


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:
	_lowerCAmelCase:						List[Any]   				=       False

if is_vision_available():
	from PIL import Image

	from transformers import PixaStructImageProcessor




class       lowercase_					(unittest.TestCase   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_=7       ,  lowercase_=3       ,  lowercase_=18       ,  lowercase_=30       ,  lowercase_=400       ,  lowercase_=None       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=None       ,  )  -> str:
											a__      =size if size is not None else {'height': 20, 'width': 20}
											a__      =parent
											a__      =batch_size
											a__      =num_channels
											a__      =image_size
											a__      =min_resolution
											a__      =max_resolution
											a__      =size
											a__      =do_normalize
											a__      =do_convert_rgb
											a__      =[512, 1024, 2048, 4096]
											a__      =patch_size if patch_size is not None else {'height': 16, 'width': 16}





				def 						__UpperCamelCase    (				self)  -> str:
											return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





				def 						__UpperCamelCase    (				self)  -> Any:
											a__      ='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
											a__      =Image.open(requests.get(lowercase_       ,  stream=lowercase_).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_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =PixaStructImageProcessor if is_vision_available() else None





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =PixaStructImageProcessingTester(self)





				@property
				def 						__UpperCamelCase    (				self)  -> List[Any]:
											return self.image_processor_tester.prepare_image_processor_dict()





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(lowercase_       ,  'do_normalize'))
											self.assertTrue(hasattr(lowercase_       ,  'do_convert_rgb'))





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =self.image_processor_tester.prepare_dummy_image()

											a__      =self.image_processing_class(**self.image_processor_dict)
											a__      =2048

											a__      =image_processor(lowercase_       ,  return_tensors='pt'       ,  max_patches=lowercase_)
											self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,  torch.tensor(0.06_06)       ,  atol=1e-3       ,  rtol=1e-3))





				def 						__UpperCamelCase    (				self)  -> Any:
											# Initialize image_processor
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  Image.Image)

											# Test not batched input
											a__      =(
											    (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
																		a__      =image_processor(
																		    image_inputs[0]       ,  return_tensors='pt'       ,  max_patches=lowercase_).flattened_patches
																		self.assertEqual(
																		    encoded_images.shape       ,  (1, max_patch, expected_hidden_dim)       ,  )

																		# Test batched
																		a__      =image_processor(
																		    lowercase_       ,  return_tensors='pt'       ,  max_patches=lowercase_).flattened_patches
																		self.assertEqual(
																		    encoded_images.shape       ,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,  )





				def 						__UpperCamelCase    (				self)  -> Any:
											# Initialize image_processor
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  Image.Image)

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

											a__      =True

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

																		a__      ='Hello'

																		a__      =image_processor(
																		    image_inputs[0]       ,  return_tensors='pt'       ,  max_patches=lowercase_       ,  header_text=lowercase_).flattened_patches
																		self.assertEqual(
																		    encoded_images.shape       ,  (1, max_patch, expected_hidden_dim)       ,  )

																		# Test batched
																		a__      =image_processor(
																		    lowercase_       ,  return_tensors='pt'       ,  max_patches=lowercase_       ,  header_text=lowercase_).flattened_patches
																		self.assertEqual(
																		    encoded_images.shape       ,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,  )





				def 						__UpperCamelCase    (				self)  -> Any:
											# Initialize image_processor
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_       ,  numpify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  np.ndarray)

											a__      =(
											    (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
																		a__      =image_processor(
																		    image_inputs[0]       ,  return_tensors='pt'       ,  max_patches=lowercase_).flattened_patches
																		self.assertEqual(
																		    encoded_images.shape       ,  (1, max_patch, expected_hidden_dim)       ,  )

																		# Test batched
																		a__      =image_processor(
																		    lowercase_       ,  return_tensors='pt'       ,  max_patches=lowercase_).flattened_patches
																		self.assertEqual(
																		    encoded_images.shape       ,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,  )





				def 						__UpperCamelCase    (				self)  -> Dict:
											# Initialize image_processor
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_       ,  torchify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  torch.Tensor)

											# Test not batched input
											a__      =(
											    (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
																		a__      =image_processor(
																		    image_inputs[0]       ,  return_tensors='pt'       ,  max_patches=lowercase_).flattened_patches
																		self.assertEqual(
																		    encoded_images.shape       ,  (1, max_patch, expected_hidden_dim)       ,  )

																		# Test batched
																		a__      =image_processor(
																		    lowercase_       ,  return_tensors='pt'       ,  max_patches=lowercase_).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_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =PixaStructImageProcessor if is_vision_available() else None





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =PixaStructImageProcessingTester(self       ,  num_channels=4)
											a__      =3





				@property
				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											return self.image_processor_tester.prepare_image_processor_dict()





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(lowercase_       ,  'do_normalize'))
											self.assertTrue(hasattr(lowercase_       ,  'do_convert_rgb'))





				def 						__UpperCamelCase    (				self)  -> int:
											# Initialize image_processor
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  Image.Image)

											# Test not batched input
											a__      =(
											    (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
																		a__      =image_processor(
																		    image_inputs[0]       ,  return_tensors='pt'       ,  max_patches=lowercase_).flattened_patches
																		self.assertEqual(
																		    encoded_images.shape       ,  (1, max_patch, expected_hidden_dim)       ,  )

																		# Test batched
																		a__      =image_processor(
																		    lowercase_       ,  return_tensors='pt'       ,  max_patches=lowercase_).flattened_patches
																		self.assertEqual(
																		    encoded_images.shape       ,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,  )




A_			:					Union[str, Any]    			=     '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A_			:					str    			=     [{'type': 'code', 'content': INSTALL_CONTENT}]
A_			:					Any    			=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}



