






import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand
def 		_a       ( a					:Tuple					)				->     Tuple:
 return EnvironmentCommand()
def 		_a       ( a					:List[str]					)				->     str:
 return EnvironmentCommand(args.accelerate_config_file					)






class 						lowercase_   (      _lowercase  ):






      '''simple docstring'''




      @staticmethod
      def  __lowerCAmelCase   ( __UpperCAmelCase :		ArgumentParser    )       ->Any:




       """simple docstring"""


       a						    = parser.add_parser('''env'''    )
       download_parser.set_defaults(func=A_    )
       download_parser.add_argument(
           '''--accelerate-config_file'''					,   default=A_					,   help='''The accelerate config file to use for the default values in the launching script.'''					,   )
       download_parser.set_defaults(func=A_    )







      def __init__( self :		List[Any]					,   __UpperCAmelCase :		Optional[int]					,   *__UpperCAmelCase :		Tuple    )       ->None:




       """simple docstring"""


       a						    = accelerate_config_file







      def  __lowerCAmelCase   ( self :		Dict    )       ->Union[str, Any]:




       """simple docstring"""


       a						    = '''not installed'''
       if is_safetensors_available():
        import safetensors

        a						    = safetensors.__version__
       elif importlib.util.find_spec('''safetensors'''    ) is not None:
        import safetensors

        a						    = F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

       a						    = '''not installed'''
       a						    = a						    = '''not found'''
       if is_accelerate_available():
        import accelerate
        from accelerate.commands.config import default_config_file, load_config_from_file

        a						    = accelerate.__version__
        # Get the default from the config file.
        if self._accelerate_config_file is not None or os.path.isfile(A_    ):
         a						    = load_config_from_file(self._accelerate_config_file    ).to_dict()

        a						    = (
            '''\n'''.join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]    )
            if isinstance(A_					,   A_    )
            else F"""\t{accelerate_config}"""
        )

       a						    = '''not installed'''
       a						    = '''NA'''
       if is_torch_available():
        import torch

        a						    = torch.__version__
        a						    = torch.cuda.is_available()

       a						    = '''not installed'''
       a						    = '''NA'''
       if is_tf_available():
        import tensorflow as tf

        a						    = tf.__version__
        try:
         # deprecated in v2.1
         a						    = tf.test.is_gpu_available()
        except AttributeError:
         # returns list of devices, convert to bool
         a						    = bool(tf.config.list_physical_devices('''GPU'''    )    )

       a						    = '''not installed'''
       a						    = '''not installed'''
       a						    = '''not installed'''
       a						    = '''NA'''
       if is_flax_available():
        import flax
        import jax
        import jaxlib

        a						    = flax.__version__
        a						    = jax.__version__
        a						    = jaxlib.__version__
        a						    = jax.lib.xla_bridge.get_backend().platform

       a						    = {
           '''`transformers` version''': version,
           '''Platform''': platform.platform(),
           '''Python version''': platform.python_version(),
           '''Huggingface_hub version''': huggingface_hub.__version__,
           '''Safetensors version''': F"""{safetensors_version}""",
           '''Accelerate version''': F"""{accelerate_version}""",
           '''Accelerate config''': F"""{accelerate_config_str}""",
           '''PyTorch version (GPU?)''': F"""{pt_version} ({pt_cuda_available})""",
           '''Tensorflow version (GPU?)''': F"""{tf_version} ({tf_cuda_available})""",
           '''Flax version (CPU?/GPU?/TPU?)''': F"""{flax_version} ({jax_backend})""",
           '''Jax version''': F"""{jax_version}""",
           '''JaxLib version''': F"""{jaxlib_version}""",
           '''Using GPU in script?''': '''<fill in>''',
           '''Using distributed or parallel set-up in script?''': '''<fill in>''',
       }

       print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''    )
       print(self.format_dict(A_    )    )

       return info







      @staticmethod
      def  __lowerCAmelCase   ( __UpperCAmelCase :		Union[str, Any]    )       ->Dict:




       """simple docstring"""


       return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]    ) + "\n"




"""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


_lowercase				 =					datasets.utils.logging.get_logger(__name__)

_lowercase				 =					['''names''', '''prefix''']
_lowercase				 =					['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
_lowercase				 =					['''encoding_errors''', '''on_bad_lines''']
_lowercase				 =					['''date_format''']







@dataclass
class 					lowerCAmelCase_					(   datasets.BuilderConfig     ):



						'''simple docstring'''


						_lowerCamelCase:     str   =     ","
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     Optional[Union[int, List[int], str]]   =     "infer"
						_lowerCamelCase:     Optional[List[str]]   =     None
						_lowerCamelCase:     Optional[List[str]]   =     None
						_lowerCamelCase:     Optional[Union[int, str, List[int], List[str]]]   =     None
						_lowerCamelCase:     Optional[Union[List[int], List[str]]]   =     None
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     bool   =     True
						_lowerCamelCase:     Optional[Literal["c", "python", "pyarrow"]]   =     None
						_lowerCamelCase:     Dict[Union[int, str], Callable[[Any], Any]]   =     None
						_lowerCamelCase:     Optional[list]   =     None
						_lowerCamelCase:     Optional[list]   =     None
						_lowerCamelCase:     bool   =     False
						_lowerCamelCase:     Optional[Union[int, List[int]]]   =     None
						_lowerCamelCase:     Optional[int]   =     None
						_lowerCamelCase:     Optional[Union[str, List[str]]]   =     None
						_lowerCamelCase:     bool   =     True
						_lowerCamelCase:     bool   =     True
						_lowerCamelCase:     bool   =     False
						_lowerCamelCase:     bool   =     True
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     str   =     "."
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     str   =     '"'
						_lowerCamelCase:     int   =     0
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     bool   =     True
						_lowerCamelCase:     bool   =     True
						_lowerCamelCase:     int   =     0
						_lowerCamelCase:     bool   =     True
						_lowerCamelCase:     bool   =     False
						_lowerCamelCase:     Optional[str]   =     None
						_lowerCamelCase:     int   =     10000
						_lowerCamelCase:     Optional[datasets.Features]   =     None
						_lowerCamelCase:     Optional[str]   =     "strict"
						_lowerCamelCase:     Literal["error", "warn", "skip"]   =     "error"
						_lowerCamelCase:     Optional[str]   =     None


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Any:
							if self.delimiter is not None:
								A     = self.delimiter
							if self.column_names is not None:
								A     = self.column_names




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Any:
							A     = {
							    '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'''


						_lowerCamelCase:     Any   =     CsvConfig


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Optional[int]:
							return datasets.DatasetInfo(features=self.config.features   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   Any   )  -> str:
							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     = dl_manager.download_and_extract(self.config.data_files   )
							if isinstance(A_	,(str, list, tuple)   ):
								A     = data_files
								if isinstance(A_	,A_   ):
									A     = [files]
								A     = [dl_manager.iter_files(A_   ) for file in files]
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,gen_kwargs={'files': files}   )]
							A     = []
							for split_name, files in data_files.items():
								if isinstance(A_	,A_   ):
									A     = [files]
								A     = [dl_manager.iter_files(A_   ) for file in files]
								splits.append(datasets.SplitGenerator(name=A_	,gen_kwargs={'files': files}   )   )
							return splits


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   pa.Table   )  -> pa.Table:
							if self.config.features is not None:
								A     = self.config.features.arrow_schema
								if all(not require_storage_cast(A_   ) for feature in self.config.features.values()   ):
									# cheaper cast
									A     = 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     = table_cast(A_	,A_   )
							return pa_table




						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   Union[str, Any]   )  -> List[Any]:
							A     = self.config.features.arrow_schema if self.config.features else None
							# dtype allows reading an int column as str
							A     = (
							    {
							        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     = pd.read_csv(A_	,iterator=A_	,dtype=A_	,**self.config.pd_read_csv_kwargs   )
								try:
									for batch_idx, df in enumerate(A_   ):
										A     = 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