


'''simple docstring'''
import unittest

import numpy as np
import requests

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

		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
		_UpperCAmelCase											=					False

if is_vision_available():
		from PIL import Image

		from transformers import PixaStructImageProcessor
class        lowercase     (							unittest.TestCase ):

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_   ,       UpperCamelCase_=7   ,       UpperCamelCase_=3   ,       UpperCamelCase_=18   ,       UpperCamelCase_=30   ,       UpperCamelCase_=400   ,       UpperCamelCase_=None   ,       UpperCamelCase_=True   ,       UpperCamelCase_=True   ,       UpperCamelCase_=None   ,       ):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			size if size is not None else {'''height''': 20, '''width''': 20}
							UpperCamelCase__       :Tuple							 =			parent
							UpperCamelCase__       :Dict							 =			batch_size
							UpperCamelCase__       :Optional[Any]							 =			num_channels
							UpperCamelCase__       :Any							 =			image_size
							UpperCamelCase__       :str							 =			min_resolution
							UpperCamelCase__       :int							 =			max_resolution
							UpperCamelCase__       :Dict							 =			size
							UpperCamelCase__       :Optional[Any]							 =			do_normalize
							UpperCamelCase__       :Tuple							 =			do_convert_rgb
							UpperCamelCase__       :str							 =			[512, 1024, 2048, 4096]
							UpperCamelCase__       :Any							 =			patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
							UpperCamelCase__       :Optional[int]							 =			Image.open(requests.get(UpperCamelCase_   ,       stream=UpperCamelCase_							).raw							).convert('''RGB'''							)
							return raw_image



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,		reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'     ,		)
@require_torch
@require_vision
class        lowercase     (							A__     ,		unittest.TestCase ):

		"""simple docstring"""

		_a   			=    PixaStructImageProcessor if is_vision_available() else None




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			PixaStructImageProcessingTester(self							)




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(UpperCamelCase_   ,       '''do_normalize'''							)							)
							self.assertTrue(hasattr(UpperCamelCase_   ,       '''do_convert_rgb'''							)							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			self.image_processor_tester.prepare_dummy_image()

							UpperCamelCase__       :Any							 =			self.image_processing_class(**self.image_processor_dict							)
							UpperCamelCase__       :Union[str, Any]							 =			2048

							UpperCamelCase__       :Any							 =			image_processor(UpperCamelCase_   ,       return_tensors='''pt'''   ,       max_patches=UpperCamelCase_							)
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean()   ,       torch.tensor(0.0606							)   ,       atol=1e-3   ,       rtol=1e-3							)							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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

							# Test not batched input
							UpperCamelCase__       :Optional[int]							 =			(
							    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
												# Test not batched input
												UpperCamelCase__       :str							 =			image_processor(
												    image_inputs[0]   ,       return_tensors='''pt'''   ,       max_patches=UpperCamelCase_							).flattened_patches
												self.assertEqual(
												    encoded_images.shape   ,       (1, max_patch, expected_hidden_dim)   ,       )

												# Test batched
												UpperCamelCase__       :str							 =			image_processor(
												    UpperCamelCase_   ,       return_tensors='''pt'''   ,       max_patches=UpperCamelCase_							).flattened_patches
												self.assertEqual(
												    encoded_images.shape   ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,       )




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							UpperCamelCase__       :List[str]							 =			prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=UpperCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(UpperCamelCase_   ,       Image.Image							)

							# Test not batched input
							UpperCamelCase__       :Dict							 =			(
							    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
							    * self.image_processor_tester.num_channels
							) + 2

							UpperCamelCase__       :str							 =			True

							for max_patch in self.image_processor_tester.max_patches:
												# Test not batched input
												with self.assertRaises(UpperCamelCase_							):
																	UpperCamelCase__       :Optional[int]							 =			image_processor(
																	    image_inputs[0]   ,       return_tensors='''pt'''   ,       max_patches=UpperCamelCase_							).flattened_patches

												UpperCamelCase__       :List[str]							 =			'''Hello'''

												UpperCamelCase__       :str							 =			image_processor(
												    image_inputs[0]   ,       return_tensors='''pt'''   ,       max_patches=UpperCamelCase_   ,       header_text=UpperCamelCase_							).flattened_patches
												self.assertEqual(
												    encoded_images.shape   ,       (1, max_patch, expected_hidden_dim)   ,       )

												# Test batched
												UpperCamelCase__       :List[Any]							 =			image_processor(
												    UpperCamelCase_   ,       return_tensors='''pt'''   ,       max_patches=UpperCamelCase_   ,       header_text=UpperCamelCase_							).flattened_patches
												self.assertEqual(
												    encoded_images.shape   ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,       )




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			self.image_processing_class(**self.image_processor_dict							)
							# create random numpy tensors
							UpperCamelCase__       :Optional[int]							 =			prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=UpperCamelCase_   ,       numpify=UpperCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(UpperCamelCase_   ,       np.ndarray							)

							UpperCamelCase__       :Dict							 =			(
							    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
												# Test not batched input
												UpperCamelCase__       :int							 =			image_processor(
												    image_inputs[0]   ,       return_tensors='''pt'''   ,       max_patches=UpperCamelCase_							).flattened_patches
												self.assertEqual(
												    encoded_images.shape   ,       (1, max_patch, expected_hidden_dim)   ,       )

												# Test batched
												UpperCamelCase__       :str							 =			image_processor(
												    UpperCamelCase_   ,       return_tensors='''pt'''   ,       max_patches=UpperCamelCase_							).flattened_patches
												self.assertEqual(
												    encoded_images.shape   ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,       )




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							UpperCamelCase__       :Optional[int]							 =			prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=UpperCamelCase_   ,       torchify=UpperCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(UpperCamelCase_   ,       torch.Tensor							)

							# Test not batched input
							UpperCamelCase__       :Union[str, Any]							 =			(
							    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
												# Test not batched input
												UpperCamelCase__       :Any							 =			image_processor(
												    image_inputs[0]   ,       return_tensors='''pt'''   ,       max_patches=UpperCamelCase_							).flattened_patches
												self.assertEqual(
												    encoded_images.shape   ,       (1, max_patch, expected_hidden_dim)   ,       )

												# Test batched
												UpperCamelCase__       :List[Any]							 =			image_processor(
												    UpperCamelCase_   ,       return_tensors='''pt'''   ,       max_patches=UpperCamelCase_							).flattened_patches
												self.assertEqual(
												    encoded_images.shape   ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,       )



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,		reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'     ,		)
@require_torch
@require_vision
class        lowercase     (							A__     ,		unittest.TestCase ):

		"""simple docstring"""

		_a   			=    PixaStructImageProcessor if is_vision_available() else None




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			PixaStructImageProcessingTester(self   ,       num_channels=4							)
							UpperCamelCase__       :Any							 =			3




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(UpperCamelCase_   ,       '''do_normalize'''							)							)
							self.assertTrue(hasattr(UpperCamelCase_   ,       '''do_convert_rgb'''							)							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							UpperCamelCase__       :Union[str, Any]							 =			prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=UpperCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(UpperCamelCase_   ,       Image.Image							)

							# Test not batched input
							UpperCamelCase__       :Any							 =			(
							    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
							    * (self.image_processor_tester.num_channels - 1)
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
												# Test not batched input
												UpperCamelCase__       :List[Any]							 =			image_processor(
												    image_inputs[0]   ,       return_tensors='''pt'''   ,       max_patches=UpperCamelCase_							).flattened_patches
												self.assertEqual(
												    encoded_images.shape   ,       (1, max_patch, expected_hidden_dim)   ,       )

												# Test batched
												UpperCamelCase__       :str							 =			image_processor(
												    UpperCamelCase_   ,       return_tensors='''pt'''   ,       max_patches=UpperCamelCase_							).flattened_patches
												self.assertEqual(
												    encoded_images.shape   ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,       )



'''simple docstring'''
from argparse import ArgumentParser

from .env import EnvironmentCommand


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



					'''simple docstring'''






					UpperCamelCase__       :Union[str, Any]							 =			ArgumentParser('''Diffusers CLI tool'''			, usage='''diffusers-cli <command> [<args>]'''       )
					UpperCamelCase__       :Union[str, Any]							 =			parser.add_subparsers(help='''diffusers-cli command helpers'''       )

					# Register commands
					EnvironmentCommand.register_subcommand(__a       )

					# Let's go
					UpperCamelCase__       :Optional[int]							 =			parser.parse_args()

					if not hasattr(__a			, '''func'''       ):
										parser.print_help()
										exit(1       )

					# Run
					UpperCamelCase__       :Optional[int]							 =			args.func(__a       )
					service.run()


if __name__ == "__main__":
		main()