
'''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'''],
				    )     ,  )




'''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)


