code: 
  
  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 __UpperCamelCase    :
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__=1_0_0							,			lowerCamelCase__=1_3							,			lowerCamelCase__=3_0							,			lowerCamelCase__=2							,			lowerCamelCase__=3							,			lowerCamelCase__=True							,			lowerCamelCase__=True							,			lowerCamelCase__=3_2							,			lowerCamelCase__=4							,			lowerCamelCase__=4							,			lowerCamelCase__=3_7							,			lowerCamelCase__="gelu"							,			lowerCamelCase__=0.1							,			lowerCamelCase__=0.1							,			lowerCamelCase__=1_0							,			lowerCamelCase__=0.02							,			lowerCamelCase__=3							,			lowerCamelCase__=None							,			lowerCamelCase__=[0, 1, 2, 3]							,			):
  						UpperCAmelCase__:  Optional[Any]		       =   parent
  						UpperCAmelCase__:  List[str]		       =   1_0_0
  						UpperCAmelCase__:  List[str]		       =   batch_size
  						UpperCAmelCase__:  str		       =   image_size
  						UpperCAmelCase__:  Union[str, Any]		       =   patch_size
  						UpperCAmelCase__:  List[str]		       =   num_channels
  						UpperCAmelCase__:  List[Any]		       =   is_training
  						UpperCAmelCase__:  str		       =   use_labels
  						UpperCAmelCase__:  Dict		       =   hidden_size
  						UpperCAmelCase__:  Any		       =   num_hidden_layers
  						UpperCAmelCase__:  Any		       =   num_attention_heads
  						UpperCAmelCase__:  List[Any]		       =   intermediate_size
  						UpperCAmelCase__:  List[str]		       =   hidden_act
  						UpperCAmelCase__:  Dict		       =   hidden_dropout_prob
  						UpperCAmelCase__:  Optional[Any]		       =   attention_probs_dropout_prob
  						UpperCAmelCase__:  int		       =   type_sequence_label_size
  						UpperCAmelCase__:  Optional[Any]		       =   initializer_range
  						UpperCAmelCase__:  List[str]		       =   scope
  						UpperCAmelCase__:  List[Any]		       =   out_indices
  						UpperCAmelCase__:  List[str]		       =   num_labels
  
  						# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
  						UpperCAmelCase__:  Dict		       =   (image_size // patch_size) ** 2
  						UpperCAmelCase__:  List[Any]		       =   num_patches + 1
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  str		       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
  
  						UpperCAmelCase__:  Dict		       =   None
  						UpperCAmelCase__:  Optional[int]		       =   None
  						if self.use_labels:
  							UpperCAmelCase__:  List[Any]		       =   ids_tensor([self.batch_size]							,			self.type_sequence_label_size	)
  							UpperCAmelCase__:  Tuple		       =   ids_tensor([self.batch_size, self.image_size, self.image_size]							,			self.num_labels	)
  
  						UpperCAmelCase__:  List[Any]		       =   self.get_config()
  
  						return config, pixel_values, labels, pixel_labels
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						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=lowerCamelCase__							,			initializer_range=self.initializer_range							,			out_indices=self.out_indices							,			)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Dict		       =   DataaVecVisionModel(config=lowerCamelCase__	)
  						model.to(lowerCamelCase__	)
  						model.eval()
  						UpperCAmelCase__:  int		       =   model(lowerCamelCase__	)
  						# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
  						UpperCAmelCase__:  Optional[Any]		       =   (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							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Optional[int]		       =   self.type_sequence_label_size
  						UpperCAmelCase__:  Optional[Any]		       =   DataaVecVisionForImageClassification(lowerCamelCase__	)
  						model.to(lowerCamelCase__	)
  						model.eval()
  						UpperCAmelCase__:  str		       =   model(lowerCamelCase__							,			labels=lowerCamelCase__	)
  						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Union[str, Any]		       =   self.num_labels
  						UpperCAmelCase__:  Optional[Any]		       =   DataaVecVisionForSemanticSegmentation(lowerCamelCase__	)
  						model.to(lowerCamelCase__	)
  						model.eval()
  						UpperCAmelCase__:  int		       =   model(lowerCamelCase__	)
  						self.parent.assertEqual(
  						    result.logits.shape							,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
  						UpperCAmelCase__:  Optional[Any]		       =   model(lowerCamelCase__							,			labels=lowerCamelCase__	)
  						self.parent.assertEqual(
  						    result.logits.shape							,			(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[Any]		       =   self.prepare_config_and_inputs()
  						UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   config_and_inputs
  						UpperCAmelCase__:  Union[str, Any]		       =   {"pixel_values": pixel_values}
  						return config, inputs_dict
  
  @require_torch
  class __UpperCamelCase    (							_a				,_a				,unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      (
  					    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
  					    if is_torch_available()
  					    else ()
  					)
  					__magic_name__     			=      (
  					    {
  					        "feature-extraction": DataaVecVisionModel,
  					        "image-classification": DataaVecVisionForImageClassification,
  					        "image-segmentation": DataaVecVisionForSemanticSegmentation,
  					    }
  					    if is_torch_available()
  					    else {}
  					)
  
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[Any]		       =   DataaVecVisionModelTester(self	)
  						UpperCAmelCase__:  int		       =   ConfigTester(
  						    self							,			config_class=lowerCamelCase__							,			has_text_modality=lowerCamelCase__							,			hidden_size=3_7	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						self.config_tester.run_common_tests()
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						# 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	):
  						pass
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Dict		       =   self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  							UpperCAmelCase__:  List[Any]		       =   model_class(lowerCamelCase__	)
  							self.assertIsInstance(model.get_input_embeddings()							,			(nn.Module)	)
  							UpperCAmelCase__:  Optional[int]		       =   model.get_output_embeddings()
  							self.assertTrue(x is None or isinstance(lowerCamelCase__							,			nn.Linear	)	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  							UpperCAmelCase__:  int		       =   model_class(lowerCamelCase__	)
  							UpperCAmelCase__:  Dict		       =   inspect.signature(model.forward	)
  							# signature.parameters is an OrderedDict => so arg_names order is deterministic
  							UpperCAmelCase__:  int		       =   [*signature.parameters.keys()]
  
  							UpperCAmelCase__:  List[Any]		       =   ["pixel_values"]
  							self.assertListEqual(arg_names[:1]							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_model(*lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[int]		       =   self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_for_image_segmentation(*lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						if not self.model_tester.is_training:
  							return
  
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Tuple		       =   self.model_tester.prepare_config_and_inputs_for_common()
  						UpperCAmelCase__:  Optional[Any]		       =   True
  
  						for model_class in self.all_model_classes:
  							if model_class in [*get_values(lowerCamelCase__	)]:
  								continue
  
  							UpperCAmelCase__:  List[str]		       =   model_class(lowerCamelCase__	)
  							model.to(lowerCamelCase__	)
  							model.train()
  							UpperCAmelCase__:  List[str]		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__							,			return_labels=lowerCamelCase__	)
  							UpperCAmelCase__:  int		       =   model(**lowerCamelCase__	).loss
  							loss.backward()
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Optional[int]		       =   self.model_tester.prepare_config_and_inputs_for_common()
  						if not self.model_tester.is_training:
  							return
  
  						UpperCAmelCase__:  str		       =   False
  						UpperCAmelCase__:  Dict		       =   True
  
  						for model_class in self.all_model_classes:
  							if model_class in [*get_values(lowerCamelCase__	)] 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":
  								UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  Dict		       =   inputs_dict["pixel_values"].shape
  								UpperCAmelCase__:  Tuple		       =   torch.zeros(
  								    [self.model_tester.batch_size, height, width]							,			device=lowerCamelCase__	).long()
  							UpperCAmelCase__:  Any		       =   model_class(lowerCamelCase__	)
  							model.gradient_checkpointing_enable()
  							model.to(lowerCamelCase__	)
  							model.train()
  							UpperCAmelCase__:  List[str]		       =   self._prepare_for_class(lowerCamelCase__							,			lowerCamelCase__							,			return_labels=lowerCamelCase__	)
  							UpperCAmelCase__:  str		       =   model(**lowerCamelCase__	).loss
  							loss.backward()
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__       ,	UpperCAmelCase__:  int		       =   self.model_tester.prepare_config_and_inputs_for_common()
  
  						UpperCAmelCase__:  List[str]		       =   _config_zero_init(lowerCamelCase__	)
  						for model_class in self.all_model_classes:
  							UpperCAmelCase__:  Union[str, Any]		       =   model_class(config=lowerCamelCase__	)
  							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							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__=2e-4							,			lowerCamelCase__="outputs"							,			lowerCamelCase__=None	):
  						# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
  						super().check_pt_tf_outputs(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  int		       =   self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__	)
  
  
  
  
  
  					@slow
  					def       _UpperCAmelCase						(							self	):
  						for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  							UpperCAmelCase__:  List[str]		       =   DataaVecVisionModel.from_pretrained(lowerCamelCase__	)
  							self.assertIsNotNone(lowerCamelCase__	)
  
  
  
  def  _A  (			):
  	UpperCAmelCase__:  List[Any]		       =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
  	return image
  
  @require_torch
  @require_vision
  class __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					@cached_property
  					def       _UpperCAmelCase						(							self	):
  						return (
  						    BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k"	) if is_vision_available() else None
  						)
  
  
  
  
  
  					@slow
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[Any]		       =   DataaVecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k"	).to(
  						    lowerCamelCase__	)
  
  						UpperCAmelCase__:  str		       =   self.default_image_processor
  						UpperCAmelCase__:  Any		       =   prepare_img()
  						UpperCAmelCase__:  Optional[int]		       =   image_processor(images=lowerCamelCase__							,			return_tensors="pt"	).to(lowerCamelCase__	)
  
  						# forward pass
  						with torch.no_grad():
  							UpperCAmelCase__:  Dict		       =   model(**lowerCamelCase__	)
  						UpperCAmelCase__:  Optional[int]		       =   outputs.logits
  
  						# verify the logits
  						UpperCAmelCase__:  str		       =   torch.Size((1, 1_0_0_0)	)
  						self.assertEqual(logits.shape							,			lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[Any]		       =   torch.tensor([0.3_277, -0.1_395, 0.0_911]	).to(lowerCamelCase__	)
  
  						self.assertTrue(torch.allclose(logits[0, :3]							,			lowerCamelCase__							,			atol=1e-4	)	)
  
  						UpperCAmelCase__:  str		       =   [model.config.labelaid[i] for i in ["remote control, remote", "tabby, tabby cat"]]
  						self.assertEqual(logits[0].topk(2	).indices.cpu().tolist()							,			lowerCamelCase__	)
style_context: 
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,):
  	UpperCAmelCase__:  Tuple		       =   list(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  int		       =   list(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Dict		       =   len(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  int		       =   len(SCREAMING_SNAKE_CASE			)
  
  	UpperCAmelCase__:  Any		       =   [
  	    [0 for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
  	]
  	UpperCAmelCase__:  List[Any]		       =   [
  	    ["0" for _ in range(len_destination_seq + 1			)] for _ in range(len_source_seq + 1			)
  	]
  
  	for i in range(1  ,len_source_seq + 1			):
  		UpperCAmelCase__:  List[Any]		       =   i * delete_cost
  		UpperCAmelCase__:  str		       =   f"D{source_seq[i - 1]:c}"
  
  	for i in range(1  ,len_destination_seq + 1			):
  		UpperCAmelCase__:  Union[str, Any]		       =   i * insert_cost
  		UpperCAmelCase__:  Optional[Any]		       =   f"I{destination_seq[i - 1]:c}"
  
  	for i in range(1  ,len_source_seq + 1			):
  		for j in range(1  ,len_destination_seq + 1			):
  			if source_seq[i - 1] == destination_seq[j - 1]:
  				UpperCAmelCase__:  Any		       =   costs[i - 1][j - 1] + copy_cost
  				UpperCAmelCase__:  Tuple		       =   f"C{source_seq[i - 1]:c}"
  			else:
  				UpperCAmelCase__:  Union[str, Any]		       =   costs[i - 1][j - 1] + replace_cost
  				UpperCAmelCase__:  Optional[int]		       =   f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]			)
  
  			if costs[i - 1][j] + delete_cost < costs[i][j]:
  				UpperCAmelCase__:  Optional[int]		       =   costs[i - 1][j] + delete_cost
  				UpperCAmelCase__:  int		       =   f"D{source_seq[i - 1]:c}"
  
  			if costs[i][j - 1] + insert_cost < costs[i][j]:
  				UpperCAmelCase__:  Any		       =   costs[i][j - 1] + insert_cost
  				UpperCAmelCase__:  Tuple		       =   f"I{destination_seq[j - 1]:c}"
  
  	return costs, ops
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	if i == 0 and j == 0:
  		return []
  	else:
  		if ops[i][j][0] == "C" or ops[i][j][0] == "R":
  			UpperCAmelCase__:  Union[str, Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,j - 1			)
  			seq.append(ops[i][j]			)
  			return seq
  		elif ops[i][j][0] == "D":
  			UpperCAmelCase__:  Optional[Any]		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,i - 1  ,SCREAMING_SNAKE_CASE			)
  			seq.append(ops[i][j]			)
  			return seq
  		else:
  			UpperCAmelCase__:  Dict		       =   assemble_transformation(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,j - 1			)
  			seq.append(ops[i][j]			)
  			return seq
  
  
  if __name__ == "__main__":
  					_lowerCAmelCase   ,  _lowerCAmelCase     :			Dict    =compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)
  
  					_lowerCAmelCase     :			List[Any]    =len(operations)
  					_lowerCAmelCase     :			Optional[int]    =len(operations[0])
  					_lowerCAmelCase     :			Dict    =assemble_transformation(operations, m - 1, n - 1)
  
  					_lowerCAmelCase     :			int    =list("""Python""")
  					_lowerCAmelCase     :			Optional[int]    =0
  					_lowerCAmelCase     :			List[Any]    =0
  
  					with open("""min_cost.txt""", """w""") as file:
  										for op in sequence:
  															print("""""".join(string))
  
  															if op[0] == "C":
  																				file.write("""%-16s""" % """Copy %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost -= 1
  															elif op[0] == "R":
  																				_lowerCAmelCase     :			Tuple    =op[2]
  
  																				file.write("""%-16s""" % ("""Replace %c""" % op[1] + """ with """ + str(op[2])))
  																				file.write("""\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 1
  															elif op[0] == "D":
  																				string.pop(i)
  
  																				file.write("""%-16s""" % """Delete %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 2
  															else:
  																				string.insert(i, op[1])
  
  																				file.write("""%-16s""" % """Insert %c""" % op[1])
  																				file.write("""\t\t\t""" + """""".join(string))
  																				file.write("""\r\n""")
  
  																				cost += 2
  
  															i += 1
  
  										print("""""".join(string))
  										print("""Cost: """, cost)
  
  										file.write("""\r\nMinimum cost: """ + str(cost))