





"""simple docstring"""




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  __UpperCamelCase       (		a__		):






							def 					__a     (     self   )			->				List[str]:
								a   :	List[Any]			=       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  __UpperCamelCase       :






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






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Optional[Any]			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

								a   :	Dict			=       self.get_config()

								return config, pixel_values, labels, pixel_labels






							def 					__a     (     self   )			->				List[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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				int:
								a   :	Tuple			=       MobileViTModel(config=lowerCAmelCase__   )
								model.to(lowerCAmelCase__   )
								model.eval()
								a   :	Any			=       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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Dict:
								a   :	Optional[int]			=       self.num_labels
								a   :	Optional[Any]			=       MobileViTForImageClassification(lowerCAmelCase__   )
								model.to(lowerCAmelCase__   )
								model.eval()
								a   :	int			=       model(lowerCAmelCase__   ,      labels=lowerCAmelCase__   )
								self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels)   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Optional[Any]:
								a   :	List[str]			=       self.num_labels
								a   :	List[Any]			=       MobileViTForSemanticSegmentation(lowerCAmelCase__   )
								model.to(lowerCAmelCase__   )
								model.eval()
								a   :	Optional[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,
								    )   ,      )
								a   :	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 					__a     (     self   )			->				str:
								a   :	Tuple			=       self.prepare_config_and_inputs()
								a, a, a, a   :	Any			=       config_and_inputs
								a   :	Dict			=       {"pixel_values": pixel_values}
								return config, inputs_dict


@require_torch
class  __UpperCamelCase       (		a__	, a__	, unittest.TestCase		):
							lowerCamelCase     :      str         =(
							    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)
							lowerCamelCase     :      List[Any]         =(
							    {
							        """feature-extraction""": MobileViTModel,
							        """image-classification""": MobileViTForImageClassification,
							        """image-segmentation""": MobileViTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCamelCase     :      Optional[int]         =False
							lowerCamelCase     :      List[str]         =False
							lowerCamelCase     :      List[str]         =False
							lowerCamelCase     :      Any         =False






							def 					__a     (     self   )			->				Any:
								a   :	Optional[Any]			=       MobileViTModelTester(self   )
								a   :	Union[str, Any]			=       MobileViTConfigTester(self   ,      config_class=lowerCAmelCase__   ,      has_text_modality=lowerCAmelCase__   )






							def 					__a     (     self   )			->				Union[str, Any]:
								self.config_tester.run_common_tests()






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






							@unittest.skip(reason="MobileViT does not support input and output embeddings"   )
							def 					__a     (     self   )			->				int:
								pass






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






							def 					__a     (     self   )			->				Any:
								a, a   :	int			=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									a   :	Union[str, Any]			=       model_class(lowerCAmelCase__   )
									a   :	Dict			=       inspect.signature(model.forward   )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									a   :	int			=       [*signature.parameters.keys()]

									a   :	Union[str, Any]			=       ["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 					__a     (     self   )			->				Union[str, Any]:
								pass






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






							def 					__a     (     self   )			->				str:
								def check_hidden_states_output(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	Optional[int]			=       model_class(lowerCAmelCase__   )
									model.to(lowerCAmelCase__   )
									model.eval()

									with torch.no_grad():
										a   :	int			=       model(**self._prepare_for_class(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

									a   :	Any			=       outputs.hidden_states

									a   :	int			=       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.
									a   :	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   )

								a, a   :	int			=       self.model_tester.prepare_config_and_inputs_for_common()

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

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									a   :	Optional[Any]			=       True

									check_hidden_states_output(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Any			=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	List[Any]			=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__   )






							@slow
							def 					__a     (     self   )			->				Any:
								for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a   :	str			=       MobileViTModel.from_pretrained(lowerCAmelCase__   )
									self.assertIsNotNone(lowerCAmelCase__   )






def    _SCREAMING_SNAKE_CASE	(			)				->Optional[Any]:






	'''simple docstring'''






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







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






							@cached_property
							def 					__a     (     self   )			->				str:
								return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small"   ) if is_vision_available() else None






							@slow
							def 					__a     (     self   )			->				int:
								a   :	List[str]			=       MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"   ).to(lowerCAmelCase__   )

								a   :	Any			=       self.default_image_processor
								a   :	Dict			=       prepare_img()
								a   :	List[str]			=       image_processor(images=lowerCAmelCase__   ,      return_tensors="pt"   ).to(lowerCAmelCase__   )

								# forward pass
								with torch.no_grad():
									a   :	List[str]			=       model(**lowerCAmelCase__   )

								# verify the logits
								a   :	Any			=       torch.Size((1, 1000)   )
								self.assertEqual(outputs.logits.shape   ,      lowerCAmelCase__   )

								a   :	Optional[Any]			=       torch.tensor([-1.9_364, -1.2_327, -0.4_653]   ).to(lowerCAmelCase__   )

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






							@slow
							def 					__a     (     self   )			->				str:
								a   :	Optional[Any]			=       MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"   )
								a   :	int			=       model.to(lowerCAmelCase__   )

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

								a   :	Optional[int]			=       prepare_img()
								a   :	Any			=       image_processor(images=lowerCAmelCase__   ,      return_tensors="pt"   ).to(lowerCAmelCase__   )

								# forward pass
								with torch.no_grad():
									a   :	Tuple			=       model(**lowerCAmelCase__   )
								a   :	Union[str, Any]			=       outputs.logits

								# verify the logits
								a   :	Dict			=       torch.Size((1, 21, 32, 32)   )
								self.assertEqual(logits.shape   ,      lowerCAmelCase__   )

								a   :	List[str]			=       torch.tensor(
								    [
								        [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
								        [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
								        [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
								    ]   ,      device=lowerCAmelCase__   ,      )

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






							@slow
							def 					__a     (     self   )			->				str:
								a   :	Optional[int]			=       MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"   )
								a   :	Dict			=       model.to(lowerCAmelCase__   )

								a   :	Optional[Any]			=       MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"   )

								a   :	Union[str, Any]			=       prepare_img()
								a   :	Tuple			=       image_processor(images=lowerCAmelCase__   ,      return_tensors="pt"   ).to(lowerCAmelCase__   )

								# forward pass
								with torch.no_grad():
									a   :	Tuple			=       model(**lowerCAmelCase__   )

								a   :	Union[str, Any]			=       outputs.logits.detach().cpu()

								a   :	int			=       image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__   ,      target_sizes=[(50, 60)]   )
								a   :	Tuple			=       torch.Size((50, 60)   )
								self.assertEqual(segmentation[0].shape   ,      lowerCAmelCase__   )

								a   :	Tuple			=       image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__   )
								a   :	Optional[int]			=       torch.Size((32, 32)   )
								self.assertEqual(segmentation[0].shape   ,      lowerCAmelCase__   )









"""simple docstring"""




import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					List[str]					, _lowercase		:					Optional[Any]      )				->str:






	'''simple docstring'''






	a   :	Union[str, Any]			=       1.5
	a   :	List[str]			=       int(factor * num_class_images      )
	a   :	Optional[Any]			=       ClipClient(
	    url="https://knn.laion.ai/knn-service"					, indice_name="laion_400m"					, num_images=_lowercase					, aesthetic_weight=0.1      )

	os.makedirs(F"""{class_data_dir}/images"""					, exist_ok=_lowercase      )
	if len(list(Path(F"""{class_data_dir}/images"""      ).iterdir()      )      ) >= num_class_images:
		return

	while True:
		a   :	List[Any]			=       client.query(text=_lowercase      )
		if len(_lowercase      ) >= factor * num_class_images or num_images > 1E4:
			break
		else:
			a   :	Optional[int]			=       int(factor * num_images      )
			a   :	str			=       ClipClient(
			    url="https://knn.laion.ai/knn-service"					, indice_name="laion_400m"					, num_images=_lowercase					, aesthetic_weight=0.1					, )

	a   :	Optional[int]			=       0
	a   :	str			=       0
	a   :	Any			=       tqdm(desc="downloading real regularization images"					, total=_lowercase      )

	with open(F"""{class_data_dir}/caption.txt"""					, "w"      ) as fa, open(F"""{class_data_dir}/urls.txt"""					, "w"      ) as fa, open(
	    F"""{class_data_dir}/images.txt"""					, "w"      ) as fa:
		while total < num_class_images:
			a   :	Optional[Any]			=       class_images[count]
			count += 1
			try:
				a   :	str			=       requests.get(images["url"]      )
				if img.status_code == 200:
					a   :	int			=       Image.open(BytesIO(img.content      )      )
					with open(F"""{class_data_dir}/images/{total}.jpg"""					, "wb"      ) as f:
						f.write(img.content      )
					fa.write(images["caption"] + "\n"      )
					fa.write(images["url"] + "\n"      )
					fa.write(F"""{class_data_dir}/images/{total}.jpg""" + "\n"      )
					total += 1
					pbar.update(1      )
				else:
					continue
			except Exception:
				continue
	return






def    _SCREAMING_SNAKE_CASE	(			)				->Dict:






	'''simple docstring'''






	a   :	Optional[int]			=       argparse.ArgumentParser(""					, add_help=_lowercase      )
	parser.add_argument("--class_prompt"					, help="text prompt to retrieve images"					, required=_lowercase					, type=_lowercase      )
	parser.add_argument("--class_data_dir"					, help="path to save images"					, required=_lowercase					, type=_lowercase      )
	parser.add_argument("--num_class_images"					, help="number of images to download"					, default=200					, type=_lowercase      )
	return parser.parse_args()


if __name__ == "__main__":
		a		:				List[Any]  			=    parse_args()
		retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)


