

"""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 EfficientNetImageProcessor

class        _UpperCamelCase	(						unittest.TestCase	):


  """simple docstring"""





  def __init__( self					: List[Any]       ,       snake_case					: Dict       ,       snake_case					: Optional[int]=13       ,       snake_case					: str=3       ,       snake_case					: Tuple=18       ,       snake_case					: int=30       ,       snake_case					: int=400       ,       snake_case					: Optional[Any]=True       ,       snake_case					: Dict=None       ,       snake_case					: str=True       ,       snake_case					: int=[0.5, 0.5, 0.5]       ,       snake_case					: Tuple=[0.5, 0.5, 0.5]       ,       )	->			Tuple:


         '''simple docstring'''





         __magic_name__			:		Any        =     size if size is not None else {"""height""": 18, """width""": 18}
         __magic_name__			:		Tuple        =     parent
         __magic_name__			:		Optional[Any]        =     batch_size
         __magic_name__			:		List[Any]        =     num_channels
         __magic_name__			:		Any        =     image_size
         __magic_name__			:		int        =     min_resolution
         __magic_name__			:		List[Any]        =     max_resolution
         __magic_name__			:		List[str]        =     do_resize
         __magic_name__			:		Tuple        =     size
         __magic_name__			:		Tuple        =     do_normalize
         __magic_name__			:		int        =     image_mean
         __magic_name__			:		Tuple        =     image_std




  def 							_UpperCAmelCase      ( self					: str      )	->			Optional[int]:


         '''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        _UpperCamelCase	(						snake_case_   ,				unittest.TestCase	):


  """simple docstring"""





  snake_case_        =    EfficientNetImageProcessor if is_vision_available() else None



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


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     EfficientNetImageProcessorTester(self      )



  @property
  def 							_UpperCAmelCase      ( self					: Optional[Any]      )	->			str:


         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()



  def 							_UpperCAmelCase      ( self					: Union[str, Any]      )	->			Dict:


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     self.image_processing_class(**self.image_processor_dict      )
         self.assertTrue(hasattr(lowercase_       ,       '''image_mean'''      )      )
         self.assertTrue(hasattr(lowercase_       ,       '''image_std'''      )      )
         self.assertTrue(hasattr(lowercase_       ,       '''do_normalize'''      )      )
         self.assertTrue(hasattr(lowercase_       ,       '''do_resize'''      )      )
         self.assertTrue(hasattr(lowercase_       ,       '''size'''      )      )



  def 							_UpperCAmelCase      ( self					: Union[str, Any]      )	->			str:


         '''simple docstring'''





         __magic_name__			:		str        =     self.image_processing_class.from_dict(self.image_processor_dict      )
         self.assertEqual(image_processor.size       ,       {'''height''': 18, '''width''': 18}      )

         __magic_name__			:		List[str]        =     self.image_processing_class.from_dict(self.image_processor_dict       ,       size=42      )
         self.assertEqual(image_processor.size       ,       {'''height''': 42, '''width''': 42}      )



  def 							_UpperCAmelCase      ( self					: Dict      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     self.image_processing_class(**self.image_processor_dict      )
         # create random PIL images
         __magic_name__			:		Union[str, Any]        =     prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=lowercase_      )
         for image in image_inputs:
                self.assertIsInstance(lowercase_       ,       Image.Image      )

         # Test not batched input
         __magic_name__			:		Optional[int]        =     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
         __magic_name__			:		Optional[Any]        =     image_processing(lowercase_       ,       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 							_UpperCAmelCase      ( self					: Optional[int]      )	->			Tuple:


         '''simple docstring'''





         __magic_name__			:		Dict        =     self.image_processing_class(**self.image_processor_dict      )
         # create random numpy tensors
         __magic_name__			:		Tuple        =     prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=lowercase_       ,       numpify=lowercase_      )
         for image in image_inputs:
                self.assertIsInstance(lowercase_       ,       np.ndarray      )

         # Test not batched input
         __magic_name__			:		Optional[int]        =     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
         __magic_name__			:		Optional[Any]        =     image_processing(lowercase_       ,       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 							_UpperCAmelCase      ( self					: List[str]      )	->			Optional[int]:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     self.image_processing_class(**self.image_processor_dict      )
         # create random PyTorch tensors
         __magic_name__			:		Tuple        =     prepare_image_inputs(self.image_processor_tester       ,       equal_resolution=lowercase_       ,       torchify=lowercase_      )
         for image in image_inputs:
                self.assertIsInstance(lowercase_       ,       torch.Tensor      )

         # Test not batched input
         __magic_name__			:		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
         __magic_name__			:		Optional[Any]        =     image_processing(lowercase_       ,       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'''],
             )       ,       )





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__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					json.load(open(SCREAMING_SNAKE_CASE__			,"""r"""  )  )
						lowerCAmelCase	:   Tuple					     =					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}"""  )
						lowerCAmelCase	:   str					     =					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__":
		lowerCAmelCase					:				Optional[Any]   	=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.'
		)
		lowerCAmelCase					:				str   	=parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

