





import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
    from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=13					,  lowercase=64					,  lowercase=2					,  lowercase=3					,  lowercase=True					,  lowercase=True					,  lowercase=32					,  lowercase=5					,  lowercase=4					,  lowercase=37					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=10					,  lowercase=0.02					,  lowercase=[1, 16, 4, 4]					,  lowercase=None					,  )			->						List[Any]:







          '''simple docstring'''

          a__						:	Optional[int] =				parent
          a__						:	Optional[int] =				batch_size
          a__						:	Any =				image_size
          a__						:	Optional[Any] =				patch_size
          a__						:	Optional[Any] =				num_channels
          a__						:	int =				is_training
          a__						:	List[str] =				use_labels
          a__						:	List[str] =				hidden_size
          a__						:	Tuple =				num_hidden_layers
          a__						:	Optional[Any] =				num_attention_heads
          a__						:	Union[str, Any] =				intermediate_size
          a__						:	Optional[int] =				hidden_act
          a__						:	Optional[Any] =				hidden_dropout_prob
          a__						:	Any =				attention_probs_dropout_prob
          a__						:	Any =				type_sequence_label_size
          a__						:	Tuple =				initializer_range
          a__						:	Tuple =				scope
          a__						:	int =				backbone_featmap_shape

          # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          # the number of patches is based on the feature map of the backbone, which by default uses an output stride
          # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
          a__						:	Any =				(self.image_size // 32) ** 2
          a__						:	List[Any] =				num_patches + 1

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	List[str] =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

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

          return config, pixel_values, labels

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	List[str] =				{
              'global_padding': 'same',
              'layer_type': 'bottleneck',
              'depths': [3, 4, 9],
              'out_features': ['stage1', 'stage2', 'stage3'],
              'embedding_dynamic_padding': True,
              'hidden_sizes': [4, 8, 16, 32],
              'num_groups': 2,
          }

          return ViTHybridConfig(
              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=lowercase					,  initializer_range=self.initializer_range					,  backbone_featmap_shape=self.backbone_featmap_shape					,  backbone_config=lowercase					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	List[str] =				ViTHybridModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Union[str, Any] =				model(lowercase)
          self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Dict =				self.type_sequence_label_size
          a__						:	Union[str, Any] =				ViTHybridForImageClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Tuple =				model(lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.type_sequence_label_size))

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

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





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Optional[Any]         =  (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
   __A   :				List[str]         =  (
       {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
       if is_torch_available()
       else {}
   )
   __A   :				Any         =  False
   __A   :				Optional[int]         =  False
   __A   :				Optional[Any]         =  False

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	Any =				ViTHybridModelTester(self)
          a__						:	Any =				ConfigTester(self					,  config_class=lowercase					,  has_text_modality=lowercase					,  hidden_size=37)

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   @unittest.skip(reason='ViT does not use inputs_embeds')
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__ ,							a__						:	List[str] =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	str =				model_class(lowercase)
                 self.assertIsInstance(model.get_input_embeddings()					,  (nn.Module))
                 a__						:	str =				model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(lowercase					,  nn.Linear))

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__ ,							a__						:	str =				self.model_tester.prepare_config_and_inputs_for_common()

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

                 a__						:	Dict =				['pixel_values']
                 self.assertListEqual(arg_names[:1]					,  lowercase)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	List[Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	Any =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__ ,							a__						:	Tuple =				self.model_tester.prepare_config_and_inputs_for_common()

          a__						:	Tuple =				_config_zero_init(lowercase)
          for model_class in self.all_model_classes:
                 a__						:	List[Any] =				model_class(config=lowercase)
                 # Skip the check for the backbone
                 for name, module in model.named_modules():
                        if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                               a__						:	Dict =				[F'{name}.{key}' for key in module.state_dict().keys()]
                               break

                 for name, param in model.named_parameters():
                        if param.requires_grad:
                               if name in backbone_params:
                                      continue
                               self.assertIn(
                                   ((param.data.mean() * 1e9).round() / 1e9).item()					,  [0.0, 1.0]					,  msg=F'Parameter {name} of model {model_class} seems not properly initialized'					,  )

   @slow
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Optional[Any] =				ViTHybridModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)



def A_	(		)  ->		int:
       a__						:	Dict =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image





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




   """simple docstring"""



   @cached_property
   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          return (
              ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
              if is_vision_available()
              else None
          )

   @slow
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	List[str] =				ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
              lowercase)

          a__						:	List[str] =				self.default_image_processor
          a__						:	List[Any] =				prepare_img()
          a__						:	Any =				image_processor(images=lowercase					,  return_tensors='pt').to(lowercase)

          # forward pass
          with torch.no_grad():
                 a__						:	Optional[Any] =				model(**lowercase)

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

          a__						:	Any =				torch.tensor([-1.90_90, -0.49_93, -0.23_89]).to(lowercase)

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

   @slow
   @require_accelerate
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	List[str] =				ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384')
          a__						:	Union[str, Any] =				ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'					,  device_map='auto')

          a__						:	Any =				prepare_img()

          a__						:	str =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	List[Any] =				model(**lowercase)
          a__						:	int =				outputs.logits
          # model predicts one of the 1000 ImageNet classes
          a__						:	List[str] =				logits.argmax(-1).item()

          self.assertTrue(model.config.idalabel[predicted_class_idx]					,  'tabby, tabby cat')




"""simple docstring"""





import os

_a		       =							{'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1_000}







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Union[str, Any]			     =    0

   UpperCAmelCase_					:					List[str]			     =    0
   while index < len(__lowerCamelCase  ) - 1:
      UpperCAmelCase_					:					Tuple			     =    SYMBOLS[numerals[index]]
      UpperCAmelCase_					:					List[str]			     =    SYMBOLS[numerals[index + 1]]
      if current_value < next_value:
         total_value -= current_value
      else:
         total_value += current_value
      index += 1
   total_value += SYMBOLS[numerals[index]]

   return total_value







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					List[str]			     =    ""

   UpperCAmelCase_					:					Any			     =    num // 1000
   numerals += m_count * "M"
   num %= 1000

   UpperCAmelCase_					:					Any			     =    num // 100
   if c_count == 9:
      numerals += "CM"
      c_count -= 9
   elif c_count == 4:
      numerals += "CD"
      c_count -= 4
   if c_count >= 5:
      numerals += "D"
      c_count -= 5
   numerals += c_count * "C"
   num %= 100

   UpperCAmelCase_					:					str			     =    num // 10
   if x_count == 9:
      numerals += "XC"
      x_count -= 9
   elif x_count == 4:
      numerals += "XL"
      x_count -= 4
   if x_count >= 5:
      numerals += "L"
      x_count -= 5
   numerals += x_count * "X"
   num %= 10

   if num == 9:
      numerals += "IX"
      num -= 9
   elif num == 4:
      numerals += "IV"
      num -= 4
   if num >= 5:
      numerals += "V"
      num -= 5
   numerals += num * "I"

   return numerals







def 	__a     (		__lowerCamelCase = "/p089_roman.txt"  ):
   UpperCAmelCase_					:					int			     =    0

   with open(os.path.dirname(__lowerCamelCase  ) + roman_numerals_filename  ) as filea:
      UpperCAmelCase_					:					Optional[Any]			     =    filea.readlines()

   for line in lines:
      UpperCAmelCase_					:					Tuple			     =    line.strip()
      UpperCAmelCase_					:					Optional[Any]			     =    parse_roman_numerals(__lowerCamelCase  )
      UpperCAmelCase_					:					Tuple			     =    generate_roman_numerals(__lowerCamelCase  )
      savings += len(__lowerCamelCase  ) - len(__lowerCamelCase  )

   return savings


if __name__ == "__main__":
    print(f"""{solution() = }""")


