




'''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_    	=				datasets.utils.logging.get_logger(__name__)

UpperCamelCase_    	=				["names", "prefix"]
UpperCamelCase_    	=				["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
UpperCamelCase_    	=				["encoding_errors", "on_bad_lines"]
UpperCamelCase_    	=				["date_format"]






@dataclass
class 							_a				(			datasets.BuilderConfig ):





       '''simple docstring'''






       A						:				str        =						","
       A						:				Optional[str]        =						None
       A						:				Optional[Union[int, List[int], str]]        =						"infer"
       A						:				Optional[List[str]]        =						None
       A						:				Optional[List[str]]        =						None
       A						:				Optional[Union[int, str, List[int], List[str]]]        =						None
       A						:				Optional[Union[List[int], List[str]]]        =						None
       A						:				Optional[str]        =						None
       A						:				bool        =						True
       A						:				Optional[Literal["c", "python", "pyarrow"]]        =						None
       A						:				Dict[Union[int, str], Callable[[Any], Any]]        =						None
       A						:				Optional[list]        =						None
       A						:				Optional[list]        =						None
       A						:				bool        =						False
       A						:				Optional[Union[int, List[int]]]        =						None
       A						:				Optional[int]        =						None
       A						:				Optional[Union[str, List[str]]]        =						None
       A						:				bool        =						True
       A						:				bool        =						True
       A						:				bool        =						False
       A						:				bool        =						True
       A						:				Optional[str]        =						None
       A						:				str        =						"."
       A						:				Optional[str]        =						None
       A						:				str        =						'"'
       A						:				int        =						0
       A						:				Optional[str]        =						None
       A						:				Optional[str]        =						None
       A						:				Optional[str]        =						None
       A						:				Optional[str]        =						None
       A						:				bool        =						True
       A						:				bool        =						True
       A						:				int        =						0
       A						:				bool        =						True
       A						:				bool        =						False
       A						:				Optional[str]        =						None
       A						:				int        =						10_000
       A						:				Optional[datasets.Features]        =						None
       A						:				Optional[str]        =						"strict"
       A						:				Literal["error", "warn", "skip"]        =						"error"
       A						:				Optional[str]        =						None







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             if self.delimiter is not None:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						self.delimiter
             if self.column_names is not None:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						self.column_names







       @property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						{
                 '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 							_a				(			datasets.ArrowBasedBuilder ):





       '''simple docstring'''






       A						:				Tuple        =						CsvConfig







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self,							A  ):







             '''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}"  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						dl_manager.download_and_extract(self.config.data_files  )
             if isinstance(A,							(str, list, tuple)  ):
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						data_files
                   if isinstance(A,							A  ):
                         SCREAMING_SNAKE_CASE   :      Dict    					=						[files]
                   SCREAMING_SNAKE_CASE   :      int    					=						[dl_manager.iter_files(A  ) for file in files]
                   return [datasets.SplitGenerator(name=datasets.Split.TRAIN,							gen_kwargs={'files': files}  )]
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[]
             for split_name, files in data_files.items():
                   if isinstance(A,							A  ):
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						[files]
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						[dl_manager.iter_files(A  ) for file in files]
                   splits.append(datasets.SplitGenerator(name=A,							gen_kwargs={'files': files}  )  )
             return splits







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if self.config.features is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.config.features.arrow_schema
                   if all(not require_storage_cast(A  ) for feature in self.config.features.values()  ):
                         # cheaper cast
                         SCREAMING_SNAKE_CASE   :      Any    					=						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
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						table_cast(A,							A  )
             return pa_table







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.config.features.arrow_schema if self.config.features else None
             # dtype allows reading an int column as str
             SCREAMING_SNAKE_CASE   :      List[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  )  ):
                   SCREAMING_SNAKE_CASE   :      Any    					=						pd.read_csv(A,							iterator=A,							dtype=A,							**self.config.pd_read_csv_kwargs  )
                   try:
                         for batch_idx, df in enumerate(A  ):
                               SCREAMING_SNAKE_CASE   :      Dict    					=						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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
    '''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
    '''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
    '''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
    '''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
    '''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
    '''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[Any]     =      '''xmod'''

 def __init__(					self			:			str       ,				_UpperCAmelCase			:			Optional[Any]=30_522       ,				_UpperCAmelCase			:			Any=768       ,				_UpperCAmelCase			:			int=12       ,				_UpperCAmelCase			:			List[str]=12       ,				_UpperCAmelCase			:			Dict=3_072       ,				_UpperCAmelCase			:			Union[str, Any]="gelu"       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Tuple=0.1       ,				_UpperCAmelCase			:			List[str]=512       ,				_UpperCAmelCase			:			Dict=2       ,				_UpperCAmelCase			:			Optional[int]=0.02       ,				_UpperCAmelCase			:			Any=1E-1_2       ,				_UpperCAmelCase			:			Tuple=1       ,				_UpperCAmelCase			:			int=0       ,				_UpperCAmelCase			:			List[Any]=2       ,				_UpperCAmelCase			:			List[str]="absolute"       ,				_UpperCAmelCase			:			Tuple=True       ,				_UpperCAmelCase			:			Tuple=None       ,				_UpperCAmelCase			:			int=False       ,				_UpperCAmelCase			:			Union[str, Any]=2       ,				_UpperCAmelCase			:			List[Any]=False       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[str]=True       ,				_UpperCAmelCase			:			Tuple=("en_XX",)       ,				_UpperCAmelCase			:			List[str]=None       ,				**_UpperCAmelCase			:			Optional[Any]       ,				):
       super().__init__(pad_token_id=_UpperCAmelCase       ,				bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase						)

       _A     =  vocab_size
       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  hidden_act
       _A     =  intermediate_size
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  max_position_embeddings
       _A     =  type_vocab_size
       _A     =  initializer_range
       _A     =  layer_norm_eps
       _A     =  position_embedding_type
       _A     =  use_cache
       _A     =  classifier_dropout
       _A     =  pre_norm
       _A     =  adapter_reduction_factor
       _A     =  adapter_layer_norm
       _A     =  adapter_reuse_layer_norm
       _A     =  ln_before_adapter
       _A     =  list(_UpperCAmelCase						)
       _A     =  default_language





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 @property
 def      lowerCAmelCase_				(					self			:			Dict						):
       if self.task == "multiple-choice":
             _A     =  {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
             _A     =  {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
           ]						)

