



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 SwinaSRImageProcessor
							from transformers.image_transforms import get_image_size







class      __lowerCAmelCase       (     unittest.TestCase		):



			def __init__(		self  :    Union[str, Any]					,						A  :    Dict					,						A  :    List[str]=7					,						A  :    int=3					,						A  :    Optional[Any]=18					,						A  :    int=30					,						A  :    Union[str, Any]=4_00					,						A  :    Dict=True					,						A  :    Any=1 / 2_55					,						A  :    Any=True					,						A  :    Tuple=8					,						)  ->       List[Any]:



						"""simple docstring"""






						_UpperCAmelCase									=  parent
						_UpperCAmelCase									=  batch_size
						_UpperCAmelCase									=  num_channels
						_UpperCAmelCase									=  image_size
						_UpperCAmelCase									=  min_resolution
						_UpperCAmelCase									=  max_resolution
						_UpperCAmelCase									=  do_rescale
						_UpperCAmelCase									=  rescale_factor
						_UpperCAmelCase									=  do_pad
						_UpperCAmelCase									=  pad_size



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



						"""simple docstring"""






						return {
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						    "pad_size": self.pad_size,
						}



			def        _lowerCamelCase			(		self  :    Optional[int]					,						A  :    Union[str, Any]=False					,						A  :    int=False					,						A  :    Optional[int]=False)  ->       Optional[int]:



						"""simple docstring"""






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

						if equal_resolution:
									_UpperCAmelCase									=  []
									for i in range(self.batch_size):
												image_inputs.append(
												    np.random.randint(
												        2_55					,						size=(self.num_channels, self.max_resolution, self.max_resolution)					,						dtype=np.uinta))
						else:
									_UpperCAmelCase									=  []
									for i in range(self.batch_size):
												_UpperCAmelCase			,	_UpperCAmelCase									=  np.random.choice(np.arange(self.min_resolution					,						self.max_resolution)					,						2)
												image_inputs.append(np.random.randint(2_55					,						size=(self.num_channels, width, height)					,						dtype=np.uinta))

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

						if torchify:
									_UpperCAmelCase									=  [torch.from_numpy(A) for x in image_inputs]

						return image_inputs







@require_torch
@require_vision
class      __lowerCAmelCase       (     A							,		unittest.TestCase		):
			UpperCamelCase     =	SwinaSRImageProcessor if is_vision_available() else None



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



						"""simple docstring"""






						_UpperCAmelCase									=  SwinaSRImageProcessingTester(self)



			@property
			def        _lowerCamelCase			(		self  :    List[Any])  ->       Any:



						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



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



						"""simple docstring"""






						_UpperCAmelCase									=  self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(A					,						'do_rescale'))
						self.assertTrue(hasattr(A					,						'rescale_factor'))
						self.assertTrue(hasattr(A					,						'do_pad'))
						self.assertTrue(hasattr(A					,						'pad_size'))



			def        _lowerCamelCase			(		self  :    List[Any])  ->       List[Any]:



						"""simple docstring"""






						pass



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



						"""simple docstring"""






						_UpperCAmelCase			,	_UpperCAmelCase									=  get_image_size(A)
						_UpperCAmelCase									=  self.image_processor_tester.pad_size

						_UpperCAmelCase									=  (old_height // size + 1) * size - old_height
						_UpperCAmelCase									=  (old_width // size + 1) * size - old_width
						return old_height + pad_height, old_width + pad_width



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



						"""simple docstring"""






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

						# Test not batched input
						_UpperCAmelCase									=  image_processor(image_inputs[0]					,						return_tensors='pt').pixel_values
						_UpperCAmelCase			,	_UpperCAmelCase									=  self.calculate_expected_size(np.array(image_inputs[0]))
						self.assertEqual(
						    encoded_images.shape					,						(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )					,						)



			def        _lowerCamelCase			(		self  :    Tuple)  ->       Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase									=  self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						_UpperCAmelCase									=  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
						_UpperCAmelCase									=  image_processor(image_inputs[0]					,						return_tensors='pt').pixel_values
						_UpperCAmelCase			,	_UpperCAmelCase									=  self.calculate_expected_size(image_inputs[0])
						self.assertEqual(
						    encoded_images.shape					,						(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )					,						)



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



						"""simple docstring"""






						_UpperCAmelCase									=  self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						_UpperCAmelCase									=  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
						_UpperCAmelCase									=  image_processor(image_inputs[0]					,						return_tensors='pt').pixel_values
						_UpperCAmelCase			,	_UpperCAmelCase									=  self.calculate_expected_size(image_inputs[0])
						self.assertEqual(
						    encoded_images.shape					,						(
						        1,
						        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 				A      (	_UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  str       )							->					Union[str, Any]:







			'''simple docstring'''
			# Construct configuration depending on size
			_UpperCAmelCase									=  {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  MODELS[model_size]  # set model hyperparameters
			_UpperCAmelCase									=  ImageGPTConfig(n_embd=_UpperCAmelCase				,  n_layer=_UpperCAmelCase				,  n_head=_UpperCAmelCase       )
			_UpperCAmelCase									=  ImageGPTForCausalLM(_UpperCAmelCase       )

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

			# Save pytorch-model
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_UpperCAmelCase									=  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__":
							UpperCAmelCase__  =			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."
							)
							UpperCAmelCase__  =			parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)
