





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


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

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



@dataclass
class    __lowerCAmelCase			(     datasets.BuilderConfig	):
			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			     = 10_000
			lowerCamelCase_	:	Optional[datasets.Features]			     = None
			lowerCamelCase_	:	Optional[str]			     = "strict"
			lowerCamelCase_	:	Literal["error", "warn", "skip"]			     = "error"
			lowerCamelCase_	:	Optional[str]			     = None






			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							if self.delimiter is not None:
											snake_case_		:       str           =    self.delimiter
							if self.column_names is not None:
											snake_case_		:       Optional[Any]           =    self.column_names






			@property
			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							snake_case_		:       int           =    {
							    "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()   ,				__UpperCAmelCase       ):
															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	):
			lowerCamelCase_	:	List[str]			     = CsvConfig






			def      lowerCamelCase    (self       )	->  Optional[Any]:


							'''simple docstring'''



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






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  int:


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






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Dict:


							'''simple docstring'''



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






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       List[str]           =    self.config.features.arrow_schema if self.config.features else None
							# dtype allows reading an int column as str
							snake_case_		:       Dict           =    (
							    {
							        name: dtype.to_pandas_dtype() if not require_storage_cast(__UpperCAmelCase       ) 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(__UpperCAmelCase       )       ):
											snake_case_		:       List[Any]           =    pd.read_csv(__UpperCAmelCase   ,				iterator=__UpperCAmelCase   ,				dtype=__UpperCAmelCase   ,				**self.config.pd_read_csv_kwargs       )
											try:
															for batch_idx, df in enumerate(__UpperCAmelCase       ):
																			snake_case_		:       str           =    pa.Table.from_pandas(__UpperCAmelCase       )
																			# 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(__UpperCAmelCase       )
											except ValueError as e:
															logger.error(F'''Failed to read file \'{file}\' with error {type(__UpperCAmelCase       )}: {e}'''       )
															raise





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCamelCase		       =      {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TrOCRForCausalLM""",
						    """TrOCRPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


