import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
    from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=13		,				lowerCAmelCase_=32		,				lowerCAmelCase_=3		,				lowerCAmelCase_=4		,				lowerCAmelCase_=[10, 20, 30, 40]		,				lowerCAmelCase_=[2, 2, 3, 2]		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=37		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=10		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=["stage2", "stage3", "stage4"]		,				lowerCAmelCase_=[2, 3, 4]		,				lowerCAmelCase_=None		,				)  ->					str:
        _A        =  parent
        _A        =  batch_size
        _A        =  image_size
        _A        =  num_channels
        _A        =  num_stages
        _A        =  hidden_sizes
        _A        =  depths
        _A        =  is_training
        _A        =  use_labels
        _A        =  intermediate_size
        _A        =  hidden_act
        _A        =  num_labels
        _A        =  initializer_range
        _A        =  out_features
        _A        =  out_indices
        _A        =  scope



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

        _A        =  None
        if self.use_labels:
               _A        =  ids_tensor([self.batch_size]		,				self.num_labels							)

        _A        =  self.get_config()

        return config, pixel_values, labels



 def      UpperCAmelCase							(					self							)  ->					str:
        return ConvNextVaConfig(
            num_channels=self.num_channels		,				hidden_sizes=self.hidden_sizes		,				depths=self.depths		,				num_stages=self.num_stages		,				hidden_act=self.hidden_act		,				is_decoder=lowerCAmelCase_		,				initializer_range=self.initializer_range		,				out_features=self.out_features		,				out_indices=self.out_indices		,				num_labels=self.num_labels		,				)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Tuple:
        _A        =  ConvNextVaModel(config=lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_							)
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape		,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,				)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Dict:
        _A        =  ConvNextVaForImageClassification(lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_		,				labels=lowerCAmelCase_							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Any:
        _A        =  ConvNextVaBackbone(config=lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_							)

        # verify hidden states
        self.parent.assertEqual(len(result.feature_maps							)		,				len(config.out_features							)							)
        self.parent.assertListEqual(list(result.feature_maps[0].shape							)		,				[self.batch_size, self.hidden_sizes[1], 4, 4]							)

        # verify channels
        self.parent.assertEqual(len(model.channels							)		,				len(config.out_features							)							)
        self.parent.assertListEqual(model.channels		,				config.hidden_sizes[1:]							)

        # verify backbone works with out_features=None
        _A        =  None
        _A        =  ConvNextVaBackbone(config=lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_							)

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps							)		,				1							)
        self.parent.assertListEqual(list(result.feature_maps[0].shape							)		,				[self.batch_size, self.hidden_sizes[-1], 1, 1]							)

        # verify channels
        self.parent.assertEqual(len(model.channels							)		,				1							)
        self.parent.assertListEqual(model.channels		,				[config.hidden_sizes[-1]]							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.prepare_config_and_inputs()
        _A   ,  _A   ,  _A        =  config_and_inputs
        _A        =  {"""pixel_values""": pixel_values}
        return config, inputs_dict



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  self.prepare_config_and_inputs()
        _A   ,  _A   ,  _A        =  config_and_inputs
        _A        =  {"""pixel_values""": pixel_values, """labels""": labels}
        return config, inputs_dict



@require_torch
class 				a							(			__lowerCAmelCase       ,			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:str    			=		(
     (
         ConvNextVaModel,
         ConvNextVaForImageClassification,
         ConvNextVaBackbone,
     )
     if is_torch_available()
     else ()
 )
 lowerCamelCase	:Optional[int]    			=		(
     {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
     if is_torch_available()
     else {}
 )

 lowerCamelCase	:List[str]    			=		False
 lowerCamelCase	:int    			=		False
 lowerCamelCase	:List[str]    			=		False
 lowerCamelCase	:Union[str, Any]    			=		False
 lowerCamelCase	:str    			=		False



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  ConvNextVaModelTester(self							)
        _A        =  ConfigTester(self		,				config_class=lowerCAmelCase_		,				has_text_modality=lowerCAmelCase_		,				hidden_size=37							)



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        return



 @unittest.skip(reason="""ConvNextV2 does not use inputs_embeds"""							)
 def      UpperCAmelCase							(					self							)  ->					Dict:
        pass



 @unittest.skip(reason="""ConvNextV2 does not support input and output embeddings"""							)
 def      UpperCAmelCase							(					self							)  ->					Any:
        pass



 @unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""							)
 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        pass



 def      UpperCAmelCase							(					self							)  ->					int:
        if not self.model_tester.is_training:
               return

        for model_class in self.all_model_classes:
               _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_with_labels()
               _A        =  True

               if model_class.__name__ in [
                   *get_values(lowerCAmelCase_							),
                   *get_values(lowerCAmelCase_							),
               ]:
                      continue

               _A        =  model_class(lowerCAmelCase_							)
               model.to(lowerCAmelCase_							)
               model.train()
               _A        =  self._prepare_for_class(lowerCAmelCase_		,				lowerCAmelCase_		,				return_labels=lowerCAmelCase_							)
               _A        =  model(**lowerCAmelCase_							).loss
               loss.backward()



 def      UpperCAmelCase							(					self							)  ->					int:
        if not self.model_tester.is_training:
               return

        for model_class in self.all_model_classes:
               _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_with_labels()
               _A        =  False
               _A        =  True

               if (
                   model_class.__name__
                   in [*get_values(lowerCAmelCase_							), *get_values(lowerCAmelCase_							)]
                   or not model_class.supports_gradient_checkpointing
               ):
                      continue

               _A        =  model_class(lowerCAmelCase_							)
               model.to(lowerCAmelCase_							)
               model.gradient_checkpointing_enable()
               model.train()
               _A        =  self._prepare_for_class(lowerCAmelCase_		,				lowerCAmelCase_		,				return_labels=lowerCAmelCase_							)
               _A        =  model(**lowerCAmelCase_							).loss
               loss.backward()



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

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

               _A        =  ["""pixel_values"""]
               self.assertListEqual(arg_names[:1]		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        def check_hidden_states_output(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							):
               _A        =  model_class(lowerCAmelCase_							)
               model.to(lowerCAmelCase_							)
               model.eval()

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

               _A        =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               _A        =  self.model_tester.num_stages
               self.assertEqual(len(lowerCAmelCase_							)		,				expected_num_stages + 1							)

               # ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]							)		,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,				)

        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _A        =  True
               check_hidden_states_output(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _A        =  True

               check_hidden_states_output(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_							)



 @slow
 def      UpperCAmelCase							(					self							)  ->					str:
        for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _A        =  ConvNextVaModel.from_pretrained(lowerCAmelCase_							)
               self.assertIsNotNone(lowerCAmelCase_							)






def  snake_case  (       )						->   List[str]:
       _A        =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
       return image







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







 """simple docstring"""






 @cached_property
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224"""							) if is_vision_available() else None



 @slow
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224"""							).to(lowerCAmelCase_							)

        _A        =  self.default_image_processor
        _A        =  prepare_img()
        _A        =  preprocessor(images=lowerCAmelCase_		,				return_tensors="""pt"""							).to(lowerCAmelCase_							)

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

        # verify the logits
        _A        =  torch.Size((1, 10_00)							)
        self.assertEqual(outputs.logits.shape		,				lowerCAmelCase_							)

        _A        =  torch.tensor([0.9996, 0.1966, -0.4386]							).to(lowerCAmelCase_							)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]		,				lowerCAmelCase_		,				atol=1E-4							)							)





from __future__ import annotations

_SCREAMING_SNAKE_CASE										=						{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					None:
        _A        =  graph
        # mapping node to its parent in resulting breadth first tree
        _A        =  {}
        _A        =  source_vertex



 def      UpperCAmelCase							(					self							)  ->					None:
        _A        =  {self.source_vertex}
        _A        =  None
        _A        =  [self.source_vertex]  # first in first out queue

        while queue:
               _A        =  queue.pop(0							)
               for adjacent_vertex in self.graph[vertex]:
                      if adjacent_vertex not in visited:
                             visited.add(lowerCAmelCase_							)
                             _A        =  vertex
                             queue.append(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					str:
        if target_vertex == self.source_vertex:
               return self.source_vertex

        _A        =  self.parent.get(lowerCAmelCase_							)
        if target_vertex_parent is None:
               _A        =  (
                   F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
               )
               raise ValueError(lowerCAmelCase_							)

        return self.shortest_path(lowerCAmelCase_							) + F'''->{target_vertex}'''


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))




