


'''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


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

A				:					List[Any]    						=			['''names''', '''prefix''']
A				:					Tuple    						=			['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
A				:					List[Any]    						=			['''encoding_errors''', '''on_bad_lines''']
A				:					Union[str, Any]    						=			['''date_format''']


@dataclass
class 			__lowerCamelCase			(	datasets.BuilderConfig	):
    """simple docstring"""
    a													=   ","
    a													=   None
    a													=   "infer"
    a													=   None
    a													=   None
    a													=   None
    a													=   None
    a													=   None
    a													=   True
    a													=   None
    a													=   None
    a													=   None
    a													=   None
    a													=   False
    a													=   None
    a													=   None
    a													=   None
    a													=   True
    a													=   True
    a													=   False
    a													=   True
    a													=   None
    a													=   "."
    a													=   None
    a													=   '"'
    a													=   0
    a													=   None
    a													=   None
    a													=   None
    a													=   None
    a													=   True
    a													=   True
    a													=   0
    a													=   True
    a													=   False
    a													=   None
    a													=   1_0000
    a													=   None
    a													=   "strict"
    a													=   "error"
    a													=   None


    def        A   (  self      :							str):




           if self.delimiter is not None:
                  _A					:     Any     = self.delimiter
           if self.column_names is not None:
                  _A					:     List[Any]     = self.column_names


    @property
    def        A   (  self      :							Optional[int]):
           _A					:     Dict     = {
               "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()				,							lowerCAmelCase__):
                         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"""
    a													=   CsvConfig


    def        A   (  self      :							Optional[Any]):
           return datasets.DatasetInfo(features=self.config.features)


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


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							pa.Table):




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


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










'''simple docstring'''







from typing import TYPE_CHECKING

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


A				:					Optional[int]    						=			{
    '''configuration_mask2former''': [
        '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Mask2FormerConfig''',
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Tuple    						=			['''Mask2FormerImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Dict    						=			[
       '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Mask2FormerForUniversalSegmentation''',
       '''Mask2FormerModel''',
       '''Mask2FormerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_maskaformer import MaskaFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskaformer import (
          MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskaFormerForUniversalSegmentation,
          MaskaFormerModel,
          MaskaFormerPreTrainedModel,
      )


else:
   import sys

   A				:					Optional[Any]    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)






