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 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Union[str, Any]   ,       snake_case__:	List[str]   ,       snake_case__:	Dict=100   ,       snake_case__:	str=13   ,       snake_case__:	List[Any]=30   ,       snake_case__:	List[Any]=2   ,       snake_case__:	Union[str, Any]=3   ,       snake_case__:	List[Any]=True   ,       snake_case__:	List[str]=True   ,       snake_case__:	str=32   ,       snake_case__:	List[Any]=4   ,       snake_case__:	Tuple=4   ,       snake_case__:	Optional[Any]=37   ,       snake_case__:	Dict="gelu"   ,       snake_case__:	Any=0.1   ,       snake_case__:	List[Any]=0.1   ,       snake_case__:	int=10   ,       snake_case__:	List[str]=0.02   ,       snake_case__:	Dict=3   ,       snake_case__:	str=None   ,       snake_case__:	Union[str, Any]=[0, 1, 2, 3]   ,       )  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	parent
         _lowercase     =	100
         _lowercase     =	batch_size
         _lowercase     =	image_size
         _lowercase     =	patch_size
         _lowercase     =	num_channels
         _lowercase     =	is_training
         _lowercase     =	use_labels
         _lowercase     =	hidden_size
         _lowercase     =	num_hidden_layers
         _lowercase     =	num_attention_heads
         _lowercase     =	intermediate_size
         _lowercase     =	hidden_act
         _lowercase     =	hidden_dropout_prob
         _lowercase     =	attention_probs_dropout_prob
         _lowercase     =	type_sequence_label_size
         _lowercase     =	initializer_range
         _lowercase     =	scope
         _lowercase     =	out_indices
         _lowercase     =	num_labels
  
         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         _lowercase     =	(image_size // patch_size) ** 2
         _lowercase     =	num_patches + 1
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
  
         _lowercase     =	None
         _lowercase     =	None
         if self.use_labels:
             _lowercase     =	ids_tensor([self.batch_size]   ,       self.type_sequence_label_size	)
             _lowercase     =	ids_tensor([self.batch_size, self.image_size, self.image_size]   ,       self.num_labels	)
  
         _lowercase     =	self.get_config()
  
         return config, pixel_values, labels, pixel_labels
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         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=snake_case__   ,       initializer_range=self.initializer_range   ,       out_indices=self.out_indices   ,       )
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str   ,       snake_case__:	List[Any]   ,       snake_case__:	List[str]   ,       snake_case__:	Any   ,       snake_case__:	List[Any]	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	DataaVecVisionModel(config=snake_case__	)
         model.to(snake_case__	)
         model.eval()
         _lowercase     =	model(snake_case__	)
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         _lowercase     =	(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 			SCREAMING_SNAKE_CASE__				(    self:	Any   ,       snake_case__:	Tuple   ,       snake_case__:	List[str]   ,       snake_case__:	Any   ,       snake_case__:	Dict	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.type_sequence_label_size
         _lowercase     =	DataaVecVisionForImageClassification(snake_case__	)
         model.to(snake_case__	)
         model.eval()
         _lowercase     =	model(snake_case__   ,       labels=snake_case__	)
         self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.type_sequence_label_size)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]   ,       snake_case__:	Union[str, Any]   ,       snake_case__:	Optional[int]   ,       snake_case__:	Dict   ,       snake_case__:	str	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.num_labels
         _lowercase     =	DataaVecVisionForSemanticSegmentation(snake_case__	)
         model.to(snake_case__	)
         model.eval()
         _lowercase     =	model(snake_case__	)
         self.parent.assertEqual(
             result.logits.shape   ,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
         _lowercase     =	model(snake_case__   ,       labels=snake_case__	)
         self.parent.assertEqual(
             result.logits.shape   ,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.prepare_config_and_inputs()
         _lowercase ,    _lowercase ,    _lowercase ,    _lowercase     =	config_and_inputs
         _lowercase     =	{'pixel_values': pixel_values}
         return config, inputs_dict
  
  
  
  
  
  
  @require_torch
  class 					snake_case_							(       lowercase_      ,			lowercase_      ,			unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						(
         (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     __lowerCAmelCase      =						(
         {
             """feature-extraction""": DataaVecVisionModel,
             """image-classification""": DataaVecVisionForImageClassification,
             """image-segmentation""": DataaVecVisionForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )
  
     __lowerCAmelCase      =						False
     __lowerCAmelCase      =						False
     __lowerCAmelCase      =						False
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	DataaVecVisionModelTester(self	)
         _lowercase     =	ConfigTester(
             self   ,       config_class=snake_case__   ,       has_text_modality=snake_case__   ,       hidden_size=37	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         self.config_tester.run_common_tests()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         pass
  
     @require_torch_multi_gpu
     @unittest.skip(
         reason='Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'	)
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         pass
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	str	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
             _lowercase     =	model_class(snake_case__	)
             self.assertIsInstance(model.get_input_embeddings()   ,       (nn.Module)	)
             _lowercase     =	model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(snake_case__   ,       nn.Linear	)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
             _lowercase     =	model_class(snake_case__	)
             _lowercase     =	inspect.signature(model.forward	)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             _lowercase     =	[*signature.parameters.keys()]
  
             _lowercase     =	['pixel_values']
             self.assertListEqual(arg_names[:1]   ,       snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_segmentation(*snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         if not self.model_tester.is_training:
             return
  
         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
         _lowercase     =	True
  
         for model_class in self.all_model_classes:
             if model_class in [*get_values(snake_case__	)]:
                 continue
  
             _lowercase     =	model_class(snake_case__	)
             model.to(snake_case__	)
             model.train()
             _lowercase     =	self._prepare_for_class(snake_case__   ,       snake_case__   ,       return_labels=snake_case__	)
             _lowercase     =	model(**snake_case__	).loss
             loss.backward()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
             return
  
         _lowercase     =	False
         _lowercase     =	True
  
         for model_class in self.all_model_classes:
             if model_class in [*get_values(snake_case__	)] 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     =	inputs_dict['pixel_values'].shape
                 _lowercase     =	torch.zeros(
                     [self.model_tester.batch_size, height, width]   ,       device=snake_case__	).long()
             _lowercase     =	model_class(snake_case__	)
             model.gradient_checkpointing_enable()
             model.to(snake_case__	)
             model.train()
             _lowercase     =	self._prepare_for_class(snake_case__   ,       snake_case__   ,       return_labels=snake_case__	)
             _lowercase     =	model(**snake_case__	).loss
             loss.backward()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase ,    _lowercase     =	self.model_tester.prepare_config_and_inputs_for_common()
  
         _lowercase     =	_config_zero_init(snake_case__	)
         for model_class in self.all_model_classes:
             _lowercase     =	model_class(config=snake_case__	)
             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 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]   ,       snake_case__:	Tuple   ,       snake_case__:	str   ,       snake_case__:	Any   ,       snake_case__:	List[Any]=2E-4   ,       snake_case__:	Optional[int]="outputs"   ,       snake_case__:	List[Any]=None	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         super().check_pt_tf_outputs(snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__   ,       snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case__	)
  
     @slow
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _lowercase     =	DataaVecVisionModel.from_pretrained(snake_case__	)
             self.assertIsNotNone(snake_case__	)
  
  
  
  
  
  
  def        UpperCAmelCase      (   ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
      return image
  
  @require_torch
  @require_vision
  class 					snake_case_							(       unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     @cached_property
     def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         return (
             BeitImageProcessor.from_pretrained('facebook/data2vec-vision-base-ft1k'	) if is_vision_available() else None
         )
  
     @slow
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k'	).to(
             snake_case__	)
  
         _lowercase     =	self.default_image_processor
         _lowercase     =	prepare_img()
         _lowercase     =	image_processor(images=snake_case__   ,       return_tensors='pt'	).to(snake_case__	)
  
         # forward pass
         with torch.no_grad():
             _lowercase     =	model(**snake_case__	)
         _lowercase     =	outputs.logits
  
         # verify the logits
         _lowercase     =	torch.Size((1, 1_000)	)
         self.assertEqual(logits.shape   ,       snake_case__	)
  
         _lowercase     =	torch.tensor([0.32_77, -0.13_95, 0.09_11]	).to(snake_case__	)
  
         self.assertTrue(torch.allclose(logits[0, :3]   ,       snake_case__   ,       atol=1E-4	)	)
  
         _lowercase     =	[model.config.labelaid[i] for i in ['remote control, remote', 'tabby, tabby cat']]
         self.assertEqual(logits[0].topk(2	).indices.cpu().tolist()   ,       snake_case__	)
  
style_context: 
  
  
  
  
  
  
  import argparse
  import json
  from pathlib import Path
  
  import requests
  import torch
  from datasets import load_dataset
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import (
      BeitConfig,
      BeitForImageClassification,
      BeitForMaskedImageModeling,
      BeitForSemanticSegmentation,
      BeitImageProcessor,
  )
  from transformers.image_utils import PILImageResampling
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowerCamelCase										=  logging.get_logger(__name__)
  def        UpperCAmelCase      (   _lowercase,      _lowercase=False,      _lowercase=False      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	'backbone.' if is_semantic else ''
  
      _lowercase     =	[]
      for i in range(config.num_hidden_layers      ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''')      )
          rename_keys.append(
              (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''')      )
          rename_keys.append(
              (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''')      )
  
      # projection layer + position embeddings
      rename_keys.extend(
          [
              (f'''{prefix}cls_token''', 'beit.embeddings.cls_token'),
              (f'''{prefix}patch_embed.proj.weight''', 'beit.embeddings.patch_embeddings.projection.weight'),
              (f'''{prefix}patch_embed.proj.bias''', 'beit.embeddings.patch_embeddings.projection.bias'),
          ]      )
  
      if has_lm_head:
          # mask token + shared relative position bias + layernorm
          rename_keys.extend(
              [
                  ('mask_token', 'beit.embeddings.mask_token'),
                  (
                      'rel_pos_bias.relative_position_bias_table',
                      'beit.encoder.relative_position_bias.relative_position_bias_table',
                  ),
                  (
                      'rel_pos_bias.relative_position_index',
                      'beit.encoder.relative_position_bias.relative_position_index',
                  ),
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
              ]      )
      elif is_semantic:
          # semantic segmentation classification heads
          rename_keys.extend(
              [
                  ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
                  ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
                  ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
                  ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
              ]      )
      else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
                  ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]      )
  
      return rename_keys
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase=False,      _lowercase=False      ):
  
  
      '''simple docstring'''
  
  
  
      for i in range(config.num_hidden_layers      ):
          _lowercase     =	'backbone.' if is_semantic else ''
          # queries, keys and values
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''      )
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''      )
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''      )
  
          _lowercase     =	in_proj_weight[
              : config.hidden_size, :
          ]
          _lowercase     =	q_bias
          _lowercase     =	in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _lowercase     =	in_proj_weight[
              -config.hidden_size :, :
          ]
          _lowercase     =	v_bias
  
          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''      )
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''      )
  
          _lowercase     =	gamma_a
          _lowercase     =	gamma_a
  
          # relative_position bias table + index
          if not has_lm_head:
              # each layer has its own relative position bias
              _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''      )
              _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''      )
  
              _lowercase     =	table
              _lowercase     =	index
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	dct.pop(_lowercase      )
      _lowercase     =	val
  def        UpperCAmelCase      (   ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	'http://images.cocodataset.org/val2017/000000039769.jpg'
      _lowercase     =	Image.open(requests.get(_lowercase,      stream=_lowercase      ).raw      )
      return im
  @torch.no_grad()
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	BeitConfig()
      _lowercase     =	False
      _lowercase     =	False
      _lowercase     =	'huggingface/label-files'
      # set config parameters based on URL
      if checkpoint_url[-9:-4] == "pt22k":
          # masked image modeling
          _lowercase     =	True
          _lowercase     =	True
          _lowercase     =	True
      elif checkpoint_url[-9:-4] == "ft22k":
          # intermediate fine-tuning on ImageNet-22k
          _lowercase     =	True
          _lowercase     =	21841
          _lowercase     =	'imagenet-22k-id2label.json'
          _lowercase     =	json.load(open(hf_hub_download(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
          _lowercase     =	{int(_lowercase      ): v for k, v in idalabel.items()}
          # this dataset contains 21843 labels but the model only has 21841
          # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
          del idalabel[9205]
          del idalabel[15027]
          _lowercase     =	idalabel
          _lowercase     =	{v: k for k, v in idalabel.items()}
      elif checkpoint_url[-8:-4] == "to1k":
          # fine-tuning on ImageNet-1k
          _lowercase     =	True
          _lowercase     =	1000
          _lowercase     =	'imagenet-1k-id2label.json'
          _lowercase     =	json.load(open(hf_hub_download(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
          _lowercase     =	{int(_lowercase      ): v for k, v in idalabel.items()}
          _lowercase     =	idalabel
          _lowercase     =	{v: k for k, v in idalabel.items()}
          if "384" in checkpoint_url:
              _lowercase     =	384
          if "512" in checkpoint_url:
              _lowercase     =	512
      elif "ade20k" in checkpoint_url:
          # fine-tuning
          _lowercase     =	True
          _lowercase     =	150
          _lowercase     =	'ade20k-id2label.json'
          _lowercase     =	json.load(open(hf_hub_download(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
          _lowercase     =	{int(_lowercase      ): v for k, v in idalabel.items()}
          _lowercase     =	idalabel
          _lowercase     =	{v: k for k, v in idalabel.items()}
          _lowercase     =	640
          _lowercase     =	True
      else:
          raise ValueError('Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''      )
  
      # size of the architecture
      if "base" in checkpoint_url:
          pass
      elif "large" in checkpoint_url:
          _lowercase     =	1024
          _lowercase     =	4096
          _lowercase     =	24
          _lowercase     =	16
          if "ade20k" in checkpoint_url:
              _lowercase     =	640
              _lowercase     =	[7, 11, 15, 23]
      else:
          raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'      )
  
      # load state_dict of original model, remove and rename some keys
      _lowercase     =	torch.hub.load_state_dict_from_url(_lowercase,      map_location='cpu',      check_hash=_lowercase      )
      _lowercase     =	state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']
  
      _lowercase     =	create_rename_keys(_lowercase,      has_lm_head=_lowercase,      is_semantic=_lowercase      )
      for src, dest in rename_keys:
          rename_key(_lowercase,      _lowercase,      _lowercase      )
      read_in_q_k_v(_lowercase,      _lowercase,      has_lm_head=_lowercase,      is_semantic=_lowercase      )
      if is_semantic:
          # add prefix to decoder keys
          for key, val in state_dict.copy().items():
              _lowercase     =	state_dict.pop(_lowercase      )
              if key.startswith('backbone.fpn'      ):
                  _lowercase     =	key.replace('backbone.fpn',      'fpn'      )
              _lowercase     =	val
  
      # load HuggingFace model
      if checkpoint_url[-9:-4] == "pt22k":
          _lowercase     =	BeitForMaskedImageModeling(_lowercase      )
      elif "ade20k" in checkpoint_url:
          _lowercase     =	BeitForSemanticSegmentation(_lowercase      )
      else:
          _lowercase     =	BeitForImageClassification(_lowercase      )
      model.eval()
      model.load_state_dict(_lowercase      )
  
      # Check outputs on an image
      if is_semantic:
          _lowercase     =	BeitImageProcessor(size=config.image_size,      do_center_crop=_lowercase      )
          _lowercase     =	load_dataset('hf-internal-testing/fixtures_ade20k',      split='test'      )
          _lowercase     =	Image.open(ds[0]['file']      )
      else:
          _lowercase     =	BeitImageProcessor(
              size=config.image_size,      resample=PILImageResampling.BILINEAR,      do_center_crop=_lowercase      )
          _lowercase     =	prepare_img()
  
      _lowercase     =	image_processor(images=_lowercase,      return_tensors='pt'      )
      _lowercase     =	encoding['pixel_values']
  
      _lowercase     =	model(_lowercase      )
      _lowercase     =	outputs.logits
  
      # verify logits
      _lowercase     =	torch.Size([1, 1000]      )
      if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'      ):
          _lowercase     =	torch.Size([1, 196, 8192]      )
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'      ):
          _lowercase     =	torch.Size([1, 196, 8192]      )
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'      ):
          _lowercase     =	torch.Size([1, 21841]      )
          _lowercase     =	torch.tensor([2.2_288, 2.4_671, 0.7_395]      )
          _lowercase     =	2397
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'      ):
          _lowercase     =	torch.Size([1, 21841]      )
          _lowercase     =	torch.tensor([1.6_881, -0.2_787, 0.5_901]      )
          _lowercase     =	2396
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'      ):
          _lowercase     =	torch.tensor([0.1_241, 0.0_798, -0.6_569]      )
          _lowercase     =	285
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-1.2_385, -1.0_987, -1.0_108]      )
          _lowercase     =	281
      elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-1.5_303, -0.9_484, -0.3_147]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'      ):
          _lowercase     =	torch.tensor([0.4_610, -0.0_928, 0.2_086]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-0.4_804, 0.6_257, -0.1_837]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-0.3_062, 0.7_261, 0.4_852]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'      ):
          _lowercase     =	(1, 150, 160, 160)
          _lowercase     =	torch.tensor(
              [
                  [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
                  [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
                  [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
              ]      )
      elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'      ):
          _lowercase     =	(1, 150, 160, 160)
          _lowercase     =	torch.tensor(
              [
                  [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
                  [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
                  [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
              ]      )
      else:
          raise ValueError('Can\'t verify logits as model is not supported'      )
  
      assert logits.shape == expected_shape, "Shape of logits not as expected"
      if not has_lm_head:
          if is_semantic:
              assert torch.allclose(
                  logits[0, :3, :3, :3],      _lowercase,      atol=1e-3      ), "First elements of logits not as expected"
          else:
              print('Predicted class idx:',      logits.argmax(-1      ).item()      )
              assert torch.allclose(
                  logits[0, :3],      _lowercase,      atol=1e-3      ), "First elements of logits not as expected"
              assert logits.argmax(-1      ).item() == expected_class_idx, "Predicted class index not as expected"
  
      Path(_lowercase      ).mkdir(exist_ok=_lowercase      )
      print(f'''Saving model to {pytorch_dump_folder_path}'''      )
      model.save_pretrained(_lowercase      )
      print(f'''Saving image processor to {pytorch_dump_folder_path}'''      )
      image_processor.save_pretrained(_lowercase      )
  
  
  if __name__ == "__main__":
     lowerCamelCase										=  argparse.ArgumentParser()
  
     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth''',
         type=str,
         help='''URL to the original PyTorch checkpoint (.pth file).''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
     )
     lowerCamelCase										=  parser.parse_args()
     convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
  