




"""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       UpperCAmelCase_    (		unittest.TestCase				):




    def __init__(       self		:			Union[str, Any]      ,					snake_case_		:			Tuple      ,					snake_case_		:			Dict=7      ,					snake_case_		:			List[Any]=3      ,					snake_case_		:			Optional[int]=18      ,					snake_case_		:			Tuple=30      ,					snake_case_		:			Dict=400      ,					snake_case_		:			List[str]=True      ,					snake_case_		:			Optional[int]=None      ,					snake_case_		:			List[str]=True      ,					snake_case_		:			Optional[Any]=[0.5, 0.5, 0.5]      ,					snake_case_		:			Optional[int]=[0.5, 0.5, 0.5]      ,					) ->				List[Any]:




     '''simple docstring'''

     A__			=							size if size is not None else {"height": 18, "width": 18}
     A__			=							parent
     A__			=							batch_size
     A__			=							num_channels
     A__			=							image_size
     A__			=							min_resolution
     A__			=							max_resolution
     A__			=							do_resize
     A__			=							size
     A__			=							do_normalize
     A__			=							image_mean
     A__			=							image_std




    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				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       UpperCAmelCase_    (		A_,  unittest.TestCase				):
    lowercase__		   = ViTImageProcessor if is_vision_available() else None




    def 				__magic_name__				(       self		:			int ) ->				Any:




     '''simple docstring'''

     A__			=							ViTImageProcessingTester(self )




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




     '''simple docstring'''

     return self.image_processor_tester.prepare_image_processor_dict()




    def 				__magic_name__				(       self		:			Tuple ) ->				str:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(snake_case_      ,					"image_mean" ) )
     self.assertTrue(hasattr(snake_case_      ,					"image_std" ) )
     self.assertTrue(hasattr(snake_case_      ,					"do_normalize" ) )
     self.assertTrue(hasattr(snake_case_      ,					"do_resize" ) )
     self.assertTrue(hasattr(snake_case_      ,					"size" ) )




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




     '''simple docstring'''

     A__			=							self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size      ,					{"height": 18, "width": 18} )

     A__			=							self.image_processing_class.from_dict(self.image_processor_dict      ,					size=42 )
     self.assertEqual(image_processor.size      ,					{"height": 42, "width": 42} )




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




     '''simple docstring'''

     pass




    def 				__magic_name__				(       self		:			Optional[int] ) ->				Any:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     A__			=							prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=snake_case_ )
     for image in image_inputs:
      self.assertIsInstance(snake_case_      ,					Image.Image )

     # Test not batched input
     A__			=							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
     A__			=							image_processing(snake_case_      ,					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 				__magic_name__				(       self		:			str ) ->				Optional[Any]:




     '''simple docstring'''

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

     # Test not batched input
     A__			=							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
     A__			=							image_processing(snake_case_      ,					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 				__magic_name__				(       self		:			Dict ) ->				Union[str, Any]:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     A__			=							prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=snake_case_      ,					torchify=snake_case_ )
     for image in image_inputs:
      self.assertIsInstance(snake_case_      ,					torch.Tensor )

     # Test not batched input
     A__			=							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
     A__			=							image_processing(snake_case_      ,					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     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						Union[str, Any]:
 # Initialise PyTorch model
 A__			=							json.load(open(lowercase_     ,					"r"		)		)
 A__			=							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}"""		)
 A__			=							GPTNeoForCausalLM(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_gpt_neo(lowercase_     ,					lowercase_     ,					lowercase_		)

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""		)
 model.save_pretrained(lowercase_		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=			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."
      )
      SCREAMING_SNAKE_CASE							=			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





