






'''simple docstring'''




import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)

def 	__UpperCAmelCase       ( __magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Any   =       VideoMAEConfig()

			set_architecture_configs(__magic_name__     ,__magic_name__						)

			if "finetuned" not in model_name:
						snake_case_				:					Union[str, Any]   =       False

			if "finetuned" in model_name:
						snake_case_				:					str   =       "huggingface/label-files"
						if "kinetics" in model_name:
									snake_case_				:					List[str]   =       400
									snake_case_				:					int   =       "kinetics400-id2label.json"
						elif "ssv2" in model_name:
									snake_case_				:					Dict   =       174
									snake_case_				:					Union[str, Any]   =       "something-something-v2-id2label.json"
						else:
									raise ValueError("Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."						)
						snake_case_				:					Optional[Any]   =       json.load(open(hf_hub_download(__magic_name__     ,__magic_name__     ,repo_type="dataset"						)     ,"r"						)						)
						snake_case_				:					Optional[int]   =       {int(__magic_name__						): v for k, v in idalabel.items()}
						snake_case_				:					Optional[int]   =       idalabel
						snake_case_				:					List[str]   =       {v: k for k, v in idalabel.items()}

			return config

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    List[Any]:



			"""simple docstring"""



			if "small" in model_name:
						snake_case_				:					str   =       384
						snake_case_				:					str   =       1536
						snake_case_				:					str   =       12
						snake_case_				:					Optional[int]   =       16
						snake_case_				:					List[Any]   =       12
						snake_case_				:					Optional[int]   =       3
						snake_case_				:					Optional[int]   =       192
						snake_case_				:					Any   =       768
			elif "large" in model_name:
						snake_case_				:					Any   =       1024
						snake_case_				:					Union[str, Any]   =       4096
						snake_case_				:					Optional[int]   =       24
						snake_case_				:					str   =       16
						snake_case_				:					Tuple   =       12
						snake_case_				:					int   =       8
						snake_case_				:					Optional[int]   =       512
						snake_case_				:					Any   =       2048
			elif "huge" in model_name:
						snake_case_				:					Tuple   =       1280
						snake_case_				:					Dict   =       5120
						snake_case_				:					int   =       32
						snake_case_				:					Optional[Any]   =       16
						snake_case_				:					List[Any]   =       12
						snake_case_				:					Tuple   =       8
						snake_case_				:					Optional[int]   =       640
						snake_case_				:					List[str]   =       2560
			elif "base" not in model_name:
						raise ValueError("Model name should include either \"small\", \"base\", \"large\", or \"huge\""						)

def 	__UpperCAmelCase       ( __magic_name__						)->    Dict:



			"""simple docstring"""



			if "encoder." in name:
						snake_case_				:					Any   =       name.replace("encoder."     ,""						)
			if "cls_token" in name:
						snake_case_				:					Dict   =       name.replace("cls_token"     ,"videomae.embeddings.cls_token"						)
			if "decoder_pos_embed" in name:
						snake_case_				:					List[str]   =       name.replace("decoder_pos_embed"     ,"decoder.decoder_pos_embed"						)
			if "pos_embed" in name and "decoder" not in name:
						snake_case_				:					List[Any]   =       name.replace("pos_embed"     ,"videomae.embeddings.position_embeddings"						)
			if "patch_embed.proj" in name:
						snake_case_				:					str   =       name.replace("patch_embed.proj"     ,"videomae.embeddings.patch_embeddings.projection"						)
			if "patch_embed.norm" in name:
						snake_case_				:					int   =       name.replace("patch_embed.norm"     ,"videomae.embeddings.norm"						)
			if "decoder.blocks" in name:
						snake_case_				:					Any   =       name.replace("decoder.blocks"     ,"decoder.decoder_layers"						)
			if "blocks" in name:
						snake_case_				:					Optional[int]   =       name.replace("blocks"     ,"videomae.encoder.layer"						)
			if "attn.proj" in name:
						snake_case_				:					Union[str, Any]   =       name.replace("attn.proj"     ,"attention.output.dense"						)
			if "attn" in name and "bias" not in name:
						snake_case_				:					List[Any]   =       name.replace("attn"     ,"attention.self"						)
			if "attn" in name:
						snake_case_				:					Dict   =       name.replace("attn"     ,"attention.attention"						)
			if "norm1" in name:
						snake_case_				:					List[Any]   =       name.replace("norm1"     ,"layernorm_before"						)
			if "norm2" in name:
						snake_case_				:					Union[str, Any]   =       name.replace("norm2"     ,"layernorm_after"						)
			if "mlp.fc1" in name:
						snake_case_				:					int   =       name.replace("mlp.fc1"     ,"intermediate.dense"						)
			if "mlp.fc2" in name:
						snake_case_				:					Any   =       name.replace("mlp.fc2"     ,"output.dense"						)
			if "decoder_embed" in name:
						snake_case_				:					Optional[int]   =       name.replace("decoder_embed"     ,"decoder.decoder_embed"						)
			if "decoder_norm" in name:
						snake_case_				:					str   =       name.replace("decoder_norm"     ,"decoder.decoder_norm"						)
			if "decoder_pred" in name:
						snake_case_				:					List[Any]   =       name.replace("decoder_pred"     ,"decoder.decoder_pred"						)
			if "norm.weight" in name and "decoder" not in name and "fc" not in name:
						snake_case_				:					Union[str, Any]   =       name.replace("norm.weight"     ,"videomae.layernorm.weight"						)
			if "norm.bias" in name and "decoder" not in name and "fc" not in name:
						snake_case_				:					str   =       name.replace("norm.bias"     ,"videomae.layernorm.bias"						)
			if "head" in name and "decoder" not in name:
						snake_case_				:					Any   =       name.replace("head"     ,"classifier"						)

			return name

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Any:



			"""simple docstring"""



			for key in orig_state_dict.copy().keys():
						snake_case_				:					Optional[Any]   =       orig_state_dict.pop(__magic_name__						)

						if key.startswith("encoder."						):
									snake_case_				:					Dict   =       key.replace("encoder."     ,""						)

						if "qkv" in key:
									snake_case_				:					List[Any]   =       key.split("."						)
									if key.startswith("decoder.blocks"						):
												snake_case_				:					Any   =       config.decoder_hidden_size
												snake_case_				:					Optional[int]   =       int(key_split[2]						)
												snake_case_				:					Dict   =       "decoder.decoder_layers."
												if "weight" in key:
															snake_case_				:					int   =       val[:dim, :]
															snake_case_				:					Union[str, Any]   =       val[dim : dim * 2, :]
															snake_case_				:					Dict   =       val[-dim:, :]
									else:
												snake_case_				:					Tuple   =       config.hidden_size
												snake_case_				:					List[str]   =       int(key_split[1]						)
												snake_case_				:					str   =       "videomae.encoder.layer."
												if "weight" in key:
															snake_case_				:					List[str]   =       val[:dim, :]
															snake_case_				:					List[Any]   =       val[dim : dim * 2, :]
															snake_case_				:					Any   =       val[-dim:, :]
						else:
									snake_case_				:					Any   =       val

			return orig_state_dict

def 	__UpperCAmelCase       ( )->    int:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       hf_hub_download(
			    repo_id="hf-internal-testing/spaghetti-video"     ,filename="eating_spaghetti.npy"     ,repo_type="dataset"						)
			snake_case_				:					List[Any]   =       np.load(__magic_name__						)
			return list(__magic_name__						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)->    str:



			"""simple docstring"""



			snake_case_				:					Tuple   =       get_videomae_config(__magic_name__						)

			if "finetuned" in model_name:
						snake_case_				:					Optional[int]   =       VideoMAEForVideoClassification(__magic_name__						)
			else:
						snake_case_				:					int   =       VideoMAEForPreTraining(__magic_name__						)

			# download original checkpoint, hosted on Google Drive
			snake_case_				:					Any   =       "pytorch_model.bin"
			gdown.cached_download(__magic_name__     ,__magic_name__     ,quiet=__magic_name__						)
			snake_case_				:					List[Any]   =       torch.load(__magic_name__     ,map_location="cpu"						)
			if "model" in files:
						snake_case_				:					Union[str, Any]   =       files["model"]
			else:
						snake_case_				:					int   =       files["module"]
			snake_case_				:					Optional[Any]   =       convert_state_dict(__magic_name__     ,__magic_name__						)

			model.load_state_dict(__magic_name__						)
			model.eval()

			# verify model on basic input
			snake_case_				:					Union[str, Any]   =       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]     ,image_std=[0.5, 0.5, 0.5]						)
			snake_case_				:					List[str]   =       prepare_video()
			snake_case_				:					Union[str, Any]   =       image_processor(__magic_name__     ,return_tensors="pt"						)

			if "finetuned" not in model_name:
						snake_case_				:					Any   =       hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"     ,filename="bool_masked_pos.pt"						)
						snake_case_				:					Dict   =       torch.load(__magic_name__						)

			snake_case_				:					Any   =       model(**__magic_name__						)
			snake_case_				:					Tuple   =       outputs.logits

			snake_case_				:					Tuple   =       [
			    "videomae-small-finetuned-kinetics",
			    "videomae-small-finetuned-ssv2",
			    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
			    "videomae-base-short",
			    "videomae-base-short-finetuned-kinetics",
			    "videomae-base",
			    "videomae-base-finetuned-kinetics",
			    "videomae-large",
			    "videomae-large-finetuned-kinetics",
			    "videomae-huge-finetuned-kinetics",
			    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
			    "videomae-base-short-ssv2",
			    "videomae-base-short-finetuned-ssv2",
			    "videomae-base-ssv2",
			    "videomae-base-finetuned-ssv2",
			]

			# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
			if model_name == "videomae-small-finetuned-kinetics":
						snake_case_				:					Any   =       torch.Size([1, 400]						)
						snake_case_				:					List[Any]   =       torch.tensor([-0.9_291, -0.4_061, -0.9_307]						)
			elif model_name == "videomae-small-finetuned-ssv2":
						snake_case_				:					List[Any]   =       torch.Size([1, 174]						)
						snake_case_				:					Union[str, Any]   =       torch.tensor([0.2_671, -0.4_689, -0.8_235]						)
			elif model_name == "videomae-base":
						snake_case_				:					Dict   =       torch.Size([1, 1408, 1536]						)
						snake_case_				:					Dict   =       torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]]						)
			elif model_name == "videomae-base-short":
						snake_case_				:					Union[str, Any]   =       torch.Size([1, 1408, 1536]						)
						snake_case_				:					Optional[int]   =       torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]						)
						# we verified the loss both for normalized and unnormalized targets for this one
						snake_case_				:					List[str]   =       torch.tensor([0.5_142]						) if config.norm_pix_loss else torch.tensor([0.6_469]						)
			elif model_name == "videomae-large":
						snake_case_				:					Any   =       torch.Size([1, 1408, 1536]						)
						snake_case_				:					List[str]   =       torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]]						)
			elif model_name == "videomae-large-finetuned-kinetics":
						snake_case_				:					List[Any]   =       torch.Size([1, 400]						)
						snake_case_				:					Optional[int]   =       torch.tensor([0.0_771, 0.0_011, -0.3_625]						)
			elif model_name == "videomae-huge-finetuned-kinetics":
						snake_case_				:					List[str]   =       torch.Size([1, 400]						)
						snake_case_				:					Union[str, Any]   =       torch.tensor([0.2_433, 0.1_632, -0.4_894]						)
			elif model_name == "videomae-base-short-finetuned-kinetics":
						snake_case_				:					List[Any]   =       torch.Size([1, 400]						)
						snake_case_				:					int   =       torch.tensor([0.6_588, 0.0_990, -0.2_493]						)
			elif model_name == "videomae-base-finetuned-kinetics":
						snake_case_				:					Dict   =       torch.Size([1, 400]						)
						snake_case_				:					str   =       torch.tensor([0.3_669, -0.0_688, -0.2_421]						)
			elif model_name == "videomae-base-short-ssv2":
						snake_case_				:					Optional[Any]   =       torch.Size([1, 1408, 1536]						)
						snake_case_				:					Optional[int]   =       torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]]						)
			elif model_name == "videomae-base-short-finetuned-ssv2":
						snake_case_				:					Dict   =       torch.Size([1, 174]						)
						snake_case_				:					str   =       torch.tensor([-0.0_537, -0.1_539, -0.3_266]						)
			elif model_name == "videomae-base-ssv2":
						snake_case_				:					Union[str, Any]   =       torch.Size([1, 1408, 1536]						)
						snake_case_				:					List[str]   =       torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]]						)
			elif model_name == "videomae-base-finetuned-ssv2":
						snake_case_				:					int   =       torch.Size([1, 174]						)
						snake_case_				:					Any   =       torch.tensor([0.1_961, -0.8_337, -0.6_389]						)
			else:
						raise ValueError(F'''Model name not supported. Should be one of {model_names}'''						)

			# verify logits
			assert logits.shape == expected_shape
			if "finetuned" in model_name:
						assert torch.allclose(logits[0, :3]     ,__magic_name__     ,atol=1E-4						)
			else:
						print("Logits:"     ,logits[0, :3, :3]						)
						assert torch.allclose(logits[0, :3, :3]     ,__magic_name__     ,atol=1E-4						)
			print("Logits ok!"						)

			# verify loss, if applicable
			if model_name == "videomae-base-short":
						snake_case_				:					Tuple   =       outputs.loss
						assert torch.allclose(__magic_name__     ,__magic_name__     ,atol=1E-4						)
						print("Loss ok!"						)

			if pytorch_dump_folder_path is not None:
						print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''						)
						image_processor.save_pretrained(__magic_name__						)
						model.save_pretrained(__magic_name__						)

			if push_to_hub:
						print("Pushing to the hub..."						)
						model.push_to_hub(__magic_name__     ,organization="nielsr"						)


if __name__ == "__main__":
		__lowerCamelCase   :					List[Any]   							=					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4''',
		    type=str,
		    help=(
		        '''URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'''
		        ''' download link.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default='''/Users/nielsrogge/Documents/VideoMAE/Test''',
		    type=str,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument('''--model_name''', default='''videomae-base''', type=str, help='''Name of the model.''')
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)

		__lowerCamelCase   :					Any   							=					parser.parse_args()
		convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)












'''simple docstring'''




import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


__lowerCamelCase   :					Optional[Any]   							=					datasets.utils.logging.get_logger(__name__)

__lowerCamelCase   :					List[str]   							=					['''names''', '''prefix''']
__lowerCamelCase   :					int   							=					['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
__lowerCamelCase   :					str   							=					['''encoding_errors''', '''on_bad_lines''']
__lowerCamelCase   :					Optional[Any]   							=					['''date_format''']







@dataclass
class    A_	(datasets.BuilderConfig							):
							"""simple docstring"""




							a__   		=	","
							a__   		=	None
							a__   		=	"infer"
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	True
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	False
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	True
							a__   		=	True
							a__   		=	False
							a__   		=	True
							a__   		=	None
							a__   		=	"."
							a__   		=	None
							a__   		=	'"'
							a__   		=	0
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	None
							a__   		=	True
							a__   		=	True
							a__   		=	0
							a__   		=	True
							a__   		=	False
							a__   		=	None
							a__   		=	10000
							a__   		=	None
							a__   		=	"strict"
							a__   		=	"error"
							a__   		=	None

							def 							_A	(		self			:List[str]				)				->  Any:


										'''simple docstring'''




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






							@property
							def 							_A	(		self			:Optional[Any]				)				->  int:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       {
										    "sep": self.sep,
										    "header": self.header,
										    "names": self.names,
										    "index_col": self.index_col,
										    "usecols": self.usecols,
										    "prefix": self.prefix,
										    "mangle_dupe_cols": self.mangle_dupe_cols,
										    "engine": self.engine,
										    "converters": self.converters,
										    "true_values": self.true_values,
										    "false_values": self.false_values,
										    "skipinitialspace": self.skipinitialspace,
										    "skiprows": self.skiprows,
										    "nrows": self.nrows,
										    "na_values": self.na_values,
										    "keep_default_na": self.keep_default_na,
										    "na_filter": self.na_filter,
										    "verbose": self.verbose,
										    "skip_blank_lines": self.skip_blank_lines,
										    "thousands": self.thousands,
										    "decimal": self.decimal,
										    "lineterminator": self.lineterminator,
										    "quotechar": self.quotechar,
										    "quoting": self.quoting,
										    "escapechar": self.escapechar,
										    "comment": self.comment,
										    "encoding": self.encoding,
										    "dialect": self.dialect,
										    "error_bad_lines": self.error_bad_lines,
										    "warn_bad_lines": self.warn_bad_lines,
										    "skipfooter": self.skipfooter,
										    "doublequote": self.doublequote,
										    "memory_map": self.memory_map,
										    "float_precision": self.float_precision,
										    "chunksize": self.chunksize,
										    "encoding_errors": self.encoding_errors,
										    "on_bad_lines": self.on_bad_lines,
										    "date_format": self.date_format,
										}

										# some kwargs must not be passed if they don't have a default value
										# some others are deprecated and we can also not pass them if they are the default value
										for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
													if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()       ,							lowerCAmelCase__				):
																del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
										if not (datasets.config.PANDAS_VERSION.major >= 2):
													for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
																del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
										if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
													for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
																del pd_read_csv_kwargs[pd_read_csv_parameter]

										return pd_read_csv_kwargs







class    A_	(datasets.ArrowBasedBuilder							):
							"""simple docstring"""




							a__   		=	CsvConfig

							def 							_A	(		self			:Optional[Any]				)				->  Optional[Any]:


										'''simple docstring'''




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

							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:Dict				)				->  List[Any]:


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

							def 							_A	(		self			:List[Any]       ,							lowerCAmelCase__			:pa.Table				)				->  pa.Table:


										'''simple docstring'''




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






							def 							_A	(		self			:Dict       ,							lowerCAmelCase__			:Union[str, Any]				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       self.config.features.arrow_schema if self.config.features else None
										# dtype allows reading an int column as str
										snake_case_				:					str   =       (
										    {
										        name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCAmelCase__				) else object
										        for name, dtype, feature in zip(schema.names       ,							schema.types       ,							self.config.features.values()				)
										    }
										    if schema is not None
										    else None
										)
										for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__				)				):
													snake_case_				:					Tuple   =       pd.read_csv(lowerCAmelCase__       ,							iterator=lowerCAmelCase__       ,							dtype=lowerCAmelCase__       ,							**self.config.pd_read_csv_kwargs				)
													try:
																for batch_idx, df in enumerate(lowerCAmelCase__				):
																			snake_case_				:					Optional[int]   =       pa.Table.from_pandas(lowerCAmelCase__				)
																			# Uncomment for debugging (will print the Arrow table size and elements)
																			# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																			# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																			yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__				)
													except ValueError as e:
																logger.error(F'''Failed to read file \'{file}\' with error {type(lowerCAmelCase__				)}: {e}'''				)
																raise




