






import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
    from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class 		_snake_case  :






   def __init__(       self		,					a		,					a=13		,					a=32		,					a=2		,					a=3		,					a=16		,					a=[1, 2, 1]		,					a=[2, 2, 4]		,					a=2		,					a=2.0		,					a=True		,					a=0.0		,					a=0.0		,					a=0.1		,					a="gelu"		,					a=False		,					a=True		,					a=0.02		,					a=1E-5		,					a=True		,					a=None		,					a=True		,					a=10		,					a=8		,					)      ->       Dict:
      SCREAMING_SNAKE_CASE						= parent
      SCREAMING_SNAKE_CASE						= batch_size
      SCREAMING_SNAKE_CASE						= image_size
      SCREAMING_SNAKE_CASE						= patch_size
      SCREAMING_SNAKE_CASE						= num_channels
      SCREAMING_SNAKE_CASE						= embed_dim
      SCREAMING_SNAKE_CASE						= depths
      SCREAMING_SNAKE_CASE						= num_heads
      SCREAMING_SNAKE_CASE						= window_size
      SCREAMING_SNAKE_CASE						= mlp_ratio
      SCREAMING_SNAKE_CASE						= qkv_bias
      SCREAMING_SNAKE_CASE						= hidden_dropout_prob
      SCREAMING_SNAKE_CASE						= attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE						= drop_path_rate
      SCREAMING_SNAKE_CASE						= hidden_act
      SCREAMING_SNAKE_CASE						= use_absolute_embeddings
      SCREAMING_SNAKE_CASE						= patch_norm
      SCREAMING_SNAKE_CASE						= layer_norm_eps
      SCREAMING_SNAKE_CASE						= initializer_range
      SCREAMING_SNAKE_CASE						= is_training
      SCREAMING_SNAKE_CASE						= scope
      SCREAMING_SNAKE_CASE						= use_labels
      SCREAMING_SNAKE_CASE						= type_sequence_label_size
      SCREAMING_SNAKE_CASE						= encoder_stride






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
      SCREAMING_SNAKE_CASE						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

      SCREAMING_SNAKE_CASE						= self.get_config()

      return config, pixel_values, labels






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
      return SwinvaConfig(
          image_size=self.image_size		,					patch_size=self.patch_size		,					num_channels=self.num_channels		,					embed_dim=self.embed_dim		,					depths=self.depths		,					num_heads=self.num_heads		,					window_size=self.window_size		,					mlp_ratio=self.mlp_ratio		,					qkv_bias=self.qkv_bias		,					hidden_dropout_prob=self.hidden_dropout_prob		,					attention_probs_dropout_prob=self.attention_probs_dropout_prob		,					drop_path_rate=self.drop_path_rate		,					hidden_act=self.hidden_act		,					use_absolute_embeddings=self.use_absolute_embeddings		,					path_norm=self.patch_norm		,					layer_norm_eps=self.layer_norm_eps		,					initializer_range=self.initializer_range		,					encoder_stride=self.encoder_stride		,					)






   def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       Optional[Any]:
      SCREAMING_SNAKE_CASE						= SwinvaModel(config=__a)
      model.to(__a)
      model.eval()
      SCREAMING_SNAKE_CASE						= model(__a)

      SCREAMING_SNAKE_CASE						= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
      SCREAMING_SNAKE_CASE						= int(config.embed_dim * 2 ** (len(config.depths) - 1))

      self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, expected_seq_len, expected_dim))






   def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       List[Any]:
      SCREAMING_SNAKE_CASE						= SwinvaForMaskedImageModeling(config=__a)
      model.to(__a)
      model.eval()
      SCREAMING_SNAKE_CASE						= model(__a)
      self.parent.assertEqual(
          result.logits.shape		,					(self.batch_size, self.num_channels, self.image_size, self.image_size))

      # test greyscale images
      SCREAMING_SNAKE_CASE						= 1
      SCREAMING_SNAKE_CASE						= SwinvaForMaskedImageModeling(__a)
      model.to(__a)
      model.eval()

      SCREAMING_SNAKE_CASE						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
      SCREAMING_SNAKE_CASE						= model(__a)
      self.parent.assertEqual(result.logits.shape		,					(self.batch_size, 1, self.image_size, self.image_size))






   def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       List[Any]:
      SCREAMING_SNAKE_CASE						= self.type_sequence_label_size
      SCREAMING_SNAKE_CASE						= SwinvaForImageClassification(__a)
      model.to(__a)
      model.eval()
      SCREAMING_SNAKE_CASE						= model(__a		,					labels=__a)
      self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.type_sequence_label_size))







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



@require_torch
class 		_snake_case  (  _UpperCamelCase   ,    _UpperCamelCase   ,    unittest.TestCase					):
   _lowercase		:    List[str]			     =							(
       (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
   )
   _lowercase		:    List[Any]			     =							(
       {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
       if is_torch_available()
       else {}
   )

   _lowercase		:    Optional[Any]			     =							False
   _lowercase		:    Union[str, Any]			     =							False
   _lowercase		:    List[Any]			     =							False
   _lowercase		:    Dict			     =							False






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
      SCREAMING_SNAKE_CASE						= SwinvaModelTester(self)
      SCREAMING_SNAKE_CASE						= ConfigTester(self		,					config_class=__a		,					embed_dim=37)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
      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    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__a)






   @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.')
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
      pass






   @unittest.skip(reason='Swinv2 does not use inputs_embeds')
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
      pass






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

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






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

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

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






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()
      SCREAMING_SNAKE_CASE						= True

      for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE						= True
         SCREAMING_SNAKE_CASE						= False
         SCREAMING_SNAKE_CASE						= True
         SCREAMING_SNAKE_CASE						= model_class(__a)
         model.to(__a)
         model.eval()
         with torch.no_grad():
            SCREAMING_SNAKE_CASE						= model(**self._prepare_for_class(__a		,					__a))
         SCREAMING_SNAKE_CASE						= outputs.attentions
         SCREAMING_SNAKE_CASE						= len(self.model_tester.depths)
         self.assertEqual(len(__a)		,					__a)

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         SCREAMING_SNAKE_CASE						= True
         SCREAMING_SNAKE_CASE						= config.window_size**2
         SCREAMING_SNAKE_CASE						= model_class(__a)
         model.to(__a)
         model.eval()
         with torch.no_grad():
            SCREAMING_SNAKE_CASE						= model(**self._prepare_for_class(__a		,					__a))
         SCREAMING_SNAKE_CASE						= outputs.attentions
         self.assertEqual(len(__a)		,					__a)

         self.assertListEqual(
             list(attentions[0].shape[-3:])		,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,					)
         SCREAMING_SNAKE_CASE						= len(__a)

         # Check attention is always last and order is fine
         SCREAMING_SNAKE_CASE						= True
         SCREAMING_SNAKE_CASE						= True
         SCREAMING_SNAKE_CASE						= model_class(__a)
         model.to(__a)
         model.eval()
         with torch.no_grad():
            SCREAMING_SNAKE_CASE						= model(**self._prepare_for_class(__a		,					__a))

         if hasattr(self.model_tester		,					'num_hidden_states_types'):
            SCREAMING_SNAKE_CASE						= self.model_tester.num_hidden_states_types
         else:
            # also another +1 for reshaped_hidden_states
            SCREAMING_SNAKE_CASE						= 2
         self.assertEqual(out_len + added_hidden_states		,					len(__a))

         SCREAMING_SNAKE_CASE						= outputs.attentions

         self.assertEqual(len(__a)		,					__a)

         self.assertListEqual(
             list(self_attentions[0].shape[-3:])		,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,					)






   def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a)      ->       int:
      SCREAMING_SNAKE_CASE						= model_class(__a)
      model.to(__a)
      model.eval()

      with torch.no_grad():
         SCREAMING_SNAKE_CASE						= model(**self._prepare_for_class(__a		,					__a))

      SCREAMING_SNAKE_CASE						= outputs.hidden_states

      SCREAMING_SNAKE_CASE						= getattr(
          self.model_tester		,					'expected_num_hidden_layers'		,					len(self.model_tester.depths) + 1)
      self.assertEqual(len(__a)		,					__a)

      # Swinv2 has a different seq_length
      SCREAMING_SNAKE_CASE						= (
          config.patch_size
          if isinstance(config.patch_size		,					collections.abc.Iterable)
          else (config.patch_size, config.patch_size)
      )

      SCREAMING_SNAKE_CASE						= (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

      self.assertListEqual(
          list(hidden_states[0].shape[-2:])		,					[num_patches, self.model_tester.embed_dim]		,					)

      SCREAMING_SNAKE_CASE						= outputs.reshaped_hidden_states
      self.assertEqual(len(__a)		,					__a)

      SCREAMING_SNAKE_CASE						= reshaped_hidden_states[0].shape
      SCREAMING_SNAKE_CASE						= (
          reshaped_hidden_states[0].view(__a		,					__a		,					height * width).permute(0		,					2		,					1)
      )
      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:])		,					[num_patches, self.model_tester.embed_dim]		,					)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

      SCREAMING_SNAKE_CASE						= (
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size		,					collections.abc.Iterable)
          else (self.model_tester.image_size, self.model_tester.image_size)
      )

      for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE						= True
         self.check_hidden_states_output(__a		,					__a		,					__a		,					__a)

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

         self.check_hidden_states_output(__a		,					__a		,					__a		,					__a)






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()
      SCREAMING_SNAKE_CASE						= 3

      SCREAMING_SNAKE_CASE						= (
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size		,					collections.abc.Iterable)
          else (self.model_tester.image_size, self.model_tester.image_size)
      )
      SCREAMING_SNAKE_CASE						= (
          config.patch_size
          if isinstance(config.patch_size		,					collections.abc.Iterable)
          else (config.patch_size, config.patch_size)
      )

      SCREAMING_SNAKE_CASE						= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      SCREAMING_SNAKE_CASE						= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE						= True
         self.check_hidden_states_output(__a		,					__a		,					__a		,					(padded_height, padded_width))

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         SCREAMING_SNAKE_CASE						= True
         self.check_hidden_states_output(__a		,					__a		,					__a		,					(padded_height, padded_width))






   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*__a)






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






   @slow
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
      for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         SCREAMING_SNAKE_CASE						= SwinvaModel.from_pretrained(__a)
         self.assertIsNotNone(__a)







   def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
      SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

      SCREAMING_SNAKE_CASE						= _config_zero_init(__a)
      for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE						= model_class(config=__a)
         for name, param in model.named_parameters():
            if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
               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'''		,					)



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






   @cached_property
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
      return (
          AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256')
          if is_vision_available()
          else None
      )







   @slow
   def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
      SCREAMING_SNAKE_CASE						= SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256').to(
          __a)
      SCREAMING_SNAKE_CASE						= self.default_image_processor

      SCREAMING_SNAKE_CASE						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
      SCREAMING_SNAKE_CASE						= image_processor(images=__a		,					return_tensors='pt').to(__a)

      # forward pass
      with torch.no_grad():
         SCREAMING_SNAKE_CASE						= model(**__a)

      # verify the logits
      SCREAMING_SNAKE_CASE						= torch.Size((1, 1000))
      self.assertEqual(outputs.logits.shape		,					__a)
      SCREAMING_SNAKE_CASE						= torch.tensor([-0.39_47, -0.43_06, 0.00_26]).to(__a)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]		,					__a		,					atol=1E-4))












from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 		_snake_case  (  A__					):
			_lowercase		:    Union[str, Any]			     =							['''image_processor''', '''tokenizer''']
			_lowercase		:    Optional[int]			     =							'''Pix2StructImageProcessor'''
			_lowercase		:    str			     =							('''T5Tokenizer''', '''T5TokenizerFast''')






			def __init__(       self		,					a		,					a)      ->       Any:
						SCREAMING_SNAKE_CASE						= False
						super().__init__(a		,					a)






			def __call__(       self		,					a=None		,					a = None		,					a = True		,					a = False		,					a = None		,					a = None		,					a = 2048		,					a = 0		,					a = None		,					a = None		,					a = False		,					a = False		,					a = False		,					a = False		,					a = False		,					a = True		,					a = None		,					**a		,					)      ->       BatchEncoding:
						if images is None and text is None:
									raise ValueError('You have to specify either images or text.')

						# Get only text
						if images is None and not self.image_processor.is_vqa:
									SCREAMING_SNAKE_CASE						= self.tokenizer
									SCREAMING_SNAKE_CASE						= self.tokenizer(
									    text=a		,					add_special_tokens=a		,					padding=a		,					truncation=a		,					max_length=a		,					stride=a		,					pad_to_multiple_of=a		,					return_attention_mask=a		,					return_overflowing_tokens=a		,					return_special_tokens_mask=a		,					return_offsets_mapping=a		,					return_token_type_ids=a		,					return_length=a		,					verbose=a		,					return_tensors=a		,					**a		,					)
									return text_encoding

						if not self.image_processor.is_vqa:
									# add pixel_values
									SCREAMING_SNAKE_CASE						= self.image_processor(
									    a		,					return_tensors=a		,					max_patches=a		,					**a)
						else:
									# add pixel_values and bbox
									SCREAMING_SNAKE_CASE						= self.image_processor(
									    a		,					return_tensors=a		,					max_patches=a		,					header_text=a		,					**a)

						if text is not None and not self.image_processor.is_vqa:
									SCREAMING_SNAKE_CASE						= self.tokenizer(
									    text=a		,					add_special_tokens=a		,					padding=a		,					truncation=a		,					max_length=a		,					stride=a		,					pad_to_multiple_of=a		,					return_attention_mask=a		,					return_overflowing_tokens=a		,					return_special_tokens_mask=a		,					return_offsets_mapping=a		,					return_token_type_ids=a		,					return_length=a		,					verbose=a		,					return_tensors=a		,					**a		,					)

									if "attention_mask" in text_encoding:
												SCREAMING_SNAKE_CASE						= text_encoding.pop('attention_mask')
									if "input_ids" in text_encoding:
												SCREAMING_SNAKE_CASE						= text_encoding.pop('input_ids')
						else:
									SCREAMING_SNAKE_CASE						= None

						if text_encoding is not None:
									encoding_image_processor.update(a)

						return encoding_image_processor






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       List[Any]:
						return self.tokenizer.batch_decode(*a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					*a		,					**a)      ->       Optional[int]:
						return self.tokenizer.decode(*a		,					**a)







			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.tokenizer.model_input_names
						SCREAMING_SNAKE_CASE						= self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))




