





import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, 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 transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
			from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import MobileViTImageProcessor


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      str      =		self.config_class(**self.inputs_dict			)
			self.parent.assertTrue(hasattr(lowerCamelCase_,					'hidden_sizes'			)			)
			self.parent.assertTrue(hasattr(lowerCamelCase_,					'neck_hidden_sizes'			)			)
			self.parent.assertTrue(hasattr(lowerCamelCase_,					'num_attention_heads'			)			)


class       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						Optional[int],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Tuple=13,					lowerCamelCase_:						Tuple=32,					lowerCamelCase_:						Optional[int]=2,					lowerCamelCase_:						Dict=3,					lowerCamelCase_:						Tuple=640,					lowerCamelCase_:						Optional[int]=4,					lowerCamelCase_:						Optional[Any]="silu",					lowerCamelCase_:						Tuple=3,					lowerCamelCase_:						Union[str, Any]=32,					lowerCamelCase_:						List[str]=0.1,					lowerCamelCase_:						Any=0.1,					lowerCamelCase_:						List[Any]=0.1,					lowerCamelCase_:						Optional[Any]=0.0_2,					lowerCamelCase_:						str=True,					lowerCamelCase_:						Optional[Any]=True,					lowerCamelCase_:						List[str]=10,					lowerCamelCase_:						Any=None,					):
			lowercase__					:      Dict      =		parent
			lowercase__					:      Union[str, Any]      =		batch_size
			lowercase__					:      Dict      =		image_size
			lowercase__					:      Optional[int]      =		patch_size
			lowercase__					:      Tuple      =		num_channels
			lowercase__					:      Dict      =		last_hidden_size
			lowercase__					:      Dict      =		num_attention_heads
			lowercase__					:      Optional[Any]      =		hidden_act
			lowercase__					:      Union[str, Any]      =		conv_kernel_size
			lowercase__					:      str      =		output_stride
			lowercase__					:      Union[str, Any]      =		hidden_dropout_prob
			lowercase__					:      Dict      =		attention_probs_dropout_prob
			lowercase__					:      Union[str, Any]      =		classifier_dropout_prob
			lowercase__					:      List[str]      =		use_labels
			lowercase__					:      Dict      =		is_training
			lowercase__					:      List[Any]      =		num_labels
			lowercase__					:      Tuple      =		initializer_range
			lowercase__					:      List[str]      =		scope

	def    snake_case__(						self:						Optional[Any]			):
			lowercase__					:      str      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

			lowercase__					:      List[str]      =		None
			lowercase__					:      Dict      =		None
			if self.use_labels:
					lowercase__					:      Tuple      =		ids_tensor([self.batch_size],					self.num_labels			)
					lowercase__					:      Optional[int]      =		ids_tensor([self.batch_size, self.image_size, self.image_size],					self.num_labels			)

			lowercase__					:      Dict      =		self.get_config()

			return config, pixel_values, labels, pixel_labels

	def    snake_case__(						self:						Dict			):
			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    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						List[Any],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						Union[str, Any]			):
			lowercase__					:      Optional[Any]      =		MobileViTModel(config=lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()
			lowercase__					:      Tuple      =		model(lowerCamelCase_			)
			self.parent.assertEqual(
			    result.last_hidden_state.shape,					(
			        self.batch_size,
			        self.last_hidden_size,
			        self.image_size // self.output_stride,
			        self.image_size // self.output_stride,
			    ),					)

	def    snake_case__(						self:						str,					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						List[Any],					lowerCamelCase_:						str			):
			lowercase__					:      Optional[int]      =		self.num_labels
			lowercase__					:      Optional[int]      =		MobileViTForImageClassification(lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()
			lowercase__					:      Union[str, Any]      =		model(lowerCamelCase_,					labels=lowerCamelCase_			)
			self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)			)

	def    snake_case__(						self:						int,					lowerCamelCase_:						Any,					lowerCamelCase_:						Any,					lowerCamelCase_:						Any,					lowerCamelCase_:						Optional[Any]			):
			lowercase__					:      int      =		self.num_labels
			lowercase__					:      Any      =		MobileViTForSemanticSegmentation(lowerCamelCase_			)
			model.to(lowerCamelCase_			)
			model.eval()
			lowercase__					:      Any      =		model(lowerCamelCase_			)
			self.parent.assertEqual(
			    result.logits.shape,					(
			        self.batch_size,
			        self.num_labels,
			        self.image_size // self.output_stride,
			        self.image_size // self.output_stride,
			    ),					)
			lowercase__					:      List[str]      =		model(lowerCamelCase_,					labels=lowerCamelCase_			)
			self.parent.assertEqual(
			    result.logits.shape,					(
			        self.batch_size,
			        self.num_labels,
			        self.image_size // self.output_stride,
			        self.image_size // self.output_stride,
			    ),					)





	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      Any      =		self.prepare_config_and_inputs()
			lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      Union[str, Any]      =		config_and_inputs
			lowercase__					:      Dict      =		{'pixel_values': pixel_values}
			return config, inputs_dict


@require_torch
class       __lowerCAmelCase					(       _UpperCamelCase   ,    _UpperCamelCase   ,    unittest.TestCase      ):


	'''simple docstring'''





	_A													=				(
	    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
	    if is_torch_available()
	    else ()
	)
	_A													=				(
	    {
	        "feature-extraction": MobileViTModel,
	        "image-classification": MobileViTForImageClassification,
	        "image-segmentation": MobileViTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	_A													=				False
	_A													=				False
	_A													=				False
	_A													=				False

	def    snake_case__(						self:						int			):
			lowercase__					:      Optional[Any]      =		MobileViTModelTester(self			)
			lowercase__					:      int      =		MobileViTConfigTester(self,					config_class=lowerCamelCase_,					has_text_modality=lowerCamelCase_			)

	def    snake_case__(						self:						Optional[int]			):
			self.config_tester.run_common_tests()

	@unittest.skip(reason='MobileViT does not use inputs_embeds'			)
	def    snake_case__(						self:						Optional[int]			):
			pass

	@unittest.skip(reason='MobileViT does not support input and output embeddings'			)
	def    snake_case__(						self:						Any			):
			pass

	@unittest.skip(reason='MobileViT does not output attentions'			)
	def    snake_case__(						self:						Dict			):
			pass

	def    snake_case__(						self:						List[str]			):
			lowercase__   ,				lowercase__					:      List[Any]      =		self.model_tester.prepare_config_and_inputs_for_common()

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

					lowercase__					:      List[str]      =		['pixel_values']
					self.assertListEqual(arg_names[:1],					lowerCamelCase_			)

	@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
	def    snake_case__(						self:						str			):
			pass

	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      Union[str, Any]      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*lowerCamelCase_			)

	def    snake_case__(						self:						int			):
			def check_hidden_states_output(lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Any,					lowerCamelCase_:						List[str]			):
					lowercase__					:      Tuple      =		model_class(lowerCamelCase_			)
					model.to(lowerCamelCase_			)
					model.eval()

					with torch.no_grad():
							lowercase__					:      Optional[int]      =		model(**self._prepare_for_class(lowerCamelCase_,					lowerCamelCase_			)			)

					lowercase__					:      Optional[Any]      =		outputs.hidden_states

					lowercase__					:      List[Any]      =		5
					self.assertEqual(len(lowerCamelCase_			),					lowerCamelCase_			)

					# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
					# with the width and height being successively divided by 2.
					lowercase__					:      int      =		2
					for i in range(len(lowerCamelCase_			)			):
							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			)

			lowercase__   ,				lowercase__					:      List[str]      =		self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowercase__					:      Union[str, Any]      =		True
					check_hidden_states_output(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					lowercase__					:      List[str]      =		True

					check_hidden_states_output(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			)

	def    snake_case__(						self:						List[str]			):
			lowercase__					:      Tuple      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_			)

	def    snake_case__(						self:						int			):
			lowercase__					:      List[str]      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase_			)





	@slow
	def    snake_case__(						self:						Union[str, Any]			):
			for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowercase__					:      Union[str, Any]      =		MobileViTModel.from_pretrained(lowerCamelCase_			)
					self.assertIsNotNone(lowerCamelCase_			)


def       SCREAMING_SNAKE_CASE__  (       )   ->							Tuple:




		'''simple docstring'''
		lowercase__					:      Union[str, Any]      =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
		return image





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


	'''simple docstring'''





	@cached_property
	def    snake_case__(						self:						Optional[int]			):
			return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'			) if is_vision_available() else None

	@slow
	def    snake_case__(						self:						Any			):
			lowercase__					:      Optional[int]      =		MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'			).to(lowerCamelCase_			)

			lowercase__					:      Union[str, Any]      =		self.default_image_processor
			lowercase__					:      Union[str, Any]      =		prepare_img()
			lowercase__					:      List[str]      =		image_processor(images=lowerCamelCase_,					return_tensors='pt'			).to(lowerCamelCase_			)

			# forward pass
			with torch.no_grad():
					lowercase__					:      Any      =		model(**lowerCamelCase_			)

			# verify the logits
			lowercase__					:      List[str]      =		torch.Size((1, 1000)			)
			self.assertEqual(outputs.logits.shape,					lowerCamelCase_			)

			lowercase__					:      List[Any]      =		torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]			).to(lowerCamelCase_			)

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

	@slow
	def    snake_case__(						self:						Any			):
			lowercase__					:      str      =		MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'			)
			lowercase__					:      List[str]      =		model.to(lowerCamelCase_			)

			lowercase__					:      int      =		MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'			)

			lowercase__					:      int      =		prepare_img()
			lowercase__					:      List[str]      =		image_processor(images=lowerCamelCase_,					return_tensors='pt'			).to(lowerCamelCase_			)

			# forward pass
			with torch.no_grad():
					lowercase__					:      Dict      =		model(**lowerCamelCase_			)
			lowercase__					:      Union[str, Any]      =		outputs.logits

			# verify the logits
			lowercase__					:      Union[str, Any]      =		torch.Size((1, 21, 32, 32)			)
			self.assertEqual(logits.shape,					lowerCamelCase_			)

			lowercase__					:      int      =		torch.tensor(
			    [
			        [[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
			        [[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
			        [[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
			    ],					device=lowerCamelCase_,					)

			self.assertTrue(torch.allclose(logits[0, :3, :3, :3],					lowerCamelCase_,					atol=1E-4			)			)





	@slow
	def    snake_case__(						self:						Optional[Any]			):
			lowercase__					:      Optional[int]      =		MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'			)
			lowercase__					:      Union[str, Any]      =		model.to(lowerCamelCase_			)

			lowercase__					:      Union[str, Any]      =		MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'			)

			lowercase__					:      List[str]      =		prepare_img()
			lowercase__					:      Any      =		image_processor(images=lowerCamelCase_,					return_tensors='pt'			).to(lowerCamelCase_			)

			# forward pass
			with torch.no_grad():
					lowercase__					:      Dict      =		model(**lowerCamelCase_			)

			lowercase__					:      List[Any]      =		outputs.logits.detach().cpu()

			lowercase__					:      Optional[int]      =		image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase_,					target_sizes=[(50, 60)]			)
			lowercase__					:      List[str]      =		torch.Size((50, 60)			)
			self.assertEqual(segmentation[0].shape,					lowerCamelCase_			)

			lowercase__					:      Any      =		image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase_			)
			lowercase__					:      List[Any]      =		torch.Size((32, 32)			)
			self.assertEqual(segmentation[0].shape,					lowerCamelCase_			)









import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel


def       SCREAMING_SNAKE_CASE__  (       )   ->							int:




		'''simple docstring'''
		lowercase__					:      str      =		argparse.ArgumentParser()
		parser.add_argument(
		    '-m'   ,    '--pretrained_model_name_or_path'   ,    type=_lowercase   ,    default=_lowercase   ,    required=_lowercase   ,    help='Path to pretrained model or model identifier from huggingface.co/models.'   ,    )
		parser.add_argument(
		    '-c'   ,    '--caption'   ,    type=_lowercase   ,    default='robotic cat with wings'   ,    help='Text used to generate images.'   ,    )
		parser.add_argument(
		    '-n'   ,    '--images_num'   ,    type=_lowercase   ,    default=4   ,    help='How much images to generate.'   ,    )
		parser.add_argument(
		    '-s'   ,    '--seed'   ,    type=_lowercase   ,    default=42   ,    help='Seed for random process.'   ,    )
		parser.add_argument(
		    '-ci'   ,    '--cuda_id'   ,    type=_lowercase   ,    default=0   ,    help='cuda_id.'   ,    )
		lowercase__					:      Any      =		parser.parse_args()
		return args


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Union[str, Any]   ,    _lowercase     : Optional[int]   ,    _lowercase     : Union[str, Any]			)   ->							str:




		'''simple docstring'''
		if not len(_lowercase			) == rows * cols:
				raise ValueError('The specified number of rows and columns are not correct.'			)

		lowercase__   ,				lowercase__					:      Any      =		imgs[0].size
		lowercase__					:      int      =		Image.new('RGB'   ,    size=(cols * w, rows * h)			)
		lowercase__   ,				lowercase__					:      Any      =		grid.size

		for i, img in enumerate(_lowercase			):
				grid.paste(_lowercase   ,    box=(i % cols * w, i // cols * h)			)
		return grid


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]   ,    _lowercase     : Tuple="robotic cat with wings"   ,    _lowercase     : Optional[Any]=7.5   ,    _lowercase     : int=50   ,    _lowercase     : str=1   ,    _lowercase     : Any=42   ,    )   ->							str:




		'''simple docstring'''
		lowercase__					:      int      =		torch.Generator(pipeline.device			).manual_seed(_lowercase			)
		lowercase__					:      Any      =		pipeline(
		    _lowercase   ,    guidance_scale=_lowercase   ,    num_inference_steps=_lowercase   ,    generator=_lowercase   ,    num_images_per_prompt=_lowercase   ,    ).images
		lowercase__					:      Dict      =		int(math.sqrt(_lowercase			)			)
		lowercase__					:      int      =		image_grid(_lowercase   ,    rows=_rows   ,    cols=num_images_per_prompt // _rows			)
		return grid, images


__UpperCamelCase:	int = parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase:	Optional[int] = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
__UpperCamelCase:	List[str] = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
__UpperCamelCase:	int = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
__UpperCamelCase:	int = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

__UpperCamelCase:	Tuple = StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase:	List[str] = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
			__UpperCamelCase:	Dict = load(args.pretrained_model_name_or_path, model=unet)
			unet.eval()
			setattr(pipeline, """unet""", unet)
else:
			__UpperCamelCase:	Dict = unet.to(torch.device("""cuda""", args.cuda_id))
__UpperCamelCase:	Optional[int] = pipeline.to(unet.device)
__UpperCamelCase,	__UpperCamelCase:	Union[str, Any] = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
__UpperCamelCase:	List[str] = os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
			image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))


