):
   __A  :		Any       =			pd.read_csv(a_      ,      iterator=a_      ,      dtype=a_      ,      **self.config.pd_read_csv_kwargs						)
   try:
    for batch_idx, df in enumerate(a_						):
     __A  :		Optional[int]       =			pa.Table.from_pandas(a_						)
     # Uncomment for debugging (will print the Arrow table size and elements)
     # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
     # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
     yield (file_idx, batch_idx), self._cast_table(a_						)
   except ValueError as e:
    logger.error(F"""Failed to read file '{file}' with error {type(a_						)}: {e}"""						)
    raise










'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_ :     Dict								=      logging.get_logger(__name__)

UpperCAmelCase_ :     Optional[int]								=      {
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Tuple    							=						'''vit_msn'''


 def __init__(      self      ,      __lowercase=768      ,      __lowercase=12      ,      __lowercase=12      ,      __lowercase=3_072      ,      __lowercase="gelu"      ,      __lowercase=0.0      ,      __lowercase=0.0      ,      __lowercase=0.0_2      ,      __lowercase=1E-06      ,      __lowercase=224      ,      __lowercase=16      ,      __lowercase=3      ,      __lowercase=True      ,      **__lowercase      ,      ):

  """simple docstring"""







  super().__init__(**__lowercase						)

  __A  :		Tuple       =			hidden_size
  __A  :		Optional[int]       =			num_hidden_layers
  __A  :		str       =			num_attention_heads
  __A  :		Dict       =			intermediate_size
  __A  :		Optional[Any]       =			hidden_act
  __A  :		Union[str, Any]       =			hidden_dropout_prob
  __A  :		Optional[Any]       =			attention_probs_dropout_prob
  __A  :		List[str]       =			initializer_range
  __A  :		Dict       =			layer_norm_eps
  __A  :		List[Any]       =			image_size
  __A  :		Optional[int]       =			patch_size
  __A  :		List[Any]       =			num_channels
  __A  :		Optional[Any]       =			qkv_bias






