




import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    LayoutLMForMaskedLM,
				    LayoutLMForQuestionAnswering,
				    LayoutLMForSequenceClassification,
				    LayoutLMForTokenClassification,
				    LayoutLMModel,
				)






class    __lowerCAmelCase			:







			def __init__(	self   :      Dict   ,   snake_case__   :      Dict   ,   snake_case__   :      Optional[int]=13   ,   snake_case__   :      List[Any]=7   ,   snake_case__   :      List[Any]=True   ,   snake_case__   :      Any=True   ,   snake_case__   :      int=True   ,   snake_case__   :      Optional[Any]=True   ,   snake_case__   :      int=99   ,   snake_case__   :      Dict=32   ,   snake_case__   :      List[str]=5   ,   snake_case__   :      Union[str, Any]=4   ,   snake_case__   :      Any=37   ,   snake_case__   :      str="gelu"   ,   snake_case__   :      List[str]=0.1   ,   snake_case__   :      Any=0.1   ,   snake_case__   :      Any=512   ,   snake_case__   :      List[str]=16   ,   snake_case__   :      str=2   ,   snake_case__   :      Any=0.02   ,   snake_case__   :      Optional[Any]=3   ,   snake_case__   :      Union[str, Any]=4   ,   snake_case__   :      int=None   ,   snake_case__   :      Optional[Any]=1_000   ,   ):




									"""simple docstring"""


									_UpperCAmelCase													=					parent
									_UpperCAmelCase													=					batch_size
									_UpperCAmelCase													=					seq_length
									_UpperCAmelCase													=					is_training
									_UpperCAmelCase													=					use_input_mask
									_UpperCAmelCase													=					use_token_type_ids
									_UpperCAmelCase													=					use_labels
									_UpperCAmelCase													=					vocab_size
									_UpperCAmelCase													=					hidden_size
									_UpperCAmelCase													=					num_hidden_layers
									_UpperCAmelCase													=					num_attention_heads
									_UpperCAmelCase													=					intermediate_size
									_UpperCAmelCase													=					hidden_act
									_UpperCAmelCase													=					hidden_dropout_prob
									_UpperCAmelCase													=					attention_probs_dropout_prob
									_UpperCAmelCase													=					max_position_embeddings
									_UpperCAmelCase													=					type_vocab_size
									_UpperCAmelCase													=					type_sequence_label_size
									_UpperCAmelCase													=					initializer_range
									_UpperCAmelCase													=					num_labels
									_UpperCAmelCase													=					num_choices
									_UpperCAmelCase													=					scope
									_UpperCAmelCase													=					range_bbox







			def UpperCamelCase      (	self   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size				)

									_UpperCAmelCase													=					ids_tensor([self.batch_size, self.seq_length, 4]   ,   self.range_bbox				)
									# Ensure that bbox is legal
									for i in range(bbox.shape[0]				):
															for j in range(bbox.shape[1]				):
																					if bbox[i, j, 3] < bbox[i, j, 1]:
																											_UpperCAmelCase													=					bbox[i, j, 3]
																											_UpperCAmelCase													=					bbox[i, j, 1]
																											_UpperCAmelCase													=					t
																					if bbox[i, j, 2] < bbox[i, j, 0]:
																											_UpperCAmelCase													=					bbox[i, j, 2]
																											_UpperCAmelCase													=					bbox[i, j, 0]
																											_UpperCAmelCase													=					t

									_UpperCAmelCase													=					None
									if self.use_input_mask:
															_UpperCAmelCase													=					ids_tensor([self.batch_size, self.seq_length]   ,   vocab_size=2				)

									_UpperCAmelCase													=					None
									if self.use_token_type_ids:
															_UpperCAmelCase													=					ids_tensor([self.batch_size, self.seq_length]   ,   self.type_vocab_size				)

									_UpperCAmelCase													=					None
									_UpperCAmelCase													=					None
									_UpperCAmelCase													=					None
									if self.use_labels:
															_UpperCAmelCase													=					ids_tensor([self.batch_size]   ,   self.type_sequence_label_size				)
															_UpperCAmelCase													=					ids_tensor([self.batch_size, self.seq_length]   ,   self.num_labels				)
															_UpperCAmelCase													=					ids_tensor([self.batch_size]   ,   self.num_choices				)

									_UpperCAmelCase													=					self.get_config()

									return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







			def UpperCamelCase      (	self   :      Optional[Any]				):




									"""simple docstring"""


									return LayoutLMConfig(
									    vocab_size=self.vocab_size   ,   hidden_size=self.hidden_size   ,   num_hidden_layers=self.num_hidden_layers   ,   num_attention_heads=self.num_attention_heads   ,   intermediate_size=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   hidden_dropout_prob=self.hidden_dropout_prob   ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,   max_position_embeddings=self.max_position_embeddings   ,   type_vocab_size=self.type_vocab_size   ,   initializer_range=self.initializer_range   ,   )







			def UpperCamelCase      (	self   :      Optional[int]   ,   snake_case__   :      Dict   ,   snake_case__   :      List[Any]   ,   snake_case__   :      Dict   ,   snake_case__   :      int   ,   snake_case__   :      Any   ,   snake_case__   :      Union[str, Any]   ,   snake_case__   :      Any   ,   snake_case__   :      List[Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					LayoutLMModel(config=snake_case__				)
									model.to(snake_case__				)
									model.eval()
									_UpperCAmelCase													=					model(snake_case__   ,   snake_case__   ,   attention_mask=snake_case__   ,   token_type_ids=snake_case__				)
									_UpperCAmelCase													=					model(snake_case__   ,   snake_case__   ,   token_type_ids=snake_case__				)
									_UpperCAmelCase													=					model(snake_case__   ,   snake_case__				)
									self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)				)
									self.parent.assertEqual(result.pooler_output.shape   ,   (self.batch_size, self.hidden_size)				)







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      List[str]   ,   snake_case__   :      Optional[Any]   ,   snake_case__   :      Tuple   ,   snake_case__   :      List[Any]   ,   snake_case__   :      Optional[Any]   ,   snake_case__   :      List[Any]   ,   snake_case__   :      Optional[Any]   ,   snake_case__   :      Optional[Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					LayoutLMForMaskedLM(config=snake_case__				)
									model.to(snake_case__				)
									model.eval()
									_UpperCAmelCase													=					model(snake_case__   ,   snake_case__   ,   attention_mask=snake_case__   ,   token_type_ids=snake_case__   ,   labels=snake_case__				)
									self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.vocab_size)				)







			def UpperCamelCase      (	self   :      str   ,   snake_case__   :      Tuple   ,   snake_case__   :      List[str]   ,   snake_case__   :      List[str]   ,   snake_case__   :      Optional[int]   ,   snake_case__   :      List[str]   ,   snake_case__   :      Tuple   ,   snake_case__   :      int   ,   snake_case__   :      Dict				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.num_labels
									_UpperCAmelCase													=					LayoutLMForSequenceClassification(snake_case__				)
									model.to(snake_case__				)
									model.eval()
									_UpperCAmelCase													=					model(
									    snake_case__   ,   snake_case__   ,   attention_mask=snake_case__   ,   token_type_ids=snake_case__   ,   labels=snake_case__				)
									self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_labels)				)







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      Any   ,   snake_case__   :      Union[str, Any]   ,   snake_case__   :      List[str]   ,   snake_case__   :      Union[str, Any]   ,   snake_case__   :      Dict   ,   snake_case__   :      Tuple   ,   snake_case__   :      str   ,   snake_case__   :      List[str]				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.num_labels
									_UpperCAmelCase													=					LayoutLMForTokenClassification(config=snake_case__				)
									model.to(snake_case__				)
									model.eval()
									_UpperCAmelCase													=					model(snake_case__   ,   snake_case__   ,   attention_mask=snake_case__   ,   token_type_ids=snake_case__   ,   labels=snake_case__				)
									self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.num_labels)				)







			def UpperCamelCase      (	self   :      str   ,   snake_case__   :      Union[str, Any]   ,   snake_case__   :      Tuple   ,   snake_case__   :      str   ,   snake_case__   :      int   ,   snake_case__   :      str   ,   snake_case__   :      Optional[int]   ,   snake_case__   :      Dict   ,   snake_case__   :      Any				):




									"""simple docstring"""


									_UpperCAmelCase													=					LayoutLMForQuestionAnswering(config=snake_case__				)
									model.to(snake_case__				)
									model.eval()
									_UpperCAmelCase													=					model(
									    snake_case__   ,   bbox=snake_case__   ,   attention_mask=snake_case__   ,   token_type_ids=snake_case__   ,   start_positions=snake_case__   ,   end_positions=snake_case__   ,   )
									self.parent.assertEqual(result.start_logits.shape   ,   (self.batch_size, self.seq_length)				)
									self.parent.assertEqual(result.end_logits.shape   ,   (self.batch_size, self.seq_length)				)







			def UpperCamelCase      (	self   :      Union[str, Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.prepare_config_and_inputs()
									(
									    (
									    _UpperCAmelCase
									)							,			(
									    _UpperCAmelCase
									)							,			(
									    _UpperCAmelCase
									)							,			(
									    _UpperCAmelCase
									)							,			(
									    _UpperCAmelCase
									)							,			(
									    _UpperCAmelCase
									)							,			(
									    _UpperCAmelCase
									)							,			(
									    _UpperCAmelCase
									)							,			
									)													=					config_and_inputs
									_UpperCAmelCase													=					{
									    "input_ids": input_ids,
									    "bbox": bbox,
									    "token_type_ids": token_type_ids,
									    "attention_mask": input_mask,
									}
									return config, inputs_dict






@require_torch
class    __lowerCAmelCase			(	UpperCAmelCase__ ,		UpperCAmelCase__ ,		unittest.TestCase			):
			snake_case_				:			str								=					(
			    (
			        LayoutLMModel,
			        LayoutLMForMaskedLM,
			        LayoutLMForSequenceClassification,
			        LayoutLMForTokenClassification,
			        LayoutLMForQuestionAnswering,
			    )
			    if is_torch_available()
			    else None
			)
			snake_case_				:			str								=					(
			    {
			        "document-question-answering": LayoutLMForQuestionAnswering,
			        "feature-extraction": LayoutLMModel,
			        "fill-mask": LayoutLMForMaskedLM,
			        "text-classification": LayoutLMForSequenceClassification,
			        "token-classification": LayoutLMForTokenClassification,
			        "zero-shot": LayoutLMForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			snake_case_				:			str								=					True







			def UpperCamelCase      (	self   :      Any				):




									"""simple docstring"""


									_UpperCAmelCase													=					LayoutLMModelTester(self				)
									_UpperCAmelCase													=					ConfigTester(self   ,   config_class=snake_case__   ,   hidden_size=37				)







			def UpperCamelCase      (	self   :      Any				):




									"""simple docstring"""


									self.config_tester.run_common_tests()







			def UpperCamelCase      (	self   :      Optional[Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case__				)







			def UpperCamelCase      (	self   :      Any				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
															_UpperCAmelCase													=					type
															self.model_tester.create_and_check_model(*snake_case__				)







			def UpperCamelCase      (	self   :      List[str]				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*snake_case__				)







			def UpperCamelCase      (	self   :      List[str]				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*snake_case__				)







			def UpperCamelCase      (	self   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*snake_case__				)







			def UpperCamelCase      (	self   :      str				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*snake_case__				)







			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."				)
			def UpperCamelCase      (	self   :      Tuple				):




									"""simple docstring"""


									pass




def 							__SCREAMING_SNAKE_CASE  (      ):

						'''simple docstring'''


						_UpperCAmelCase													=					torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]     ,   device=snake_case_						)  # noqa: E231
						_UpperCAmelCase													=					torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]     ,   device=snake_case_						)  # noqa: E231
						_UpperCAmelCase													=					torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]     ,   device=snake_case_						)  # noqa: E231
						_UpperCAmelCase													=					torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]     ,   device=snake_case_						)  # noqa: E231
						# these are sequence labels (i.e. at the token level)
						_UpperCAmelCase													=					torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]     ,   device=snake_case_						)  # noqa: E231
						# fmt: on

						return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class    __lowerCAmelCase			(	unittest.TestCase			):







			@slow
			def UpperCamelCase      (	self   :      List[Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"				).to(snake_case__				)

									_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase													=					prepare_layoutlm_batch_inputs()

									# forward pass
									_UpperCAmelCase													=					model(input_ids=snake_case__   ,   bbox=snake_case__   ,   attention_mask=snake_case__   ,   token_type_ids=snake_case__				)

									# test the sequence output on [0, :3, :3]
									_UpperCAmelCase													=					torch.tensor(
									    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]   ,   device=snake_case__   ,   )

									self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]   ,   snake_case__   ,   atol=1e-3				)				)

									# test the pooled output on [1, :3]
									_UpperCAmelCase													=					torch.tensor([-0.6_580, -0.0_214, 0.8_552]   ,   device=snake_case__				)

									self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]   ,   snake_case__   ,   atol=1e-3				)				)







			@slow
			def UpperCamelCase      (	self   :      Optional[int]				):




									"""simple docstring"""


									_UpperCAmelCase													=					LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"   ,   num_labels=2				).to(
									    snake_case__				)

									_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase													=					prepare_layoutlm_batch_inputs()

									# forward pass
									_UpperCAmelCase													=					model(
									    input_ids=snake_case__   ,   bbox=snake_case__   ,   attention_mask=snake_case__   ,   token_type_ids=snake_case__   ,   labels=torch.tensor([1, 1]   ,   device=snake_case__				)   ,   )

									# test whether we get a loss as a scalar
									_UpperCAmelCase													=					outputs.loss
									_UpperCAmelCase													=					torch.Size([]				)
									self.assertEqual(loss.shape   ,   snake_case__				)

									# test the shape of the logits
									_UpperCAmelCase													=					outputs.logits
									_UpperCAmelCase													=					torch.Size((2, 2)				)
									self.assertEqual(logits.shape   ,   snake_case__				)







			@slow
			def UpperCamelCase      (	self   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"   ,   num_labels=13				).to(
									    snake_case__				)

									_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase													=					prepare_layoutlm_batch_inputs()

									# forward pass
									_UpperCAmelCase													=					model(
									    input_ids=snake_case__   ,   bbox=snake_case__   ,   attention_mask=snake_case__   ,   token_type_ids=snake_case__   ,   labels=snake_case__				)

									# test the loss calculation to be around 2.65
									# expected_loss = torch.tensor(2.65, device=torch_device)

									# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
									# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

									# test the shape of the logits
									_UpperCAmelCase													=					outputs.logits
									_UpperCAmelCase													=					torch.Size((2, 25, 13)				)
									self.assertEqual(logits.shape   ,   snake_case__				)







			@slow
			def UpperCamelCase      (	self   :      List[str]				):




									"""simple docstring"""


									_UpperCAmelCase													=					LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"				).to(snake_case__				)

									_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase													=					prepare_layoutlm_batch_inputs()

									# forward pass
									_UpperCAmelCase													=					model(input_ids=snake_case__   ,   bbox=snake_case__   ,   attention_mask=snake_case__   ,   token_type_ids=snake_case__				)

									# test the shape of the logits
									_UpperCAmelCase													=					torch.Size((2, 25)				)
									self.assertEqual(outputs.start_logits.shape   ,   snake_case__				)
									self.assertEqual(outputs.end_logits.shape   ,   snake_case__				)










import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase_     :    Union[str, Any]      =			datasets.utils.logging.get_logger(__name__)






@dataclass
class    __lowerCAmelCase			(	datasets.BuilderConfig			):
			snake_case_				:			Optional[datasets.Features]								=					None






class    __lowerCAmelCase			(	datasets.ArrowBasedBuilder			):
			snake_case_				:			str								=					ArrowConfig







			def UpperCamelCase      (	self   :      Optional[int]				):




									"""simple docstring"""


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







			def UpperCamelCase      (	self   :      str   ,   snake_case__   :      Optional[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}"""				)
									_UpperCAmelCase													=					dl_manager.download_and_extract(self.config.data_files				)
									if isinstance(snake_case__   ,   (str, list, tuple)				):
															_UpperCAmelCase													=					data_files
															if isinstance(snake_case__   ,   snake_case__				):
																					_UpperCAmelCase													=					[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															_UpperCAmelCase													=					[dl_manager.iter_files(snake_case__				) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,   gen_kwargs={"files": files}				)]
									_UpperCAmelCase													=					[]
									for split_name, files in data_files.items():
															if isinstance(snake_case__   ,   snake_case__				):
																					_UpperCAmelCase													=					[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															_UpperCAmelCase													=					[dl_manager.iter_files(snake_case__				) for file in files]
															# Infer features is they are stoed in the arrow schema
															if self.info.features is None:
																					for file in itertools.chain.from_iterable(snake_case__				):
																											with open(snake_case__   ,   "rb"				) as f:
																																	_UpperCAmelCase													=					datasets.Features.from_arrow_schema(pa.ipc.open_stream(snake_case__				).schema				)
																											break
															splits.append(datasets.SplitGenerator(name=snake_case__   ,   gen_kwargs={"files": files}				)				)
									return splits







			def UpperCamelCase      (	self   :      int   ,   snake_case__   :      pa.Table				):




									"""simple docstring"""


									if self.info.features is not None:
															# more expensive cast to support nested features with keys in a different order
															# allows str <-> int/float or str to Audio for example
															_UpperCAmelCase													=					table_cast(snake_case__   ,   self.info.features.arrow_schema				)
									return pa_table







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      int				):




									"""simple docstring"""


									for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__				)				):
															with open(snake_case__   ,   "rb"				) as f:
																					try:
																											for batch_idx, record_batch in enumerate(pa.ipc.open_stream(snake_case__				)				):
																																	_UpperCAmelCase													=					pa.Table.from_batches([record_batch]				)
																																	# 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 F"""{file_idx}_{batch_idx}""", self._cast_table(snake_case__				)
																					except ValueError as e:
																											logger.error(F"""Failed to read file '{file}' with error {type(snake_case__				)}: {e}"""				)
																											raise




