




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


@skip_mps
class  lowerCamelCase     (		_lowerCamelCase					,_lowerCamelCase					,_lowerCamelCase					,unittest.TestCase    ):






			'''simple docstring'''





			UpperCamelCase__										=StableDiffusionModelEditingPipeline
			UpperCamelCase__										=TEXT_TO_IMAGE_PARAMS
			UpperCamelCase__										=TEXT_TO_IMAGE_BATCH_PARAMS
			UpperCamelCase__										=TEXT_TO_IMAGE_IMAGE_PARAMS
			UpperCamelCase__										=TEXT_TO_IMAGE_IMAGE_PARAMS

			def 				UpperCAmelCase__							(			self      : Tuple	)    -> Tuple:
								torch.manual_seed(0	)
								__magic_name__				:						List[str]     				=				UNetaDConditionModel(
								    block_out_channels=(32, 64)							,    layers_per_block=2							,    sample_size=32							,    in_channels=4							,    out_channels=4							,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							,    cross_attention_dim=32							,    )
								__magic_name__				:						List[Any]     				=				DDIMScheduler()
								torch.manual_seed(0	)
								__magic_name__				:						Dict     				=				AutoencoderKL(
								    block_out_channels=[32, 64]							,    in_channels=3							,    out_channels=3							,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']							,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							,    latent_channels=4							,    )
								torch.manual_seed(0	)
								__magic_name__				:						Any     				=				CLIPTextConfig(
								    bos_token_id=0							,    eos_token_id=2							,    hidden_size=32							,    intermediate_size=37							,    layer_norm_eps=1E-05							,    num_attention_heads=4							,    num_hidden_layers=5							,    pad_token_id=1							,    vocab_size=1000							,    )
								__magic_name__				:						Optional[int]     				=				CLIPTextModel(lowerCamelCase_	)
								__magic_name__				:						List[Any]     				=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

								__magic_name__				:						Union[str, Any]     				=				{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components

			def 				UpperCAmelCase__							(			self      : List[Any]							,    lowerCamelCase_      : Dict							,    lowerCamelCase_      : List[str]=0	)    -> List[Any]:
								__magic_name__				:						int     				=				torch.manual_seed(lowerCamelCase_	)
								__magic_name__				:						List[Any]     				=				{
								    '''prompt''': '''A field of roses''',
								    '''generator''': generator,
								    # Setting height and width to None to prevent OOMs on CPU.
								    '''height''': None,
								    '''width''': None,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''numpy''',
								}
								return inputs

			def 				UpperCAmelCase__							(			self      : List[Any]	)    -> List[str]:
								__magic_name__				:						Union[str, Any]     				=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__magic_name__				:						Optional[int]     				=				self.get_dummy_components()
								__magic_name__				:						Any     				=				StableDiffusionModelEditingPipeline(**lowerCamelCase_	)
								__magic_name__				:						str     				=				sd_pipe.to(lowerCamelCase_	)
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

								__magic_name__				:						Any     				=				self.get_dummy_inputs(lowerCamelCase_	)
								__magic_name__				:						List[str]     				=				sd_pipe(**lowerCamelCase_	).images
								__magic_name__				:						Dict     				=				image[0, -3:, -3:, -1]
								assert image.shape == (1, 64, 64, 3)

								__magic_name__				:						Dict     				=				np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]	)

								assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2

			def 				UpperCAmelCase__							(			self      : int	)    -> str:
								__magic_name__				:						Tuple     				=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__magic_name__				:						str     				=				self.get_dummy_components()
								__magic_name__				:						Dict     				=				StableDiffusionModelEditingPipeline(**lowerCamelCase_	)
								__magic_name__				:						List[Any]     				=				sd_pipe.to(lowerCamelCase_	)
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

								__magic_name__				:						List[str]     				=				self.get_dummy_inputs(lowerCamelCase_	)
								__magic_name__				:						str     				=				'''french fries'''
								__magic_name__				:						Tuple     				=				sd_pipe(**lowerCamelCase_							,    negative_prompt=lowerCamelCase_	)
								__magic_name__				:						Dict     				=				output.images
								__magic_name__				:						List[str]     				=				image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)

								__magic_name__				:						Union[str, Any]     				=				np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]	)

								assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2

			def 				UpperCAmelCase__							(			self      : List[Any]	)    -> List[Any]:
								__magic_name__				:						Union[str, Any]     				=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__magic_name__				:						Optional[Any]     				=				self.get_dummy_components()
								__magic_name__				:						List[Any]     				=				EulerAncestralDiscreteScheduler(
								    beta_start=0.0_0_0_8_5							,    beta_end=0.0_1_2							,    beta_schedule='''scaled_linear'''	)
								__magic_name__				:						Optional[int]     				=				StableDiffusionModelEditingPipeline(**lowerCamelCase_	)
								__magic_name__				:						Dict     				=				sd_pipe.to(lowerCamelCase_	)
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

								__magic_name__				:						Dict     				=				self.get_dummy_inputs(lowerCamelCase_	)
								__magic_name__				:						Any     				=				sd_pipe(**lowerCamelCase_	).images
								__magic_name__				:						Tuple     				=				image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)

								__magic_name__				:						List[Any]     				=				np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]	)

								assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2

			def 				UpperCAmelCase__							(			self      : Union[str, Any]	)    -> str:
								__magic_name__				:						int     				=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__magic_name__				:						str     				=				self.get_dummy_components()
								__magic_name__				:						Dict     				=				PNDMScheduler()
								__magic_name__				:						Any     				=				StableDiffusionModelEditingPipeline(**lowerCamelCase_	)
								__magic_name__				:						Any     				=				sd_pipe.to(lowerCamelCase_	)
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

								__magic_name__				:						Dict     				=				self.get_dummy_inputs(lowerCamelCase_	)
								# the pipeline does not expect pndm so test if it raises error.
								with self.assertRaises(lowerCamelCase_	):
													__magic_name__				:						List[Any]     				=				sd_pipe(**lowerCamelCase_	).images

			def 				UpperCAmelCase__							(			self      : Optional[int]	)    -> Optional[int]:
								super().test_inference_batch_single_identical(expected_max_diff=5E-3	)


			def 				UpperCAmelCase__							(			self      : int	)    -> str:
								super().test_attention_slicing_forward_pass(expected_max_diff=5E-3	)






@slow
@require_torch_gpu
class  lowerCamelCase     (		unittest.TestCase    ):






			'''simple docstring'''





			def 				UpperCAmelCase__							(			self      : str	)    -> Optional[int]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()

			def 				UpperCAmelCase__							(			self      : Tuple							,    lowerCamelCase_      : Union[str, Any]=0	)    -> List[str]:
								__magic_name__				:						List[str]     				=				torch.manual_seed(lowerCamelCase_	)
								__magic_name__				:						Any     				=				{
								    '''prompt''': '''A field of roses''',
								    '''generator''': generator,
								    '''num_inference_steps''': 3,
								    '''guidance_scale''': 7.5,
								    '''output_type''': '''numpy''',
								}
								return inputs

			def 				UpperCAmelCase__							(			self      : Optional[int]	)    -> Optional[int]:
								__magic_name__				:						str     				=				'''CompVis/stable-diffusion-v1-4'''
								__magic_name__				:						Dict     				=				StableDiffusionModelEditingPipeline.from_pretrained(lowerCamelCase_							,    safety_checker=lowerCamelCase_	)
								pipe.to(lowerCamelCase_	)
								pipe.set_progress_bar_config(disable=lowerCamelCase_	)
								pipe.enable_attention_slicing()

								__magic_name__				:						Optional[int]     				=				self.get_inputs()
								__magic_name__				:						Optional[Any]     				=				pipe(**lowerCamelCase_	).images
								__magic_name__				:						int     				=				image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)

								__magic_name__				:						Any     				=				np.array(
								    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]	)

								assert np.abs(expected_slice - image_slice	).max() < 1E-2

								# make sure image changes after editing
								pipe.edit_model('''A pack of roses'''							,    '''A pack of blue roses'''	)

								__magic_name__				:						Union[str, Any]     				=				pipe(**lowerCamelCase_	).images
								__magic_name__				:						str     				=				image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)

								assert np.abs(expected_slice - image_slice	).max() > 1E-1


			def 				UpperCAmelCase__							(			self      : int	)    -> int:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								__magic_name__				:						Tuple     				=				'''CompVis/stable-diffusion-v1-4'''
								__magic_name__				:						Any     				=				DDIMScheduler.from_pretrained(lowerCamelCase_							,    subfolder='''scheduler'''	)
								__magic_name__				:						Dict     				=				StableDiffusionModelEditingPipeline.from_pretrained(
								    lowerCamelCase_							,    scheduler=lowerCamelCase_							,    safety_checker=lowerCamelCase_	)
								__magic_name__				:						List[Any]     				=				pipe.to(lowerCamelCase_	)
								pipe.set_progress_bar_config(disable=lowerCamelCase_	)
								pipe.enable_attention_slicing(1	)
								pipe.enable_sequential_cpu_offload()

								__magic_name__				:						Union[str, Any]     				=				self.get_inputs()
								__magic_name__				:						List[str]     				=				pipe(**lowerCamelCase_	)

								__magic_name__				:						Optional[int]     				=				torch.cuda.max_memory_allocated()
								# make sure that less than 4.4 GB is allocated
								assert mem_bytes < 4.4 * 10**9









from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import numpy as np
		import tensorflow as tf

		from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
		from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileViTImageProcessor




class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							def  _UpperCAmelCase  (       self							:						Tuple      )      ->       int:
										SCREAMING_SNAKE_CASE     =       self.config_class(**self.inputs_dict      )
										self.parent.assertTrue(hasattr(a							,  """hidden_sizes"""      )      )
										self.parent.assertTrue(hasattr(a							,  """neck_hidden_sizes"""      )      )
										self.parent.assertTrue(hasattr(a							,  """num_attention_heads"""      )      )





class       UpperCAmelCase_   :




							'''simple docstring'''



							def __init__(       self							:						Optional[Any]							,  a							:						Dict							,  a							:						Optional[int]=13							,  a							:						Optional[int]=32							,  a							:						List[str]=2							,  a							:						Optional[int]=3							,  a							:						str=640							,  a							:						str=4							,  a							:						Optional[int]="silu"							,  a							:						Union[str, Any]=3							,  a							:						Optional[int]=32							,  a							:						Optional[Any]=0.1							,  a							:						Optional[int]=0.1							,  a							:						int=0.1							,  a							:						List[str]=0.02							,  a							:						Tuple=True							,  a							:						Optional[Any]=True							,  a							:						List[Any]=10							,  a							:						List[Any]=None							,  )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       parent
										SCREAMING_SNAKE_CASE     =       batch_size
										SCREAMING_SNAKE_CASE     =       image_size
										SCREAMING_SNAKE_CASE     =       patch_size
										SCREAMING_SNAKE_CASE     =       num_channels
										SCREAMING_SNAKE_CASE     =       last_hidden_size
										SCREAMING_SNAKE_CASE     =       num_attention_heads
										SCREAMING_SNAKE_CASE     =       hidden_act
										SCREAMING_SNAKE_CASE     =       conv_kernel_size
										SCREAMING_SNAKE_CASE     =       output_stride
										SCREAMING_SNAKE_CASE     =       hidden_dropout_prob
										SCREAMING_SNAKE_CASE     =       attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE     =       classifier_dropout_prob
										SCREAMING_SNAKE_CASE     =       use_labels
										SCREAMING_SNAKE_CASE     =       is_training
										SCREAMING_SNAKE_CASE     =       num_labels
										SCREAMING_SNAKE_CASE     =       initializer_range
										SCREAMING_SNAKE_CASE     =       scope





							def  _UpperCAmelCase  (       self							:						Tuple      )      ->       List[Any]:
										SCREAMING_SNAKE_CASE     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

										SCREAMING_SNAKE_CASE     =       self.get_config()

										return config, pixel_values, labels, pixel_labels





							def  _UpperCAmelCase  (       self							:						List[Any]      )      ->       Union[str, Any]:
										return MobileViTConfig(
										    image_size=self.image_size							,  patch_size=self.patch_size							,  num_channels=self.num_channels							,  num_attention_heads=self.num_attention_heads							,  hidden_act=self.hidden_act							,  conv_kernel_size=self.conv_kernel_size							,  output_stride=self.output_stride							,  hidden_dropout_prob=self.hidden_dropout_prob							,  attention_probs_dropout_prob=self.attention_probs_dropout_prob							,  classifier_dropout_prob=self.classifier_dropout_prob							,  initializer_range=self.initializer_range							,  )





							def  _UpperCAmelCase  (       self							:						List[str]							,  a							:						Union[str, Any]							,  a							:						Tuple							,  a							:						Optional[int]							,  a							:						str      )      ->       Tuple:
										SCREAMING_SNAKE_CASE     =       TFMobileViTModel(config=a      )
										SCREAMING_SNAKE_CASE     =       model(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       SCREAMING_SNAKE_CASE     =       self.image_size // self.output_stride
										self.parent.assertEqual(
										    result.last_hidden_state.shape							,  (self.batch_size, self.last_hidden_size, expected_height, expected_width)      )





							def  _UpperCAmelCase  (       self							:						Any							,  a							:						Dict							,  a							:						Optional[Any]							,  a							:						List[Any]							,  a							:						List[str]      )      ->       Any:
										SCREAMING_SNAKE_CASE     =       self.num_labels
										SCREAMING_SNAKE_CASE     =       TFMobileViTForImageClassification(a      )
										SCREAMING_SNAKE_CASE     =       model(a							,  labels=a							,  training=a      )
										self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels)      )





							def  _UpperCAmelCase  (       self							:						Dict							,  a							:						List[str]							,  a							:						Union[str, Any]							,  a							:						List[Any]							,  a							:						Union[str, Any]      )      ->       Optional[int]:
										SCREAMING_SNAKE_CASE     =       self.num_labels
										SCREAMING_SNAKE_CASE     =       TFMobileViTForSemanticSegmentation(a      )
										SCREAMING_SNAKE_CASE     =       SCREAMING_SNAKE_CASE     =       self.image_size // self.output_stride

										SCREAMING_SNAKE_CASE     =       model(a							,  training=a      )
										self.parent.assertEqual(
										    result.logits.shape							,  (self.batch_size, self.num_labels, expected_height, expected_width)      )

										SCREAMING_SNAKE_CASE     =       model(a							,  labels=a							,  training=a      )
										self.parent.assertEqual(
										    result.logits.shape							,  (self.batch_size, self.num_labels, expected_height, expected_width)      )







							def  _UpperCAmelCase  (       self							:						Any      )      ->       Optional[int]:
										SCREAMING_SNAKE_CASE     =       self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       config_and_inputs
										SCREAMING_SNAKE_CASE     =       {"""pixel_values""": pixel_values}
										return config, inputs_dict





@require_tf
class       UpperCAmelCase_   ( A		,   A		,   unittest.TestCase							):




							'''simple docstring'''



							a__    	=  (
							    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
							    if is_tf_available()
							    else ()
							)
							a__    	=  (
							    {'''feature-extraction''': TFMobileViTModel, '''image-classification''': TFMobileViTForImageClassification}
							    if is_tf_available()
							    else {}
							)

							a__    	=  False
							a__    	=  False
							a__    	=  False
							a__    	=  False
							a__    	=  False





							def  _UpperCAmelCase  (       self							:						str      )      ->       Dict:
										SCREAMING_SNAKE_CASE     =       TFMobileViTModelTester(self      )
										SCREAMING_SNAKE_CASE     =       TFMobileViTConfigTester(self							,  config_class=a							,  has_text_modality=a      )





							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       Dict:
										self.config_tester.run_common_tests()





							@unittest.skip(reason="""MobileViT does not use inputs_embeds"""      )
							def  _UpperCAmelCase  (       self							:						Dict      )      ->       Tuple:
										pass





							@unittest.skip(reason="""MobileViT does not support input and output embeddings"""      )
							def  _UpperCAmelCase  (       self							:						List[Any]      )      ->       Tuple:
										pass





							@unittest.skip(reason="""MobileViT does not output attentions"""      )
							def  _UpperCAmelCase  (       self							:						str      )      ->       List[Any]:
										pass





							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       List[Any]:
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs_for_common()

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

													SCREAMING_SNAKE_CASE     =       ["""pixel_values"""]
													self.assertListEqual(arg_names[:1]							,  a      )





							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       List[Any]:
										SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*a      )





							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       Union[str, Any]:
										def check_hidden_states_output(a							:						Dict							,  a							:						Union[str, Any]							,  a							:						Optional[Any]      ):
													SCREAMING_SNAKE_CASE     =       model_class(a      )

													SCREAMING_SNAKE_CASE     =       model(**self._prepare_for_class(a							,  a      )      )

													SCREAMING_SNAKE_CASE     =       outputs.hidden_states

													SCREAMING_SNAKE_CASE     =       5
													self.assertEqual(len(a      )							,  a      )

													# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
													# with the width and height being successively divided by 2.
													SCREAMING_SNAKE_CASE     =       2
													for i in range(len(a      )      ):
																self.assertListEqual(
																    list(hidden_states[i].shape[-2:]      )							,  [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]							,  )
																divisor *= 2

													self.assertEqual(self.model_tester.output_stride							,  divisor // 2      )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE     =       True
													check_hidden_states_output(a							,  a							,  a      )

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

													check_hidden_states_output(a							,  a							,  a      )





							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*a      )





							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       Tuple:
										SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*a      )





							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""      )      ) == 0							,  reason="""TF does not support backprop for grouped convolutions on CPU."""							,  )
							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       Any:
										super().test_dataset_conversion()





							def  _UpperCAmelCase  (       self							:						Union[str, Any]							,  a							:						List[str]							,  a							:						Optional[int]							,  a							:						Any=2E-1							,  a							:						List[str]=2E-1      )      ->       Dict:
										self.assertTrue(np.allclose(a							,  a							,  atol=a							,  rtol=a      )      )





							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""      )      ) == 0							,  reason="""TF does not support backprop for grouped convolutions on CPU."""							,  )
							@slow
							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       str:
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
													if model_class.__name__ != "TFMobileViTModel":
																SCREAMING_SNAKE_CASE     =       model_class(a      )
																if getattr(a							,  """hf_compute_loss"""							,  a      ):
																			super().test_keras_fit()





							def  _UpperCAmelCase  (       self							:						int      )      ->       Optional[Any]:
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
													# set an ignore index to correctly test the masked loss used in
													# `TFMobileViTForSemanticSegmentation`.
													if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
																SCREAMING_SNAKE_CASE     =       5

													SCREAMING_SNAKE_CASE     =       model_class(a      )
													if getattr(a							,  """hf_compute_loss"""							,  a      ):
																# The number of elements in the loss should be the same as the number of elements in the label
																SCREAMING_SNAKE_CASE     =       self._prepare_for_class(inputs_dict.copy()							,  a							,  return_labels=a      )
																SCREAMING_SNAKE_CASE     =       prepared_for_class[
																    sorted(prepared_for_class.keys() - inputs_dict.keys()							,  reverse=a      )[0]
																]
																SCREAMING_SNAKE_CASE     =       added_label.shape.as_list()[:1]

																# Test that model correctly compute the loss with kwargs
																SCREAMING_SNAKE_CASE     =       self._prepare_for_class(inputs_dict.copy()							,  a							,  return_labels=a      )
																SCREAMING_SNAKE_CASE     =       {"""input_ids""", """pixel_values""", """input_features"""}
																SCREAMING_SNAKE_CASE     =       possible_input_names.intersection(set(a      )      ).pop()
																SCREAMING_SNAKE_CASE     =       prepared_for_class.pop(a      )

																SCREAMING_SNAKE_CASE     =       model(a							,  **a      )[0]
																self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

																# Test that model correctly compute the loss when we mask some positions
																SCREAMING_SNAKE_CASE     =       self._prepare_for_class(inputs_dict.copy()							,  a							,  return_labels=a      )
																SCREAMING_SNAKE_CASE     =       {"""input_ids""", """pixel_values""", """input_features"""}
																SCREAMING_SNAKE_CASE     =       possible_input_names.intersection(set(a      )      ).pop()
																SCREAMING_SNAKE_CASE     =       prepared_for_class.pop(a      )
																if "labels" in prepared_for_class:
																			SCREAMING_SNAKE_CASE     =       prepared_for_class["""labels"""].numpy()
																			if len(labels.shape      ) > 1 and labels.shape[1] != 1:
																						# labels[0] = -100
																						SCREAMING_SNAKE_CASE     =       tf.convert_to_tensor(a      )
																						SCREAMING_SNAKE_CASE     =       model(a							,  **a      )[0]
																						self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )
																						self.assertTrue(not np.any(np.isnan(loss.numpy()      )      )      )

                # Test that model correctly compute the loss with a dict
																SCREAMING_SNAKE_CASE     =       self._prepare_for_class(inputs_dict.copy()							,  a							,  return_labels=a      )
																SCREAMING_SNAKE_CASE     =       model(a      )[0]
																self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

																# Test that model correctly compute the loss with a tuple
																SCREAMING_SNAKE_CASE     =       self._prepare_for_class(inputs_dict.copy()							,  a							,  return_labels=a      )

																# Get keys that were added with the _prepare_for_class function
																SCREAMING_SNAKE_CASE     =       prepared_for_class.keys() - inputs_dict.keys()
																SCREAMING_SNAKE_CASE     =       inspect.signature(model.call      ).parameters
																SCREAMING_SNAKE_CASE     =       list(signature.keys()      )

																# Create a dictionary holding the location of the tensors in the tuple
																SCREAMING_SNAKE_CASE     =       {0: input_name}
																for label_key in label_keys:
																			SCREAMING_SNAKE_CASE     =       signature_names.index(a      )
																			SCREAMING_SNAKE_CASE     =       label_key
																SCREAMING_SNAKE_CASE     =       sorted(tuple_index_mapping.items()      )
																# Initialize a list with their default values, update the values and convert to a tuple
																SCREAMING_SNAKE_CASE     =       []

																for name in signature_names:
																			if name != "kwargs":
																						list_input.append(signature[name].default      )

																for index, value in sorted_tuple_index_mapping:
																			SCREAMING_SNAKE_CASE     =       prepared_for_class[value]

																SCREAMING_SNAKE_CASE     =       tuple(a      )

																# Send to model
																SCREAMING_SNAKE_CASE     =       model(tuple_input[:-1]      )[0]

																self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )







							@slow
							def  _UpperCAmelCase  (       self							:						str      )      ->       List[Any]:
										for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE     =       TFMobileViTModel.from_pretrained(a      )
													self.assertIsNotNone(a      )





def        lowerCamelCase_     (     ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
			return image




@require_tf
class       UpperCAmelCase_   ( unittest.TestCase							):




							'''simple docstring'''



							@slow
							def  _UpperCAmelCase  (       self							:						Dict      )      ->       Dict:
										SCREAMING_SNAKE_CASE     =       TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""      )

										SCREAMING_SNAKE_CASE     =       MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""      )
										SCREAMING_SNAKE_CASE     =       prepare_img()
										SCREAMING_SNAKE_CASE     =       image_processor(images=a							,  return_tensors="""tf"""      )

										# forward pass
										SCREAMING_SNAKE_CASE     =       model(**a							,  training=a      )

										# verify the logits
										SCREAMING_SNAKE_CASE     =       tf.TensorShape((1, 1_000)      )
										self.assertEqual(outputs.logits.shape							,  a      )

										SCREAMING_SNAKE_CASE     =       tf.constant([-1.9364, -1.2327, -0.4653]      )

										tf.debugging.assert_near(outputs.logits[0, :3]							,  a							,  atol=1E-4							,  rtol=1E-0_4      )







							@slow
							def  _UpperCAmelCase  (       self							:						int      )      ->       Tuple:
										# `from_pt` will be removed
										SCREAMING_SNAKE_CASE     =       TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""      )

										SCREAMING_SNAKE_CASE     =       MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""      )

										SCREAMING_SNAKE_CASE     =       prepare_img()
										SCREAMING_SNAKE_CASE     =       image_processor(images=a							,  return_tensors="""tf"""      )

										# forward pass
										SCREAMING_SNAKE_CASE     =       model(inputs.pixel_values							,  training=a      )
										SCREAMING_SNAKE_CASE     =       outputs.logits

										# verify the logits
										SCREAMING_SNAKE_CASE     =       tf.TensorShape((1, 21, 32, 32)      )
										self.assertEqual(logits.shape							,  a      )

										SCREAMING_SNAKE_CASE     =       tf.constant(
										    [
										        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
										        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
										        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
										    ]      )

										tf.debugging.assert_near(logits[0, :3, :3, :3]							,  a							,  rtol=1E-4							,  atol=1E-4      )






