



from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 tensorflow as tf

				from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor


class       a							:



		def __init__(     self     :Any   ,__lowercase     :int   ,__lowercase     :Union[str, Any]=1_3   ,__lowercase     :int=3_0   ,__lowercase     :str=2   ,__lowercase     :int=3   ,__lowercase     :Dict=True   ,__lowercase     :Tuple=True   ,__lowercase     :List[Any]=3_2   ,__lowercase     :Tuple=2   ,__lowercase     :Dict=4   ,__lowercase     :int=3_7   ,__lowercase     :Dict="gelu"   ,__lowercase     :Dict=0.1   ,__lowercase     :Dict=0.1   ,__lowercase     :Optional[int]=1_0   ,__lowercase     :Tuple=0.02   ,__lowercase     :Optional[int]=3   ,__lowercase     :List[Any]=None   ,):
						snake_case__	:      List[str]     		=	parent
						snake_case__	:      Optional[Any]     		=	batch_size
						snake_case__	:      List[str]     		=	image_size
						snake_case__	:      Optional[Any]     		=	patch_size
						snake_case__	:      Optional[int]     		=	num_channels
						snake_case__	:      Optional[Any]     		=	is_training
						snake_case__	:      List[str]     		=	use_labels
						snake_case__	:      Union[str, Any]     		=	hidden_size
						snake_case__	:      Tuple     		=	num_hidden_layers
						snake_case__	:      Any     		=	num_attention_heads
						snake_case__	:      List[str]     		=	intermediate_size
						snake_case__	:      str     		=	hidden_act
						snake_case__	:      Tuple     		=	hidden_dropout_prob
						snake_case__	:      Optional[int]     		=	attention_probs_dropout_prob
						snake_case__	:      Optional[int]     		=	type_sequence_label_size
						snake_case__	:      List[str]     		=	initializer_range
						snake_case__	:      str     		=	scope

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



		def 	__lowerCamelCase			(     self     :Dict  ):
						snake_case__	:      List[Any]     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

						snake_case__	:      List[str]     		=	self.get_config()

						return config, pixel_values, labels



		def 	__lowerCamelCase			(     self     :str  ):
						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=__lowerCAmelCase   ,initializer_range=self.initializer_range   ,)



		def 	__lowerCamelCase			(     self     :List[Any]   ,__lowercase     :List[str]   ,__lowercase     :Any   ,__lowercase     :Tuple  ):
						snake_case__	:      Dict     		=	TFViTModel(config=__lowerCAmelCase  )
						snake_case__	:      List[Any]     		=	model(__lowerCAmelCase   ,training=__lowerCAmelCase  )
						self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)  )

						# Test with an image with different size than the one specified in config.
						snake_case__	:      int     		=	self.image_size // 2
						snake_case__	:      Union[str, Any]     		=	pixel_values[:, :, :image_size, :image_size]
						snake_case__	:      Dict     		=	model(__lowerCAmelCase   ,interpolate_pos_encoding=__lowerCAmelCase   ,training=__lowerCAmelCase  )
						snake_case__	:      List[Any]     		=	(image_size // self.patch_size) ** 2 + 1
						self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, seq_length, self.hidden_size)  )



		def 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :int   ,__lowercase     :str   ,__lowercase     :str  ):
						snake_case__	:      Tuple     		=	self.type_sequence_label_size
						snake_case__	:      List[str]     		=	TFViTForImageClassification(__lowerCAmelCase  )
						snake_case__	:      Dict     		=	model(__lowerCAmelCase   ,labels=__lowerCAmelCase   ,training=__lowerCAmelCase  )
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.type_sequence_label_size)  )

						# Test with an image with different size than the one specified in config.
						snake_case__	:      Optional[int]     		=	self.image_size // 2
						snake_case__	:      List[Any]     		=	pixel_values[:, :, :image_size, :image_size]
						snake_case__	:      Tuple     		=	model(__lowerCAmelCase   ,interpolate_pos_encoding=__lowerCAmelCase   ,training=__lowerCAmelCase  )
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.type_sequence_label_size)  )

						# test greyscale images
						snake_case__	:      Any     		=	1
						snake_case__	:      Dict     		=	TFViTForImageClassification(__lowerCAmelCase  )
						snake_case__	:      Dict     		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
						snake_case__	:      Optional[Any]     		=	model(__lowerCAmelCase  )
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.type_sequence_label_size)  )






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


@require_tf
class       a							(			lowerCAmelCase_  ,							lowerCAmelCase_  ,							unittest.TestCase				):
		__lowerCAmelCase	:    int											=					(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
		__lowerCAmelCase	:    int											=					(
		    {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
		    if is_tf_available()
		    else {}
		)

		__lowerCAmelCase	:    Tuple											=					False
		__lowerCAmelCase	:    Optional[Any]											=					False
		__lowerCAmelCase	:    List[str]											=					False



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      Optional[int]     		=	TFViTModelTester(self  )
						snake_case__	:      List[Any]     		=	ConfigTester(self   ,config_class=__lowerCAmelCase   ,has_text_modality=__lowerCAmelCase   ,hidden_size=3_7  )



		def 	__lowerCamelCase			(     self     :Any  ):
						self.config_tester.run_common_tests()



		@unittest.skip(reason='''ViT does not use inputs_embeds'''  )
		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						pass



		@unittest.skip(reason='''ViT does not use inputs_embeds'''  )
		def 	__lowerCamelCase			(     self     :Any  ):
						pass



		def 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__		, snake_case__	:      Union[str, Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										snake_case__	:      List[str]     		=	model_class(__lowerCAmelCase  )
										self.assertIsInstance(model.get_input_embeddings()   ,(tf.keras.layers.Layer)  )
										snake_case__	:      Optional[int]     		=	model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(__lowerCAmelCase   ,tf.keras.layers.Layer  )  )



		def 	__lowerCamelCase			(     self     :Dict  ):
						snake_case__		, snake_case__	:      Tuple     		=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										snake_case__	:      Any     		=	model_class(__lowerCAmelCase  )
										snake_case__	:      Any     		=	inspect.signature(model.call  )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										snake_case__	:      List[str]     		=	[*signature.parameters.keys()]

										snake_case__	:      str     		=	['''pixel_values''']
										self.assertListEqual(arg_names[:1]   ,__lowerCAmelCase  )



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      Dict     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowerCAmelCase  )



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      Optional[Any]     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase  )






		@slow
		def 	__lowerCamelCase			(     self     :Dict  ):
						snake_case__	:      int     		=	TFViTModel.from_pretrained('''google/vit-base-patch16-224'''  )
						self.assertIsNotNone(__lowerCAmelCase  )



def 			_lowerCAmelCase       (					)   ->					Any:


				"""simple docstring"""

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





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



		@cached_property
		def 	__lowerCamelCase			(     self     :Dict  ):
						return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''  ) if is_vision_available() else None






		@slow
		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      Any     		=	TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''  )

						snake_case__	:      int     		=	self.default_image_processor
						snake_case__	:      List[str]     		=	prepare_img()
						snake_case__	:      Optional[Any]     		=	image_processor(images=__lowerCAmelCase   ,return_tensors='''tf'''  )

						# forward pass
						snake_case__	:      Any     		=	model(**__lowerCAmelCase  )

						# verify the logits
						snake_case__	:      List[str]     		=	tf.TensorShape((1, 1_0_0_0)  )
						self.assertEqual(outputs.logits.shape   ,__lowerCAmelCase  )

						snake_case__	:      Optional[Any]     		=	tf.constant([-0.2744, 0.8215, -0.0836]  )

						tf.debugging.assert_near(outputs.logits[0, :3]   ,__lowerCAmelCase   ,atol=1e-4  )


"""simple docstring"""


from __future__ import annotations

from collections import Counter
from random import random


class 				a :


    def __init__(    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					{}


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     str    ):
      _UpperCAmelCase         =					{}


    def    lowerCAmelCase_     (    self		:     str      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     float    ):
      if nodea not in self.connections:
        self.add_node(__lowerCAmelCase    )
      if nodea not in self.connections:
        self.add_node(__lowerCAmelCase    )
      _UpperCAmelCase         =					probability


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      return list(self.connections    )


    def    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     str    ):
      _UpperCAmelCase         =					0
      _UpperCAmelCase         =					random()

      for dest in self.connections[node]:
        current_probability += self.connections[node][dest]
        if current_probability > random_value:
          return dest
      return ""






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					MarkovChainGraphUndirectedUnweighted()

  for nodea, nodea, probability in transitions:
    graph.add_transition_probability(lowercase      ,lowercase      ,lowercase						)

  _UpperCAmelCase         =					Counter(graph.get_nodes()						)
  _UpperCAmelCase         =					start

  for _ in range(lowercase						):
    _UpperCAmelCase         =					graph.transition(lowercase						)
    visited[node] += 1

  return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()
