


import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
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 (
	    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
	    MODEL_MAPPING,
	    LevitForImageClassification,
	    LevitForImageClassificationWithTeacher,
	    LevitModel,
	)
	from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import LevitImageProcessor





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  self.config_class(**self.inputs_dict							)
						self.parent.assertTrue(hasattr(__a						,    'hidden_sizes'							)							)
						self.parent.assertTrue(hasattr(__a						,    'num_attention_heads'							)							)





class 							__UpperCamelCase    :




	def __init__(       self						,    __a						,    __a=13						,    __a=64						,    __a=3						,    __a=3						,    __a=2						,    __a=1						,    __a=16						,    __a=[128, 256, 384]						,    __a=[4, 6, 8]						,    __a=[2, 3, 4]						,    __a=[16, 16, 16]						,    __a=0						,    __a=[2, 2, 2]						,    __a=[2, 2, 2]						,    __a=0.02						,    __a=True						,    __a=True						,    __a=2						,    ):

						'''simple docstring'''





						__a      :	List[Any]    =  parent
						__a      :	Dict    =  batch_size
						__a      :	List[Any]    =  image_size
						__a      :	Dict    =  num_channels
						__a      :	Optional[Any]    =  kernel_size
						__a      :	str    =  stride
						__a      :	Optional[int]    =  padding
						__a      :	Union[str, Any]    =  hidden_sizes
						__a      :	Any    =  num_attention_heads
						__a      :	List[Any]    =  depths
						__a      :	Optional[Any]    =  key_dim
						__a      :	Optional[int]    =  drop_path_rate
						__a      :	Union[str, Any]    =  patch_size
						__a      :	Optional[Any]    =  attention_ratio
						__a      :	Any    =  mlp_ratio
						__a      :	Any    =  initializer_range
						__a      :	Union[str, Any]    =  [
						    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
						    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
						]
						__a      :	Optional[Any]    =  is_training
						__a      :	List[str]    =  use_labels
						__a      :	Any    =  num_labels
						__a      :	List[str]    =  initializer_range




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	int    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

						__a      :	Dict    =  None
						if self.use_labels:
											__a      :	Any    =  ids_tensor([self.batch_size]						,    self.num_labels							)

						__a      :	List[str]    =  self.get_config()
						return config, pixel_values, labels




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return LevitConfig(
						    image_size=self.image_size						,    num_channels=self.num_channels						,    kernel_size=self.kernel_size						,    stride=self.stride						,    padding=self.padding						,    patch_size=self.patch_size						,    hidden_sizes=self.hidden_sizes						,    num_attention_heads=self.num_attention_heads						,    depths=self.depths						,    key_dim=self.key_dim						,    drop_path_rate=self.drop_path_rate						,    mlp_ratio=self.mlp_ratio						,    attention_ratio=self.attention_ratio						,    initializer_range=self.initializer_range						,    down_ops=self.down_ops						,    )




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a							):

						'''simple docstring'''





						__a      :	List[str]    =  LevitModel(config=__a							)
						model.to(__a							)
						model.eval()
						__a      :	List[str]    =  model(__a							)
						__a      :	List[Any]    =  (self.image_size, self.image_size)
						__a      :	Optional[Any]    =  image_size[0], image_size[1]
						for _ in range(4							):
											__a      :	str    =  floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
											__a      :	Optional[int]    =  floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
						self.parent.assertEqual(
						    result.last_hidden_state.shape						,    (self.batch_size, ceil(height / 4							) * ceil(width / 4							), self.hidden_sizes[-1])						,    )




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a							):

						'''simple docstring'''





						__a      :	Tuple    =  self.num_labels
						__a      :	Any    =  LevitForImageClassification(__a							)
						model.to(__a							)
						model.eval()
						__a      :	Union[str, Any]    =  model(__a						,    labels=__a							)
						self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_labels)							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  self.prepare_config_and_inputs()
						__a      :	Any    =  config_and_inputs
						__a      :	List[str]    =  {'pixel_values': pixel_values}
						return config, inputs_dict





@require_torch
class 							__UpperCamelCase    (		lowerCAmelCase_		,				lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   (
	    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
	    if is_torch_available()
	    else ()
	)
	A_           =   (
	    {
	        "feature-extraction": LevitModel,
	        "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
	    }
	    if is_torch_available()
	    else {}
	)

	A_           =   False
	A_           =   False
	A_           =   False
	A_           =   False
	A_           =   False




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[Any]    =  LevitModelTester(self							)
						__a      :	List[Any]    =  ConfigTester(self						,    config_class=__a						,    has_text_modality=__a						,    hidden_size=37							)




	def 				__UpperCAmelCase							(       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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return




	@unittest.skip(reason='Levit does not use inputs_embeds'							)
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass




	@unittest.skip(reason='Levit does not support input and output embeddings'							)
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass




	@unittest.skip(reason='Levit does not output attentions'							)
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						pass




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

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

											__a      :	Optional[int]    =  ['pixel_values']
											self.assertListEqual(arg_names[:1]						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						def check_hidden_states_output(__a						,    __a						,    __a							):
											__a      :	Tuple    =  model_class(__a							)
											model.to(__a							)
											model.eval()

											with torch.no_grad():
																__a      :	Optional[Any]    =  model(**self._prepare_for_class(__a						,    __a							)							)

											__a      :	List[Any]    =  outputs.hidden_states

											__a      :	List[Any]    =  len(self.model_tester.depths							) + 1
											self.assertEqual(len(__a							)						,    __a							)

											__a      :	Any    =  (self.model_tester.image_size, self.model_tester.image_size)
											__a      :	str    =  image_size[0], image_size[1]
											for _ in range(4							):
																__a      :	int    =  floor(
																    (
																        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																        / self.model_tester.stride
																    )
																    + 1							)
																__a      :	str    =  floor(
																    (
																        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																        / self.model_tester.stride
																    )
																    + 1							)
											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-2:]							)						,    [
											        height * width,
											        self.model_tester.hidden_sizes[0],
											    ]						,    )

						__a      :	str    =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											__a      :	List[Any]    =  True
											check_hidden_states_output(__a						,    __a						,    __a							)

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

											check_hidden_states_output(__a						,    __a						,    __a							)




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

						'''simple docstring'''





						pass




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a=False							):

						'''simple docstring'''





						__a      :	Any    =  super()._prepare_for_class(__a						,    __a						,    return_labels=__a							)

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

						return inputs_dict




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Dict    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						if not self.model_tester.is_training:
											return

						__a      :	Optional[Any]    =  self.model_tester.prepare_config_and_inputs_for_common()
						__a      :	Any    =  True

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	int    =  self.model_tester.prepare_config_and_inputs_for_common()
						if not self.model_tester.is_training:
											return

						__a      :	Optional[Any]    =  False
						__a      :	Tuple    =  True

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.model_tester.prepare_config_and_inputs_for_common()

						__a      :	Dict    =  [
						    {'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							),
											    ]
											    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
											):
																continue

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

																					__a      :	List[str]    =  model_class(__a							)
																					model.to(__a							)
																					model.train()

																					__a      :	List[Any]    =  self._prepare_for_class(__a						,    __a						,    return_labels=__a							)

																					if problem_type["num_labels"] > 1:
																										__a      :	Tuple    =  inputs['labels'].unsqueeze(1							).repeat(1						,    problem_type['num_labels']							)

																					__a      :	Union[str, 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      :	Optional[int]    =  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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__a      :	Optional[Any]    =  LevitModel.from_pretrained(__a							)
											self.assertIsNotNone(__a							)






def   lowerCamelCase							():
					__a      :	Optional[int]    =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
					return image





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




	@cached_property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)





	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
						    __a							)

						__a      :	Dict    =  self.default_image_processor
						__a      :	int    =  prepare_img()
						__a      :	Dict    =  image_processor(images=__a						,    return_tensors='pt'							).to(__a							)

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

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

						__a      :	Dict    =  torch.tensor([1.0448, -0.3745, -1.8317]							).to(__a							)

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







'''simple docstring'''



import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

__lowercase     :						str	=     {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
    ' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'
}






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str = "dhaka"						,				_SCREAMING_SNAKE_CASE				:       int = 5       ):
					__a      :	Optional[Any]    =  min(_SCREAMING_SNAKE_CASE						,				50       )  # Prevent abuse!
					__a      :	Optional[Any]    =  {
					    'q': query,
					    'tbm': 'isch',
					    'hl': 'en',
					    'ijn': '0',
					}

					__a      :	Tuple    =  requests.get('https://www.google.com/search'						,				params=_SCREAMING_SNAKE_CASE						,				headers=_SCREAMING_SNAKE_CASE       )
					__a      :	Dict    =  BeautifulSoup(html.text						,				'html.parser'       )
					__a      :	List[str]    =  ''.join(
					    re.findall(r'AF_initDataCallback\(([^<]+)\);'						,				str(soup.select('script'       )       )       )       )

					__a      :	Optional[Any]    =  json.dumps(_SCREAMING_SNAKE_CASE       )
					__a      :	List[str]    =  json.loads(_SCREAMING_SNAKE_CASE       )

					__a      :	List[Any]    =  re.findall(
					    r'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",'						,				_SCREAMING_SNAKE_CASE						,				)
					if not matched_google_image_data:
										return 0

					__a      :	Tuple    =  re.sub(
					    r'\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]'						,				''						,				str(_SCREAMING_SNAKE_CASE       )						,				)

					__a      :	Optional[Any]    =  re.findall(
					    r'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]'						,				_SCREAMING_SNAKE_CASE						,				)
					for index, fixed_full_res_image in enumerate(_SCREAMING_SNAKE_CASE       ):
										if index >= max_images:
															return index
										__a      :	List[str]    =  bytes(_SCREAMING_SNAKE_CASE						,				'ascii'       ).decode(
										    'unicode-escape'       )
										__a      :	Tuple    =  bytes(_SCREAMING_SNAKE_CASE						,				'ascii'       ).decode(
										    'unicode-escape'       )
										__a      :	Dict    =  urllib.request.build_opener()
										__a      :	Union[str, Any]    =  [
										    (
										        'User-Agent',
										        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
										        ' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582',
										    )
										]
										urllib.request.install_opener(_SCREAMING_SNAKE_CASE       )
										__a      :	List[Any]    =  F"""query_{query.replace(" "						,				"_"       )}"""
										if not os.path.exists(_SCREAMING_SNAKE_CASE       ):
															os.makedirs(_SCREAMING_SNAKE_CASE       )
										urllib.request.urlretrieve(  # noqa: S310
										    _SCREAMING_SNAKE_CASE						,				F"""{path_name}/original_size_img_{index}.jpg"""       )
					return index


if __name__ == "__main__":
	try:
		__lowercase     :						Optional[int]	=     download_images_from_google_query(sys.argv[1])
		print(f'''{image_count} images were downloaded to disk.''')
	except IndexError:
		print('Please provide a search term.')
		raise



