






"""simple docstring"""



import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


_UpperCamelCase		:  List[str]						=		logging.getLogger(__name__)

_UpperCamelCase		:  List[str]						=		5_0  # max width of layer names
_UpperCamelCase		:  Tuple						=		7_0  # max width of quantizer names
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Tuple    ):





  '''simple docstring'''





  lowercase							    =       parser.add_argument_group('quant_trainer arguments'    )
  group.add_argument('--wprec'		,					type=SCREAMING_SNAKE_CASE_		,					default=8		,					help='weight precision'    )
  group.add_argument('--aprec'		,					type=SCREAMING_SNAKE_CASE_		,					default=8		,					help='activation precision'    )
  group.add_argument('--quant-per-tensor'		,					action='store_true'		,					help='per tensor weight scaling'    )
  group.add_argument('--quant-disable'		,					action='store_true'		,					help='disable all quantizers'    )
  group.add_argument('--quant-disable-embeddings'		,					action='store_true'		,					help='disable all embeddings quantizers'    )
  group.add_argument('--quant-disable-keyword'		,					type=SCREAMING_SNAKE_CASE_		,					nargs='+'		,					help='disable quantizers by keyword'    )
  group.add_argument('--quant-disable-layer-module'		,					type=SCREAMING_SNAKE_CASE_		,					help='disable quantizers by keyword under layer.'    )
  group.add_argument('--quant-enable-layer-module'		,					type=SCREAMING_SNAKE_CASE_		,					help='enable quantizers by keyword under layer'    )
  group.add_argument('--calibrator'		,					default='max'		,					help='which quantization range calibrator to use'    )
  group.add_argument('--percentile'		,					default=SCREAMING_SNAKE_CASE_		,					type=SCREAMING_SNAKE_CASE_		,					help='percentile for PercentileCalibrator'    )
  group.add_argument('--fuse-qkv'		,					action='store_true'		,					help='use the same scale factor for qkv'    )
  group.add_argument('--clip-gelu'		,					metavar='N'		,					type=SCREAMING_SNAKE_CASE_		,					help='clip gelu output maximum value to N'    )
  group.add_argument(
      '--recalibrate-weights'		,					action='store_true'		,					help=(
          'recalibrate weight amaxes by taking the max of the weights.'
          ' amaxes will be computed with the current quantization granularity (axis).'
      )		,					)
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]    ):





  '''simple docstring'''





  if args.calibrator == "max":
    lowercase							    =       'max'
  elif args.calibrator == "percentile":
    if args.percentile is None:
      raise ValueError('Specify --percentile when using percentile calibrator'    )
    lowercase							    =       'histogram'
  elif args.calibrator == "mse":
    lowercase							    =       'histogram'
  else:
    raise ValueError(f'Invalid calibrator {args.calibrator}'    )

  lowercase							    =       QuantDescriptor(num_bits=args.aprec		,					calib_method=SCREAMING_SNAKE_CASE_    )
  lowercase							    =       QuantDescriptor(num_bits=args.wprec		,					axis=(None if args.quant_per_tensor else (0,))    )
  quant_nn.QuantLinear.set_default_quant_desc_input(SCREAMING_SNAKE_CASE_    )
  quant_nn.QuantLinear.set_default_quant_desc_weight(SCREAMING_SNAKE_CASE_    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[Any]		,					__snake_case			:  List[str]		,					__snake_case			:  Union[str, Any]=False		,					__snake_case			:  Any=False    ):





  '''simple docstring'''





  logger.info('Configuring Model for Quantization'    )
  logger.info(f'using quantization package {pytorch_quantization.__file__}'    )

  if not calib:
    if args.quant_disable_embeddings:
      set_quantizer_by_name(SCREAMING_SNAKE_CASE_		,					['embeddings']		,					which='weight'		,					_disabled=SCREAMING_SNAKE_CASE_    )

    if args.quant_disable:
      set_quantizer_by_name(SCREAMING_SNAKE_CASE_		,					['']		,					_disabled=SCREAMING_SNAKE_CASE_    )

    if args.quant_disable_keyword:
      set_quantizer_by_name(SCREAMING_SNAKE_CASE_		,					args.quant_disable_keyword		,					_disabled=SCREAMING_SNAKE_CASE_    )

    if args.quant_disable_layer_module:
      set_quantizer_by_name(SCREAMING_SNAKE_CASE_		,					[r'layer.\d+.' + args.quant_disable_layer_module]		,					_disabled=SCREAMING_SNAKE_CASE_    )

    if args.quant_enable_layer_module:
      set_quantizer_by_name(SCREAMING_SNAKE_CASE_		,					[r'layer.\d+.' + args.quant_enable_layer_module]		,					_disabled=SCREAMING_SNAKE_CASE_    )

    if args.recalibrate_weights:
      recalibrate_weights(SCREAMING_SNAKE_CASE_    )

    if args.fuse_qkv:
      fuse_qkv(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_    )

  if args.clip_gelu:
    clip_gelu(SCREAMING_SNAKE_CASE_		,					args.clip_gelu    )

  # if args.local_rank in [-1, 0] and not calib:
  print_quant_summary(SCREAMING_SNAKE_CASE_    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict    ):





  '''simple docstring'''





  logger.info('Enabling Calibration'    )
  for name, module in model.named_modules():
    if name.endswith('_quantizer'    ):
      if module._calibrator is not None:
        module.disable_quant()
        module.enable_calib()
      else:
        module.disable()
      logger.info(f'{name:80}: {module}'    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[Any]		,					__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  logger.info('Loading calibrated amax'    )
  for name, module in model.named_modules():
    if name.endswith('_quantizer'    ):
      if module._calibrator is not None:
        if isinstance(module._calibrator		,					calib.MaxCalibrator    ):
          module.load_calib_amax()
        else:
          module.load_calib_amax('percentile'		,					percentile=args.percentile    )
        module.enable_quant()
        module.disable_calib()
      else:
        module.enable()
  model.cuda()
  print_quant_summary(SCREAMING_SNAKE_CASE_    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Any		,					__snake_case			:  Optional[Any]    ):





  '''simple docstring'''





  def fusea(__snake_case			:  Any		,					__snake_case			:  str		,					__snake_case			:  List[str]    ):
    for mod in [qq, qk, qv]:
      if not hasattr(SCREAMING_SNAKE_CASE_		,					'_amax'    ):
        print('          WARNING: NO AMAX BUFFER'    )
        return
    lowercase							    =       qq._amax.detach().item()
    lowercase							    =       qk._amax.detach().item()
    lowercase							    =       qv._amax.detach().item()

    lowercase							    =       max(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_    )
    qq._amax.fill_(SCREAMING_SNAKE_CASE_    )
    qk._amax.fill_(SCREAMING_SNAKE_CASE_    )
    qv._amax.fill_(SCREAMING_SNAKE_CASE_    )
    logger.info(f'          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}'    )

  for name, mod in model.named_modules():
    if name.endswith('.attention.self'    ):
      logger.info(f'FUSE_QKV: {name:{name_width}}'    )
      fusea(mod.matmul_q_input_quantizer		,					mod.matmul_k_input_quantizer		,					mod.matmul_v_input_quantizer    )
      if args.quant_per_tensor:
        fusea(mod.query._weight_quantizer		,					mod.key._weight_quantizer		,					mod.value._weight_quantizer    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  for name, mod in model.named_modules():
    if name.endswith('.output.dense'    ) and not name.endswith('attention.output.dense'    ):
      lowercase							    =       mod._input_quantizer._amax.data.detach().item()
      mod._input_quantizer._amax.data.detach().clamp_(max=SCREAMING_SNAKE_CASE_    )
      lowercase							    =       mod._input_quantizer._amax.data.detach().item()
      logger.info(f'CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}'    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[int]    ):





  '''simple docstring'''





  for name, mod in model.named_modules():
    if hasattr(SCREAMING_SNAKE_CASE_		,					'_weight_quantizer'    ) and mod._weight_quantizer.axis is not None:
      lowercase							    =       mod.weight.shape[0]
      lowercase							    =       mod._weight_quantizer._amax.detach()
      lowercase							    =       torch.ones(SCREAMING_SNAKE_CASE_		,					dtype=amax.dtype		,					device=amax.device    ) * amax
      print(f'expanding {name} {amax} -> {mod._weight_quantizer._amax}'    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  for name, mod in model.named_modules():
    if hasattr(SCREAMING_SNAKE_CASE_		,					'_weight_quantizer'    ):
      if not hasattr(mod.weight_quantizer		,					'_amax'    ):
        print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER'    )
        continue

      # determine which axes to reduce across
      # e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
      lowercase							    =       set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis    )
      lowercase							    =       set(range(len(mod.weight.size()    )    )    ) - axis_set
      lowercase							    =       pytorch_quantization.utils.reduce_amax(mod.weight		,					axis=SCREAMING_SNAKE_CASE_		,					keepdims=SCREAMING_SNAKE_CASE_    ).detach()
      logger.info(f'RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'    )
      lowercase							    =       amax
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict		,					__snake_case			:  Optional[Any]=25		,					__snake_case			:  Any=1_80		,					__snake_case			:  Optional[int]=None    ):





  '''simple docstring'''





  if ignore is None:
    lowercase							    =       []
  elif not isinstance(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_    ):
    lowercase							    =       [ignore]

  lowercase							    =       0
  for name, mod in model.named_modules():
    if not hasattr(SCREAMING_SNAKE_CASE_		,					'weight'    ):
      continue
    lowercase							    =       max(SCREAMING_SNAKE_CASE_		,					len(SCREAMING_SNAKE_CASE_    )    )

  for name, mod in model.named_modules():
    lowercase							    =       getattr(SCREAMING_SNAKE_CASE_		,					'_input_quantizer'		,					SCREAMING_SNAKE_CASE_    )
    lowercase							    =       getattr(SCREAMING_SNAKE_CASE_		,					'_weight_quantizer'		,					SCREAMING_SNAKE_CASE_    )
    if not hasattr(SCREAMING_SNAKE_CASE_		,					'weight'    ):
      continue
    if type(SCREAMING_SNAKE_CASE_    ) in ignore:
      continue
    if [True for s in ignore if type(SCREAMING_SNAKE_CASE_    ) is str and s in name]:
      continue
    lowercase							    =       f'Act:{input_q.extra_repr()}'
    lowercase							    =       f'Wgt:{weight_q.extra_repr()}'
    lowercase							    =       f'{name:{name_width}} {act_str} {wgt_str}'
    if len(SCREAMING_SNAKE_CASE_    ) <= line_width:
      logger.info(SCREAMING_SNAKE_CASE_    )
    else:
      logger.info(f'{name:{name_width}} {act_str}'    )
      logger.info(f'{"  ":{name_width}} {wgt_str}'    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int    ):





  '''simple docstring'''





  lowercase							    =       0
  for name, mod in model.named_modules():
    if isinstance(SCREAMING_SNAKE_CASE_		,					pytorch_quantization.nn.TensorQuantizer    ):
      print(f'{name:80} {mod}'    )
      count += 1
  print(f'{count} TensorQuantizers found in model'    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[Any]		,					__snake_case			:  int		,					__snake_case			:  str		,					__snake_case			:  List[str]		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  lowercase							    =       getattr(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_    )
  if quantizer_mod is not None:
    assert hasattr(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_    )
    setattr(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_    )
  else:
    logger.warning(f'{name} has no {quantizer}'    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  Optional[int]		,					__snake_case			:  List[Any]="both"		,					**__snake_case			:  Tuple    ):





  '''simple docstring'''





  lowercase							    =       f'Warning: changing {which} quantizers of {name:{qname_width}}'
  for k, v in kwargs.items():
    s += f' {k}={v}'
    if which in ["input", "both"]:
      set_quantizer(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					'_input_quantizer'		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_    )
    if which in ["weight", "both"]:
      set_quantizer(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					'_weight_quantizer'		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_    )
  logger.info(SCREAMING_SNAKE_CASE_    )



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[Any]		,					__snake_case			:  Dict		,					**__snake_case			:  List[str]    ):





  '''simple docstring'''





  for name, mod in model.named_modules():
    if hasattr(SCREAMING_SNAKE_CASE_		,					'_input_quantizer'    ) or hasattr(SCREAMING_SNAKE_CASE_		,					'_weight_quantizer'    ):
      for n in names:
        if re.search(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_    ):
          set_quantizers(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					**SCREAMING_SNAKE_CASE_    )
    elif name.endswith('_quantizer'    ):
      for n in names:
        if re.search(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_    ):
          lowercase							    =       f'Warning: changing {name:{name_width}}'
          for k, v in kwargs.items():
            s += f' {k}={v}'
            setattr(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_    )
          logger.info(SCREAMING_SNAKE_CASE_    )





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


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

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


@dataclass
class       SCREAMING_SNAKE_CASE__					(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														=     1_0000
    __lowerCamelCase						:       Optional[datasets.Features]														=     None
    __lowerCamelCase						:       Optional[str]														=     "strict"
    __lowerCamelCase						:       Literal["error", "warn", "skip"]														=     "error"
    __lowerCamelCase						:       Optional[str]														=     None





    def       snake_case_				(      self):
         if self.delimiter is not None:
              lowercase__    :   List[Any]      =       self.delimiter
         if self.column_names is not None:
              lowercase__    :   Optional[int]      =       self.column_names





    @property
    def       snake_case_				(      self):
         lowercase__    :   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()						,   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       SCREAMING_SNAKE_CASE__					(datasets.ArrowBasedBuilder     ):
    __lowerCamelCase						:       Optional[Any]														=     CsvConfig





    def       snake_case_				(      self):
         return datasets.DatasetInfo(features=self.config.features)





    def       snake_case_				(      self						,   a):
         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}""")
         lowercase__    :   Any      =       dl_manager.download_and_extract(self.config.data_files)
         if isinstance(a						,   (str, list, tuple)):
              lowercase__    :   List[str]      =       data_files
              if isinstance(a						,   a):
                   lowercase__    :   Optional[Any]      =       [files]
              lowercase__    :   Optional[int]      =       [dl_manager.iter_files(a) for file in files]
              return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,   gen_kwargs={'files': files})]
         lowercase__    :   int      =       []
         for split_name, files in data_files.items():
              if isinstance(a						,   a):
                   lowercase__    :   Optional[int]      =       [files]
              lowercase__    :   Tuple      =       [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						,   a):
         if self.config.features is not None:
              lowercase__    :   Optional[int]      =       self.config.features.arrow_schema
              if all(not require_storage_cast(a) for feature in self.config.features.values()):
                   # cheaper cast
                   lowercase__    :   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
                   lowercase__    :   Optional[Any]      =       table_cast(a						,   a)
         return pa_table





    def       snake_case_				(      self						,   a):
         lowercase__    :   List[Any]      =       self.config.features.arrow_schema if self.config.features else None
         # dtype allows reading an int column as str
         lowercase__    :   Optional[int]      =       (
             {
                 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)):
              lowercase__    :   int      =       pd.read_csv(a						,   iterator=a						,   dtype=a						,   **self.config.pd_read_csv_kwargs)
              try:
                   for batch_idx, df in enumerate(a):
                        lowercase__    :   List[str]      =       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

