

import inspect
import unittest

from transformers import ViTMSNConfig
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 torch import nn

       from transformers import ViTMSNForImageClassification, ViTMSNModel
       from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor


class       SCREAMING_SNAKE_CASE__					:





    def __init__(      self						,   a						,   a=13						,   a=30						,   a=2						,   a=3						,   a=True						,   a=True						,   a=32						,   a=5						,   a=4						,   a=37						,   a="gelu"						,   a=0.1						,   a=0.1						,   a=10						,   a=0.02						,   a=None						,   ):
         lowercase__    :   str      =       parent
         lowercase__    :   List[Any]      =       batch_size
         lowercase__    :   List[Any]      =       image_size
         lowercase__    :   Any      =       patch_size
         lowercase__    :   Optional[Any]      =       num_channels
         lowercase__    :   Union[str, Any]      =       is_training
         lowercase__    :   Optional[int]      =       use_labels
         lowercase__    :   Optional[Any]      =       hidden_size
         lowercase__    :   Any      =       num_hidden_layers
         lowercase__    :   str      =       num_attention_heads
         lowercase__    :   List[str]      =       intermediate_size
         lowercase__    :   List[str]      =       hidden_act
         lowercase__    :   Tuple      =       hidden_dropout_prob
         lowercase__    :   str      =       attention_probs_dropout_prob
         lowercase__    :   List[str]      =       type_sequence_label_size
         lowercase__    :   Union[str, Any]      =       initializer_range
         lowercase__    :   Optional[Any]      =       scope

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





    def       snake_case_				(      self):
         lowercase__    :   Any      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

         lowercase__    :   Any      =       None
         if self.use_labels:
              lowercase__    :   List[str]      =       ids_tensor([self.batch_size]						,   self.type_sequence_label_size)

         lowercase__    :   str      =       self.get_config()

         return config, pixel_values, labels





    def       snake_case_				(      self):
         return ViTMSNConfig(
             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						,   initializer_range=self.initializer_range						,   )





    def       snake_case_				(      self						,   a						,   a						,   a):
         lowercase__    :   Optional[Any]      =       ViTMSNModel(config=a)
         model.to(a)
         model.eval()
         lowercase__    :   Union[str, Any]      =       model(a)
         self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size))





    def       snake_case_				(      self						,   a						,   a						,   a):
         lowercase__    :   List[str]      =       self.type_sequence_label_size
         lowercase__    :   List[Any]      =       ViTMSNForImageClassification(a)
         model.to(a)
         model.eval()
         lowercase__    :   str      =       model(a						,   labels=a)
         print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}')
         print('Labels: {labels}')
         self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.type_sequence_label_size))

         # test greyscale images
         lowercase__    :   List[Any]      =       1
         lowercase__    :   Optional[Any]      =       ViTMSNForImageClassification(a)
         model.to(a)
         model.eval()

         lowercase__    :   Optional[int]      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
         lowercase__    :   List[str]      =       model(a)
         self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.type_sequence_label_size))





    def       snake_case_				(      self):
         lowercase__    :   Dict      =       self.prepare_config_and_inputs()
         lowercase__      , lowercase__      , lowercase__    :   Dict      =       config_and_inputs
         lowercase__    :   str      =       {'pixel_values': pixel_values}
         return config, inputs_dict



@require_torch
class       SCREAMING_SNAKE_CASE__					(__snake_case		,       __snake_case		,       unittest.TestCase     ):
    __lowerCamelCase						:       Dict														=     (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    __lowerCamelCase						:       int														=     (
        {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

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





    def       snake_case_				(      self):
         lowercase__    :   Dict      =       ViTMSNModelTester(self)
         lowercase__    :   Any      =       ConfigTester(self						,   config_class=a						,   has_text_modality=a						,   hidden_size=37)





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





    @unittest.skip(reason='ViTMSN does not use inputs_embeds')
    def       snake_case_				(      self):
         pass





    def       snake_case_				(      self):
         lowercase__      , lowercase__    :   Optional[int]      =       self.model_tester.prepare_config_and_inputs_for_common()

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





    def       snake_case_				(      self):
         lowercase__      , lowercase__    :   int      =       self.model_tester.prepare_config_and_inputs_for_common()

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

              lowercase__    :   Tuple      =       ['pixel_values']
              self.assertListEqual(arg_names[:1]						,   a)





    def       snake_case_				(      self):
         lowercase__    :   List[str]      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a)





    def       snake_case_				(      self):
         lowercase__    :   List[Any]      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*a)





    @slow
    def       snake_case_				(      self):
         for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowercase__    :   Tuple      =       ViTMSNModel.from_pretrained(a)
              self.assertIsNotNone(a)



def 	snake_case__			(    ):



     '''simple docstring'''





     lowercase__    :   str      =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
     return image


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





    @cached_property
    def       snake_case_				(      self):
         return ViTImageProcessor.from_pretrained('facebook/vit-msn-small') if is_vision_available() else None





    @slow
    def       snake_case_				(      self):
         torch.manual_seed(2)
         lowercase__    :   Tuple      =       ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small').to(a)

         lowercase__    :   Dict      =       self.default_image_processor
         lowercase__    :   Dict      =       prepare_img()
         lowercase__    :   List[str]      =       image_processor(images=a						,   return_tensors='pt').to(a)

         # forward pass
         with torch.no_grad():
              lowercase__    :   Optional[int]      =       model(**a)

         # verify the logits
         lowercase__    :   Optional[Any]      =       torch.Size((1, 1000))
         self.assertEqual(outputs.logits.shape						,   a)

         lowercase__    :   int      =       torch.tensor([-0.0_803, -0.4_454, -0.2_375]).to(a)

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




from __future__ import annotations

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    dict	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     lowercase__      , lowercase__    :   List[str]      =       set(SCREAMING_SNAKE_CASE_			), [start]

     while stack:
          lowercase__    :   Union[str, Any]      =       stack.pop()
          explored.add(SCREAMING_SNAKE_CASE_			)
          # Differences from BFS:
          # 1) pop last element instead of first one
          # 2) add adjacent elements to stack without exploring them
          for adj in reversed(graph[v]			):
               if adj not in explored:
                    stack.append(SCREAMING_SNAKE_CASE_			)
     return explored


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

if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(depth_first_search(G, '''A'''))

