code: 
  '''simple docstring'''
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
  
  
  if is_torch_available():
  						import torch
  
  if is_vision_available():
  						from PIL import Image
  
  						from transformers import ViTImageProcessor
  
  
  
  
  
  
  class   snake_case__					(			unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: Dict     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: List[Any]=7     ,  UpperCamelCase__							: List[str]=3     ,  UpperCamelCase__							: str=18     ,  UpperCamelCase__							: Optional[Any]=30     ,  UpperCamelCase__							: List[Any]=400     ,  UpperCamelCase__							: List[Any]=True     ,  UpperCamelCase__							: int=None     ,  UpperCamelCase__							: int=True     ,  UpperCamelCase__							: Any=[0.5, 0.5, 0.5]     ,  UpperCamelCase__							: str=[0.5, 0.5, 0.5]     ,  )	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[Any]     =	size if size is not None else {'''height''': 18, '''width''': 18}
  				snake_case     :					List[str]     =	parent
  				snake_case     :					Optional[Any]     =	batch_size
  				snake_case     :					List[str]     =	num_channels
  				snake_case     :					Optional[Any]     =	image_size
  				snake_case     :					Any     =	min_resolution
  				snake_case     :					List[str]     =	max_resolution
  				snake_case     :					Optional[int]     =	do_resize
  				snake_case     :					Any     =	size
  				snake_case     :					Optional[Any]     =	do_normalize
  				snake_case     :					Optional[int]     =	image_mean
  				snake_case     :					List[str]     =	image_std
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[int]			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return {
  				    "image_mean": self.image_mean,
  				    "image_std": self.image_std,
  				    "do_normalize": self.do_normalize,
  				    "do_resize": self.do_resize,
  				    "size": self.size,
  				}
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class   snake_case__					(			__SCREAMING_SNAKE_CASE						,				unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	ViTImageProcessor if is_vision_available() else None
  
  
  
  		def 			lowerCAmelCase							(			self							: List[Any]			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[Any]     =	ViTImageProcessingTester(self			)
  
  
  
  		@property
  		def 			lowerCAmelCase							(			self							: Tuple			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  		def 			lowerCAmelCase							(			self							: List[Any]			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Any     =	self.image_processing_class(**self.image_processor_dict			)
  				self.assertTrue(hasattr(UpperCamelCase__     ,  '''image_mean'''			)			)
  				self.assertTrue(hasattr(UpperCamelCase__     ,  '''image_std'''			)			)
  				self.assertTrue(hasattr(UpperCamelCase__     ,  '''do_normalize'''			)			)
  				self.assertTrue(hasattr(UpperCamelCase__     ,  '''do_resize'''			)			)
  				self.assertTrue(hasattr(UpperCamelCase__     ,  '''size'''			)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Any:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	self.image_processing_class.from_dict(self.image_processor_dict			)
  				self.assertEqual(image_processor.size     ,  {'''height''': 18, '''width''': 18}			)
  
  				snake_case     :					List[Any]     =	self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42			)
  				self.assertEqual(image_processor.size     ,  {'''height''': 42, '''width''': 42}			)
  
  
  
  		def 			lowerCAmelCase							(			self							: str			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				pass
  
  
  
  		def 			lowerCAmelCase							(			self							: Union[str, Any]			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Any     =	self.image_processing_class(**self.image_processor_dict			)
  				# create random PIL images
  				snake_case     :					Dict     =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase__			)
  				for image in image_inputs:
  						self.assertIsInstance(UpperCamelCase__     ,  Image.Image			)
  
  				# Test not batched input
  				snake_case     :					Optional[Any]     =	image_processing(image_inputs[0]     ,  return_tensors='''pt'''			).pixel_values
  				self.assertEqual(
  				    encoded_images.shape     ,  (
  				        1,
  				        self.image_processor_tester.num_channels,
  				        self.image_processor_tester.size['''height'''],
  				        self.image_processor_tester.size['''width'''],
  				    )     ,  )
  
  				# Test batched
  				snake_case     :					int     =	image_processing(UpperCamelCase__     ,  return_tensors='''pt'''			).pixel_values
  				self.assertEqual(
  				    encoded_images.shape     ,  (
  				        self.image_processor_tester.batch_size,
  				        self.image_processor_tester.num_channels,
  				        self.image_processor_tester.size['''height'''],
  				        self.image_processor_tester.size['''width'''],
  				    )     ,  )
  
  
  
  		def 			lowerCAmelCase							(			self							: int			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[Any]     =	self.image_processing_class(**self.image_processor_dict			)
  				# create random numpy tensors
  				snake_case     :					List[Any]     =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase__     ,  numpify=UpperCamelCase__			)
  				for image in image_inputs:
  						self.assertIsInstance(UpperCamelCase__     ,  np.ndarray			)
  
  				# Test not batched input
  				snake_case     :					List[str]     =	image_processing(image_inputs[0]     ,  return_tensors='''pt'''			).pixel_values
  				self.assertEqual(
  				    encoded_images.shape     ,  (
  				        1,
  				        self.image_processor_tester.num_channels,
  				        self.image_processor_tester.size['''height'''],
  				        self.image_processor_tester.size['''width'''],
  				    )     ,  )
  
  				# Test batched
  				snake_case     :					Any     =	image_processing(UpperCamelCase__     ,  return_tensors='''pt'''			).pixel_values
  				self.assertEqual(
  				    encoded_images.shape     ,  (
  				        self.image_processor_tester.batch_size,
  				        self.image_processor_tester.num_channels,
  				        self.image_processor_tester.size['''height'''],
  				        self.image_processor_tester.size['''width'''],
  				    )     ,  )
  
  
  
  		def 			lowerCAmelCase							(			self							: str			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Tuple     =	self.image_processing_class(**self.image_processor_dict			)
  				# create random PyTorch tensors
  				snake_case     :					int     =	prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=UpperCamelCase__     ,  torchify=UpperCamelCase__			)
  				for image in image_inputs:
  						self.assertIsInstance(UpperCamelCase__     ,  torch.Tensor			)
  
  				# Test not batched input
  				snake_case     :					str     =	image_processing(image_inputs[0]     ,  return_tensors='''pt'''			).pixel_values
  				self.assertEqual(
  				    encoded_images.shape     ,  (
  				        1,
  				        self.image_processor_tester.num_channels,
  				        self.image_processor_tester.size['''height'''],
  				        self.image_processor_tester.size['''width'''],
  				    )     ,  )
  
  				# Test batched
  				snake_case     :					int     =	image_processing(UpperCamelCase__     ,  return_tensors='''pt'''			).pixel_values
  				self.assertEqual(
  				    encoded_images.shape     ,  (
  				        self.image_processor_tester.batch_size,
  				        self.image_processor_tester.num_channels,
  				        self.image_processor_tester.size['''height'''],
  				        self.image_processor_tester.size['''width'''],
  				    )     ,  )
  
  
  
style_context: 
  '''simple docstring'''
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Tuple     =	json.load(open(SCREAMING_SNAKE_CASE__   ,					'''r''' ) )
  		snake_case     :					Any     =	GPTNeoConfig(
  		    hidden_size=config_json['''n_embd''']   ,					num_layers=config_json['''n_layer''']   ,					num_heads=config_json['''n_head''']   ,					attention_types=config_json['''attention_types''']   ,					max_position_embeddings=config_json['''n_positions''']   ,					resid_dropout=config_json['''res_dropout''']   ,					embed_dropout=config_json['''embed_dropout''']   ,					attention_dropout=config_json['''attn_dropout''']   ,					)
  		print(F'Building PyTorch model from configuration: {config}' )
  		snake_case     :					Union[str, Any]     =	GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__ )
  
  		# Load weights from tf checkpoint
  		load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  
  		# Save pytorch-model
  		print(F'Save PyTorch model to {pytorch_dump_path}' )
  		model.save_pretrained(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
  						lowercase__						=			argparse.ArgumentParser()
  						# Required parameters
  						parser.add_argument(
  						    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  						)
  						parser.add_argument(
  						    "--config_file",
  						    default=None,
  						    type=str,
  						    required=True,
  						    help=(
  						        "The config json file corresponding to the pre-trained mesh-tf model. \n"
  						        "This specifies the model architecture."
  						    ),
  						)
  						parser.add_argument(
  						    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  						)
  						lowercase__						=			parser.parse_args()
  						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  