





"""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 ViltImageProcessor




class 					UpperCamelCase_    (       unittest.TestCase      ):


							def __init__(							self      :int							,			__A      :List[str]							,			__A      :int=7							,			__A      :Any=3							,			__A      :Dict=18							,			__A      :List[Any]=30							,			__A      :List[Any]=400							,			__A      :List[str]=True							,			__A      :Dict=None							,			__A      :int=2							,			__A      :Dict=True							,			__A      :int=[0.5, 0.5, 0.5]							,			__A      :Tuple=[0.5, 0.5, 0.5]							,			)				->				List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= size if size is not None else {"shortest_edge": 30}
									SCREAMING_SNAKE_CASE__								= parent
									SCREAMING_SNAKE_CASE__								= batch_size
									SCREAMING_SNAKE_CASE__								= num_channels
									SCREAMING_SNAKE_CASE__								= image_size
									SCREAMING_SNAKE_CASE__								= min_resolution
									SCREAMING_SNAKE_CASE__								= max_resolution
									SCREAMING_SNAKE_CASE__								= do_resize
									SCREAMING_SNAKE_CASE__								= size
									SCREAMING_SNAKE_CASE__								= size_divisor
									SCREAMING_SNAKE_CASE__								= do_normalize
									SCREAMING_SNAKE_CASE__								= image_mean
									SCREAMING_SNAKE_CASE__								= image_std


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



									"""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,
									    "size_divisor": self.size_divisor,
									}


							def        _snake_case					(							self      :List[Any]							,			__A      :Dict							,			__A      :Dict=False						)				->				Optional[int]:



									"""simple docstring"""



									if not batched:
											SCREAMING_SNAKE_CASE__								= self.size["shortest_edge"]
											SCREAMING_SNAKE_CASE__								= image_inputs[0]
											if isinstance(_A							,			Image.Image						):
													SCREAMING_SNAKE_CASE__								= image.size
											else:
													SCREAMING_SNAKE_CASE__								= image.shape[1], image.shape[2]
											SCREAMING_SNAKE_CASE__								= size / min(_A							,			_A						)
											if h < w:
													SCREAMING_SNAKE_CASE__								= size, scale * w
											else:
													SCREAMING_SNAKE_CASE__								= scale * h, size

											SCREAMING_SNAKE_CASE__								= int((1333 / 800) * size						)
											if max(_A							,			_A						) > max_size:
													SCREAMING_SNAKE_CASE__								= max_size / max(_A							,			_A						)
													SCREAMING_SNAKE_CASE__								= newh * scale
													SCREAMING_SNAKE_CASE__								= neww * scale

											SCREAMING_SNAKE_CASE__								= int(newh + 0.5						), int(neww + 0.5						)
											SCREAMING_SNAKE_CASE__								= (
											    newh // self.size_divisor * self.size_divisor,
											    neww // self.size_divisor * self.size_divisor,
											)

									else:
											SCREAMING_SNAKE_CASE__								= []
											for image in image_inputs:
													SCREAMING_SNAKE_CASE__								= self.get_expected_values([image]						)
													expected_values.append((expected_height, expected_width)						)
											SCREAMING_SNAKE_CASE__								= max(_A							,			key=lambda __A						: item[0]						)[0]
											SCREAMING_SNAKE_CASE__								= max(_A							,			key=lambda __A						: item[1]						)[1]

									return expected_height, expected_width




@require_torch
@require_vision
class 					UpperCamelCase_    (       A__			,					unittest.TestCase      ):
							lowerCamelCase_          =   ViltImageProcessor if is_vision_available() else None


							def        _snake_case					(							self      :str						)				->				Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= ViltImageProcessingTester(self						)


							@property
							def        _snake_case					(							self      :str						)				->				Tuple:



									"""simple docstring"""



									return self.image_processor_tester.prepare_image_processor_dict()


							def        _snake_case					(							self      :Dict						)				->				Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_A							,			"""image_mean"""						)						)
									self.assertTrue(hasattr(_A							,			"""image_std"""						)						)
									self.assertTrue(hasattr(_A							,			"""do_normalize"""						)						)
									self.assertTrue(hasattr(_A							,			"""do_resize"""						)						)
									self.assertTrue(hasattr(_A							,			"""size"""						)						)
									self.assertTrue(hasattr(_A							,			"""size_divisor"""						)						)


							def        _snake_case					(							self      :Any						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.image_processing_class.from_dict(self.image_processor_dict						)
									self.assertEqual(image_processor.size							,			{"""shortest_edge""": 30}						)

									SCREAMING_SNAKE_CASE__								= self.image_processing_class.from_dict(self.image_processor_dict							,			size=42						)
									self.assertEqual(image_processor.size							,			{"""shortest_edge""": 42}						)


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



									"""simple docstring"""



									pass


							def        _snake_case					(							self      :Union[str, Any]						)				->				Optional[int]:



									"""simple docstring"""



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

									# Test not batched input
									SCREAMING_SNAKE_CASE__								= image_processing(image_inputs[0]							,			return_tensors="""pt"""						).pixel_values

									SCREAMING_SNAKE_CASE__								= self.image_processor_tester.get_expected_values(_A						)
									self.assertEqual(
									    encoded_images.shape							,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,			)

									# Test batched
									SCREAMING_SNAKE_CASE__								= image_processing(_A							,			return_tensors="""pt"""						).pixel_values

									SCREAMING_SNAKE_CASE__								= self.image_processor_tester.get_expected_values(_A							,			batched=_A						)
									self.assertEqual(
									    encoded_images.shape							,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )							,			)


							def        _snake_case					(							self      :Optional[Any]						)				->				List[Any]:



									"""simple docstring"""



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

									# Test not batched input
									SCREAMING_SNAKE_CASE__								= image_processing(image_inputs[0]							,			return_tensors="""pt"""						).pixel_values

									SCREAMING_SNAKE_CASE__								= self.image_processor_tester.get_expected_values(_A						)
									self.assertEqual(
									    encoded_images.shape							,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,			)

									# Test batched
									SCREAMING_SNAKE_CASE__								= image_processing(_A							,			return_tensors="""pt"""						).pixel_values

									SCREAMING_SNAKE_CASE__								= self.image_processor_tester.get_expected_values(_A							,			batched=_A						)
									self.assertEqual(
									    encoded_images.shape							,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )							,			)


							def        _snake_case					(							self      :List[Any]						)				->				Dict:



									"""simple docstring"""



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

									# Test not batched input
									SCREAMING_SNAKE_CASE__								= image_processing(image_inputs[0]							,			return_tensors="""pt"""						).pixel_values

									SCREAMING_SNAKE_CASE__								= self.image_processor_tester.get_expected_values(_A						)
									self.assertEqual(
									    encoded_images.shape							,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,			)

									# Test batched
									SCREAMING_SNAKE_CASE__								= image_processing(_A							,			return_tensors="""pt"""						).pixel_values

									SCREAMING_SNAKE_CASE__								= self.image_processor_tester.get_expected_values(_A							,			batched=_A						)
									self.assertEqual(
									    encoded_images.shape							,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )							,			)






import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Any   ,				UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							Union[str, Any]      ):
		# Construct configuration depending on size
		SCREAMING_SNAKE_CASE__								= {"""small""": (512, 8, 24), """medium""": (1_024, 8, 36), """large""": (1_536, 16, 48)}
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE__								= ImageGPTConfig(n_embd=UpperCamelCase__   ,				n_layer=UpperCamelCase__   ,				n_head=UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= ImageGPTForCausalLM(UpperCamelCase__      )

		# Load weights from numpy
		load_tf_weights_in_imagegpt(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__      )

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


if __name__ == "__main__":
	_lowerCamelCase							=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--imagegpt_checkpoint_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to the TensorFlow checkpoint path.',
	)
	parser.add_argument(
	    '--model_size',
	    default=None,
	    type=str,
	    required=True,
	    help='Size of the model (can be either \'small\', \'medium\' or \'large\').',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	_lowerCamelCase							=     parser.parse_args()
	convert_imagegpt_checkpoint_to_pytorch(
	    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
	)