


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 			a      (      snake_case_   ):






		def       lowerCAmelCase_  (  self							)->			Dict:
						'''simple docstring'''


						A__     :							str   				=self.config_class(**self.inputs_dict							)
						self.parent.assertTrue(hasattr(__UpperCamelCase	,     '''hidden_sizes'''							)							)
						self.parent.assertTrue(hasattr(__UpperCamelCase	,     '''neck_hidden_sizes'''							)							)
						self.parent.assertTrue(hasattr(__UpperCamelCase	,     '''num_attention_heads'''							)							)







class 			a      :






		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase=13	,     __UpperCamelCase=32	,     __UpperCamelCase=2	,     __UpperCamelCase=3	,     __UpperCamelCase=6_40	,     __UpperCamelCase=4	,     __UpperCamelCase="silu"	,     __UpperCamelCase=3	,     __UpperCamelCase=32	,     __UpperCamelCase=0.1	,     __UpperCamelCase=0.1	,     __UpperCamelCase=0.1	,     __UpperCamelCase=0.02	,     __UpperCamelCase=True	,     __UpperCamelCase=True	,     __UpperCamelCase=10	,     __UpperCamelCase=None	,     )->			List[Any]:
						'''simple docstring'''


						A__     :							Optional[int]   				=parent
						A__     :							List[str]   				=batch_size
						A__     :							int   				=image_size
						A__     :							Dict   				=patch_size
						A__     :							List[Any]   				=num_channels
						A__     :							Optional[Any]   				=last_hidden_size
						A__     :							Dict   				=num_attention_heads
						A__     :							Optional[Any]   				=hidden_act
						A__     :							int   				=conv_kernel_size
						A__     :							List[Any]   				=output_stride
						A__     :							str   				=hidden_dropout_prob
						A__     :							str   				=attention_probs_dropout_prob
						A__     :							Any   				=classifier_dropout_prob
						A__     :							Dict   				=use_labels
						A__     :							Dict   				=is_training
						A__     :							List[str]   				=num_labels
						A__     :							List[str]   				=initializer_range
						A__     :							Tuple   				=scope






		def       lowerCAmelCase_  (  self							)->			List[str]:
						'''simple docstring'''


						A__     :							Optional[Any]   				=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

						A__     :							List[Any]   				=None
						A__     :							List[Any]   				=None
						if self.use_labels:
										A__     :							Dict   				=ids_tensor([self.batch_size]	,     self.num_labels							)
										A__     :							Union[str, Any]   				=ids_tensor([self.batch_size, self.image_size, self.image_size]	,     self.num_labels							)

						A__     :							Optional[Any]   				=self.get_config()

						return config, pixel_values, labels, pixel_labels






		def       lowerCAmelCase_  (  self							)->			Any:
						'''simple docstring'''


						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       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			Optional[Any]:
						'''simple docstring'''


						A__     :							str   				=TFMobileViTModel(config=__UpperCamelCase							)
						A__     :							Optional[Any]   				=model(__UpperCamelCase	,     training=__UpperCamelCase							)
						A__     :							List[Any]   				=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       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			Dict:
						'''simple docstring'''


						A__     :							int   				=self.num_labels
						A__     :							Any   				=TFMobileViTForImageClassification(__UpperCamelCase							)
						A__     :							Any   				=model(__UpperCamelCase	,     labels=__UpperCamelCase	,     training=__UpperCamelCase							)
						self.parent.assertEqual(result.logits.shape	,     (self.batch_size, self.num_labels)							)






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			Optional[int]:
						'''simple docstring'''


						A__     :							Optional[Any]   				=self.num_labels
						A__     :							int   				=TFMobileViTForSemanticSegmentation(__UpperCamelCase							)
						A__     :							Union[str, Any]   				=self.image_size // self.output_stride

						A__     :							str   				=model(__UpperCamelCase	,     training=__UpperCamelCase							)
						self.parent.assertEqual(
						    result.logits.shape	,     (self.batch_size, self.num_labels, expected_height, expected_width)							)

						A__     :							str   				=model(__UpperCamelCase	,     labels=__UpperCamelCase	,     training=__UpperCamelCase							)
						self.parent.assertEqual(
						    result.logits.shape	,     (self.batch_size, self.num_labels, expected_height, expected_width)							)






		def       lowerCAmelCase_  (  self							)->			List[str]:
						'''simple docstring'''


						A__     :							Dict   				=self.prepare_config_and_inputs()
						A__				,  A__				,  A__				,  A__     :							Dict   				=config_and_inputs
						A__     :							Optional[Any]   				={'''pixel_values''': pixel_values}
						return config, inputs_dict







@require_tf
class 			a      (      snake_case_      ,snake_case_      ,unittest.TestCase   ):
		__lowercase													=		(
		    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
		    if is_tf_available()
		    else ()
		)
		__lowercase													=		(
		    {"""feature-extraction""": TFMobileViTModel, """image-classification""": TFMobileViTForImageClassification}
		    if is_tf_available()
		    else {}
		)

		__lowercase													=		False
		__lowercase													=		False
		__lowercase													=		False
		__lowercase													=		False
		__lowercase													=		False






		def       lowerCAmelCase_  (  self							)->			List[str]:
						'''simple docstring'''


						A__     :							Optional[Any]   				=TFMobileViTModelTester(self							)
						A__     :							Union[str, Any]   				=TFMobileViTConfigTester(self	,     config_class=__UpperCamelCase	,     has_text_modality=__UpperCamelCase							)






		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
						'''simple docstring'''


						self.config_tester.run_common_tests()






		@unittest.skip(reason='''MobileViT does not use inputs_embeds'''							)
		def       lowerCAmelCase_  (  self							)->			Any:
						'''simple docstring'''


						pass






		@unittest.skip(reason='''MobileViT does not support input and output embeddings'''							)
		def       lowerCAmelCase_  (  self							)->			int:
						'''simple docstring'''


						pass






		@unittest.skip(reason='''MobileViT does not output attentions'''							)
		def       lowerCAmelCase_  (  self							)->			List[Any]:
						'''simple docstring'''


						pass






		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
						'''simple docstring'''


						A__				,  A__     :							Any   				=self.model_tester.prepare_config_and_inputs_for_common()

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

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






		def       lowerCAmelCase_  (  self							)->			List[Any]:
						'''simple docstring'''


						A__     :							str   				=self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCamelCase							)






		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
						'''simple docstring'''


						def check_hidden_states_output(__UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							):
										A__     :							Optional[Any]   				=model_class(__UpperCamelCase							)

										A__     :							Union[str, Any]   				=model(**self._prepare_for_class(__UpperCamelCase	,     __UpperCamelCase							)							)

										A__     :							Union[str, Any]   				=outputs.hidden_states

										A__     :							str   				=5
										self.assertEqual(len(__UpperCamelCase							)	,     __UpperCamelCase							)

										# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
										# with the width and height being successively divided by 2.
										A__     :							Optional[Any]   				=2
										for i in range(len(__UpperCamelCase							)							):
														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__     :							Dict   				=self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										A__     :							Optional[int]   				=True
										check_hidden_states_output(__UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)

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

										check_hidden_states_output(__UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)






		def       lowerCAmelCase_  (  self							)->			Optional[int]:
						'''simple docstring'''


						A__     :							Optional[Any]   				=self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase							)






		def       lowerCAmelCase_  (  self							)->			List[Any]:
						'''simple docstring'''


						A__     :							str   				=self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase							)






		@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       lowerCAmelCase_  (  self							)->			Any:
						'''simple docstring'''


						super().test_dataset_conversion()






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase=2E-1	,     __UpperCamelCase=2E-1							)->			List[str]:
						'''simple docstring'''


						self.assertTrue(np.allclose(__UpperCamelCase	,     __UpperCamelCase	,     atol=__UpperCamelCase	,     rtol=__UpperCamelCase							)							)






		@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       lowerCAmelCase_  (  self							)->			Dict:
						'''simple docstring'''


						A__				,  A__     :							List[str]   				=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":
														A__     :							Optional[Any]   				=model_class(__UpperCamelCase							)
														if getattr(__UpperCamelCase	,     '''hf_compute_loss'''	,     __UpperCamelCase							):
																		super().test_keras_fit()






		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
						'''simple docstring'''


						A__				,  A__     :							str   				=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":
														A__     :							Dict   				=5

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

														# Test that model correctly compute the loss with kwargs
														A__     :							List[str]   				=self._prepare_for_class(inputs_dict.copy()	,     __UpperCamelCase	,     return_labels=__UpperCamelCase							)
														A__     :							Union[str, Any]   				={'''input_ids''', '''pixel_values''', '''input_features'''}
														A__     :							Union[str, Any]   				=possible_input_names.intersection(set(__UpperCamelCase							)							).pop()
														A__     :							Any   				=prepared_for_class.pop(__UpperCamelCase							)

														A__     :							Any   				=model(__UpperCamelCase	,     **__UpperCamelCase							)[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
														A__     :							str   				=self._prepare_for_class(inputs_dict.copy()	,     __UpperCamelCase	,     return_labels=__UpperCamelCase							)
														A__     :							Dict   				={'''input_ids''', '''pixel_values''', '''input_features'''}
														A__     :							int   				=possible_input_names.intersection(set(__UpperCamelCase							)							).pop()
														A__     :							Any   				=prepared_for_class.pop(__UpperCamelCase							)
														if "labels" in prepared_for_class:
																		A__     :							Union[str, Any]   				=prepared_for_class['''labels'''].numpy()
																		if len(labels.shape							) > 1 and labels.shape[1] != 1:
																						# labels[0] = -100
																						A__     :							Union[str, Any]   				=tf.convert_to_tensor(__UpperCamelCase							)
																						A__     :							Any   				=model(__UpperCamelCase	,     **__UpperCamelCase							)[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
														A__     :							str   				=self._prepare_for_class(inputs_dict.copy()	,     __UpperCamelCase	,     return_labels=__UpperCamelCase							)
														A__     :							Optional[Any]   				=model(__UpperCamelCase							)[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
														A__     :							Optional[Any]   				=self._prepare_for_class(inputs_dict.copy()	,     __UpperCamelCase	,     return_labels=__UpperCamelCase							)

														# Get keys that were added with the _prepare_for_class function
														A__     :							Dict   				=prepared_for_class.keys() - inputs_dict.keys()
														A__     :							List[Any]   				=inspect.signature(model.call							).parameters
														A__     :							Dict   				=list(signature.keys()							)

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

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

														for index, value in sorted_tuple_index_mapping:
																		A__     :							str   				=prepared_for_class[value]

														A__     :							Optional[Any]   				=tuple(__UpperCamelCase							)

														# Send to model
														A__     :							Optional[Any]   				=model(tuple_input[:-1]							)[0]

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






		@slow
		def       lowerCAmelCase_  (  self							)->			List[str]:
						'''simple docstring'''


						for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										A__     :							Any   				=TFMobileViTModel.from_pretrained(__UpperCamelCase							)
										self.assertIsNotNone(__UpperCamelCase							)







def   SCREAMING_SNAKE_CASE__						(							)			->							Optional[int]:
				A__     :							Union[str, Any]   				=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
				return image




@require_tf
class 			a      (      unittest.TestCase   ):






		@slow
		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
						'''simple docstring'''


						A__     :							Union[str, Any]   				=TFMobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''							)

						A__     :							Optional[int]   				=MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''							)
						A__     :							str   				=prepare_img()
						A__     :							Optional[Any]   				=image_processor(images=__UpperCamelCase	,     return_tensors='''tf'''							)

						# forward pass
						A__     :							Tuple   				=model(**__UpperCamelCase	,     training=__UpperCamelCase							)

						# verify the logits
						A__     :							int   				=tf.TensorShape((1, 10_00)							)
						self.assertEqual(outputs.logits.shape	,     __UpperCamelCase							)

						A__     :							List[Any]   				=tf.constant([-1.9364, -1.2327, -0.4653]							)

						tf.debugging.assert_near(outputs.logits[0, :3]	,     __UpperCamelCase	,     atol=1E-4	,     rtol=1E-04							)






		@slow
		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
						'''simple docstring'''


						A__     :							List[str]   				=TFMobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''							)

						A__     :							List[str]   				=MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''							)

						A__     :							Dict   				=prepare_img()
						A__     :							List[Any]   				=image_processor(images=__UpperCamelCase	,     return_tensors='''tf'''							)

						# forward pass
						A__     :							List[Any]   				=model(inputs.pixel_values	,     training=__UpperCamelCase							)
						A__     :							Tuple   				=outputs.logits

						# verify the logits
						A__     :							Any   				=tf.TensorShape((1, 21, 32, 32)							)
						self.assertEqual(logits.shape	,     __UpperCamelCase							)

						A__     :							Tuple   				=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]	,     __UpperCamelCase	,     rtol=1E-4	,     atol=1E-4							)







def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Dict:
				A__     :							Dict   				=len(snake_case_	)
				for i in range(length - 1	):
								A__     :							List[Any]   				=False
								for j in range(length - 1 - i	):
												if collection[j] > collection[j + 1]:
																A__     :							Dict   				=True
																A__				,  A__     :							Union[str, Any]   				=collection[j + 1], collection[j]
								if not swapped:
												break  # Stop iteration if the collection is sorted.
				return collection


if __name__ == "__main__":
						import doctest
						import time

						doctest.testmod()

						__lowerCamelCase       :       Union[str, Any]				   =			input("Enter numbers separated by a comma:").strip()
						__lowerCamelCase       :       Optional[int]				   =			[int(item) for item in user_input.split(",")]
						__lowerCamelCase       :       Optional[int]				   =			time.process_time()
						print(*bubble_sort(unsorted), sep=",")
						print(F"Processing time: {(time.process_time() - start)%1E9 + 7}")



