





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


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import BlipImageProcessor




class 					UpperCamelCase_    (       unittest.TestCase      ):


							def __init__(							self      :Union[str, Any]							,			__A      :Any							,			__A      :Dict=7							,			__A      :Union[str, Any]=3							,			__A      :Any=18							,			__A      :Union[str, Any]=30							,			__A      :Any=400							,			__A      :int=True							,			__A      :Optional[Any]=None							,			__A      :Optional[int]=True							,			__A      :List[str]=False							,			__A      :int=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]							,			__A      :Any=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]							,			__A      :Optional[int]=True							,			)				->				Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= size if size is not None else {"""height""": 20, """width""": 20}
									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__								= do_normalize
									SCREAMING_SNAKE_CASE__								= image_mean
									SCREAMING_SNAKE_CASE__								= image_std
									SCREAMING_SNAKE_CASE__								= do_pad
									SCREAMING_SNAKE_CASE__								= do_convert_rgb


							def        _snake_case					(							self      :Tuple						)				->				Tuple:



									"""simple docstring"""



									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_convert_rgb": self.do_convert_rgb,
									    "do_pad": self.do_pad,
									}


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



									"""simple docstring"""



									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

									if equal_resolution:
											SCREAMING_SNAKE_CASE__								= []
											for i in range(self.batch_size						):
													image_inputs.append(
													    np.random.randint(
													        255							,			size=(self.num_channels, self.max_resolution, self.max_resolution)							,			dtype=np.uinta						)						)
									else:
											SCREAMING_SNAKE_CASE__								= []
											for i in range(self.batch_size						):
													SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= np.random.choice(np.arange(self.min_resolution							,			self.max_resolution						)							,			2						)
													image_inputs.append(np.random.randint(255							,			size=(self.num_channels, width, height)							,			dtype=np.uinta						)						)

									if not numpify and not torchify:
											# PIL expects the channel dimension as last dimension
											SCREAMING_SNAKE_CASE__								= [Image.fromarray(np.moveaxis(__A							,			0							,			-1						)						) for x in image_inputs]

									if torchify:
											SCREAMING_SNAKE_CASE__								= [torch.from_numpy(__A						) for x in image_inputs]

									return image_inputs




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


							def        _snake_case					(							self      :Dict						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BlipImageProcessingTester(self						)


							@property
							def        _snake_case					(							self      :List[str]						)				->				Dict:



									"""simple docstring"""



									return self.image_processor_tester.prepare_image_processor_dict()


							def        _snake_case					(							self      :Tuple						)				->				List[str]:



									"""simple docstring"""



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


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



									"""simple docstring"""



									pass


							def        _snake_case					(							self      :Tuple						)				->				List[str]:



									"""simple docstring"""



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

									# Test not batched input
									SCREAMING_SNAKE_CASE__								= image_processor(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
									SCREAMING_SNAKE_CASE__								= image_processor(__A							,			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        _snake_case					(							self      :Optional[Any]						)				->				Tuple:



									"""simple docstring"""



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

									# Test not batched input
									SCREAMING_SNAKE_CASE__								= image_processor(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
									SCREAMING_SNAKE_CASE__								= image_processor(__A							,			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        _snake_case					(							self      :Optional[int]						)				->				int:



									"""simple docstring"""



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

									# Test not batched input
									SCREAMING_SNAKE_CASE__								= image_processor(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
									SCREAMING_SNAKE_CASE__								= image_processor(__A							,			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"""],
									    )							,			)




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


							def        _snake_case					(							self      :str						)				->				Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BlipImageProcessingTester(self							,			num_channels=4						)
									SCREAMING_SNAKE_CASE__								= 3


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



									"""simple docstring"""



									return self.image_processor_tester.prepare_image_processor_dict()


							def        _snake_case					(							self      :str						)				->				Any:



									"""simple docstring"""



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


							def        _snake_case					(							self      :List[str]						)				->				str:



									"""simple docstring"""



									pass


							def        _snake_case					(							self      :Tuple						)				->				Tuple:



									"""simple docstring"""



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

									# Test not batched input
									SCREAMING_SNAKE_CASE__								= image_processor(image_inputs[0]							,			return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape							,			(
									        1,
									        self.expected_encoded_image_num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )							,			)

									# Test batched
									SCREAMING_SNAKE_CASE__								= image_processor(__A							,			return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape							,			(
									        self.image_processor_tester.batch_size,
									        self.expected_encoded_image_num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""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
	)