

import inspect
import unittest

from transformers import MobileNetVaConfig
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 MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
					from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import MobileNetVaImageProcessor

class 			a					(		UpperCAmelCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self.config_class(**self.inputs_dict		)
										self.parent.assertTrue(hasattr(A_  ,   "tf_padding"		)		)
										self.parent.assertTrue(hasattr(A_  ,   "depth_multiplier"		)		)







class 			a					:


						def __init__(    self  ,   A_  ,   A_=13  ,   A_=3  ,   A_=32  ,   A_=0.25  ,   A_=8  ,   A_=8  ,   A_=6  ,   A_=32  ,   A_=True  ,   A_=True  ,   A_=True  ,   A_="relu6"  ,   A_=1280  ,   A_=0.1  ,   A_=0.02  ,   A_=True  ,   A_=True  ,   A_=10  ,   A_=None  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      parent
										_UpperCAmelCase    :						Tuple							=      batch_size
										_UpperCAmelCase    :						Dict							=      num_channels
										_UpperCAmelCase    :						int							=      image_size
										_UpperCAmelCase    :						Union[str, Any]							=      depth_multiplier
										_UpperCAmelCase    :						Optional[int]							=      depth_divisible_by
										_UpperCAmelCase    :						Optional[Any]							=      min_depth
										_UpperCAmelCase    :						List[Any]							=      expand_ratio
										_UpperCAmelCase    :						List[Any]							=      tf_padding
										_UpperCAmelCase    :						List[str]							=      output_stride
										_UpperCAmelCase    :						List[Any]							=      first_layer_is_expansion
										_UpperCAmelCase    :						Tuple							=      finegrained_output
										_UpperCAmelCase    :						Any							=      hidden_act
										_UpperCAmelCase    :						List[Any]							=      last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier		)
										_UpperCAmelCase    :						Optional[int]							=      classifier_dropout_prob
										_UpperCAmelCase    :						List[Any]							=      use_labels
										_UpperCAmelCase    :						Tuple							=      is_training
										_UpperCAmelCase    :						List[str]							=      num_labels
										_UpperCAmelCase    :						int							=      initializer_range
										_UpperCAmelCase    :						Optional[Any]							=      scope


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

										_UpperCAmelCase    :						Any							=      self.get_config()

										return config, pixel_values, labels, pixel_labels


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return MobileNetVaConfig(
										    num_channels=self.num_channels  ,   image_size=self.image_size  ,   depth_multiplier=self.depth_multiplier  ,   depth_divisible_by=self.depth_divisible_by  ,   min_depth=self.min_depth  ,   expand_ratio=self.expand_ratio  ,   output_stride=self.output_stride  ,   first_layer_is_expansion=self.first_layer_is_expansion  ,   finegrained_output=self.finegrained_output  ,   hidden_act=self.hidden_act  ,   tf_padding=self.tf_padding  ,   classifier_dropout_prob=self.classifier_dropout_prob  ,   initializer_range=self.initializer_range  ,   )


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      MobileNetVaModel(config=A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase    :						Optional[int]							=      model(A_		)
										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,
										    )  ,   )
										self.parent.assertEqual(
										    result.pooler_output.shape  ,   (self.batch_size, self.last_hidden_size)  ,   )


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.num_labels
										_UpperCAmelCase    :						Optional[Any]							=      MobileNetVaForImageClassification(A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase    :						int							=      model(A_  ,   labels=A_		)
										self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.num_labels)		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.num_labels
										_UpperCAmelCase    :						int							=      MobileNetVaForSemanticSegmentation(A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase    :						Dict							=      model(A_		)
										self.parent.assertEqual(
										    result.logits.shape  ,   (
										        self.batch_size,
										        self.num_labels,
										        self.image_size // self.output_stride,
										        self.image_size // self.output_stride,
										    )  ,   )
										_UpperCAmelCase    :						List[str]							=      model(A_  ,   labels=A_		)
										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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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







@require_torch
class 			a					(		UpperCAmelCase							,	UpperCAmelCase							,	unittest.TestCase			):
						_lowercase        =     (
						    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						_lowercase        =     (
						    {
						        "feature-extraction": MobileNetVaModel,
						        "image-classification": MobileNetVaForImageClassification,
						        "image-segmentation": MobileNetVaForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						_lowercase        =     False
						_lowercase        =     False
						_lowercase        =     False
						_lowercase        =     False


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      MobileNetVaModelTester(self		)
										_UpperCAmelCase    :						int							=      MobileNetVaConfigTester(self  ,   config_class=A_  ,   has_text_modality=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						@unittest.skip(reason="MobileNetV2 does not use inputs_embeds"		)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						@unittest.skip(reason="MobileNetV2 does not support input and output embeddings"		)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						@unittest.skip(reason="MobileNetV2 does not output attentions"		)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self.model_tester.prepare_config_and_inputs_for_common()

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

														_UpperCAmelCase    :						List[Any]							=      ["pixel_values"]
														self.assertListEqual(arg_names[:1]  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										def check_hidden_states_output(A_  ,   A_  ,   A_		):
														_UpperCAmelCase    :						Any							=      model_class(A_		)
														model.to(A_		)
														model.eval()

														with torch.no_grad():
																		_UpperCAmelCase    :						Union[str, Any]							=      model(**self._prepare_for_class(A_  ,   A_		)		)

														_UpperCAmelCase    :						str							=      outputs.hidden_states

														_UpperCAmelCase    :						Union[str, Any]							=      16
														self.assertEqual(len(A_		)  ,   A_		)

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

										for model_class in self.all_model_classes:
														_UpperCAmelCase    :						str							=      True
														check_hidden_states_output(A_  ,   A_  ,   A_		)

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

														check_hidden_states_output(A_  ,   A_  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*A_		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						Union[str, Any]							=      MobileNetVaModel.from_pretrained(A_		)
														self.assertIsNotNone(A_		)







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





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


						@cached_property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return (
										    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224"		) if is_vision_available() else None
										)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"		).to(A_		)

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

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase    :						Any							=      model(**A_		)

										# verify the logits
										_UpperCAmelCase    :						int							=      torch.Size((1, 1001)		)
										self.assertEqual(outputs.logits.shape  ,   A_		)

										_UpperCAmelCase    :						str							=      torch.tensor([0.24_45, -1.19_93, 0.19_05]		).to(A_		)

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





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"		)
										_UpperCAmelCase    :						List[str]							=      model.to(A_		)

										_UpperCAmelCase    :						int							=      MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"		)

										_UpperCAmelCase    :						str							=      prepare_img()
										_UpperCAmelCase    :						Optional[int]							=      image_processor(images=A_  ,   return_tensors="pt"		).to(A_		)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase    :						int							=      model(**A_		)
										_UpperCAmelCase    :						Tuple							=      outputs.logits

										# verify the logits
										_UpperCAmelCase    :						str							=      torch.Size((1, 21, 65, 65)		)
										self.assertEqual(logits.shape  ,   A_		)

										_UpperCAmelCase    :						List[str]							=      torch.tensor(
										    [
										        [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
										        [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
										        [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
										    ]  ,   device=A_  ,   )

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





def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					str	)				->    bool:
				_UpperCAmelCase    :						Optional[Any]							=      len(lowerCAmelCase	) + 1
				_UpperCAmelCase    :						Optional[int]							=      len(lowerCAmelCase	) + 1

				# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
				# length i of input_string matches with prefix string of length j of
				# given pattern.
				# "dp" stands for dynamic programming.
				_UpperCAmelCase    :						List[str]							=      [[0 for i in range(lowerCAmelCase	)] for j in range(lowerCAmelCase	)]

				# since string of zero length match pattern of zero length
				_UpperCAmelCase    :						List[Any]							=      1

				# since pattern of zero length will never match with string of non-zero length
				for i in range(1					, lowerCAmelCase	):
								_UpperCAmelCase    :						Dict							=      0

				# since string of zero length will match with pattern where there
				# is at least one * alternatively
				for j in range(1					, lowerCAmelCase	):
								_UpperCAmelCase    :						Tuple							=      dp[0][j - 2] if pattern[j - 1] == "*" else 0

				# now using bottom-up approach to find for all remaining lengths
				for i in range(1					, lowerCAmelCase	):
								for j in range(1					, lowerCAmelCase	):
												if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																_UpperCAmelCase    :						Optional[Any]							=      dp[i - 1][j - 1]

												elif pattern[j - 1] == "*":
																if dp[i][j - 2] == 1:
																				_UpperCAmelCase    :						List[str]							=      1
																elif pattern[j - 2] in (input_string[i - 1], "."):
																				_UpperCAmelCase    :						str							=      dp[i - 1][j]
																else:
																				_UpperCAmelCase    :						int							=      0
												else:
																_UpperCAmelCase    :						List[Any]							=      0

				return bool(dp[-1][-1]	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					# inputing the strings
					# input_string = input("input a string :")
					# pattern = input("input a pattern :")

					SCREAMING_SNAKE_CASE_            = 'aab'
					SCREAMING_SNAKE_CASE_            = 'c*a*b'

					# using function to check whether given string matches the given pattern
					if match_pattern(input_string, pattern):
										print(F'''{input_string} matches the given pattern {pattern}''')
					else:
										print(F'''{input_string} does not match with the given pattern {pattern}''')


