





"""simple docstring"""
import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)



def   __lowerCamelCase     (							a_  :	Optional[Any]				,     a_  :	List[Any]=False				,     a_  :	Any=False				,     a_  :	Union[str, Any]="data2vec."							)    ->   str:
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						'''backbone.''' if is_semantic else ''''''

      __SCREAMING_SNAKE_CASE     :Tuple	       =						[]
      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'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')							)
            rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')							)
            rename_keys.append(
                (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')							)
            rename_keys.append(
                (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')							)
            rename_keys.append(
                (f'''{prefix}blocks.{i}.norm2.weight''', f'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')							)
            rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')							)
            rename_keys.append(
                (f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')							)
            rename_keys.append(
                (f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')							)
            rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')							)
            rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')							)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              (f'''{prefix}cls_token''', f'''{hf_prefix}embeddings.cls_token'''),
              (f'''{prefix}patch_embed.proj.weight''', f'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
              (f'''{prefix}patch_embed.proj.bias''', f'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
          ]							)

      if has_lm_head:
            # mask token + shared relative position bias + layernorm
            rename_keys.extend(
                [
                    ('''mask_token''', f'''{hf_prefix}embeddings.mask_token'''),
                    (
                        '''rel_pos_bias.relative_position_bias_table''',
                        f'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
                    ),
                    (
                        '''rel_pos_bias.relative_position_index''',
                        f'''{hf_prefix}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''', f'''{hf_prefix}pooler.layernorm.weight'''),
                    ('''fc_norm.bias''', f'''{hf_prefix}pooler.layernorm.bias'''),
                    ('''head.weight''', '''classifier.weight'''),
                    ('''head.bias''', '''classifier.bias'''),
                ]							)

      return rename_keys



def   __lowerCamelCase     (							a_  :	Tuple				,     a_  :	Optional[Any]				,     a_  :	List[Any]=False				,     a_  :	Any=False				,     a_  :	str="data2vec_vision."							)    ->   Optional[Any]:
      for i in range(config.num_hidden_layers							):
            __SCREAMING_SNAKE_CASE     :int	       =						'''backbone.''' if is_semantic else ''''''
            # queries, keys and values
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''							)
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''							)
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''							)

            __SCREAMING_SNAKE_CASE     :Dict	       =						in_proj_weight[
                : config.hidden_size, :
            ]
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						q_bias
            __SCREAMING_SNAKE_CASE     :Any	       =						in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            __SCREAMING_SNAKE_CASE     :Any	       =						in_proj_weight[
                -config.hidden_size :, :
            ]
            __SCREAMING_SNAKE_CASE     :str	       =						v_bias

            # gamma_1 and gamma_2
            # we call them lambda because otherwise they are renamed when using .from_pretrained
            __SCREAMING_SNAKE_CASE     :Any	       =						state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''							)
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''							)

            __SCREAMING_SNAKE_CASE     :str	       =						gamma_a
            __SCREAMING_SNAKE_CASE     :Dict	       =						gamma_a

            # relative_position bias table + index
            if not has_lm_head:
                  # each layer has its own relative position bias
                  __SCREAMING_SNAKE_CASE     :int	       =						state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''							)
                  __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''							)

                  __SCREAMING_SNAKE_CASE     :Optional[int]	       =						table
                  __SCREAMING_SNAKE_CASE     :Any	       =						index



def   __lowerCamelCase     (							)    ->   Dict:
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						argparse.ArgumentParser(
          '''Convert Data2VecVision to HF for image classification and pretraining'''				,     add_help=a_							)
      parser.add_argument('''--hf_checkpoint_name'''				,     type=a_							)
      parser.add_argument('''--input_size'''				,     default=2_24				,     type=a_				,     help='''images input size'''							)
      parser.add_argument('''--beit_checkpoint'''				,     default=''''''				,     help='''beit checkpoint'''							)

      return parser.parse_args()



def   __lowerCamelCase     (							a_  :	Any				,     a_  :	Union[str, Any]				,     a_  :	int							)    ->   int:
      def load_state_dict(a_  :	List[Any]				,     a_  :	Optional[int]				,     a_  :	Tuple=""				,     a_  :	Tuple="relative_position_index"							):
            __SCREAMING_SNAKE_CASE     :int	       =						[]
            __SCREAMING_SNAKE_CASE     :Tuple	       =						[]
            __SCREAMING_SNAKE_CASE     :Tuple	       =						[]
            # copy state_dict so _load_from_state_dict can modify it
            __SCREAMING_SNAKE_CASE     :Any	       =						getattr(a_				,     '''_metadata'''				,     a_							)
            __SCREAMING_SNAKE_CASE     :Any	       =						state_dict.copy()
            if metadata is not None:
                  __SCREAMING_SNAKE_CASE     :List[str]	       =						metadata

            def load(a_  :	Union[str, Any]				,     a_  :	str=""							):
                  __SCREAMING_SNAKE_CASE     :str	       =						{} if metadata is None else metadata.get(prefix[:-1]				,     {}							)
                  module._load_from_state_dict(
                      a_				,     a_				,     a_				,     a_				,     a_				,     a_				,     a_							)
                  for name, child in module._modules.items():
                        if child is not None:
                              load(a_				,     prefix + name + '''.'''							)

            load(a_				,     prefix=a_							)

            __SCREAMING_SNAKE_CASE     :Any	       =						[]
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[]
            for key in missing_keys:
                  __SCREAMING_SNAKE_CASE     :Tuple	       =						True
                  for ignore_key in ignore_missing.split('''|'''							):
                        if ignore_key in key:
                              __SCREAMING_SNAKE_CASE     :List[Any]	       =						False
                              break
                  if keep_flag:
                        warn_missing_keys.append(a_							)
                  else:
                        ignore_missing_keys.append(a_							)

            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						warn_missing_keys

            if len(a_							) > 0:
                  print(
                      '''Weights of {} not initialized from pretrained model: {}'''.format(
                          model.__class__.__name__				,     a_							)							)
            if len(a_							) > 0:
                  print('''Weights from pretrained model not used in {}: {}'''.format(model.__class__.__name__				,     a_							)							)
            if len(a_							) > 0:
                  print(
                      '''Ignored weights of {} not initialized from pretrained model: {}'''.format(
                          model.__class__.__name__				,     a_							)							)
            if len(a_							) > 0:
                  print('''\n'''.join(a_							)							)

      __SCREAMING_SNAKE_CASE     :int	       =						{
          '''pretrained''': False,
          '''use_shared_rel_pos_bias''': True,
          '''use_abs_pos_emb''': False,
          '''init_values''': 0.1,
      }

      if is_finetuned:
            model_kwargs.update(
                {
                    '''num_classes''': 10_00,
                    '''use_mean_pooling''': True,
                    '''init_scale''': 0.001,
                    '''use_rel_pos_bias''': True,
                }							)

      __SCREAMING_SNAKE_CASE     :List[Any]	       =						create_model(
          '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''				,     **a_				,     )
      __SCREAMING_SNAKE_CASE     :str	       =						model.patch_embed.patch_size
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						(args.input_size // patch_size[0], args.input_size // patch_size[1])
      __SCREAMING_SNAKE_CASE     :str	       =						torch.load(args.beit_checkpoint				,     map_location='''cpu'''							)

      print(f'''Load ckpt from {args.beit_checkpoint}'''							)
      __SCREAMING_SNAKE_CASE     :int	       =						None
      for model_key in ("model", "module"):
            if model_key in checkpoint:
                  __SCREAMING_SNAKE_CASE     :Any	       =						checkpoint[model_key]
                  print(f'''Load state_dict by model_key = {model_key}'''							)
                  break

      __SCREAMING_SNAKE_CASE     :int	       =						list(checkpoint_model.keys()							)
      for key in all_keys:
            if "relative_position_index" in key:
                  checkpoint_model.pop(a_							)

            if "relative_position_bias_table" in key:
                  __SCREAMING_SNAKE_CASE     :int	       =						checkpoint_model[key]
                  __SCREAMING_SNAKE_CASE     :int	       =						rel_pos_bias.size()
                  __SCREAMING_SNAKE_CASE     :Any	       =						model.state_dict()[key].size()
                  __SCREAMING_SNAKE_CASE     :Dict	       =						model.patch_embed.patch_shape
                  if dst_patch_shape[0] != dst_patch_shape[1]:
                        raise NotImplementedError()

      load_state_dict(a_				,     a_				,     prefix=''''''							)

      return model



def   __lowerCamelCase     (							)    ->   Optional[Any]:
      __SCREAMING_SNAKE_CASE     :Any	       =						get_args()

      __SCREAMING_SNAKE_CASE     :Any	       =						'''ft1k''' in args.hf_checkpoint_name
      __SCREAMING_SNAKE_CASE     :Any	       =						'''large''' in args.hf_checkpoint_name

      if is_finetuned:
            # To convert Beit's data2vec_vision to HF you need to copy
            # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
            # into this folder.
            import modeling_finetune  # noqa: F401
      else:
            # To convert Beit's data2vec_vision to HF you need to copy
            # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
            # into this folder
            # IMPORTANT: Note that for now we've only converted the down-stream
            # model and not the full pretrained model. This means for the integration
            # test you need to add a `return x` after the following line:
            # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
            # to make the integration test pass.
            import modeling_cyclical  # noqa: F401

      # 1. Create model config
      __SCREAMING_SNAKE_CASE     :Tuple	       =						DataaVecVisionConfig()
      if is_finetuned:
            __SCREAMING_SNAKE_CASE     :Any	       =						True
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						False
            __SCREAMING_SNAKE_CASE     :Tuple	       =						True
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						10_00

            __SCREAMING_SNAKE_CASE     :str	       =						'''huggingface/label-files'''
            __SCREAMING_SNAKE_CASE     :List[str]	       =						'''imagenet-1k-id2label.json'''
            __SCREAMING_SNAKE_CASE     :List[str]	       =						json.load(open(hf_hub_download(a_				,     a_				,     repo_type='''dataset'''							)				,     '''r'''							)							)
            __SCREAMING_SNAKE_CASE     :Any	       =						{int(a_							): v for k, v in idalabel.items()}
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						idalabel
            __SCREAMING_SNAKE_CASE     :str	       =						{v: k for k, v in idalabel.items()}
      else:
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						False
            __SCREAMING_SNAKE_CASE     :str	       =						True
            __SCREAMING_SNAKE_CASE     :str	       =						False

      if is_large:
            __SCREAMING_SNAKE_CASE     :Any	       =						10_24
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						40_96
            __SCREAMING_SNAKE_CASE     :int	       =						24
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						16

      # 2. Load Beit model
      __SCREAMING_SNAKE_CASE     :Tuple	       =						load_beit_model(a_				,     a_				,     a_							)
      orig_model.eval()

      # 3. Forward Beit model
      __SCREAMING_SNAKE_CASE     :Dict	       =						BeitImageProcessor(size=config.image_size				,     do_center_crop=a_							)
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''							)
      __SCREAMING_SNAKE_CASE     :Tuple	       =						image_processor(images=a_				,     return_tensors='''pt'''							)
      __SCREAMING_SNAKE_CASE     :Any	       =						encoding['''pixel_values''']

      __SCREAMING_SNAKE_CASE     :List[str]	       =						(pixel_values,) if is_finetuned else (pixel_values, None)
      with torch.no_grad():
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						orig_model(*a_							)

      # 4. Load HF Data2VecVision model
      if is_finetuned:
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						DataaVecVisionForImageClassification(a_							)
            hf_model.eval()
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						False
            __SCREAMING_SNAKE_CASE     :List[str]	       =						'''data2vec_vision.'''
      else:
            __SCREAMING_SNAKE_CASE     :Tuple	       =						DataaVecVisionModel(a_							)
            hf_model.eval()
            __SCREAMING_SNAKE_CASE     :List[str]	       =						True
            __SCREAMING_SNAKE_CASE     :Tuple	       =						''''''

      __SCREAMING_SNAKE_CASE     :Tuple	       =						create_rename_keys(a_				,     hf_prefix=a_				,     has_lm_head=a_							)
      __SCREAMING_SNAKE_CASE     :Any	       =						orig_model.state_dict()
      for src, dest in rename_keys:
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						state_dict.pop(a_							)
            __SCREAMING_SNAKE_CASE     :Tuple	       =						val

      read_in_q_k_v(a_				,     a_				,     hf_prefix=a_				,     has_lm_head=a_							)
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						hf_model.load_state_dict(a_				,     strict=a_							)
      print('''HF missing'''				,     a_							)
      print('''HF unexpected_keys'''				,     a_							)

      # 5. Forward HF Data2VecVision model
      with torch.no_grad():
            __SCREAMING_SNAKE_CASE     :int	       =						hf_model(a_							)

      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

      # 6. Compare
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						torch.max(torch.abs(hf_output - orig_model_output							)							).item()

      print(f'''max_absolute_diff = {max_absolute_diff}'''							)
      __SCREAMING_SNAKE_CASE     :Dict	       =						torch.allclose(a_				,     a_				,     atol=1e-3							)
      print('''Do both models output the same tensors?'''				,     '''🔥''' if success else '''💩'''							)
      if not success:
            raise Exception('''Something went wRoNg'''							)

      # 7. Save
      print(f'''Saving to {args.hf_checkpoint_name}'''							)
      hf_model.save_pretrained(args.hf_checkpoint_name							)
      image_processor.save_pretrained(args.hf_checkpoint_name							)


if __name__ == "__main__":
  main()
  # Run the following to convert checkpoints
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./pretrained_base.pt \
  #          --hf_checkpoint_name "./data2vec-vision-base"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./finetuned_base.pt \
  #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./pretrained_large.pt \
  #          --hf_checkpoint_name "./data2vec-vision-large"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./finetuned_large.pt \
  #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"






"""simple docstring"""
import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
  from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor






class       _SCREAMING_SNAKE_CASE(     A						):




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Any	       =						self.config_class(**self.inputs_dict							)
             self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__	,'''hidden_sizes'''							)							)
             self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__	,'''num_attention_heads'''							)							)
             self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__	,'''num_encoder_blocks'''							)							)






class       _SCREAMING_SNAKE_CASE:




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=13	,SCREAMING_SNAKE_CASE__=64	,SCREAMING_SNAKE_CASE__=3	,SCREAMING_SNAKE_CASE__=4	,SCREAMING_SNAKE_CASE__=[2, 2, 2, 2]	,SCREAMING_SNAKE_CASE__=[8, 4, 2, 1]	,SCREAMING_SNAKE_CASE__=[16, 32, 64, 1_28]	,SCREAMING_SNAKE_CASE__=[1, 4, 8, 16]	,SCREAMING_SNAKE_CASE__=[1, 2, 4, 8]	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__="gelu"	,SCREAMING_SNAKE_CASE__=0.1	,SCREAMING_SNAKE_CASE__=0.1	,SCREAMING_SNAKE_CASE__=0.0_2	,SCREAMING_SNAKE_CASE__=16	,SCREAMING_SNAKE_CASE__=3	,SCREAMING_SNAKE_CASE__=None	,)    ->   List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						parent
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						batch_size
             __SCREAMING_SNAKE_CASE     :str	       =						image_size
             __SCREAMING_SNAKE_CASE     :Dict	       =						num_channels
             __SCREAMING_SNAKE_CASE     :str	       =						num_encoder_blocks
             __SCREAMING_SNAKE_CASE     :Any	       =						sr_ratios
             __SCREAMING_SNAKE_CASE     :Tuple	       =						depths
             __SCREAMING_SNAKE_CASE     :Dict	       =						hidden_sizes
             __SCREAMING_SNAKE_CASE     :Any	       =						downsampling_rates
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						num_attention_heads
             __SCREAMING_SNAKE_CASE     :Dict	       =						is_training
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						use_labels
             __SCREAMING_SNAKE_CASE     :List[str]	       =						hidden_act
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						hidden_dropout_prob
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE     :int	       =						initializer_range
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						decoder_hidden_size
             __SCREAMING_SNAKE_CASE     :Any	       =						num_labels
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						scope




       def 					_UpperCamelCase  (		self							)    ->   str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             __SCREAMING_SNAKE_CASE     :Any	       =						None
             if self.use_labels:
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						ids_tensor([self.batch_size, self.image_size, self.image_size]	,self.num_labels							)

             __SCREAMING_SNAKE_CASE     :int	       =						self.get_config()
             return config, pixel_values, labels




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





             """simple docstring"""






             return GLPNConfig(
                 image_size=self.image_size	,num_channels=self.num_channels	,num_encoder_blocks=self.num_encoder_blocks	,depths=self.depths	,hidden_sizes=self.hidden_sizes	,num_attention_heads=self.num_attention_heads	,hidden_act=self.hidden_act	,hidden_dropout_prob=self.hidden_dropout_prob	,attention_probs_dropout_prob=self.attention_probs_dropout_prob	,initializer_range=self.initializer_range	,decoder_hidden_size=self.decoder_hidden_size	,)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						GLPNModel(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE     :int	       =						model(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Dict	       =						self.image_size // (self.downsampling_rates[-1] * 2)
             self.parent.assertEqual(
                 result.last_hidden_state.shape	,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Any	       =						self.num_labels
             __SCREAMING_SNAKE_CASE     :List[str]	       =						GLPNForDepthEstimation(SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						model(SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.predicted_depth.shape	,(self.batch_size, self.image_size, self.image_size)							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						model(SCREAMING_SNAKE_CASE__	,labels=SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.predicted_depth.shape	,(self.batch_size, self.image_size, self.image_size)							)




       def 					_UpperCamelCase  (		self							)    ->   Any:





             """simple docstring"""






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






@require_torch
class       _SCREAMING_SNAKE_CASE(     A					,			A					,			unittest.TestCase						):
       SCREAMING_SNAKE_CASE_     :   Optional[Any]					=						(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE_     :   int					=						(
           {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
       )

       SCREAMING_SNAKE_CASE_     :   Optional[Any]					=						False
       SCREAMING_SNAKE_CASE_     :   str					=						False
       SCREAMING_SNAKE_CASE_     :   Dict					=						False




       def 					_UpperCamelCase  (		self							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						GLPNModelTester(self							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						GLPNConfigTester(self	,config_class=SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   Any:





             """simple docstring"""






             self.config_tester.run_common_tests()




       def 					_UpperCamelCase  (		self							)    ->   Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE__							)




       @unittest.skip('''GLPN does not use inputs_embeds'''							)
       def 					_UpperCamelCase  (		self							)    ->   Tuple:





             """simple docstring"""






             pass




       @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''							)
       def 					_UpperCamelCase  (		self							)    ->   str:





             """simple docstring"""






             pass




       def 					_UpperCamelCase  (		self							)    ->   List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.model_tester.prepare_config_and_inputs_for_common()

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

                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						['''pixel_values''']
                   self.assertListEqual(arg_names[:1]	,SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :List[Any]	       =						self.model_tester.prepare_config_and_inputs_for_common()
             __SCREAMING_SNAKE_CASE     :Any	       =						True

             for model_class in self.all_model_classes:
                   __SCREAMING_SNAKE_CASE     :str	       =						True
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						False
                   __SCREAMING_SNAKE_CASE     :int	       =						True
                   __SCREAMING_SNAKE_CASE     :Dict	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.eval()
                   with torch.no_grad():
                         __SCREAMING_SNAKE_CASE     :str	       =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)							)
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						outputs.attentions

                   __SCREAMING_SNAKE_CASE     :List[str]	       =						sum(self.model_tester.depths							)
                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,SCREAMING_SNAKE_CASE__							)

                   # check that output_attentions also work using config
                   del inputs_dict["output_attentions"]
                   __SCREAMING_SNAKE_CASE     :Dict	       =						True
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.eval()
                   with torch.no_grad():
                         __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)							)
                   __SCREAMING_SNAKE_CASE     :str	       =						outputs.attentions

                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,SCREAMING_SNAKE_CASE__							)

                   # verify the first attentions (first block, first layer)
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						(self.model_tester.image_size // 4) ** 2
                   __SCREAMING_SNAKE_CASE     :Any	       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                   self.assertListEqual(
                       list(attentions[0].shape[-3:]							)	,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]	,)

                   # verify the last attentions (last block, last layer)
                   __SCREAMING_SNAKE_CASE     :str	       =						(self.model_tester.image_size // 32) ** 2
                   __SCREAMING_SNAKE_CASE     :Any	       =						(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
                   self.assertListEqual(
                       list(attentions[-1].shape[-3:]							)	,[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]	,)
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						len(SCREAMING_SNAKE_CASE__							)

                   # Check attention is always last and order is fine
                   __SCREAMING_SNAKE_CASE     :Any	       =						True
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						True
                   __SCREAMING_SNAKE_CASE     :Any	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.eval()
                   with torch.no_grad():
                         __SCREAMING_SNAKE_CASE     :Dict	       =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)							)

                   self.assertEqual(out_len + 1	,len(SCREAMING_SNAKE_CASE__							)							)

                   __SCREAMING_SNAKE_CASE     :int	       =						outputs.attentions

                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,SCREAMING_SNAKE_CASE__							)
                   # verify the first attentions (first block, first layer)
                   __SCREAMING_SNAKE_CASE     :str	       =						(self.model_tester.image_size // 4) ** 2
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                   self.assertListEqual(
                       list(self_attentions[0].shape[-3:]							)	,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]	,)




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





             """simple docstring"""






             def check_hidden_states_output(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE     :Any	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.eval()

                   with torch.no_grad():
                         __SCREAMING_SNAKE_CASE     :List[str]	       =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)							)

                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						outputs.hidden_states

                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						self.model_tester.num_encoder_blocks
                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,SCREAMING_SNAKE_CASE__							)

                   # verify the first hidden states (first block)
                   self.assertListEqual(
                       list(hidden_states[0].shape[-3:]							)	,[
                           self.model_tester.hidden_sizes[0],
                           self.model_tester.image_size // 4,
                           self.model_tester.image_size // 4,
                       ]	,)

             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Dict	       =						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						True
                   check_hidden_states_output(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   __SCREAMING_SNAKE_CASE     :int	       =						True

                   check_hidden_states_output(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             if not self.model_tester.is_training:
                   return

             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Dict	       =						self.model_tester.prepare_config_and_inputs_for_common()
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						True

             for model_class in self.all_model_classes:
                   if model_class in get_values(SCREAMING_SNAKE_CASE__							):
                         continue
                   # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
                   # this can then be incorporated into _prepare_for_class in test_modeling_common.py
                   if model_class.__name__ == "GLPNForDepthEstimation":
                         __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Any	       =						inputs_dict['''pixel_values'''].shape
                         __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						torch.zeros(
                             [self.model_tester.batch_size, height, width]	,device=SCREAMING_SNAKE_CASE__							).long()
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.train()
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,return_labels=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :str	       =						model(**SCREAMING_SNAKE_CASE__							).loss
                   loss.backward()




       @slow
       def 					_UpperCamelCase  (		self							)    ->   Optional[Any]:





             """simple docstring"""






             for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __SCREAMING_SNAKE_CASE     :str	       =						GLPNModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)




def   __lowerCamelCase     (							)    ->   List[str]:
      __SCREAMING_SNAKE_CASE     :int	       =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
      return image







@require_torch
@require_vision
@slow
class       _SCREAMING_SNAKE_CASE(     unittest.TestCase						):




       @slow
       def 					_UpperCamelCase  (		self							)    ->   Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Any	       =						GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :str	       =						prepare_img()
             __SCREAMING_SNAKE_CASE     :int	       =						image_processor(images=SCREAMING_SNAKE_CASE__	,return_tensors='''pt'''							).to(SCREAMING_SNAKE_CASE__							)

             # forward pass
             with torch.no_grad():
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						model(**SCREAMING_SNAKE_CASE__							)

             # verify the predicted depth
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						torch.Size([1, 4_80, 6_40]							)
             self.assertEqual(outputs.predicted_depth.shape	,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :str	       =						torch.tensor(
                 [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]							).to(SCREAMING_SNAKE_CASE__							)

             self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]	,SCREAMING_SNAKE_CASE__	,atol=1E-4							)							)