


"""simple docstring"""
import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
 import jax

 from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class 	lowercase__				(					unittest.TestCase				):





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=13      ,   SCREAMING_SNAKE_CASE=30      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=32      ,   SCREAMING_SNAKE_CASE=5      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=37      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=10      ,   SCREAMING_SNAKE_CASE=0.02      ,   )  ->  Dict:
      _lowerCamelCase	:					int   =		parent
      _lowerCamelCase	:					List[Any]   =		batch_size
      _lowerCamelCase	:					Tuple   =		image_size
      _lowerCamelCase	:					Dict   =		patch_size
      _lowerCamelCase	:					Union[str, Any]   =		num_channels
      _lowerCamelCase	:					List[Any]   =		is_training
      _lowerCamelCase	:					Any   =		use_labels
      _lowerCamelCase	:					List[Any]   =		hidden_size
      _lowerCamelCase	:					Any   =		num_hidden_layers
      _lowerCamelCase	:					Optional[int]   =		num_attention_heads
      _lowerCamelCase	:					Union[str, Any]   =		intermediate_size
      _lowerCamelCase	:					Any   =		hidden_act
      _lowerCamelCase	:					Optional[Any]   =		hidden_dropout_prob
      _lowerCamelCase	:					Optional[Any]   =		attention_probs_dropout_prob
      _lowerCamelCase	:					str   =		type_sequence_label_size
      _lowerCamelCase	:					List[str]   =		initializer_range

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





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					str   =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      _lowerCamelCase	:					int   =		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=SCREAMING_SNAKE_CASE      ,   initializer_range=self.initializer_range      ,   )

      return config, pixel_values





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      _lowerCamelCase	:					Dict   =		FlaxViTModel(config=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		model(SCREAMING_SNAKE_CASE)
      # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
      _lowerCamelCase	:					Dict   =		(self.image_size, self.image_size)
      _lowerCamelCase	:					int   =		(self.patch_size, self.patch_size)
      _lowerCamelCase	:					Union[str, Any]   =		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
      self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, num_patches + 1, self.hidden_size))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					str   =		self.type_sequence_label_size
      _lowerCamelCase	:					List[Any]   =		FlaxViTForImageClassification(config=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		model(SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size))

      # test greyscale images
      _lowerCamelCase	:					Dict   =		1
      _lowerCamelCase	:					int   =		FlaxViTForImageClassification(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
      _lowerCamelCase	:					Union[str, Any]   =		model(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					Optional[Any]   =		self.prepare_config_and_inputs()
      (
          (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    
      )	:					str   =		config_and_inputs
      _lowerCamelCase	:					Union[str, Any]   =		{"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_flax
class 	lowercase__				(					A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()





   def 		UpperCamelCase_  (							self)  ->  None:
      _lowerCamelCase	:					Dict   =		FlaxViTModelTester(self)
      _lowerCamelCase	:					Any   =		ConfigTester(self      ,   config_class=SCREAMING_SNAKE_CASE      ,   has_text_modality=SCREAMING_SNAKE_CASE      ,   hidden_size=37)





   def 		UpperCamelCase_  (							self)  ->  int:
      self.config_tester.run_common_tests()





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Optional[int]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Optional[Any]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _lowerCamelCase	:					int   =		model_class(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Optional[Any]   =		inspect.signature(model.__call__)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCamelCase	:					Optional[int]   =		[*signature.parameters.keys()]

         _lowerCamelCase	:					Tuple   =		["""pixel_values"""]
         self.assertListEqual(arg_names[:1]      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase   ,    _lowerCamelCase	:					str   =		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__):
            _lowerCamelCase	:					List[Any]   =		self._prepare_for_class(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
            _lowerCamelCase	:					List[str]   =		model_class(SCREAMING_SNAKE_CASE)

            @jax.jit
            def model_jitted(SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE):
               return model(pixel_values=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

            with self.subTest("""JIT Enabled"""):
               _lowerCamelCase	:					List[str]   =		model_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

            with self.subTest("""JIT Disabled"""):
               with jax.disable_jit():
                  _lowerCamelCase	:					List[Any]   =		model_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

            self.assertEqual(len(SCREAMING_SNAKE_CASE)      ,   len(SCREAMING_SNAKE_CASE))
            for jitted_output, output in zip(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE):
               self.assertEqual(jitted_output.shape      ,   output.shape)





   @slow
   def 		UpperCamelCase_  (							self)  ->  List[str]:
      for model_class_name in self.all_model_classes:
         _lowerCamelCase	:					Tuple   =		model_class_name.from_pretrained("""google/vit-base-patch16-224""")
         _lowerCamelCase	:					List[Any]   =		model(np.ones((1, 3, 224, 224)))
         self.assertIsNotNone(SCREAMING_SNAKE_CASE)



'''simple docstring'''
lowerCAmelCase					:    Optional[Any]           = {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}







def 		A_(     A						:		dict		,     A						:		str		,     A						:		Optional[Any]):
							UpperCamelCase   			=     set()
							# keep track of all the paths to be checked
							UpperCamelCase   			=     [[start]]

							# return path if start is goal
							if start == goal:
														return [start]

							# keeps looping until all possible paths have been checked
							while queue:
														# pop the first path from the queue
														UpperCamelCase   			=     queue.pop(0)
														# get the last node from the path
														UpperCamelCase   			=     path[-1]
														if node not in explored:
																					UpperCamelCase   			=     graph[node]
																					# go through all neighbour nodes, construct a new path and
																					# push it into the queue
																					for neighbour in neighbours:
																												UpperCamelCase   			=     list(A)
																												new_path.append(A)
																												queue.append(A)
																												# return path if neighbour is goal
																												if neighbour == goal:
																																			return new_path

            # mark node as explored
																					explored.add(A)

    # in case there's no path between the 2 nodes
							return []







def 		A_(     A						:		dict		,     A						:		str		,     A						:		Tuple):
							if not graph or start not in graph or target not in graph:
														return -1
							if start == target:
														return 0
							UpperCamelCase   			=     [start]
							UpperCamelCase   			=     set(A)
							# Keep tab on distances from `start` node.
							UpperCamelCase   			=     {start: 0, target: -1}
							while queue:
														UpperCamelCase   			=     queue.pop(0)
														if node == target:
																					UpperCamelCase   			=     (
																					    dist[node] if dist[target] == -1 else min(dist[target]		,     dist[node])
																					)
														for adjacent in graph[node]:
																					if adjacent not in visited:
																												visited.add(A)
																												queue.append(A)
																												UpperCamelCase   			=     dist[node] + 1
							return dist[target]


if __name__ == "__main__":
						print(bfs_shortest_path(demo_graph, 'G', 'D'))  # returns ['G', 'C', 'A', 'B', 'D']
						print(bfs_shortest_path_distance(demo_graph, 'G', 'D'))  # returns 4




