



from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers.models.layoutlm.modeling_tf_layoutlm import (
	    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFLayoutLMForMaskedLM,
	    TFLayoutLMForQuestionAnswering,
	    TFLayoutLMForSequenceClassification,
	    TFLayoutLMForTokenClassification,
	    TFLayoutLMModel,
	)


class 	__snake_case   :
		def __init__(							self    ,a_    ,a_=13    ,a_=7    ,a_=True    ,a_=True    ,a_=True    ,a_=True    ,a_=99    ,a_=32    ,a_=2    ,a_=4    ,a_=37    ,a_="gelu"    ,a_=0.1    ,a_=0.1    ,a_=512    ,a_=16    ,a_=2    ,a_=0.02    ,a_=3    ,a_=4    ,a_=None    ,a_=1000    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  parent
						lowerCAmelCase__		  =  batch_size
						lowerCAmelCase__		  =  seq_length
						lowerCAmelCase__		  =  is_training
						lowerCAmelCase__		  =  use_input_mask
						lowerCAmelCase__		  =  use_token_type_ids
						lowerCAmelCase__		  =  use_labels
						lowerCAmelCase__		  =  vocab_size
						lowerCAmelCase__		  =  hidden_size
						lowerCAmelCase__		  =  num_hidden_layers
						lowerCAmelCase__		  =  num_attention_heads
						lowerCAmelCase__		  =  intermediate_size
						lowerCAmelCase__		  =  hidden_act
						lowerCAmelCase__		  =  hidden_dropout_prob
						lowerCAmelCase__		  =  attention_probs_dropout_prob
						lowerCAmelCase__		  =  max_position_embeddings
						lowerCAmelCase__		  =  type_vocab_size
						lowerCAmelCase__		  =  type_sequence_label_size
						lowerCAmelCase__		  =  initializer_range
						lowerCAmelCase__		  =  num_labels
						lowerCAmelCase__		  =  num_choices
						lowerCAmelCase__		  =  scope
						lowerCAmelCase__		  =  range_bbox
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size						)

						# convert bbox to numpy since TF does not support item assignment
						lowerCAmelCase__		  =  ids_tensor([self.batch_size, self.seq_length, 4]    ,self.range_bbox						).numpy()
						# 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]:
																		lowerCAmelCase__		  =  bbox[i, j, 3]
																		lowerCAmelCase__		  =  bbox[i, j, 1]
																		lowerCAmelCase__		  =  t
														if bbox[i, j, 2] < bbox[i, j, 0]:
																		lowerCAmelCase__		  =  bbox[i, j, 2]
																		lowerCAmelCase__		  =  bbox[i, j, 0]
																		lowerCAmelCase__		  =  t
						lowerCAmelCase__		  =  tf.convert_to_tensor(a_						)

						lowerCAmelCase__		  =  None
						if self.use_input_mask:
										lowerCAmelCase__		  =  random_attention_mask([self.batch_size, self.seq_length]						)

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

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

						lowerCAmelCase__		  =  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    ,)

						return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  TFLayoutLMModel(config=a_						)

						lowerCAmelCase__		  =  model(a_    ,a_    ,attention_mask=a_    ,token_type_ids=a_						)
						lowerCAmelCase__		  =  model(a_    ,a_    ,token_type_ids=a_						)
						lowerCAmelCase__		  =  model(a_    ,a_						)
						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 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  TFLayoutLMForMaskedLM(config=a_						)

						lowerCAmelCase__		  =  model(a_    ,a_    ,attention_mask=a_    ,token_type_ids=a_    ,labels=a_						)
						self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.seq_length, self.vocab_size)						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.num_labels
						lowerCAmelCase__		  =  TFLayoutLMForSequenceClassification(config=a_						)

						lowerCAmelCase__		  =  model(a_    ,a_    ,attention_mask=a_    ,token_type_ids=a_						)
						self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.num_labels
						lowerCAmelCase__		  =  TFLayoutLMForTokenClassification(config=a_						)

						lowerCAmelCase__		  =  model(a_    ,a_    ,attention_mask=a_    ,token_type_ids=a_    ,labels=a_						)
						self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.seq_length, self.num_labels)						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  TFLayoutLMForQuestionAnswering(config=a_						)

						lowerCAmelCase__		  =  model(a_    ,a_    ,attention_mask=a_    ,token_type_ids=a_						)
						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 						SCREAMING_SNAKE_CASE_					(							self						):







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



@require_tf
class 	__snake_case   ( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, unittest.TestCase ):
		SCREAMING_SNAKE_CASE__								=	(
		    (
		        TFLayoutLMModel,
		        TFLayoutLMForMaskedLM,
		        TFLayoutLMForTokenClassification,
		        TFLayoutLMForSequenceClassification,
		        TFLayoutLMForQuestionAnswering,
		    )
		    if is_tf_available()
		    else ()
		)
		SCREAMING_SNAKE_CASE__								=	(
		    {
		        'feature-extraction': TFLayoutLMModel,
		        'fill-mask': TFLayoutLMForMaskedLM,
		        'text-classification': TFLayoutLMForSequenceClassification,
		        'token-classification': TFLayoutLMForTokenClassification,
		        'zero-shot': TFLayoutLMForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	10
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  TFLayoutLMModelTester(self						)
						lowerCAmelCase__		  =  ConfigTester(self    ,config_class=a_    ,hidden_size=37						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						self.config_tester.run_common_tests()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*a_						)
		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__		  =  TFLayoutLMModel.from_pretrained(a_						)
										self.assertIsNotNone(a_						)



		@unittest.skip('Onnx compliancy broke with TF 2.10'						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						pass







def     UpperCAmelCase_		(					)	->     Tuple:



				"""simple docstring"""





				lowerCAmelCase__		  =  tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]					)  # noqa: E231
				lowerCAmelCase__		  =  tf.convert_to_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],]					)  # noqa: E231
				lowerCAmelCase__		  =  tf.convert_to_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]]]					)  # noqa: E231
				lowerCAmelCase__		  =  tf.convert_to_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]]					)  # noqa: E231
				# these are sequence labels (i.e. at the token level)
				lowerCAmelCase__		  =  tf.convert_to_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]]					)  # noqa: E231
				# fmt: on

				return input_ids, attention_mask, bbox, token_type_ids, labels


@require_tf
class 	__snake_case   ( unittest.TestCase ):
		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'						)

						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  prepare_layoutlm_batch_inputs()

						# forward pass
						lowerCAmelCase__		  =  model(input_ids=a_    ,bbox=a_    ,attention_mask=a_    ,token_type_ids=a_						)

						# test the sequence output on [0, :3, :3]
						lowerCAmelCase__		  =  tf.convert_to_tensor(
						    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]    ,)

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

						# test the pooled output on [1, :3]
						lowerCAmelCase__		  =  tf.convert_to_tensor([-0.6580, -0.0214, 0.8552]						)

						self.assertTrue(np.allclose(outputs.pooler_output[1, :3]    ,a_    ,atol=1e-3						)						)
		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# initialize model with randomly initialized sequence classification head
						lowerCAmelCase__		  =  TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'    ,num_labels=2						)

						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  prepare_layoutlm_batch_inputs()

						# forward pass
						lowerCAmelCase__		  =  model(
						    input_ids=a_    ,bbox=a_    ,attention_mask=a_    ,token_type_ids=a_    ,labels=tf.convert_to_tensor([1, 1]						)    ,)

						# test whether we get a loss as a scalar
						lowerCAmelCase__		  =  outputs.loss
						lowerCAmelCase__		  =  (2,)
						self.assertEqual(loss.shape    ,a_						)

						# test the shape of the logits
						lowerCAmelCase__		  =  outputs.logits
						lowerCAmelCase__		  =  (2, 2)
						self.assertEqual(logits.shape    ,a_						)
		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# initialize model with randomly initialized token classification head
						lowerCAmelCase__		  =  TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'    ,num_labels=13						)

						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  prepare_layoutlm_batch_inputs()

						# forward pass
						lowerCAmelCase__		  =  model(
						    input_ids=a_    ,bbox=a_    ,attention_mask=a_    ,token_type_ids=a_    ,labels=a_						)

						# test the shape of the logits
						lowerCAmelCase__		  =  outputs.logits
						lowerCAmelCase__		  =  tf.convert_to_tensor((2, 25, 13)						)
						self.assertEqual(logits.shape    ,a_						)



		@slow
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# initialize model with randomly initialized token classification head
						lowerCAmelCase__		  =  TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'						)

						lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__		  =  prepare_layoutlm_batch_inputs()

						# forward pass
						lowerCAmelCase__		  =  model(input_ids=a_    ,bbox=a_    ,attention_mask=a_    ,token_type_ids=a_						)

						# test the shape of the logits
						lowerCAmelCase__		  =  tf.convert_to_tensor((2, 25)						)
						self.assertEqual(outputs.start_logits.shape    ,a_						)
						self.assertEqual(outputs.end_logits.shape    ,a_						)





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	:   Any    =   datasets.utils.logging.get_logger(__name__)

_lowerCAmelCase	:   Any    =   ["names", "prefix"]
_lowerCAmelCase	:   str    =   ["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
_lowerCAmelCase	:   List[str]    =   ["encoding_errors", "on_bad_lines"]
_lowerCAmelCase	:   int    =   ["date_format"]


@dataclass
class 	__snake_case   ( datasets.BuilderConfig ):
		SCREAMING_SNAKE_CASE__								=	","
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	"infer"
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	"."
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	'"'
		SCREAMING_SNAKE_CASE__								=	0
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	0
		SCREAMING_SNAKE_CASE__								=	True
		SCREAMING_SNAKE_CASE__								=	False
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	10000
		SCREAMING_SNAKE_CASE__								=	None
		SCREAMING_SNAKE_CASE__								=	"strict"
		SCREAMING_SNAKE_CASE__								=	"error"
		SCREAMING_SNAKE_CASE__								=	None
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.delimiter is not None:
										lowerCAmelCase__		  =  self.delimiter
						if self.column_names is not None:
										lowerCAmelCase__		  =  self.column_names



		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  {
						    '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 	__snake_case   ( datasets.ArrowBasedBuilder ):
		SCREAMING_SNAKE_CASE__								=	CsvConfig
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return datasets.DatasetInfo(features=self.config.features						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""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}'						)
						lowerCAmelCase__		  =  dl_manager.download_and_extract(self.config.data_files						)
						if isinstance(a_    ,(str, list, tuple)						):
										lowerCAmelCase__		  =  data_files
										if isinstance(a_    ,a_						):
														lowerCAmelCase__		  =  [files]
										lowerCAmelCase__		  =  [dl_manager.iter_files(a_						) for file in files]
										return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,gen_kwargs={'files': files}						)]
						lowerCAmelCase__		  =  []
						for split_name, files in data_files.items():
										if isinstance(a_    ,a_						):
														lowerCAmelCase__		  =  [files]
										lowerCAmelCase__		  =  [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    ,a_						):







						"""simple docstring"""
						if self.config.features is not None:
										lowerCAmelCase__		  =  self.config.features.arrow_schema
										if all(not require_storage_cast(a_						) for feature in self.config.features.values()						):
														# cheaper cast
														lowerCAmelCase__		  =  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
														lowerCAmelCase__		  =  table_cast(a_    ,a_						)
						return pa_table



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.config.features.arrow_schema if self.config.features else None
						# dtype allows reading an int column as str
						lowerCAmelCase__		  =  (
						    {
						        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_						)						):
										lowerCAmelCase__		  =  pd.read_csv(a_    ,iterator=a_    ,dtype=a_    ,**self.config.pd_read_csv_kwargs						)
										try:
														for batch_idx, df in enumerate(a_						):
																		lowerCAmelCase__		  =  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
