


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 MobileNetVaImageProcessor







class        snake_case_   (unittest.TestCase      ):
					def __init__(      self   :List[Any]			,__snake_case   :Union[str, Any]			,__snake_case   :Dict=7			,__snake_case   :int=3			,__snake_case   :List[str]=18			,__snake_case   :Optional[Any]=30			,__snake_case   :List[str]=4_00			,__snake_case   :int=True			,__snake_case   :Optional[int]=None			,__snake_case   :Union[str, Any]=True			,__snake_case   :Optional[Any]=None			,)					->    Any:
												a__             =     size if size is not None else {'shortest_edge': 20}
												a__             =     crop_size if crop_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_center_crop
												a__             =     crop_size



					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    str:
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_center_crop": self.do_center_crop,
												    "crop_size": self.crop_size,
												}







@require_torch
@require_vision
class        snake_case_   (lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Union[str, Any]          =			MobileNetVaImageProcessor if is_vision_available() else None
					def  lowerCamelCase__(      self   :str      )					->    str:
												a__             =     MobileNetVaImageProcessingTester(self      )
					@property
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Dict:
												return self.image_processor_tester.prepare_image_processor_dict()
					def  lowerCamelCase__(      self   :List[str]      )					->    List[Any]:
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(__snake_case			,'do_resize'      )      )
												self.assertTrue(hasattr(__snake_case			,'size'      )      )
												self.assertTrue(hasattr(__snake_case			,'do_center_crop'      )      )
												self.assertTrue(hasattr(__snake_case			,'crop_size'      )      )
					def  lowerCamelCase__(      self   :int      )					->    Union[str, Any]:
												a__             =     self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size			,{'shortest_edge': 20}      )
												self.assertEqual(image_processor.crop_size			,{'height': 18, 'width': 18}      )

												a__             =     self.image_processing_class.from_dict(self.image_processor_dict			,size=42			,crop_size=84      )
												self.assertEqual(image_processor.size			,{'shortest_edge': 42}      )
												self.assertEqual(image_processor.crop_size			,{'height': 84, 'width': 84}      )
					def  lowerCamelCase__(      self   :str      )					->    int:
												pass
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    List[Any]:
												# Initialize image_processing
												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.crop_size['height'],
												        self.image_processor_tester.crop_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.crop_size['height'],
												        self.image_processor_tester.crop_size['width'],
												    )			,)
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Any:
												# Initialize image_processing
												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.crop_size['height'],
												        self.image_processor_tester.crop_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.crop_size['height'],
												        self.image_processor_tester.crop_size['width'],
												    )			,)



					def  lowerCamelCase__(      self   :Tuple      )					->    List[str]:
												# Initialize image_processing
												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.crop_size['height'],
												        self.image_processor_tester.crop_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.crop_size['height'],
												        self.image_processor_tester.crop_size['width'],
												    )			,)








import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				int   ):
							# Construct model
							if gpta_config_file == "":
														a__             =     GPTaConfig()
							else:
														a__             =     GPTaConfig.from_json_file(__lowerCAmelCase   )
							a__             =     GPTaModel(__lowerCAmelCase   )

							# Load weights from numpy
							load_tf_weights_in_gpta(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase   )

							# Save pytorch-model
							a__             =     pytorch_dump_folder_path + '/' + WEIGHTS_NAME
							a__             =     pytorch_dump_folder_path + '/' + CONFIG_NAME
							print(F'Save PyTorch model to {pytorch_weights_dump_path}'   )
							torch.save(model.state_dict()					,    __lowerCAmelCase   )
							print(F'Save configuration file to {pytorch_config_dump_path}'   )
							with open(__lowerCAmelCase					,    'w'					,    encoding='utf-8'   ) as f:
														f.write(config.to_json_string()   )


if __name__ == "__main__":
						snake_case							:       List[Any]     			=      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--gpt2_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--gpt2_config_file''',
						    default='''''',
						    type=str,
						    help=(
						        '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						snake_case							:       Any     			=      parser.parse_args()
						convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




