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 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    def      _a    (    self ):
       lowerCAmelCase_:							str       =	self.config_class(**self.inputs_dict )
       self.parent.assertTrue(hasattr(lowerCamelCase__       ,						"tf_padding" ) )
       self.parent.assertTrue(hasattr(lowerCamelCase__       ,						"depth_multiplier" ) )





class 					_lowercase    :






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__=13       ,						lowerCamelCase__=3       ,						lowerCamelCase__=32       ,						lowerCamelCase__=0.2_5       ,						lowerCamelCase__=8       ,						lowerCamelCase__=8       ,						lowerCamelCase__=6       ,						lowerCamelCase__=32       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__="relu6"       ,						lowerCamelCase__=1_280       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=10       ,						lowerCamelCase__=None       ,						):
       lowerCAmelCase_:							Union[str, Any]       =	parent
       lowerCAmelCase_:							int       =	batch_size
       lowerCAmelCase_:							Tuple       =	num_channels
       lowerCAmelCase_:							Dict       =	image_size
       lowerCAmelCase_:							str       =	depth_multiplier
       lowerCAmelCase_:							Optional[int]       =	depth_divisible_by
       lowerCAmelCase_:							List[Any]       =	min_depth
       lowerCAmelCase_:							Optional[int]       =	expand_ratio
       lowerCAmelCase_:							Any       =	tf_padding
       lowerCAmelCase_:							Union[str, Any]       =	output_stride
       lowerCAmelCase_:							Optional[int]       =	first_layer_is_expansion
       lowerCAmelCase_:							Optional[Any]       =	finegrained_output
       lowerCAmelCase_:							List[str]       =	hidden_act
       lowerCAmelCase_:							Any       =	last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
       lowerCAmelCase_:							List[str]       =	classifier_dropout_prob
       lowerCAmelCase_:							Dict       =	use_labels
       lowerCAmelCase_:							Tuple       =	is_training
       lowerCAmelCase_:							int       =	num_labels
       lowerCAmelCase_:							Any       =	initializer_range
       lowerCAmelCase_:							Dict       =	scope




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

       lowerCAmelCase_:							Optional[int]       =	None
       lowerCAmelCase_:							Optional[Any]       =	None
       if self.use_labels:
          lowerCAmelCase_:							List[str]       =	ids_tensor([self.batch_size]       ,						self.num_labels )
          lowerCAmelCase_:							Optional[int]       =	ids_tensor([self.batch_size, self.image_size, self.image_size]       ,						self.num_labels )

       lowerCAmelCase_:							int       =	self.get_config()

       return config, pixel_values, labels, pixel_labels




    def      _a    (    self ):
       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      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Union[str, Any]       =	MobileNetVaModel(config=lowerCamelCase__ )
       model.to(lowerCamelCase__ )
       model.eval()
       lowerCAmelCase_:							Optional[int]       =	model(lowerCamelCase__ )
       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      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							List[str]       =	self.num_labels
       lowerCAmelCase_:							Tuple       =	MobileNetVaForImageClassification(lowerCamelCase__ )
       model.to(lowerCamelCase__ )
       model.eval()
       lowerCAmelCase_:							Dict       =	model(lowerCamelCase__       ,						labels=lowerCamelCase__ )
       self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.num_labels) )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Any       =	self.num_labels
       lowerCAmelCase_:							Dict       =	MobileNetVaForSemanticSegmentation(lowerCamelCase__ )
       model.to(lowerCamelCase__ )
       model.eval()
       lowerCAmelCase_:							Union[str, Any]       =	model(lowerCamelCase__ )
       self.parent.assertEqual(
           result.logits.shape       ,						(
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )       ,						)
       lowerCAmelCase_:							List[str]       =	model(lowerCamelCase__       ,						labels=lowerCamelCase__ )
       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      _a    (    self ):
       lowerCAmelCase_:							Tuple       =	self.prepare_config_and_inputs()
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							str       =	config_and_inputs
       lowerCAmelCase_:							List[Any]       =	{"pixel_values": pixel_values}
       return config, inputs_dict





@require_torch
class 					_lowercase    (     UpperCAmelCase__ ,  UpperCAmelCase__ ,  unittest.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Dict          =    (
        (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
        if is_torch_available()
        else ()
    )
    SCREAMING_SNAKE_CASE:				Optional[int]          =    (
        {
            'feature-extraction': MobileNetVaModel,
            'image-classification': MobileNetVaForImageClassification,
            'image-segmentation': MobileNetVaForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    SCREAMING_SNAKE_CASE:				List[str]          =    False
    SCREAMING_SNAKE_CASE:				int          =    False
    SCREAMING_SNAKE_CASE:				List[str]          =    False
    SCREAMING_SNAKE_CASE:				List[str]          =    False




    def      _a    (    self ):
       lowerCAmelCase_:							Union[str, Any]       =	MobileNetVaModelTester(self )
       lowerCAmelCase_:							Optional[int]       =	MobileNetVaConfigTester(self       ,						config_class=lowerCamelCase__       ,						has_text_modality=lowerCamelCase__ )




    def      _a    (    self ):
       self.config_tester.run_common_tests()




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




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




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




    def      _a    (    self ):
       lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowerCAmelCase_:							Optional[int]       =	model_class(lowerCamelCase__ )
          lowerCAmelCase_:							Union[str, Any]       =	inspect.signature(model.forward )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowerCAmelCase_:							Optional[int]       =	[*signature.parameters.keys()]

          lowerCAmelCase_:							Union[str, Any]       =	["pixel_values"]
          self.assertListEqual(arg_names[:1]       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							Tuple       =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase__ )




    def      _a    (    self ):
       def check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							Any       =	model_class(lowerCamelCase__ )
          model.to(lowerCamelCase__ )
          model.eval()

          with torch.no_grad():
             lowerCAmelCase_:							Any       =	model(**self._prepare_for_class(lowerCamelCase__       ,						lowerCamelCase__ ) )

          lowerCAmelCase_:							List[str]       =	outputs.hidden_states

          lowerCAmelCase_:							Optional[int]       =	16
          self.assertEqual(len(lowerCamelCase__ )       ,						lowerCamelCase__ )

       lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          lowerCAmelCase_:							List[str]       =	True
          check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )

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

          check_hidden_states_output(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							List[Any]       =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							str       =	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__ )




    @slow
    def      _a    (    self ):
       for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_:							int       =	MobileNetVaModel.from_pretrained(lowerCamelCase__ )
          self.assertIsNotNone(lowerCamelCase__ )





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







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






    '''simple docstring'''




    @cached_property
    def      _a    (    self ):
       return (
           MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224" ) if is_vision_available() else None
       )




    @slow
    def      _a    (    self ):
       lowerCAmelCase_:							Any       =	MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224" ).to(lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	self.default_image_processor
       lowerCAmelCase_:							Any       =	prepare_img()
       lowerCAmelCase_:							int       =	image_processor(images=lowerCamelCase__       ,						return_tensors="pt" ).to(lowerCamelCase__ )

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

       # verify the logits
       lowerCAmelCase_:							List[Any]       =	torch.Size((1, 1_001) )
       self.assertEqual(outputs.logits.shape       ,						lowerCamelCase__ )

       lowerCAmelCase_:							Any       =	torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5] ).to(lowerCamelCase__ )

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




    @slow
    def      _a    (    self ):
       lowerCAmelCase_:							Optional[Any]       =	MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513" )
       lowerCAmelCase_:							Union[str, Any]       =	model.to(lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513" )

       lowerCAmelCase_:							Tuple       =	prepare_img()
       lowerCAmelCase_:							List[str]       =	image_processor(images=lowerCamelCase__       ,						return_tensors="pt" ).to(lowerCamelCase__ )

       # forward pass
       with torch.no_grad():
          lowerCAmelCase_:							str       =	model(**lowerCamelCase__ )
       lowerCAmelCase_:							str       =	outputs.logits

       # verify the logits
       lowerCAmelCase_:							Optional[Any]       =	torch.Size((1, 21, 65, 65) )
       self.assertEqual(logits.shape       ,						lowerCamelCase__ )

       lowerCAmelCase_:							Dict       =	torch.tensor(
           [
               [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
               [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
               [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
           ]       ,						device=lowerCamelCase__       ,						)

       self.assertTrue(torch.allclose(logits[0, :3, :3, :3]       ,						lowerCamelCase__       ,						atol=1E-4 ) )
from __future__ import annotations
def 		snake_case__	(		lowercase							,				lowercase					):
   print(F'''Vertex\tShortest Distance from vertex {src}'''					)
   for i, d in enumerate(lowercase					):
      print(F'''{i}\t\t{d}'''					)
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   for j in range(lowercase					):
      lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	(graph[j][k] for k in ["src", "dst", "weight"])
      if distance[u] != float("inf"					) and distance[u] + w < distance[v]:
         return True
   return False
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							int       =	[float("inf"					)] * vertex_count
   lowerCAmelCase_:							Dict       =	0.0

   for _ in range(vertex_count - 1					):
      for j in range(lowercase					):
         lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Any       =	(graph[j][k] for k in ["src", "dst", "weight"])

         if distance[u] != float("inf"					) and distance[u] + w < distance[v]:
            lowerCAmelCase_:							List[str]       =	distance[u] + w

   lowerCAmelCase_:							str       =	check_negative_cycle(lowercase							,				lowercase							,				lowercase					)
   if negative_cycle_exists:
      raise Exception("Negative cycle found"					)

   return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a  :      List[str]            =    int(input("""Enter number of vertices: """).strip())
 a  :      Any            =    int(input("""Enter number of edges: """).strip())

 a  :      list[dict[str, int]]            =    [{} for _ in range(E)]

 for i in range(E):
  print("""Edge """, i + 1)
  a     , a     , a  :      Optional[int]            =    (
      int(x)
      for x in input("""Enter source, destination, weight: """).strip().split(""" """)
  )
  a  :      Optional[int]            =    {"""src""": src, """dst""": dest, """weight""": weight}

 a  :      List[Any]            =    int(input("""\nEnter shortest path source:""").strip())
 a  :      Tuple            =    bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)