import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 (
          MODEL_MAPPING,
          DataaVecVisionForImageClassification,
          DataaVecVisionForSemanticSegmentation,
          DataaVecVisionModel,
      )
      from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import BeitImageProcessor



class     snake_case_			:






   def __init__( self							:	Dict      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	Tuple=1_00      ,		lowercase_							:	Union[str, Any]=13      ,		lowercase_							:	List[Any]=30      ,		lowercase_							:	Optional[Any]=2      ,		lowercase_							:	Tuple=3      ,		lowercase_							:	List[str]=True      ,		lowercase_							:	Dict=True      ,		lowercase_							:	Any=32      ,		lowercase_							:	Dict=4      ,		lowercase_							:	Any=4      ,		lowercase_							:	Dict=37      ,		lowercase_							:	Dict="gelu"      ,		lowercase_							:	str=0.1      ,		lowercase_							:	Optional[int]=0.1      ,		lowercase_							:	Union[str, Any]=10      ,		lowercase_							:	Optional[Any]=0.02      ,		lowercase_							:	str=3      ,		lowercase_							:	Any=None      ,		lowercase_							:	str=[0, 1, 2, 3]      ,		)   ->		Tuple:
       lowercase__					:  Optional[int]          =							parent
       lowercase__					:  Any          =							1_00
       lowercase__					:  Dict          =							batch_size
       lowercase__					:  str          =							image_size
       lowercase__					:  Any          =							patch_size
       lowercase__					:  List[str]          =							num_channels
       lowercase__					:  str          =							is_training
       lowercase__					:  Any          =							use_labels
       lowercase__					:  int          =							hidden_size
       lowercase__					:  List[str]          =							num_hidden_layers
       lowercase__					:  Dict          =							num_attention_heads
       lowercase__					:  List[str]          =							intermediate_size
       lowercase__					:  Union[str, Any]          =							hidden_act
       lowercase__					:  Union[str, Any]          =							hidden_dropout_prob
       lowercase__					:  List[str]          =							attention_probs_dropout_prob
       lowercase__					:  Any          =							type_sequence_label_size
       lowercase__					:  Union[str, Any]          =							initializer_range
       lowercase__					:  Optional[int]          =							scope
       lowercase__					:  str          =							out_indices
       lowercase__					:  Tuple          =							num_labels

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






   def  __UpperCamelCase	( self							:	str    )   ->		str:
       lowercase__					:  Tuple          =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

       lowercase__					:  Optional[int]          =							self.get_config()

       return config, pixel_values, labels, pixel_labels






   def  __UpperCamelCase	( self							:	Optional[int]    )   ->		int:
       return DataaVecVisionConfig(
           vocab_size=self.vocab_size      ,		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      ,		out_indices=self.out_indices      ,		)






   def  __UpperCamelCase	( self							:	Union[str, Any]      ,		lowercase_							:	Any      ,		lowercase_							:	Dict      ,		lowercase_							:	Any      ,		lowercase_							:	Union[str, Any]    )   ->		Any:
       lowercase__					:  Union[str, Any]          =							DataaVecVisionModel(config=lowercase_    )
       model.to(lowercase_    )
       model.eval()
       lowercase__					:  List[str]          =							model(lowercase_    )
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
       lowercase__					:  int          =							(self.image_size // self.patch_size) ** 2
       self.parent.assertEqual(result.last_hidden_state.shape      ,		(self.batch_size, num_patches + 1, self.hidden_size)    )






   def  __UpperCamelCase	( self							:	str      ,		lowercase_							:	Optional[Any]      ,		lowercase_							:	Dict      ,		lowercase_							:	int      ,		lowercase_							:	Dict    )   ->		Dict:
       lowercase__					:  Optional[Any]          =							self.type_sequence_label_size
       lowercase__					:  List[Any]          =							DataaVecVisionForImageClassification(lowercase_    )
       model.to(lowercase_    )
       model.eval()
       lowercase__					:  Optional[Any]          =							model(lowercase_      ,		labels=lowercase_    )
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.type_sequence_label_size)    )






   def  __UpperCamelCase	( self							:	int      ,		lowercase_							:	List[Any]      ,		lowercase_							:	str      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	Optional[Any]    )   ->		List[Any]:
       lowercase__					:  Union[str, Any]          =							self.num_labels
       lowercase__					:  List[Any]          =							DataaVecVisionForSemanticSegmentation(lowercase_    )
       model.to(lowercase_    )
       model.eval()
       lowercase__					:  Union[str, Any]          =							model(lowercase_    )
       self.parent.assertEqual(
           result.logits.shape      ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)    )
       lowercase__					:  Optional[Any]          =							model(lowercase_      ,		labels=lowercase_    )
       self.parent.assertEqual(
           result.logits.shape      ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)    )







   def  __UpperCamelCase	( self							:	Union[str, Any]    )   ->		Any:
       lowercase__					:  Any          =							self.prepare_config_and_inputs()
       lowercase__   ,  lowercase__   ,  lowercase__   ,  lowercase__					:  Tuple          =							config_and_inputs
       lowercase__					:  Optional[int]          =							{"pixel_values": pixel_values}
       return config, inputs_dict



@require_torch
class     snake_case_			(						__A    ,__A    ,unittest.TestCase			):
   __A      :						Tuple   =							(
       (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   __A      :						Dict   =							(
       {
           "feature-extraction": DataaVecVisionModel,
           "image-classification": DataaVecVisionForImageClassification,
           "image-segmentation": DataaVecVisionForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   __A      :						Tuple   =							False
   __A      :						Union[str, Any]   =							False
   __A      :						List[str]   =							False






   def  __UpperCamelCase	( self							:	Optional[Any]    )   ->		Union[str, Any]:
       lowercase__					:  Dict          =							DataaVecVisionModelTester(self    )
       lowercase__					:  List[Any]          =							ConfigTester(
           self      ,		config_class=lowercase_      ,		has_text_modality=lowercase_      ,		hidden_size=37    )






   def  __UpperCamelCase	( self							:	List[Any]    )   ->		Optional[int]:
       self.config_tester.run_common_tests()






   def  __UpperCamelCase	( self							:	Any    )   ->		str:
       # Data2VecVision does not use inputs_embeds
       pass






   @require_torch_multi_gpu
   @unittest.skip(
       reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"    )
   def  __UpperCamelCase	( self							:	List[str]    )   ->		List[Any]:
       pass






   def  __UpperCamelCase	( self							:	Tuple    )   ->		Optional[Any]:
       lowercase__   ,  lowercase__					:  List[str]          =							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           lowercase__					:  List[Any]          =							model_class(lowercase_    )
           self.assertIsInstance(model.get_input_embeddings()      ,		(nn.Module)    )
           lowercase__					:  Optional[int]          =							model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(lowercase_      ,		nn.Linear    )    )






   def  __UpperCamelCase	( self							:	Union[str, Any]    )   ->		Tuple:
       lowercase__   ,  lowercase__					:  str          =							self.model_tester.prepare_config_and_inputs_for_common()

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

           lowercase__					:  Any          =							["pixel_values"]
           self.assertListEqual(arg_names[:1]      ,		lowercase_    )






   def  __UpperCamelCase	( self							:	str    )   ->		int:
       lowercase__					:  Any          =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_    )






   def  __UpperCamelCase	( self							:	Optional[Any]    )   ->		List[str]:
       lowercase__					:  str          =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_segmentation(*lowercase_    )






   def  __UpperCamelCase	( self							:	Optional[Any]    )   ->		Tuple:
       if not self.model_tester.is_training:
           return

       lowercase__   ,  lowercase__					:  Union[str, Any]          =							self.model_tester.prepare_config_and_inputs_for_common()
       lowercase__					:  Optional[Any]          =							True

       for model_class in self.all_model_classes:
           if model_class in [*get_values(lowercase_    )]:
               continue

           lowercase__					:  List[str]          =							model_class(lowercase_    )
           model.to(lowercase_    )
           model.train()
           lowercase__					:  Any          =							self._prepare_for_class(lowercase_      ,		lowercase_      ,		return_labels=lowercase_    )
           lowercase__					:  List[Any]          =							model(**lowercase_    ).loss
           loss.backward()






   def  __UpperCamelCase	( self							:	List[Any]    )   ->		List[Any]:
       lowercase__   ,  lowercase__					:  Dict          =							self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
           return

       lowercase__					:  int          =							False
       lowercase__					:  Dict          =							True

       for model_class in self.all_model_classes:
           if model_class in [*get_values(lowercase_    )] or not model_class.supports_gradient_checkpointing:
               continue
           # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
           # this can then be incorporated into _prepare_for_class in test_modeling_common.py
           elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
               lowercase__   ,  lowercase__   ,  lowercase__   ,  lowercase__					:  Union[str, Any]          =							inputs_dict["pixel_values"].shape
               lowercase__					:  Tuple          =							torch.zeros(
                   [self.model_tester.batch_size, height, width]      ,		device=lowercase_    ).long()
           lowercase__					:  str          =							model_class(lowercase_    )
           model.gradient_checkpointing_enable()
           model.to(lowercase_    )
           model.train()
           lowercase__					:  str          =							self._prepare_for_class(lowercase_      ,		lowercase_      ,		return_labels=lowercase_    )
           lowercase__					:  Optional[Any]          =							model(**lowercase_    ).loss
           loss.backward()






   def  __UpperCamelCase	( self							:	Any    )   ->		Dict:
       lowercase__   ,  lowercase__					:  List[str]          =							self.model_tester.prepare_config_and_inputs_for_common()

       lowercase__					:  Dict          =							_config_zero_init(lowercase_    )
       for model_class in self.all_model_classes:
           lowercase__					:  Any          =							model_class(config=lowercase_    )
           for name, param in model.named_parameters():
               # we skip lambda parameters as these require special initial values
               # determined by config.layer_scale_init_value
               if "lambda" in name:
                   continue
               if 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'''      ,		)






   def  __UpperCamelCase	( self							:	Dict      ,		lowercase_							:	List[Any]      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	List[Any]      ,		lowercase_							:	int=2E-4      ,		lowercase_							:	List[str]="outputs"      ,		lowercase_							:	List[Any]=None    )   ->		Tuple:
       # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
       super().check_pt_tf_outputs(lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_    )






   def  __UpperCamelCase	( self							:	str    )   ->		Optional[Any]:
       lowercase__					:  List[str]          =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowercase_    )







   @slow
   def  __UpperCamelCase	( self							:	Any    )   ->		Dict:
       for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowercase__					:  str          =							DataaVecVisionModel.from_pretrained(lowercase_    )
           self.assertIsNotNone(lowercase_    )



def      lowercase_							(     ):
    lowercase__					:  Dict          =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
    return image



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






   @cached_property
   def  __UpperCamelCase	( self							:	Optional[Any]    )   ->		List[str]:
       return (
           BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k"    ) if is_vision_available() else None
       )







   @slow
   def  __UpperCamelCase	( self							:	Union[str, Any]    )   ->		int:
       lowercase__					:  Any          =							DataaVecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k"    ).to(
           lowercase_    )

       lowercase__					:  List[Any]          =							self.default_image_processor
       lowercase__					:  str          =							prepare_img()
       lowercase__					:  List[Any]          =							image_processor(images=lowercase_      ,		return_tensors="pt"    ).to(lowercase_    )

       # forward pass
       with torch.no_grad():
           lowercase__					:  int          =							model(**lowercase_    )
       lowercase__					:  List[Any]          =							outputs.logits

       # verify the logits
       lowercase__					:  Optional[int]          =							torch.Size((1, 10_00)    )
       self.assertEqual(logits.shape      ,		lowercase_    )

       lowercase__					:  int          =							torch.tensor([0.32_77, -0.13_95, 0.09_11]    ).to(lowercase_    )

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

       lowercase__					:  int          =							[model.config.labelaid[i] for i in ["remote control, remote", "tabby, tabby cat"]]
       self.assertEqual(logits[0].topk(2    ).indices.cpu().tolist()      ,		lowercase_    )




class     snake_case_			:






   def __init__( self							:	Optional[int]      ,		lowercase_							:	Optional[int]=None    )   ->		Optional[int]:
       lowercase__					:  List[Any]          =							data
       lowercase__					:  Any          =							None







   def __repr__( self							:	Optional[Any]    )   ->		int:
       lowercase__					:  Optional[int]          =							""
       lowercase__					:  Any          =							self
       while temp:
           string_rep += F'''<{temp.data}> ---> '''
           lowercase__					:  List[Any]          =							temp.next
       string_rep += "<END>"
       return string_rep



def      lowercase_							(     _lowerCamelCase				:			List[Any]):

    # if elements_list is empty
    if not elements_list:
        raise Exception("The Elements List is empty")

    # Set first element as Head
    lowercase__					:  List[str]          =							Node(elements_list[0])
    lowercase__					:  Tuple          =							head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        lowercase__					:  Tuple          =							Node(_lowerCamelCase)
        lowercase__					:  Union[str, Any]          =							current.next
    return head


UpperCamelCase    				=		[1, 3, 5, 32, 44, 12, 43]
print(f"List: {list_data}")
print('''Creating Linked List from List.''')
UpperCamelCase    				=		make_linked_list(list_data)
print('''Linked List:''')

print(linked_list)



