


import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import CvtForImageClassification, CvtModel
	from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor





class 							lowercase_					(			UpperCamelCase__):






							"""simple docstring"""







							def     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		self.config_class(**self.inputs_dict		)
													self.parent.assertTrue(hasattr(_lowercase      ,     """embed_dim"""		)		)
													self.parent.assertTrue(hasattr(_lowercase      ,     """num_heads"""		)		)





class 							lowercase_					:






							"""simple docstring"""







							def __init__( self      ,     _UpperCAmelCase      ,     _UpperCAmelCase=13      ,     _UpperCAmelCase=64      ,     _UpperCAmelCase=3      ,     _UpperCAmelCase=[16, 48, 96]      ,     _UpperCAmelCase=[1, 3, 6]      ,     _UpperCAmelCase=[1, 2, 10]      ,     _UpperCAmelCase=[7, 3, 3]      ,     _UpperCAmelCase=[4, 2, 2]      ,     _UpperCAmelCase=[2, 1, 1]      ,     _UpperCAmelCase=[2, 2, 2]      ,     _UpperCAmelCase=[False, False, True]      ,     _UpperCAmelCase=[0.0, 0.0, 0.0]      ,     _UpperCAmelCase=0.0_2      ,     _UpperCAmelCase=1e-1_2      ,     _UpperCAmelCase=True      ,     _UpperCAmelCase=True      ,     _UpperCAmelCase=2      ,     ):







													"""simple docstring"""


													a_		      =		parent
													a_		      =		batch_size
													a_		      =		image_size
													a_		      =		patch_sizes
													a_		      =		patch_stride
													a_		      =		patch_padding
													a_		      =		is_training
													a_		      =		use_labels
													a_		      =		num_labels
													a_		      =		num_channels
													a_		      =		embed_dim
													a_		      =		num_heads
													a_		      =		stride_kv
													a_		      =		depth
													a_		      =		cls_token
													a_		      =		attention_drop_rate
													a_		      =		initializer_range
													a_		      =		layer_norm_eps


							def     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

													a_		      =		None
													if self.use_labels:
																			a_		      =		ids_tensor([self.batch_size]      ,     self.num_labels		)

													a_		      =		self.get_config()
													return config, pixel_values, labels


							def     lowercase__  ( self		):







													"""simple docstring"""


													return CvtConfig(
													    image_size=self.image_size      ,     num_labels=self.num_labels      ,     num_channels=self.num_channels      ,     embed_dim=self.embed_dim      ,     num_heads=self.num_heads      ,     patch_sizes=self.patch_sizes      ,     patch_padding=self.patch_padding      ,     patch_stride=self.patch_stride      ,     stride_kv=self.stride_kv      ,     depth=self.depth      ,     cls_token=self.cls_token      ,     attention_drop_rate=self.attention_drop_rate      ,     initializer_range=self.initializer_range      ,     )


							def     lowercase__  ( self      ,     _UpperCAmelCase      ,     _UpperCAmelCase      ,     _UpperCAmelCase		):







													"""simple docstring"""


													a_		      =		CvtModel(config=_lowercase		)
													model.to(_lowercase		)
													model.eval()
													a_		      =		model(_lowercase		)
													a_		      =		(self.image_size, self.image_size)
													a_      , a_		      =		image_size[0], image_size[1]
													for i in range(len(self.depth		)		):
																			a_		      =		floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1		)
																			a_		      =		floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1		)
													self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, self.embed_dim[-1], height, width)		)


							def     lowercase__  ( self      ,     _UpperCAmelCase      ,     _UpperCAmelCase      ,     _UpperCAmelCase		):







													"""simple docstring"""


													a_		      =		self.num_labels
													a_		      =		CvtForImageClassification(_lowercase		)
													model.to(_lowercase		)
													model.eval()
													a_		      =		model(_lowercase      ,     labels=_lowercase		)
													self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.num_labels)		)






							def     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		self.prepare_config_and_inputs()
													a_      , a_      , a_		      =		config_and_inputs
													a_		      =		{"""pixel_values""": pixel_values}
													return config, inputs_dict





@require_torch
class 							lowercase_					(			UpperCamelCase__      ,UpperCamelCase__      ,unittest.TestCase):






							"""simple docstring"""







							snake_case_										=		(CvtModel, CvtForImageClassification) if is_torch_available() else ()
							snake_case_										=		(
							    {'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
							    if is_torch_available()
							    else {}
							)

							snake_case_										=		False
							snake_case_										=		False
							snake_case_										=		False
							snake_case_										=		False
							snake_case_										=		False


							def     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		CvtModelTester(self		)
													a_		      =		ConfigTester(self      ,     config_class=_lowercase      ,     has_text_modality=_lowercase      ,     hidden_size=37		)


							def     lowercase__  ( self		):







													"""simple docstring"""


													self.create_and_test_config_common_properties()
													self.config_tester.create_and_test_config_to_json_string()
													self.config_tester.create_and_test_config_to_json_file()
													self.config_tester.create_and_test_config_from_and_save_pretrained()
													self.config_tester.create_and_test_config_with_num_labels()
													self.config_tester.check_config_can_be_init_without_params()
													self.config_tester.check_config_arguments_init()


							def     lowercase__  ( self		):







													"""simple docstring"""


													return


							@unittest.skip(reason="""Cvt does not output attentions"""		)
							def     lowercase__  ( self		):







													"""simple docstring"""


													pass


							@unittest.skip(reason="""Cvt does not use inputs_embeds"""		)
							def     lowercase__  ( self		):







													"""simple docstring"""


													pass


							@unittest.skip(reason="""Cvt does not support input and output embeddings"""		)
							def     lowercase__  ( self		):







													"""simple docstring"""


													pass


							def     lowercase__  ( self		):







													"""simple docstring"""


													a_      , a_		      =		self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			a_		      =		model_class(_lowercase		)
																			a_		      =		inspect.signature(model.forward		)
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			a_		      =		[*signature.parameters.keys()]

																			a_		      =		["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]      ,     _lowercase		)


							def     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_lowercase		)


							def     lowercase__  ( self		):







													"""simple docstring"""


													def check_hidden_states_output(_UpperCAmelCase      ,     _UpperCAmelCase      ,     _UpperCAmelCase		):
																			a_		      =		model_class(_lowercase		)
																			model.to(_lowercase		)
																			model.eval()

																			with torch.no_grad():
																									a_		      =		model(**self._prepare_for_class(_lowercase      ,     _lowercase		)		)

																			a_		      =		outputs.hidden_states

																			a_		      =		len(self.model_tester.depth		)
																			self.assertEqual(len(_lowercase		)      ,     _lowercase		)

																			# verify the first hidden states (first block)
																			self.assertListEqual(
																			    list(hidden_states[0].shape[-3:]		)      ,     [
																			        self.model_tester.embed_dim[0],
																			        self.model_tester.image_size // 4,
																			        self.model_tester.image_size // 4,
																			    ]      ,     )

													a_      , a_		      =		self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			a_		      =		True
																			check_hidden_states_output(_lowercase      ,     _lowercase      ,     _lowercase		)

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			a_		      =		True

																			check_hidden_states_output(_lowercase      ,     _lowercase      ,     _lowercase		)


							def     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*_lowercase		)


							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
							def     lowercase__  ( self		):







													"""simple docstring"""


													pass






							@slow
							def     lowercase__  ( self		):







													"""simple docstring"""


													for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			a_		      =		CvtModel.from_pretrained(_lowercase		)
																			self.assertIsNotNone(_lowercase		)




def 	lowerCamelCase_							(       ):



						"""simple docstring"""




						a_		      =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
						return image





@require_torch
@require_vision
class 							lowercase_					(			unittest.TestCase):






							"""simple docstring"""







							@cached_property
							def     lowercase__  ( self		):







													"""simple docstring"""


													return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)






							@slow
							def     lowercase__  ( self		):







													"""simple docstring"""


													a_		      =		CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(_lowercase		)

													a_		      =		self.default_image_processor
													a_		      =		prepare_img()
													a_		      =		image_processor(images=_lowercase      ,     return_tensors="""pt"""		).to(_lowercase		)

													# forward pass
													with torch.no_grad():
																			a_		      =		model(**_lowercase		)

													# verify the logits
													a_		      =		torch.Size((1, 1_000)		)
													self.assertEqual(outputs.logits.shape      ,     _lowercase		)

													a_		      =		torch.tensor([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0]		).to(_lowercase		)

													self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,     _lowercase      ,     atol=1e-4		)		)





'''simple docstring'''
from PIL import Image





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Image:

			def brightness(SCREAMING_SNAKE_CASE_     ) -> float:
						return 128 + level + (c - 128)

			if not -255.0 <= level <= 255.0:
						raise ValueError('level must be between -255.0 (black) and 255.0 (white)'     )
			return img.point(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
							# Load image
							with Image.open("image_data/lena.jpg") as img:
														# Change brightness to 100
														_SCREAMING_SNAKE_CASE			=						change_brightness(img, 1_00)
														brigt_img.save("image_data/lena_brightness.png", format="png")




