
import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

			from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
			from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor





class       _lowerCAmelCase   :




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=13							,							_UpperCamelCase=30							,							_UpperCamelCase=2							,							_UpperCamelCase=3							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=32							,							_UpperCamelCase=5							,							_UpperCamelCase=4							,							_UpperCamelCase=37							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=10							,							_UpperCamelCase=0.02							,							_UpperCamelCase=None							,							_UpperCamelCase=2							,							)							-> List[Any]:
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      image_size
							lowerCAmelCase_         =      patch_size
							lowerCAmelCase_         =      num_channels
							lowerCAmelCase_         =      is_training
							lowerCAmelCase_         =      use_labels
							lowerCAmelCase_         =      hidden_size
							lowerCAmelCase_         =      num_hidden_layers
							lowerCAmelCase_         =      num_attention_heads
							lowerCAmelCase_         =      intermediate_size
							lowerCAmelCase_         =      hidden_act
							lowerCAmelCase_         =      hidden_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      type_sequence_label_size
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      scope
							lowerCAmelCase_         =      encoder_stride

							# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							lowerCAmelCase_         =      (image_size // patch_size) ** 2
							lowerCAmelCase_         =      num_patches + 1




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

							lowerCAmelCase_         =      None
							if self.use_labels:
										lowerCAmelCase_         =      ids_tensor([self.batch_size]							,							self.type_sequence_label_size	)

							lowerCAmelCase_         =      self.get_config()

							return config, pixel_values, labels




				def 	__a		(   self	)							-> List[Any]:
							return ViTConfig(
							    image_size=self.image_size							,							patch_size=self.patch_size							,							num_channels=self.num_channels							,							hidden_size=self.hidden_size							,							num_hidden_layers=self.num_hidden_layers							,							num_attention_heads=self.num_attention_heads							,							intermediate_size=self.intermediate_size							,							hidden_act=self.hidden_act							,							hidden_dropout_prob=self.hidden_dropout_prob							,							attention_probs_dropout_prob=self.attention_probs_dropout_prob							,							is_decoder=_a							,							initializer_range=self.initializer_range							,							encoder_stride=self.encoder_stride							,							)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Any:
							lowerCAmelCase_         =      ViTModel(config=_a	)
							model.to(_a	)
							model.eval()
							lowerCAmelCase_         =      model(_a	)
							self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Dict:
							lowerCAmelCase_         =      ViTForMaskedImageModeling(config=_a	)
							model.to(_a	)
							model.eval()
							lowerCAmelCase_         =      model(_a	)
							self.parent.assertEqual(
							    result.reconstruction.shape							,							(self.batch_size, self.num_channels, self.image_size, self.image_size)	)

							# test greyscale images
							lowerCAmelCase_         =      1
							lowerCAmelCase_         =      ViTForMaskedImageModeling(_a	)
							model.to(_a	)
							model.eval()

							lowerCAmelCase_         =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
							lowerCAmelCase_         =      model(_a	)
							self.parent.assertEqual(result.reconstruction.shape							,							(self.batch_size, 1, self.image_size, self.image_size)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> List[str]:
							lowerCAmelCase_         =      self.type_sequence_label_size
							lowerCAmelCase_         =      ViTForImageClassification(_a	)
							model.to(_a	)
							model.eval()
							lowerCAmelCase_         =      model(_a							,							labels=_a	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.type_sequence_label_size)	)

							# test greyscale images
							lowerCAmelCase_         =      1
							lowerCAmelCase_         =      ViTForImageClassification(_a	)
							model.to(_a	)
							model.eval()

							lowerCAmelCase_         =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
							lowerCAmelCase_         =      model(_a	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.type_sequence_label_size)	)




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      self.prepare_config_and_inputs()
							(
							    lowerCAmelCase_   
							)         =      config_and_inputs
							lowerCAmelCase_         =      {'''pixel_values''': pixel_values}
							return config, inputs_dict





@require_torch
class       _lowerCAmelCase   (      __lowercase   , __lowercase   , unittest.TestCase			):
				_lowercase     						=(
				    (
				        ViTModel,
				        ViTForImageClassification,
				        ViTForMaskedImageModeling,
				    )
				    if is_torch_available()
				    else ()
				)
				_lowercase     						=(
				    {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
				    if is_torch_available()
				    else {}
				)
				_lowercase     						=True

				_lowercase     						=False
				_lowercase     						=False
				_lowercase     						=False




				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      ViTModelTester(self	)
							lowerCAmelCase_         =      ConfigTester(self							,							config_class=_a							,							has_text_modality=_a							,							hidden_size=37	)




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




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




				def 	__a		(   self	)							-> Tuple:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowerCAmelCase_         =      model_class(_a	)
										self.assertIsInstance(model.get_input_embeddings()							,							(nn.Module)	)
										lowerCAmelCase_         =      model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(_a							,							nn.Linear	)	)




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs_for_common()

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

										lowerCAmelCase_         =      ['''pixel_values''']
										self.assertListEqual(arg_names[:1]							,							_a	)




				def 	__a		(   self	)							-> Optional[Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_a	)




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*_a	)




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_a	)




				@slow
				def 	__a		(   self	)							-> Union[str, Any]:
							for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase_         =      ViTModel.from_pretrained(_a	)
										self.assertIsNotNone(_a	)






def   lowerCamelCase__							(							):




			"""simple docstring"""


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





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




				@cached_property
				def 	__a		(   self	)							-> Union[str, Any]:
							return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"	) if is_vision_available() else None




				@slow
				def 	__a		(   self	)							-> Tuple:
							lowerCAmelCase_         =      ViTForImageClassification.from_pretrained("google/vit-base-patch16-224"	).to(_a	)

							lowerCAmelCase_         =      self.default_image_processor
							lowerCAmelCase_         =      prepare_img()
							lowerCAmelCase_         =      image_processor(images=_a							,							return_tensors="pt"	).to(_a	)

							# forward pass
							with torch.no_grad():
										lowerCAmelCase_         =      model(**_a	)

							# verify the logits
							lowerCAmelCase_         =      torch.Size((1, 1_000)	)
							self.assertEqual(outputs.logits.shape							,							_a	)

							lowerCAmelCase_         =      torch.tensor([-0.2744, 0.8215, -0.0836]	).to(_a	)

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




				@slow
				def 	__a		(   self	)							-> int:
							# ViT models have an `interpolate_pos_encoding` argument in their forward method,
							# allowing to interpolate the pre-trained position embeddings in order to use
							# the model on higher resolutions. The DINO model by Facebook AI leverages this
							# to visualize self-attention on higher resolution images.
							lowerCAmelCase_         =      ViTModel.from_pretrained("facebook/dino-vits8"	).to(_a	)

							lowerCAmelCase_         =      ViTImageProcessor.from_pretrained("facebook/dino-vits8"							,							size=480	)
							lowerCAmelCase_         =      prepare_img()
							lowerCAmelCase_         =      image_processor(images=_a							,							return_tensors="pt"	)
							lowerCAmelCase_         =      inputs.pixel_values.to(_a	)

							# forward pass
							with torch.no_grad():
										lowerCAmelCase_         =      model(_a							,							interpolate_pos_encoding=_a	)

							# verify the logits
							lowerCAmelCase_         =      torch.Size((1, 3_601, 384)	)
							self.assertEqual(outputs.last_hidden_state.shape							,							_a	)

							lowerCAmelCase_         =      torch.tensor(
							    [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]	).to(_a	)

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




				@slow
				@require_accelerate
				@require_torch_gpu
				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      ViTModel.from_pretrained("facebook/dino-vits8"							,							torch_dtype=torch.floataa							,							device_map="auto"	)
							lowerCAmelCase_         =      self.default_image_processor

							lowerCAmelCase_         =      prepare_img()
							lowerCAmelCase_         =      image_processor(images=_a							,							return_tensors="pt"	)
							lowerCAmelCase_         =      inputs.pixel_values.to(_a	)

							# forward pass to make sure inference works in fp16
							with torch.no_grad():
										lowerCAmelCase_         =      model(_a	)






from random import randint
from tempfile import TemporaryFile

import numpy as np



def   __UpperCAmelCase					(      __a :     Optional[Any]						,__a :     int						,__a :     Any      )     ->	int:

					"""simple docstring"""

					_a      :       int   =						0
					if start < end:
										_a      :       Tuple   =						randint(__a						,__a      )
										_a      :       Tuple   =						a[end]
										_a      :       List[str]   =						a[pivot]
										_a      :       Any   =						temp

										_a ,   _a      :       Optional[int]   =						_in_place_partition(__a						,__a						,__a      )
										count += _in_place_quick_sort(__a						,__a						,p - 1      )
										count += _in_place_quick_sort(__a						,p + 1						,__a      )
					return count



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Tuple						,__a :     Dict      )     ->	Dict:

					"""simple docstring"""

					_a      :       Dict   =						0
					_a      :       Tuple   =						randint(__a						,__a      )
					_a      :       List[Any]   =						a[end]
					_a      :       str   =						a[pivot]
					_a      :       str   =						temp
					_a      :       Dict   =						start - 1
					for index in range(__a						,__a      ):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															_a      :       int   =						new_pivot_index + 1
															_a      :       Any   =						a[new_pivot_index]
															_a      :       Optional[int]   =						a[index]
															_a      :       str   =						temp

					_a      :       Union[str, Any]   =						a[new_pivot_index + 1]
					_a      :       Tuple   =						a[end]
					_a      :       Any   =						temp
					return new_pivot_index + 1, count


a__												=						TemporaryFile()
a__												=						100  # 1000 elements are to be sorted


a__						,     a__												=						0, 1  # mean and standard deviation
a__												=						np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a__												=						np.load(outfile)
a__												=						len(M) - 1
a__												=						_in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)







print(z)



