






import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
							import torch
							from torch import nn

							from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
							from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor


class   __SCREAMING_SNAKE_CASE:
							def __init__(     self:      List[Any]       ,	UpperCamelCase:      Any       ,	UpperCamelCase:      Union[str, Any]=13       ,	UpperCamelCase:      str=32       ,	UpperCamelCase:      Optional[int]=2       ,	UpperCamelCase:      Optional[Any]=3       ,	UpperCamelCase:      List[Any]=16       ,	UpperCamelCase:      Union[str, Any]=[1, 2, 1]       ,	UpperCamelCase:      List[Any]=[2, 2, 4]       ,	UpperCamelCase:      Union[str, Any]=2       ,	UpperCamelCase:      str=2.0       ,	UpperCamelCase:      Dict=True       ,	UpperCamelCase:      Optional[Any]=0.0       ,	UpperCamelCase:      Optional[int]=0.0       ,	UpperCamelCase:      str=0.1       ,	UpperCamelCase:      str="gelu"       ,	UpperCamelCase:      List[str]=False       ,	UpperCamelCase:      Union[str, Any]=True       ,	UpperCamelCase:      Dict=0.02       ,	UpperCamelCase:      List[str]=1e-5       ,	UpperCamelCase:      str=True       ,	UpperCamelCase:      Dict=None       ,	UpperCamelCase:      List[str]=True       ,	UpperCamelCase:      str=10       ,	UpperCamelCase:      Union[str, Any]=8       ,	)		->		Optional[Any]:
											snake_case__									=     parent
											snake_case__									=     batch_size
											snake_case__									=     image_size
											snake_case__									=     patch_size
											snake_case__									=     num_channels
											snake_case__									=     embed_dim
											snake_case__									=     depths
											snake_case__									=     num_heads
											snake_case__									=     window_size
											snake_case__									=     mlp_ratio
											snake_case__									=     qkv_bias
											snake_case__									=     hidden_dropout_prob
											snake_case__									=     attention_probs_dropout_prob
											snake_case__									=     drop_path_rate
											snake_case__									=     hidden_act
											snake_case__									=     use_absolute_embeddings
											snake_case__									=     patch_norm
											snake_case__									=     layer_norm_eps
											snake_case__									=     initializer_range
											snake_case__									=     is_training
											snake_case__									=     scope
											snake_case__									=     use_labels
											snake_case__									=     type_sequence_label_size
											snake_case__									=     encoder_stride
							def lowerCAmelCase_ (     self:      Dict						)		->		Optional[int]:
											snake_case__									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

											snake_case__									=     None
											if self.use_labels:
															snake_case__									=     ids_tensor([self.batch_size]       ,	self.type_sequence_label_size						)

											snake_case__									=     self.get_config()

											return config, pixel_values, labels
							def lowerCAmelCase_ (     self:      List[Any]						)		->		List[str]:
											return SwinvaConfig(
											    image_size=self.image_size       ,	patch_size=self.patch_size       ,	num_channels=self.num_channels       ,	embed_dim=self.embed_dim       ,	depths=self.depths       ,	num_heads=self.num_heads       ,	window_size=self.window_size       ,	mlp_ratio=self.mlp_ratio       ,	qkv_bias=self.qkv_bias       ,	hidden_dropout_prob=self.hidden_dropout_prob       ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,	drop_path_rate=self.drop_path_rate       ,	hidden_act=self.hidden_act       ,	use_absolute_embeddings=self.use_absolute_embeddings       ,	path_norm=self.patch_norm       ,	layer_norm_eps=self.layer_norm_eps       ,	initializer_range=self.initializer_range       ,	encoder_stride=self.encoder_stride       ,	)
							def lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Union[str, Any]       ,	UpperCamelCase:      str						)		->		List[Any]:
											snake_case__									=     SwinvaModel(config=UpperCamelCase						)
											model.to(UpperCamelCase						)
											model.eval()
											snake_case__									=     model(UpperCamelCase						)

											snake_case__									=     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
											snake_case__									=     int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

											self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, expected_seq_len, expected_dim)						)
							def lowerCAmelCase_ (     self:      Optional[int]       ,	UpperCamelCase:      str       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Tuple						)		->		str:
											snake_case__									=     SwinvaForMaskedImageModeling(config=UpperCamelCase						)
											model.to(UpperCamelCase						)
											model.eval()
											snake_case__									=     model(UpperCamelCase						)
											self.parent.assertEqual(
											    result.logits.shape       ,	(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

											# test greyscale images
											snake_case__									=     1
											snake_case__									=     SwinvaForMaskedImageModeling(UpperCamelCase						)
											model.to(UpperCamelCase						)
											model.eval()

											snake_case__									=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
											snake_case__									=     model(UpperCamelCase						)
											self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, 1, self.image_size, self.image_size)						)
							def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      Any       ,	UpperCamelCase:      Tuple       ,	UpperCamelCase:      Dict						)		->		List[Any]:
											snake_case__									=     self.type_sequence_label_size
											snake_case__									=     SwinvaForImageClassification(UpperCamelCase						)
											model.to(UpperCamelCase						)
											model.eval()
											snake_case__									=     model(UpperCamelCase       ,	labels=UpperCamelCase						)
											self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.type_sequence_label_size)						)


							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Any:
											snake_case__									=     self.prepare_config_and_inputs()
											snake_case__							,						snake_case__							,						snake_case__									=     config_and_inputs
											snake_case__									=     {'pixel_values': pixel_values}
											return config, inputs_dict


@require_torch
class   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       (
							    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
							)
							_UpperCAmelCase     =       (
							    {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							def lowerCAmelCase_ (     self:      Any						)		->		List[str]:
											snake_case__									=     SwinvaModelTester(self						)
											snake_case__									=     ConfigTester(self       ,	config_class=UpperCamelCase       ,	embed_dim=37						)
							def lowerCAmelCase_ (     self:      Any						)		->		List[str]:
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__									=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCamelCase						)
							@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[Any]:
											pass
							@unittest.skip(reason='Swinv2 does not use inputs_embeds'						)
							def lowerCAmelCase_ (     self:      Any						)		->		List[Any]:
											pass
							def lowerCAmelCase_ (     self:      int						)		->		Any:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															snake_case__									=     model_class(UpperCamelCase						)
															self.assertIsInstance(model.get_input_embeddings()       ,	(nn.Module)						)
															snake_case__									=     model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(UpperCamelCase       ,	nn.Linear						)						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															snake_case__									=     model_class(UpperCamelCase						)
															snake_case__									=     inspect.signature(model.forward						)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															snake_case__									=     [*signature.parameters.keys()]

															snake_case__									=     ['pixel_values']
															self.assertListEqual(arg_names[:1]       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Union[str, Any]:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()
											snake_case__									=     True

											for model_class in self.all_model_classes:
															snake_case__									=     True
															snake_case__									=     False
															snake_case__									=     True
															snake_case__									=     model_class(UpperCamelCase						)
															model.to(UpperCamelCase						)
															model.eval()
															with torch.no_grad():
																			snake_case__									=     model(**self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase						)						)
															snake_case__									=     outputs.attentions
															snake_case__									=     len(self.model_tester.depths						)
															self.assertEqual(len(UpperCamelCase						)       ,	UpperCamelCase						)

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															snake_case__									=     True
															snake_case__									=     config.window_size**2
															snake_case__									=     model_class(UpperCamelCase						)
															model.to(UpperCamelCase						)
															model.eval()
															with torch.no_grad():
																			snake_case__									=     model(**self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase						)						)
															snake_case__									=     outputs.attentions
															self.assertEqual(len(UpperCamelCase						)       ,	UpperCamelCase						)

															self.assertListEqual(
															    list(attentions[0].shape[-3:]						)       ,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,	)
															snake_case__									=     len(UpperCamelCase						)

															# Check attention is always last and order is fine
															snake_case__									=     True
															snake_case__									=     True
															snake_case__									=     model_class(UpperCamelCase						)
															model.to(UpperCamelCase						)
															model.eval()
															with torch.no_grad():
																			snake_case__									=     model(**self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase						)						)

															if hasattr(self.model_tester       ,	'num_hidden_states_types'						):
																			snake_case__									=     self.model_tester.num_hidden_states_types
															else:
																			# also another +1 for reshaped_hidden_states
																			snake_case__									=     2
															self.assertEqual(out_len + added_hidden_states       ,	len(UpperCamelCase						)						)

															snake_case__									=     outputs.attentions

															self.assertEqual(len(UpperCamelCase						)       ,	UpperCamelCase						)

															self.assertListEqual(
															    list(self_attentions[0].shape[-3:]						)       ,	[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,	)
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      Optional[int]       ,	UpperCamelCase:      Union[str, Any]       ,	UpperCamelCase:      int       ,	UpperCamelCase:      Dict						)		->		str:
											snake_case__									=     model_class(UpperCamelCase						)
											model.to(UpperCamelCase						)
											model.eval()

											with torch.no_grad():
															snake_case__									=     model(**self._prepare_for_class(UpperCamelCase       ,	UpperCamelCase						)						)

											snake_case__									=     outputs.hidden_states

											snake_case__									=     getattr(
											    self.model_tester       ,	'expected_num_hidden_layers'       ,	len(self.model_tester.depths						) + 1						)
											self.assertEqual(len(UpperCamelCase						)       ,	UpperCamelCase						)

											# Swinv2 has a different seq_length
											snake_case__									=     (
											    config.patch_size
											    if isinstance(config.patch_size       ,	collections.abc.Iterable						)
											    else (config.patch_size, config.patch_size)
											)

											snake_case__									=     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]						)       ,	[num_patches, self.model_tester.embed_dim]       ,	)

											snake_case__									=     outputs.reshaped_hidden_states
											self.assertEqual(len(UpperCamelCase						)       ,	UpperCamelCase						)

											snake_case__							,						snake_case__							,						snake_case__							,						snake_case__									=     reshaped_hidden_states[0].shape
											snake_case__									=     (
											    reshaped_hidden_states[0].view(UpperCamelCase       ,	UpperCamelCase       ,	height * width						).permute(0       ,	2       ,	1						)
											)
											self.assertListEqual(
											    list(reshaped_hidden_states.shape[-2:]						)       ,	[num_patches, self.model_tester.embed_dim]       ,	)
							def lowerCAmelCase_ (     self:      int						)		->		int:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											snake_case__									=     (
											    self.model_tester.image_size
											    if isinstance(self.model_tester.image_size       ,	collections.abc.Iterable						)
											    else (self.model_tester.image_size, self.model_tester.image_size)
											)

											for model_class in self.all_model_classes:
															snake_case__									=     True
															self.check_hidden_states_output(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															snake_case__									=     True

															self.check_hidden_states_output(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Dict						)		->		List[str]:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()
											snake_case__									=     3

											snake_case__									=     (
											    self.model_tester.image_size
											    if isinstance(self.model_tester.image_size       ,	collections.abc.Iterable						)
											    else (self.model_tester.image_size, self.model_tester.image_size)
											)
											snake_case__									=     (
											    config.patch_size
											    if isinstance(config.patch_size       ,	collections.abc.Iterable						)
											    else (config.patch_size, config.patch_size)
											)

											snake_case__									=     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
											snake_case__									=     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

											for model_class in self.all_model_classes:
															snake_case__									=     True
															self.check_hidden_states_output(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	(padded_height, padded_width)						)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															snake_case__									=     True
															self.check_hidden_states_output(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase       ,	(padded_height, padded_width)						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Union[str, Any]:
											snake_case__									=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase						)
							def lowerCAmelCase_ (     self:      str						)		->		Optional[int]:
											snake_case__									=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*UpperCamelCase						)
							@slow
							def lowerCAmelCase_ (     self:      Any						)		->		Optional[Any]:
											for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case__									=     SwinvaModel.from_pretrained(UpperCamelCase						)
															self.assertIsNotNone(UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Any						)		->		Tuple:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											snake_case__									=     _config_zero_init(UpperCamelCase						)
											for model_class in self.all_model_classes:
															snake_case__									=     model_class(config=UpperCamelCase						)
															for name, param in model.named_parameters():
																			if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
																							self.assertIn(
																							    ((param.data.mean() * 1e9).round() / 1e9).item()       ,	[0.0, 1.0]       ,	msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''       ,	)


@require_vision
@require_torch
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							@cached_property
							def lowerCAmelCase_ (     self:      Any						)		->		Any:
											return (
											    AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'						)
											    if is_vision_available()
											    else None
											)


							@slow
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Optional[int]:
											snake_case__									=     SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'						).to(
											    UpperCamelCase						)
											snake_case__									=     self.default_image_processor

											snake_case__									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
											snake_case__									=     image_processor(images=UpperCamelCase       ,	return_tensors='pt'						).to(UpperCamelCase						)

											# forward pass
											with torch.no_grad():
															snake_case__									=     model(**UpperCamelCase						)

											# verify the logits
											snake_case__									=     torch.Size((1, 10_00)						)
											self.assertEqual(outputs.logits.shape       ,	UpperCamelCase						)
											snake_case__									=     torch.tensor([-0.3_947, -0.4_306, 0.0_026]						).to(UpperCamelCase						)
											self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,	UpperCamelCase       ,	atol=1e-4						)						)












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


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

__UpperCamelCase					:		Union[str, Any]	  =						["""names""", """prefix"""]
__UpperCamelCase					:		List[str]	  =						["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
__UpperCamelCase					:		Dict	  =						["""encoding_errors""", """on_bad_lines"""]
__UpperCamelCase					:		Any	  =						["""date_format"""]


@dataclass
class   __SCREAMING_SNAKE_CASE(	datasets.BuilderConfig	):
							_UpperCAmelCase     =       ","
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       "infer"
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       "."
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       '"'
							_UpperCAmelCase     =       0
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       0
							_UpperCAmelCase     =       True
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       1_0_0_0_0
							_UpperCAmelCase     =       None
							_UpperCAmelCase     =       "strict"
							_UpperCAmelCase     =       "error"
							_UpperCAmelCase     =       None
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											if self.delimiter is not None:
															snake_case__									=     self.delimiter
											if self.column_names is not None:
															snake_case__									=     self.column_names


							@property
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		List[str]:
											snake_case__									=     {
											    '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   __SCREAMING_SNAKE_CASE(	datasets.ArrowBasedBuilder	):
							_UpperCAmelCase     =       CsvConfig
							def lowerCAmelCase_ (     self:      str						)		->		int:
											return datasets.DatasetInfo(features=self.config.features						)
							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      Dict						)		->		int:
											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__									=     dl_manager.download_and_extract(self.config.data_files						)
											if isinstance(UpperCamelCase       ,	(str, list, tuple)						):
															snake_case__									=     data_files
															if isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			snake_case__									=     [files]
															snake_case__									=     [dl_manager.iter_files(UpperCamelCase						) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,	gen_kwargs={'files': files}						)]
											snake_case__									=     []
											for split_name, files in data_files.items():
															if isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			snake_case__									=     [files]
															snake_case__									=     [dl_manager.iter_files(UpperCamelCase						) for file in files]
															splits.append(datasets.SplitGenerator(name=UpperCamelCase       ,	gen_kwargs={'files': files}						)						)
											return splits
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      pa.Table						)		->		pa.Table:
											if self.config.features is not None:
															snake_case__									=     self.config.features.arrow_schema
															if all(not require_storage_cast(UpperCamelCase						) for feature in self.config.features.values()						):
																			# cheaper cast
																			snake_case__									=     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__									=     table_cast(UpperCamelCase       ,	UpperCamelCase						)
											return pa_table


							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      Tuple						)		->		Tuple:
											snake_case__									=     self.config.features.arrow_schema if self.config.features else None
											# dtype allows reading an int column as str
											snake_case__									=     (
											    {
											        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__									=     pd.read_csv(UpperCamelCase       ,	iterator=UpperCamelCase       ,	dtype=UpperCamelCase       ,	**self.config.pd_read_csv_kwargs						)
															try:
																			for batch_idx, df in enumerate(UpperCamelCase						):
																							snake_case__									=     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




