code: 
  
  
  '''simple docstring'''
  
  import argparse
  import json
  import sys
  from dataclasses import dataclass, field
  from functools import partial
  from pathlib import Path
  from typing import List
  
  import torch
  import torch.nn as nn
  from huggingface_hub import cached_download, hf_hub_download
  from torch import Tensor
  
  from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
  from transformers.models.deprecated.van.modeling_van import VanLayerScaling
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowerCAmelCase   :Dict								=    logging.get_logger(__name__)
  @dataclass
  class _lowerCamelCase :
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    nn.Module
  		A_			:    List[nn.Module]   					=       field(default_factory=lowercase__							)
  		A_			:    list   					=       field(default_factory=lowercase__							)
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int						,  _A	:		List[str]						,  _A	:		Tensor						,  _A	:		Tensor    )						->				Union[str, Any]:
  			__magic_name__ :					Optional[int]  			=  len(list(m.modules()    )    ) == 1 or isinstance(_A						,  nn.Convad    ) or isinstance(_A						,  nn.BatchNormad    )
  			if has_not_submodules:
  				if not isinstance(_A						,  _A    ):
  					self.traced.append(_A    )
  
  
  
  
  
  
  
  		def __call__(      self	:		Union[str, Any]						,  _A	:		Tensor    )						->				Union[str, Any]:
  			for m in self.module.modules():
  				self.handles.append(m.register_forward_hook(self._forward_hook    )    )
  			self.module(_A    )
  			[x.remove() for x in self.handles]
  			return self
  
  
  
  
  
  
  
  		@property
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Union[str, Any]:
  			# check the len of the state_dict keys to see if we have learnable params
  			return list(filter(lambda _A    : len(list(x.state_dict().keys()    )    ) > 0						,  self.traced    )    )
  
  
  
  
  
  
  
  @dataclass
  class _lowerCamelCase :
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    nn.Module
  		A_			:    nn.Module
  		A_			:    int   					=       0
  		A_			:    List   					=       field(default_factory=lowercase__							)
  		A_			:    List   					=       field(default_factory=lowercase__							)
  
  
  
  
  
  
  
  		def __call__(      self	:		Tuple						,  _A	:		Tensor    )						->				List[Any]:
  			__magic_name__ :					Dict  			=  Tracker(self.dest    )(_A    ).parametrized
  			__magic_name__ :					List[Any]  			=  Tracker(self.src    )(_A    ).parametrized
  
  			__magic_name__ :					List[Any]  			=  list(filter(lambda _A    : type(_A    ) not in self.src_skip						,  _A    )    )
  			__magic_name__ :					List[Any]  			=  list(filter(lambda _A    : type(_A    ) not in self.dest_skip						,  _A    )    )
  
  			if len(_A    ) != len(_A    ):
  				raise Exception(
  				    F'Numbers of operations are different. Source module has {len(_A    )} operations while'
  				    F' destination module has {len(_A    )}.'    )
  
  			for dest_m, src_m in zip(_A						,  _A    ):
  				dest_m.load_state_dict(src_m.state_dict()    )
  				if self.verbose == 1:
  					print(F'Transfered from={src_m} to={dest_m}'    )
  
  
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			nn.Module	,	lowerCAmelCase       :			nn.Module	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Any  			=  from_model.state_dict()
  	__magic_name__ :					List[str]  			=  our_model.state_dict()
  	__magic_name__ :					Optional[int]  			=  our_model.config
  	__magic_name__ :					List[str]  			=  []
  	for stage_idx in range(len(config.hidden_sizes	)	):
  		for block_id in range(config.depths[stage_idx]	):
  			__magic_name__ :					List[str]  			=  f'block{stage_idx + 1}.{block_id}.layer_scale_1'
  			__magic_name__ :					Optional[int]  			=  f'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'
  
  			all_keys.append((from_key, to_key)	)
  			__magic_name__ :					List[str]  			=  f'block{stage_idx + 1}.{block_id}.layer_scale_2'
  			__magic_name__ :					Optional[Any]  			=  f'van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'
  
  			all_keys.append((from_key, to_key)	)
  
  	for from_key, to_key in all_keys:
  		__magic_name__ :					Optional[Any]  			=  from_state_dict.pop(lowerCAmelCase	)
  
  	our_model.load_state_dict(lowerCAmelCase	)
  	return our_model
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			str	,	lowerCAmelCase       :			VanConfig	,	lowerCAmelCase       :			str	,	lowerCAmelCase       :			nn.Module	,	lowerCAmelCase       :			Path	,	lowerCAmelCase       :			bool = True	,	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	print(f'Downloading weights for {name}...'	)
  	__magic_name__ :					str  			=  cached_download(lowerCAmelCase	)
  	print(f'Converting {name}...'	)
  	__magic_name__ :					List[Any]  			=  torch.load(lowerCAmelCase	)['state_dict']
  	from_model.load_state_dict(lowerCAmelCase	)
  	from_model.eval()
  	with torch.no_grad():
  		__magic_name__ :					int  			=  VanForImageClassification(lowerCAmelCase	).eval()
  		__magic_name__ :					int  			=  ModuleTransfer(src=lowerCAmelCase	,	dest=lowerCAmelCase	)
  		__magic_name__ :					List[str]  			=  torch.randn((1, 3, 224, 224)	)
  		module_transfer(lowerCAmelCase	)
  		__magic_name__ :					Tuple  			=  copy_parameters(lowerCAmelCase	,	lowerCAmelCase	)
  
  	if not torch.allclose(from_model(lowerCAmelCase	)	,	our_model(lowerCAmelCase	).logits	):
  		raise ValueError('The model logits don\'t match the original one.'	)
  
  	__magic_name__ :					Any  			=  name
  	print(lowerCAmelCase	)
  
  	if push_to_hub:
  		our_model.push_to_hub(
  		    repo_path_or_name=save_directory / checkpoint_name	,	commit_message='Add model'	,	use_temp_dir=lowerCAmelCase	,	)
  
  		# we can use the convnext one
  		__magic_name__ :					Optional[int]  			=  AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'	)
  		image_processor.push_to_hub(
  		    repo_path_or_name=save_directory / checkpoint_name	,	commit_message='Add image processor'	,	use_temp_dir=lowerCAmelCase	,	)
  
  		print(f'Pushed {checkpoint_name}'	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Path	,	lowerCAmelCase       :			str = None	,	lowerCAmelCase       :			bool = True	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					List[str]  			=  'imagenet-1k-id2label.json'
  	__magic_name__ :					List[Any]  			=  1000
  
  	__magic_name__ :					Any  			=  'huggingface/label-files'
  	__magic_name__ :					List[Any]  			=  num_labels
  	__magic_name__ :					int  			=  json.load(open(hf_hub_download(lowerCAmelCase	,	lowerCAmelCase	,	repo_type='dataset'	)	,	'r'	)	)
  	__magic_name__ :					List[str]  			=  {int(lowerCAmelCase	): v for k, v in idalabel.items()}
  
  	__magic_name__ :					str  			=  idalabel
  	__magic_name__ :					Union[str, Any]  			=  {v: k for k, v in idalabel.items()}
  
  	__magic_name__ :					int  			=  partial(lowerCAmelCase	,	num_labels=lowerCAmelCase	,	idalabel=lowerCAmelCase	,	labelaid=lowerCAmelCase	)
  
  	__magic_name__ :					int  			=  {
  	    'van-tiny': ImageNetPreTrainedConfig(
  	        hidden_sizes=[32, 64, 160, 256]	,	depths=[3, 3, 5, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
  	    'van-small': ImageNetPreTrainedConfig(
  	        hidden_sizes=[64, 128, 320, 512]	,	depths=[2, 2, 4, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
  	    'van-base': ImageNetPreTrainedConfig(
  	        hidden_sizes=[64, 128, 320, 512]	,	depths=[3, 3, 12, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
  	    'van-large': ImageNetPreTrainedConfig(
  	        hidden_sizes=[64, 128, 320, 512]	,	depths=[3, 5, 27, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
  	}
  
  	__magic_name__ :					Optional[int]  			=  {
  	    'van-tiny': van_tiny,
  	    'van-small': van_small,
  	    'van-base': van_base,
  	    'van-large': van_large,
  	}
  
  	__magic_name__ :					Tuple  			=  {
  	    'van-tiny': (
  	        'https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'
  	    ),
  	    'van-small': (
  	        'https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'
  	    ),
  	    'van-base': (
  	        'https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'
  	    ),
  	    'van-large': (
  	        'https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'
  	    ),
  	}
  
  	if model_name:
  		convert_weight_and_push(
  		    lowerCAmelCase	,	names_to_config[model_name]	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=lowerCAmelCase	,	push_to_hub=lowerCAmelCase	,	)
  	else:
  		for model_name, config in names_to_config.items():
  			convert_weight_and_push(
  			    lowerCAmelCase	,	lowerCAmelCase	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=lowerCAmelCase	,	push_to_hub=lowerCAmelCase	,	)
  
  
  if __name__ == "__main__":
  							lowerCAmelCase   :List[Any]								=    argparse.ArgumentParser()
  							# Required parameters
  							parser.add_argument(
  							    '''--model-name''',
  							    default=None,
  							    type=str,
  							    help=(
  							        '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
  							        ''' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'''
  							    ),
  							)
  							parser.add_argument(
  							    '''--pytorch_dump_folder_path''',
  							    default=None,
  							    type=Path,
  							    required=True,
  							    help='''Path to the output PyTorch model directory.''',
  							)
  							parser.add_argument(
  							    '''--van_dir''',
  							    required=True,
  							    type=Path,
  							    help=(
  							        '''A path to VAN\'s original implementation directory. You can download from here:'''
  							        ''' https://github.com/Visual-Attention-Network/VAN-Classification'''
  							    ),
  							)
  							parser.add_argument(
  							    '''--push_to_hub''',
  							    default=True,
  							    type=bool,
  							    required=False,
  							    help='''If True, push model and image processor to the hub.''',
  							)
  
  							lowerCAmelCase   :Optional[int]								=    parser.parse_args()
  							lowerCAmelCase   :Path								=    args.pytorch_dump_folder_path
  							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  							lowerCAmelCase   :int								=    args.van_dir
  							# append the path to the parents to maskformer dir
  							sys.path.append(str(van_dir.parent))
  							from van.models.van import van_base, van_large, van_small, van_tiny
  
  							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
style_context: 
  
  
  '''simple docstring'''
  
  import os
  import pickle
  import tempfile
  import unittest
  from typing import Callable, Optional
  
  import numpy as np
  
  from transformers import (
      BatchEncoding,
      BertTokenizer,
      BertTokenizerFast,
      PreTrainedTokenizer,
      PreTrainedTokenizerFast,
      TensorType,
      TokenSpan,
      is_tokenizers_available,
  )
  from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
  from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow
  
  
  if is_tokenizers_available():
  							from tokenizers import Tokenizer
  							from tokenizers.models import WordPiece
  class _lowerCamelCase (		unittest.TestCase							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Any						,  _A	:		Optional[Any]    )						->				Optional[int]:
  			__magic_name__ :					Optional[int]  			=  list(tokenizer_class.max_model_input_sizes.keys()    )
  			for model_name in sa_models[:1]:
  				__magic_name__ :					List[str]  			=  tokenizer_class.from_pretrained(_A    )
  				self.assertIsNotNone(_A    )
  				self.assertIsInstance(_A						,  _A    )
  				self.assertIsInstance(_A						,  _A    )
  
  				for special_tok in tokenizer.all_special_tokens:
  					self.assertIsInstance(_A						,  _A    )
  					__magic_name__ :					Optional[Any]  			=  tokenizer.convert_tokens_to_ids(_A    )
  					self.assertIsInstance(_A						,  _A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[Any]						,  _A	:		BatchEncoding						,  _A	:		Optional[Callable] = None    )						->				Tuple:
  			__magic_name__ :					Union[str, Any]  			=  pickle.dumps(_A    )
  			self.assertIsNotNone(_A    )
  
  			__magic_name__ :					Optional[Any]  			=  pickle.loads(_A    )
  
  			# Ensure is_fast is correctly restored
  			self.assertEqual(be_restored.is_fast						,  be_original.is_fast    )
  
  			# Ensure encodings are potentially correctly restored
  			if be_original.is_fast:
  				self.assertIsNotNone(be_restored.encodings    )
  			else:
  				self.assertIsNone(be_restored.encodings    )
  
  			# Ensure the keys are the same
  			for original_v, restored_v in zip(be_original.values()						,  be_restored.values()    ):
  				if equal_op:
  					self.assertTrue(equal_op(_A						,  _A    )    )
  				else:
  					self.assertEqual(_A						,  _A    )
  
  
  
  
  
  
  
  		@slow
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				Optional[Any]:
  			self.check_tokenizer_from_pretrained(_A    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				List[Any]:
  			self.assertEqual(TensorType('tf'    )						,  TensorType.TENSORFLOW    )
  			self.assertEqual(TensorType('pt'    )						,  TensorType.PYTORCH    )
  			self.assertEqual(TensorType('np'    )						,  TensorType.NUMPY    )
  
  
  
  
  
  
  
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Optional[int]:
  			import numpy as np
  
  			__magic_name__ :					Dict  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					str  			=  BertTokenizerFast.from_pretrained('bert-base-cased'    )
  
  			# Python no tensor
  			with self.subTest('BatchEncoding (Python, return_tensors=None)'    ):
  				self.assert_dump_and_restore(tokenizer_p('Small example to encode'    )    )
  
  			with self.subTest('BatchEncoding (Python, return_tensors=NUMPY)'    ):
  				self.assert_dump_and_restore(
  				    tokenizer_p('Small example to encode'						,  return_tensors=TensorType.NUMPY    )						,  np.array_equal    )
  
  			with self.subTest('BatchEncoding (Rust, return_tensors=None)'    ):
  				self.assert_dump_and_restore(tokenizer_r('Small example to encode'    )    )
  
  			with self.subTest('BatchEncoding (Rust, return_tensors=NUMPY)'    ):
  				self.assert_dump_and_restore(
  				    tokenizer_r('Small example to encode'						,  return_tensors=TensorType.NUMPY    )						,  np.array_equal    )
  
  
  
  
  
  
  
  		@require_tf
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				List[str]:
  			import tensorflow as tf
  
  			def tf_array_equals(_A	:		str						,  _A	:		Union[str, Any]    ):
  				return tf.reduce_all(tf.equal(_A						,  _A    )    )
  
  			__magic_name__ :					str  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Optional[Any]  			=  BertTokenizerFast.from_pretrained('bert-base-cased'    )
  
  			with self.subTest('BatchEncoding (Python, return_tensors=TENSORFLOW)'    ):
  				self.assert_dump_and_restore(
  				    tokenizer_p('Small example to encode'						,  return_tensors=TensorType.TENSORFLOW    )						,  _A    )
  
  			with self.subTest('BatchEncoding (Rust, return_tensors=TENSORFLOW)'    ):
  				self.assert_dump_and_restore(
  				    tokenizer_r('Small example to encode'						,  return_tensors=TensorType.TENSORFLOW    )						,  _A    )
  
  
  
  
  
  
  
  		@require_torch
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		Any    )						->				List[str]:
  			import torch
  
  			__magic_name__ :					Union[str, Any]  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Optional[int]  			=  BertTokenizerFast.from_pretrained('bert-base-cased'    )
  
  			with self.subTest('BatchEncoding (Python, return_tensors=PYTORCH)'    ):
  				self.assert_dump_and_restore(
  				    tokenizer_p('Small example to encode'						,  return_tensors=TensorType.PYTORCH    )						,  torch.equal    )
  
  			with self.subTest('BatchEncoding (Rust, return_tensors=PYTORCH)'    ):
  				self.assert_dump_and_restore(
  				    tokenizer_r('Small example to encode'						,  return_tensors=TensorType.PYTORCH    )						,  torch.equal    )
  
  
  
  
  
  
  
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Dict:
  			__magic_name__ :					Optional[int]  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Dict  			=  BertTokenizerFast.from_pretrained('bert-base-cased'    )
  
  			with self.subTest('Python Tokenizer'    ):
  				self.assertFalse(tokenizer_p('Small example to_encode'    ).is_fast    )
  
  			with self.subTest('Rust Tokenizer'    ):
  				self.assertTrue(tokenizer_r('Small example to_encode'    ).is_fast    )
  
  
  
  
  
  
  
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Optional[int]:
  			__magic_name__ :					Optional[int]  			=  BertTokenizerFast.from_pretrained('bert-base-cased'    )
  			__magic_name__ :					Tuple  			=  tokenizer_r(['Test', '\xad', 'test']						,  is_split_into_words=_A    )
  
  			self.assertEqual(encoded.word_to_tokens(0    )						,  TokenSpan(start=1						,  end=2    )    )
  			self.assertEqual(encoded.word_to_tokens(1    )						,  _A    )
  			self.assertEqual(encoded.word_to_tokens(2    )						,  TokenSpan(start=2						,  end=3    )    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				Optional[Any]:
  			__magic_name__ :					Any  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					Dict  			=  batch.convert_to_tensors(tensor_type='np'    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (2, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (2,)    )
  			# test converting the converted
  			with CaptureStderr() as cs:
  				__magic_name__ :					Any  			=  batch.convert_to_tensors(tensor_type='np'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					Optional[Any]  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					List[Any]  			=  batch.convert_to_tensors(tensor_type='np'						,  prepend_batch_axis=_A    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (1, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (1,)    )
  
  
  
  
  
  
  
  		@require_torch
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Any:
  			__magic_name__ :					Optional[int]  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					List[Any]  			=  batch.convert_to_tensors(tensor_type='pt'    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (2, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (2,)    )
  			# test converting the converted
  			with CaptureStderr() as cs:
  				__magic_name__ :					Union[str, Any]  			=  batch.convert_to_tensors(tensor_type='pt'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					Any  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					str  			=  batch.convert_to_tensors(tensor_type='pt'						,  prepend_batch_axis=_A    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (1, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (1,)    )
  
  
  
  
  
  
  
  		@require_tf
  		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				List[str]:
  			__magic_name__ :					str  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					List[str]  			=  batch.convert_to_tensors(tensor_type='tf'    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (2, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (2,)    )
  			# test converting the converted
  			with CaptureStderr() as cs:
  				__magic_name__ :					Optional[int]  			=  batch.convert_to_tensors(tensor_type='tf'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					Union[str, Any]  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					Union[str, Any]  			=  batch.convert_to_tensors(tensor_type='tf'						,  prepend_batch_axis=_A    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (1, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (1,)    )
  
  
  
  
  
  
  
  		@require_flax
  		def      __lowerCAmelCase	(      self	:		str    )						->				Optional[Any]:
  			__magic_name__ :					List[Any]  			=  BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
  			__magic_name__ :					int  			=  batch.convert_to_tensors(tensor_type='jax'    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (2, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (2,)    )
  			# test converting the converted
  			with CaptureStderr() as cs:
  				__magic_name__ :					Optional[Any]  			=  batch.convert_to_tensors(tensor_type='jax'    )
  			self.assertFalse(len(cs.err    )						,  msg=F'should have no warning, but got {cs.err}'    )
  
  			__magic_name__ :					str  			=  BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
  			__magic_name__ :					Dict  			=  batch.convert_to_tensors(tensor_type='jax'						,  prepend_batch_axis=_A    )
  			self.assertEqual(tensor_batch['inputs'].shape						,  (1, 3)    )
  			self.assertEqual(tensor_batch['labels'].shape						,  (1,)    )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		str    )						->				int:
  			__magic_name__ :					int  			=  [{'input_ids': np.array([0, 1, 2]    )}, {'input_ids': np.array([0, 1, 2, 3]    )}]
  			__magic_name__ :					Dict  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  
  			__magic_name__ :					Optional[int]  			=  tokenizer.pad(_A						,  padding=_A    )
  			self.assertTrue(isinstance(batch['input_ids']						,  np.ndarray    )    )
  			self.assertEqual(batch['input_ids'].tolist()						,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  			__magic_name__ :					Dict  			=  tokenizer.pad(_A						,  padding=_A						,  return_tensors='np'    )
  			self.assertTrue(isinstance(batch['input_ids']						,  np.ndarray    )    )
  			self.assertEqual(batch['input_ids'].tolist()						,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  
  
  
  
  
  
  
  		@require_torch
  		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				str:
  			import torch
  
  			__magic_name__ :					List[str]  			=  [{'input_ids': torch.tensor([0, 1, 2]    )}, {'input_ids': torch.tensor([0, 1, 2, 3]    )}]
  			__magic_name__ :					List[str]  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  
  			__magic_name__ :					List[Any]  			=  tokenizer.pad(_A						,  padding=_A    )
  			self.assertTrue(isinstance(batch['input_ids']						,  torch.Tensor    )    )
  			self.assertEqual(batch['input_ids'].tolist()						,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  			__magic_name__ :					Union[str, Any]  			=  tokenizer.pad(_A						,  padding=_A						,  return_tensors='pt'    )
  			self.assertTrue(isinstance(batch['input_ids']						,  torch.Tensor    )    )
  			self.assertEqual(batch['input_ids'].tolist()						,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  
  
  
  
  
  
  
  		@require_tf
  		def      __lowerCAmelCase	(      self	:		Any    )						->				Optional[int]:
  			import tensorflow as tf
  
  			__magic_name__ :					Tuple  			=  [{'input_ids': tf.constant([0, 1, 2]    )}, {'input_ids': tf.constant([0, 1, 2, 3]    )}]
  			__magic_name__ :					Any  			=  BertTokenizer.from_pretrained('bert-base-cased'    )
  
  			__magic_name__ :					Any  			=  tokenizer.pad(_A						,  padding=_A    )
  			self.assertTrue(isinstance(batch['input_ids']						,  tf.Tensor    )    )
  			self.assertEqual(batch['input_ids'].numpy().tolist()						,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  			__magic_name__ :					Union[str, Any]  			=  tokenizer.pad(_A						,  padding=_A						,  return_tensors='tf'    )
  			self.assertTrue(isinstance(batch['input_ids']						,  tf.Tensor    )    )
  			self.assertEqual(batch['input_ids'].numpy().tolist()						,  [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
  
  
  
  
  
  
  
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		Any    )						->				str:
  			__magic_name__ :					str  			=  Tokenizer(WordPiece(unk_token='[UNK]'    )    )
  			PreTrainedTokenizerFast(tokenizer_object=_A    )
  
  
  
  
  
  
  
  		@require_tokenizers
  		def      __lowerCAmelCase	(      self	:		Dict    )						->				Any:
  			__magic_name__ :					List[Any]  			=  Tokenizer(WordPiece(unk_token='[UNK]'    )    )
  			with tempfile.TemporaryDirectory() as tmpdirname:
  				bert_tokenizer.save(os.path.join(_A						,  'tokenizer.json'    )    )
  				PreTrainedTokenizerFast(tokenizer_file=os.path.join(_A						,  'tokenizer.json'    )    )