

import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

			from transformers import (
			    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
			    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
			    MODEL_MAPPING,
			    DeiTForImageClassification,
			    DeiTForImageClassificationWithTeacher,
			    DeiTForMaskedImageModeling,
			    DeiTModel,
			)
			from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import DeiTImageProcessor






class      lowerCamelCase_      :




	def __init__(     self						:     Tuple					,      __A						:     List[Any]					,      __A						:     List[Any]=13					,      __A						:     List[str]=30					,      __A						:     Optional[int]=2					,      __A						:     Optional[Any]=3					,      __A						:     Optional[Any]=True					,      __A						:     List[Any]=True					,      __A						:     Dict=32					,      __A						:     List[str]=5					,      __A						:     Tuple=4					,      __A						:     Dict=37					,      __A						:     List[Any]="gelu"					,      __A						:     str=0.1					,      __A						:     Dict=0.1					,      __A						:     Optional[Any]=10					,      __A						:     Optional[int]=0.0_2					,      __A						:     int=3					,      __A						:     Union[str, Any]=None					,      __A						:     Union[str, Any]=2					,      ):
		__A :			List[Any]		    =			parent
		__A :			List[Any]		    =			batch_size
		__A :			List[str]		    =			image_size
		__A :			Optional[Any]		    =			patch_size
		__A :			Union[str, Any]		    =			num_channels
		__A :			Union[str, Any]		    =			is_training
		__A :			Any		    =			use_labels
		__A :			Any		    =			hidden_size
		__A :			str		    =			num_hidden_layers
		__A :			List[Any]		    =			num_attention_heads
		__A :			Optional[Any]		    =			intermediate_size
		__A :			Dict		    =			hidden_act
		__A :			str		    =			hidden_dropout_prob
		__A :			Optional[int]		    =			attention_probs_dropout_prob
		__A :			Union[str, Any]		    =			type_sequence_label_size
		__A :			Union[str, Any]		    =			initializer_range
		__A :			Optional[int]		    =			scope
		__A :			List[str]		    =			encoder_stride

		# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
		__A :			Tuple		    =			(image_size // patch_size) ** 2
		__A :			int		    =			num_patches + 2




	def        lowerCAmelCase_       (     self						:     Dict							):
		__A :			List[Any]		    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

		__A :			List[str]		    =			None
		if self.use_labels:
			__A :			Union[str, Any]		    =			ids_tensor([self.batch_size]					,      self.type_sequence_label_size							)

		__A :			str		    =			self.get_config()

		return config, pixel_values, labels




	def        lowerCAmelCase_       (     self						:     str							):
		return DeiTConfig(
		    image_size=self.image_size					,      patch_size=self.patch_size					,      num_channels=self.num_channels					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      hidden_act=self.hidden_act					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      is_decoder=__A					,      initializer_range=self.initializer_range					,      encoder_stride=self.encoder_stride					,      )




	def        lowerCAmelCase_       (     self						:     str					,      __A						:     Dict					,      __A						:     List[str]					,      __A						:     Any							):
		__A :			Any		    =			DeiTModel(config=__A							)
		model.to(__A							)
		model.eval()
		__A :			Optional[int]		    =			model(__A							)
		self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)							)




	def        lowerCAmelCase_       (     self						:     str					,      __A						:     Dict					,      __A						:     int					,      __A						:     Tuple							):
		__A :			List[str]		    =			DeiTForMaskedImageModeling(config=__A							)
		model.to(__A							)
		model.eval()
		__A :			int		    =			model(__A							)
		self.parent.assertEqual(
		    result.reconstruction.shape					,      (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

		# test greyscale images
		__A :			Dict		    =			1
		__A :			Tuple		    =			DeiTForMaskedImageModeling(__A							)
		model.to(__A							)
		model.eval()

		__A :			Optional[Any]		    =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
		__A :			Union[str, Any]		    =			model(__A							)
		self.parent.assertEqual(result.reconstruction.shape					,      (self.batch_size, 1, self.image_size, self.image_size)							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     int					,      __A						:     Any					,      __A						:     List[Any]							):
		__A :			Union[str, Any]		    =			self.type_sequence_label_size
		__A :			Optional[Any]		    =			DeiTForImageClassification(__A							)
		model.to(__A							)
		model.eval()
		__A :			Union[str, Any]		    =			model(__A					,      labels=__A							)
		self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)							)

		# test greyscale images
		__A :			Optional[int]		    =			1
		__A :			Union[str, Any]		    =			DeiTForImageClassification(__A							)
		model.to(__A							)
		model.eval()

		__A :			Optional[int]		    =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
		__A :			Optional[Any]		    =			model(__A					,      labels=__A							)
		self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)							)




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			Optional[Any]		    =			self.prepare_config_and_inputs()
		(
		    (
		    __A
		)    ,				(
		    __A
		)    ,				(
		    __A
		)    ,				
		) :			Optional[int]		    =			config_and_inputs
		__A :			str		    =			{"""pixel_values""": pixel_values}
		return config, inputs_dict






@require_torch
class      lowerCamelCase_      (	_lowercase						,      _lowercase						,      unittest.TestCase	):
	_lowercase		:   Optional[int]     					=				(
	    (
	        DeiTModel,
	        DeiTForImageClassification,
	        DeiTForImageClassificationWithTeacher,
	        DeiTForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	_lowercase		:   Dict     					=				(
	    {
	        '''feature-extraction''': DeiTModel,
	        '''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
	    }
	    if is_torch_available()
	    else {}
	)

	_lowercase		:   Any     					=				False
	_lowercase		:   int     					=				False
	_lowercase		:   Optional[int]     					=				False




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Dict		    =			DeiTModelTester(self							)
		__A :			int		    =			ConfigTester(self					,      config_class=__A					,      has_text_modality=__A					,      hidden_size=37							)




	def        lowerCAmelCase_       (     self						:     Tuple							):
		self.config_tester.run_common_tests()




	@unittest.skip(reason="""DeiT does not use inputs_embeds"""							)
	def        lowerCAmelCase_       (     self						:     Any							):
		pass




	def        lowerCAmelCase_       (     self						:     int							):
		__A    ,				__A :			Dict		    =			self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__A :			Any		    =			model_class(__A							)
			self.assertIsInstance(model.get_input_embeddings()					,      (nn.Module)							)
			__A :			Any		    =			model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(__A					,      nn.Linear							)							)




	def        lowerCAmelCase_       (     self						:     str							):
		__A    ,				__A :			Union[str, Any]		    =			self.model_tester.prepare_config_and_inputs_for_common()

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

			__A :			Union[str, Any]		    =			["""pixel_values"""]
			self.assertListEqual(arg_names[:1]					,      __A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			Dict		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__A							)




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			Union[str, Any]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_image_modeling(*__A							)




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Union[str, Any]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*__A							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     int					,      __A						:     Dict					,      __A						:     Optional[Any]=False							):
		__A :			Any		    =			super()._prepare_for_class(__A					,      __A					,      return_labels=__A							)

		if return_labels:
			if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
				del inputs_dict["labels"]

		return inputs_dict




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		if not self.model_tester.is_training:
			return

		__A    ,				__A :			int		    =			self.model_tester.prepare_config_and_inputs_for_common()
		__A :			Any		    =			True

		for model_class in self.all_model_classes:
			# DeiTForImageClassificationWithTeacher supports inference-only
			if (
			    model_class in get_values(__A							)
			    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
			):
				continue
			__A :			List[str]		    =			model_class(__A							)
			model.to(__A							)
			model.train()
			__A :			Dict		    =			self._prepare_for_class(__A					,      __A					,      return_labels=__A							)
			__A :			Any		    =			model(**__A							).loss
			loss.backward()




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A    ,				__A :			Optional[Any]		    =			self.model_tester.prepare_config_and_inputs_for_common()
		if not self.model_tester.is_training:
			return

		__A :			Optional[Any]		    =			False
		__A :			List[Any]		    =			True

		for model_class in self.all_model_classes:
			if model_class in get_values(__A							) or not model_class.supports_gradient_checkpointing:
				continue
			# DeiTForImageClassificationWithTeacher supports inference-only
			if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
				continue
			__A :			Any		    =			model_class(__A							)
			model.gradient_checkpointing_enable()
			model.to(__A							)
			model.train()
			__A :			Union[str, Any]		    =			self._prepare_for_class(__A					,      __A					,      return_labels=__A							)
			__A :			int		    =			model(**__A							).loss
			loss.backward()




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A    ,				__A :			Optional[int]		    =			self.model_tester.prepare_config_and_inputs_for_common()

		__A :			str		    =			[
		    {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
		    {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
		    {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
		]

		for model_class in self.all_model_classes:
			if (
			    model_class
			    not in [
			        *get_values(__A							),
			        *get_values(__A							),
			    ]
			    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
			):
				continue

			for problem_type in problem_types:
				with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}"""							):
					__A :			Tuple		    =			problem_type["""title"""]
					__A :			Optional[int]		    =			problem_type["""num_labels"""]

					__A :			Union[str, Any]		    =			model_class(__A							)
					model.to(__A							)
					model.train()

					__A :			int		    =			self._prepare_for_class(__A					,      __A					,      return_labels=__A							)

					if problem_type["num_labels"] > 1:
						__A :			int		    =			inputs["""labels"""].unsqueeze(1							).repeat(1					,      problem_type["""num_labels"""]							)

					__A :			Optional[Any]		    =			inputs["""labels"""].to(problem_type["""dtype"""]							)

					# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
					# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
					# they have the same size." which is a symptom something in wrong for the regression problem.
					# See https://github.com/huggingface/transformers/issues/11780
					with warnings.catch_warnings(record=__A							) as warning_list:
						__A :			List[str]		    =			model(**__A							).loss
					for w in warning_list:
						if "Using a target size that is different to the input size" in str(w.message							):
							raise ValueError(
							    F"""Something is going wrong in the regression problem: intercepted {w.message}"""							)

					loss.backward()




	@slow
	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			__A :			Optional[Any]		    =			DeiTModel.from_pretrained(__A							)
			self.assertIsNotNone(__A							)






def 							__SCREAMING_SNAKE_CASE     (   )					->				Any:
	__A :			Any		    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
	return image







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




	@cached_property
	def        lowerCAmelCase_       (     self						:     List[Any]							):
		return (
		    DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""							)
		    if is_vision_available()
		    else None
		)




	@slow
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Any		    =			DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""							).to(
		    __A							)

		__A :			int		    =			self.default_image_processor
		__A :			List[str]		    =			prepare_img()
		__A :			Optional[Any]		    =			image_processor(images=__A					,      return_tensors="""pt"""							).to(__A							)

		# forward pass
		with torch.no_grad():
			__A :			int		    =			model(**__A							)

		# verify the logits
		__A :			Optional[Any]		    =			torch.Size((1, 1000)							)
		self.assertEqual(outputs.logits.shape					,      __A							)

		__A :			str		    =			torch.tensor([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]							).to(__A							)

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




	@slow
	@require_accelerate
	@require_torch_gpu
	def        lowerCAmelCase_       (     self						:     str							):
		__A :			List[str]		    =			DeiTModel.from_pretrained(
		    """facebook/deit-base-distilled-patch16-224"""					,      torch_dtype=torch.floataa					,      device_map="""auto"""							)
		__A :			Union[str, Any]		    =			self.default_image_processor

		__A :			Any		    =			prepare_img()
		__A :			List[str]		    =			image_processor(images=__A					,      return_tensors="""pt"""							)
		__A :			List[Any]		    =			inputs.pixel_values.to(__A							)

		# forward pass to make sure inference works in fp16
		with torch.no_grad():
			__A :			Tuple		    =			model(__A							)








import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)





