



'''simple docstring'''

import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase						      =       logging.get_logger(__name__)

set_seed(770)


__lowerCAmelCase						      =       {
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


__lowerCAmelCase						      =       {
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

__lowerCAmelCase						      =       os.path.dirname(os.path.abspath(__file__))
__lowerCAmelCase						      =       os.path.join(os.path.expanduser('''~'''), '''.cache''')
__lowerCAmelCase						      =       os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False  )      ->				Optional[int]:
  _a					:				int				=							model_type
  if use_small:
    key += "_small"
  return os.path.join(lowerCAmelCase_						,      REMOTE_MODEL_PATHS[key]['file_name']  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				int:
  os.makedirs(lowerCAmelCase_						,      exist_ok=lowerCAmelCase_  )
  hf_hub_download(repo_id=lowerCAmelCase_						,      filename=lowerCAmelCase_						,      local_dir=lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_=False						,      lowerCAmelCase_="text"  )      ->				List[str]:
  if model_type == "text":
    _a					:				List[str]				=							BarkSemanticModel
    _a					:				Optional[Any]				=							BarkSemanticConfig
    _a					:				Any				=							BarkSemanticGenerationConfig
  elif model_type == "coarse":
    _a					:				Tuple				=							BarkCoarseModel
    _a					:				str				=							BarkCoarseConfig
    _a					:				str				=							BarkCoarseGenerationConfig
  elif model_type == "fine":
    _a					:				List[str]				=							BarkFineModel
    _a					:				Optional[Any]				=							BarkFineConfig
    _a					:				str				=							BarkFineGenerationConfig
  else:
    raise NotImplementedError()
  _a					:				Dict				=							f"""{model_type}_small""" if use_small else model_type
  _a					:				Union[str, Any]				=							REMOTE_MODEL_PATHS[model_key]
  if not os.path.exists(lowerCAmelCase_  ):
    logger.info(f"""{model_type} model not found, downloading into `{CACHE_DIR}`."""  )
    _download(model_info['repo_id']						,      model_info['file_name']  )
  _a					:				int				=							torch.load(lowerCAmelCase_						,      map_location=lowerCAmelCase_  )
  # this is a hack
  _a					:				List[Any]				=							checkpoint['model_args']
  if "input_vocab_size" not in model_args:
    _a					:				Dict				=							model_args['vocab_size']
    _a					:				Dict				=							model_args['vocab_size']
    del model_args["vocab_size"]

  # convert Bark model arguments to HF Bark model arguments
  _a					:				List[Any]				=							model_args.pop('n_head'  )
  _a					:				Any				=							model_args.pop('n_embd'  )
  _a					:				List[Any]				=							model_args.pop('n_layer'  )

  _a					:				Optional[int]				=							ConfigClass(**checkpoint['model_args']  )
  _a					:				List[str]				=							ModelClass(config=lowerCAmelCase_  )
  _a					:				Tuple				=							GenerationConfigClass()

  _a					:				Optional[Any]				=							model_generation_config
  _a					:				Optional[Any]				=							checkpoint['model']
  # fixup checkpoint
  _a					:				int				=							'_orig_mod.'
  for k, v in list(state_dict.items()  ):
    if k.startswith(lowerCAmelCase_  ):
      # replace part of the key with corresponding layer name in HF implementation
      _a					:				str				=							k[len(lowerCAmelCase_  ) :]
      for old_layer_name in new_layer_name_dict:
        _a					:				List[Any]				=							new_k.replace(lowerCAmelCase_						,      new_layer_name_dict[old_layer_name]  )

      _a					:				List[Any]				=							state_dict.pop(lowerCAmelCase_  )

  _a					:				List[Any]				=							set(state_dict.keys()  ) - set(model.state_dict().keys()  )
  _a					:				Tuple				=							{k for k in extra_keys if not k.endswith('.attn.bias'  )}
  _a					:				Tuple				=							set(model.state_dict().keys()  ) - set(state_dict.keys()  )
  _a					:				Optional[Any]				=							{k for k in missing_keys if not k.endswith('.attn.bias'  )}
  if len(lowerCAmelCase_  ) != 0:
    raise ValueError(f"""extra keys found: {extra_keys}"""  )
  if len(lowerCAmelCase_  ) != 0:
    raise ValueError(f"""missing keys: {missing_keys}"""  )
  model.load_state_dict(lowerCAmelCase_						,      strict=lowerCAmelCase_  )
  _a					:				Dict				=							model.num_parameters(exclude_embeddings=lowerCAmelCase_  )
  _a					:				Tuple				=							checkpoint['best_val_loss'].item()
  logger.info(f"""model loaded: {round(n_params/1E6						,      1  )}M params, {round(lowerCAmelCase_						,      3  )} loss"""  )
  model.eval()
  model.to(lowerCAmelCase_  )
  del checkpoint, state_dict

  return model



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_=False						,      lowerCAmelCase_="text"  )      ->				List[Any]:
  if model_type not in ("text", "coarse", "fine"):
    raise NotImplementedError()

  _a					:				Optional[int]				=							'cpu'  # do conversion on cpu

  _a					:				Tuple				=							_get_ckpt_path(lowerCAmelCase_						,      use_small=lowerCAmelCase_  )
  _a					:				List[Any]				=							_load_model(lowerCAmelCase_						,      lowerCAmelCase_						,      model_type=lowerCAmelCase_						,      use_small=lowerCAmelCase_  )

  # load bark initial model
  _a					:				Any				=							_bark_load_model(lowerCAmelCase_						,      'cpu'						,      model_type=lowerCAmelCase_						,      use_small=lowerCAmelCase_  )

  if model_type == "text":
    _a					:				int				=							bark_model['model']

  if model.num_parameters(exclude_embeddings=lowerCAmelCase_  ) != bark_model.get_num_params():
    raise ValueError('initial and new models don\'t have the same number of parameters'  )

  # check if same output as the bark model
  _a					:				Any				=							5
  _a					:				List[str]				=							10

  if model_type in ["text", "coarse"]:
    _a					:				Dict				=							torch.randint(256						,      (batch_size, sequence_length)						,      dtype=torch.int  )
    _a					:				Dict				=							bark_model(lowerCAmelCase_  )[0]

    _a					:				Tuple				=							model(lowerCAmelCase_  )

    # take last logits
    _a					:				Optional[int]				=							output_new_model_total.logits[:, [-1], :]

  else:
    _a					:				List[str]				=							3
    _a					:				List[Any]				=							8
    _a					:				Tuple				=							torch.randint(256						,      (batch_size, sequence_length, n_codes_total)						,      dtype=torch.int  )

    _a					:				Union[str, Any]				=							model(lowerCAmelCase_						,      lowerCAmelCase_  )
    _a					:				int				=							bark_model(lowerCAmelCase_						,      lowerCAmelCase_  )

    _a					:				List[str]				=							output_new_model_total.logits

  # output difference should come from the difference of self-attention implementation design
  if output_new_model.shape != output_old_model.shape:
    raise ValueError('initial and new outputs don\'t have the same shape'  )
  if (output_new_model - output_old_model).abs().max().item() > 1E-3:
    raise ValueError('initial and new outputs are not equal'  )

  Path(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
  model.save_pretrained(lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )      ->				Any:
  _a					:				Any				=							os.path.join(lowerCAmelCase_						,      lowerCAmelCase_  )

  _a					:				int				=							BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
  _a					:				Any				=							BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
  _a					:				List[Any]				=							BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase_						,      'config.json'  )  )
  _a					:				List[str]				=							EncodecConfig.from_pretrained('facebook/encodec_24khz'  )

  _a					:				str				=							BarkSemanticModel.from_pretrained(lowerCAmelCase_  )
  _a					:				Dict				=							BarkCoarseModel.from_pretrained(lowerCAmelCase_  )
  _a					:				int				=							BarkFineModel.from_pretrained(lowerCAmelCase_  )
  _a					:				List[Any]				=							EncodecModel.from_pretrained('facebook/encodec_24khz'  )

  _a					:				Any				=							BarkConfig.from_sub_model_configs(
      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )

  _a					:				List[str]				=							BarkGenerationConfig.from_sub_model_configs(
      semantic.generation_config						,      coarseAcoustic.generation_config						,      fineAcoustic.generation_config  )

  _a					:				Optional[Any]				=							BarkModel(lowerCAmelCase_  )

  _a					:				List[str]				=							semantic
  _a					:				Union[str, Any]				=							coarseAcoustic
  _a					:				Optional[int]				=							fineAcoustic
  _a					:				Optional[Any]				=							codec

  _a					:				List[Any]				=							bark_generation_config

  Path(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
  bark.save_pretrained(lowerCAmelCase_						,      repo_id=lowerCAmelCase_						,      push_to_hub=lowerCAmelCase_  )


if __name__ == "__main__":
       __lowerCAmelCase						      =       argparse.ArgumentParser()
       # Required parameters

       parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
       parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

       __lowerCAmelCase						      =       parser.parse_args()

       load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)









'''simple docstring'''

import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils 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, _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, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
							from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import DPTImageProcessor

class      __magic_name__						:







							def __init__(							self :	Any     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Optional[int]=2     ,_UpperCAmelCase :	Tuple=32     ,_UpperCAmelCase :	Dict=16     ,_UpperCAmelCase :	Optional[Any]=3     ,_UpperCAmelCase :	Tuple=True     ,_UpperCAmelCase :	Dict=True     ,_UpperCAmelCase :	str=32     ,_UpperCAmelCase :	Tuple=4     ,_UpperCAmelCase :	List[Any]=[0, 1, 2, 3]     ,_UpperCAmelCase :	List[str]=4     ,_UpperCAmelCase :	Any=37     ,_UpperCAmelCase :	List[Any]="gelu"     ,_UpperCAmelCase :	Optional[int]=0.1     ,_UpperCAmelCase :	Tuple=0.1     ,_UpperCAmelCase :	Optional[Any]=0.02     ,_UpperCAmelCase :	Optional[Any]=3     ,_UpperCAmelCase :	List[str]=[1, 384, 24, 24]     ,_UpperCAmelCase :	Union[str, Any]=True     ,_UpperCAmelCase :	List[str]=None     ,):
									_a					:				int				=							parent
									_a					:				Optional[Any]				=							batch_size
									_a					:				str				=							image_size
									_a					:				str				=							patch_size
									_a					:				Any				=							num_channels
									_a					:				Optional[Any]				=							is_training
									_a					:				str				=							use_labels
									_a					:				Union[str, Any]				=							hidden_size
									_a					:				int				=							num_hidden_layers
									_a					:				str				=							backbone_out_indices
									_a					:				List[Any]				=							num_attention_heads
									_a					:				Any				=							intermediate_size
									_a					:				List[Any]				=							hidden_act
									_a					:				int				=							hidden_dropout_prob
									_a					:				Optional[int]				=							attention_probs_dropout_prob
									_a					:				Optional[int]				=							initializer_range
									_a					:				Tuple				=							num_labels
									_a					:				Tuple				=							backbone_featmap_shape
									_a					:				Optional[int]				=							scope
									_a					:				Dict				=							is_hybrid
									# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
									_a					:				str				=							(image_size // patch_size) ** 2
									_a					:				Optional[int]				=							num_patches + 1







							def 							__lowercase      (							self :	Dict		):
									_a					:				str				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_a					:				Any				=							None
									if self.use_labels:
											_a					:				Union[str, Any]				=							ids_tensor([self.batch_size, self.image_size, self.image_size]     ,self.num_labels		)

									_a					:				int				=							self.get_config()

									return config, pixel_values, labels







							def 							__lowercase      (							self :	int		):
									_a					:				Dict				=							{
									    'global_padding': 'same',
									    'layer_type': 'bottleneck',
									    'depths': [3, 4, 9],
									    'out_features': ['stage1', 'stage2', 'stage3'],
									    'embedding_dynamic_padding': True,
									    'hidden_sizes': [96, 192, 384, 768],
									    'num_groups': 2,
									}

									return DPTConfig(
									    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     ,backbone_out_indices=self.backbone_out_indices     ,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=_UpperCAmelCase     ,initializer_range=self.initializer_range     ,is_hybrid=self.is_hybrid     ,backbone_config=_UpperCAmelCase     ,backbone_featmap_shape=self.backbone_featmap_shape     ,)







							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[Any]		):
									_a					:				Optional[Any]				=							DPTModel(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				List[Any]				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)		)







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	List[Any]     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Any		):
									_a					:				List[Any]				=							self.num_labels
									_a					:				Dict				=							DPTForDepthEstimation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				str				=							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.predicted_depth.shape     ,(self.batch_size, self.image_size, self.image_size)		)







							def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	List[Any]		):
									_a					:				Optional[int]				=							self.num_labels
									_a					:				Optional[int]				=							DPTForSemanticSegmentation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a					:				Optional[int]				=							model(_UpperCAmelCase     ,labels=_UpperCAmelCase		)
									self.parent.assertEqual(
									    result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size, self.image_size)		)







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				List[str]				=							self.prepare_config_and_inputs()
									_a	, _a	, _a					:				List[Any]				=							config_and_inputs
									_a					:				List[Any]				=							{'pixel_values': pixel_values}
									return config, inputs_dict

@require_torch
class      __magic_name__						(   _UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   Optional[int]           = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							lowerCAmelCase	:   int           = (
							    {
							        'depth-estimation': DPTForDepthEstimation,
							        'feature-extraction': DPTModel,
							        'image-segmentation': DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase	:   Union[str, Any]           = False
							lowerCAmelCase	:   Dict           = False
							lowerCAmelCase	:   List[str]           = False







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Union[str, Any]				=							DPTModelTester(self		)
									_a					:				int				=							ConfigTester(self     ,config_class=_UpperCAmelCase     ,has_text_modality=_UpperCAmelCase     ,hidden_size=37		)







							def 							__lowercase      (							self :	Any		):
									self.config_tester.run_common_tests()







							@unittest.skip(reason='DPT does not use inputs_embeds'		)
							def 							__lowercase      (							self :	Tuple		):
									pass







							def 							__lowercase      (							self :	List[str]		):
									_a	, _a					:				Any				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a					:				Union[str, Any]				=							model_class(_UpperCAmelCase		)
											self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module)		)
											_a					:				List[Any]				=							model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_UpperCAmelCase     ,nn.Linear		)		)







							def 							__lowercase      (							self :	Any		):
									_a	, _a					:				List[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a					:				Optional[int]				=							model_class(_UpperCAmelCase		)
											_a					:				Tuple				=							inspect.signature(model.forward		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_a					:				Union[str, Any]				=							[*signature.parameters.keys()]

											_a					:				Optional[Any]				=							['pixel_values']
											self.assertListEqual(arg_names[:1]     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	str		):
									_a					:				Tuple				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	int		):
									_a					:				int				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	str		):
									_a					:				Optional[Any]				=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
													continue

											_a	, _a					:				Optional[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()
											_a					:				Tuple				=							True

											if model_class in get_values(_UpperCAmelCase		):
													continue

											_a					:				str				=							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.train()
											_a					:				Optional[int]				=							self._prepare_for_class(_UpperCAmelCase     ,_UpperCAmelCase     ,return_labels=_UpperCAmelCase		)
											_a					:				Optional[Any]				=							model(**_UpperCAmelCase		).loss
											loss.backward()







							def 							__lowercase      (							self :	Union[str, Any]		):
									for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
													continue

											_a	, _a					:				List[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()
											_a					:				Optional[Any]				=							False
											_a					:				List[Any]				=							True

											if model_class in get_values(_UpperCAmelCase		) or not model_class.supports_gradient_checkpointing:
													continue
											_a					:				str				=							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.gradient_checkpointing_enable()
											model.train()
											_a					:				int				=							self._prepare_for_class(_UpperCAmelCase     ,_UpperCAmelCase     ,return_labels=_UpperCAmelCase		)
											_a					:				Optional[Any]				=							model(**_UpperCAmelCase		).loss
											loss.backward()







							def 							__lowercase      (							self :	str		):
									_a	, _a					:				str				=							self.model_tester.prepare_config_and_inputs_for_common()

									_a					:				List[str]				=							_config_zero_init(_UpperCAmelCase		)
									for model_class in self.all_model_classes:
											_a					:				Tuple				=							model_class(config=_UpperCAmelCase		)
											# Skip the check for the backbone
											_a					:				Any				=							[]
											for name, module in model.named_modules():
													if module.__class__.__name__ == "DPTViTHybridEmbeddings":
															_a					:				Union[str, Any]				=							[F"""{name}.{key}""" for key in module.state_dict().keys()]
															break

											for name, param in model.named_parameters():
													if param.requires_grad:
															if name in backbone_params:
																	continue
															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"""     ,)







							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
							def 							__lowercase      (							self :	Optional[Any]		):
									pass







							@slow
							def 							__lowercase      (							self :	Union[str, Any]		):
									for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											_a					:				List[Any]				=							DPTModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)







							def 							__lowercase      (							self :	Any		):
									# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
									_a	, _a					:				List[Any]				=							self.model_tester.prepare_config_and_inputs_for_common()
									_a					:				Optional[Any]				=							'add'
									with self.assertRaises(_UpperCAmelCase		):
											_a					:				List[str]				=							DPTForDepthEstimation(_UpperCAmelCase		)



def 					__lowerCamelCase					(				)      ->				Optional[Any]:
		_a					:				Optional[Any]				=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

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







							def 							__lowercase      (							self :	Any		):
									_a					:				Optional[Any]				=							DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'		)
									_a					:				Union[str, Any]				=							DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'		).to(_UpperCAmelCase		)

									_a					:				Optional[int]				=							prepare_img()
									_a					:				Optional[Any]				=							image_processor(images=_UpperCAmelCase     ,return_tensors='pt'		).to(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a					:				Tuple				=							model(**_UpperCAmelCase		)
											_a					:				Union[str, Any]				=							outputs.predicted_depth

									# verify the predicted depth
									_a					:				str				=							torch.Size((1, 384, 384)		)
									self.assertEqual(predicted_depth.shape     ,_UpperCAmelCase		)

									_a					:				int				=							torch.tensor(
									    [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]		).to(_UpperCAmelCase		)

									self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100     ,_UpperCAmelCase     ,atol=1E-4		)		)




