


import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__a	 =      logging.get_logger(__name__)
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->str:
   UpperCAmelCase      					=  OrderedDict()
   for key, value in state_dict.items():
      if key.startswith("""module.encoder""" ):
         UpperCAmelCase      					=  key.replace("""module.encoder"""	,    """glpn.encoder""" )
      if key.startswith("""module.decoder""" ):
         UpperCAmelCase      					=  key.replace("""module.decoder"""	,    """decoder.stages""" )
      if "patch_embed" in key:
         # replace for example patch_embed1 by patch_embeddings.0
         UpperCAmelCase      					=  key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
         UpperCAmelCase      					=  key.replace(F"""patch_embed{idx}"""	,    F"""patch_embeddings.{int(UpperCamelCase__ )-1}""" )
      if "norm" in key:
         UpperCAmelCase      					=  key.replace("""norm"""	,    """layer_norm""" )
      if "glpn.encoder.layer_norm" in key:
         # replace for example layer_norm1 by layer_norm.0
         UpperCAmelCase      					=  key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
         UpperCAmelCase      					=  key.replace(F"""layer_norm{idx}"""	,    F"""layer_norm.{int(UpperCamelCase__ )-1}""" )
      if "layer_norm1" in key:
         UpperCAmelCase      					=  key.replace("""layer_norm1"""	,    """layer_norm_1""" )
      if "layer_norm2" in key:
         UpperCAmelCase      					=  key.replace("""layer_norm2"""	,    """layer_norm_2""" )
      if "block" in key:
         # replace for example block1 by block.0
         UpperCAmelCase      					=  key[key.find("""block""" ) + len("""block""" )]
         UpperCAmelCase      					=  key.replace(F"""block{idx}"""	,    F"""block.{int(UpperCamelCase__ )-1}""" )
      if "attn.q" in key:
         UpperCAmelCase      					=  key.replace("""attn.q"""	,    """attention.self.query""" )
      if "attn.proj" in key:
         UpperCAmelCase      					=  key.replace("""attn.proj"""	,    """attention.output.dense""" )
      if "attn" in key:
         UpperCAmelCase      					=  key.replace("""attn"""	,    """attention.self""" )
      if "fc1" in key:
         UpperCAmelCase      					=  key.replace("""fc1"""	,    """dense1""" )
      if "fc2" in key:
         UpperCAmelCase      					=  key.replace("""fc2"""	,    """dense2""" )
      if "linear_pred" in key:
         UpperCAmelCase      					=  key.replace("""linear_pred"""	,    """classifier""" )
      if "linear_fuse" in key:
         UpperCAmelCase      					=  key.replace("""linear_fuse.conv"""	,    """linear_fuse""" )
         UpperCAmelCase      					=  key.replace("""linear_fuse.bn"""	,    """batch_norm""" )
      if "linear_c" in key:
         # replace for example linear_c4 by linear_c.3
         UpperCAmelCase      					=  key[key.find("""linear_c""" ) + len("""linear_c""" )]
         UpperCAmelCase      					=  key.replace(F"""linear_c{idx}"""	,    F"""linear_c.{int(UpperCamelCase__ )-1}""" )
      if "bot_conv" in key:
         UpperCAmelCase      					=  key.replace("""bot_conv"""	,    """0.convolution""" )
      if "skip_conv1" in key:
         UpperCAmelCase      					=  key.replace("""skip_conv1"""	,    """1.convolution""" )
      if "skip_conv2" in key:
         UpperCAmelCase      					=  key.replace("""skip_conv2"""	,    """2.convolution""" )
      if "fusion1" in key:
         UpperCAmelCase      					=  key.replace("""fusion1"""	,    """1.fusion""" )
      if "fusion2" in key:
         UpperCAmelCase      					=  key.replace("""fusion2"""	,    """2.fusion""" )
      if "fusion3" in key:
         UpperCAmelCase      					=  key.replace("""fusion3"""	,    """3.fusion""" )
      if "fusion" in key and "conv" in key:
         UpperCAmelCase      					=  key.replace("""conv"""	,    """convolutional_layer""" )
      if key.startswith("""module.last_layer_depth""" ):
         UpperCAmelCase      					=  key.replace("""module.last_layer_depth"""	,    """head.head""" )
      UpperCAmelCase      					=  value

   return new_state_dict
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[Any]:
   # for each of the encoder blocks:
   for i in range(config.num_encoder_blocks ):
      for j in range(config.depths[i] ):
         # read in weights + bias of keys and values (which is a single matrix in the original implementation)
         UpperCAmelCase      					=  state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
         UpperCAmelCase      					=  state_dict.pop(F"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" )
         # next, add keys and values (in that order) to the state dict
         UpperCAmelCase      					=  kv_weight[
             : config.hidden_sizes[i], :
         ]
         UpperCAmelCase      					=  kv_bias[: config.hidden_sizes[i]]
         UpperCAmelCase      					=  kv_weight[
             config.hidden_sizes[i] :, :
         ]
         UpperCAmelCase      					=  kv_bias[config.hidden_sizes[i] :]
def 	_UpperCamelCase					(    )    ->Dict:
   UpperCAmelCase      					=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   UpperCAmelCase      					=  Image.open(requests.get(UpperCamelCase__	,    stream=UpperCamelCase__ ).raw )

   return image
@torch.no_grad()
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_=False	,    lowerCAmelCase_=None )    ->Optional[int]:
   UpperCAmelCase      					=  GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]	,    decoder_hidden_size=6_4	,    depths=[3, 8, 2_7, 3] )

   # load image processor (only resize + rescale)
   UpperCAmelCase      					=  GLPNImageProcessor()

   # prepare image
   UpperCAmelCase      					=  prepare_img()
   UpperCAmelCase      					=  image_processor(images=UpperCamelCase__	,    return_tensors="""pt""" ).pixel_values

   logger.info("""Converting model...""" )

   # load original state dict
   UpperCAmelCase      					=  torch.load(UpperCamelCase__	,    map_location=torch.device("""cpu""" ) )

   # rename keys
   UpperCAmelCase      					=  rename_keys(UpperCamelCase__ )

   # key and value matrices need special treatment
   read_in_k_v(UpperCamelCase__	,    UpperCamelCase__ )

   # create HuggingFace model and load state dict
   UpperCAmelCase      					=  GLPNForDepthEstimation(UpperCamelCase__ )
   model.load_state_dict(UpperCamelCase__ )
   model.eval()

   # forward pass
   UpperCAmelCase      					=  model(UpperCamelCase__ )
   UpperCAmelCase      					=  outputs.predicted_depth

   # verify output
   if model_name is not None:
      if "nyu" in model_name:
         UpperCAmelCase      					=  torch.tensor(
             [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
      elif "kitti" in model_name:
         UpperCAmelCase      					=  torch.tensor(
             [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
      else:
         raise ValueError(F"""Unknown model name: {model_name}""" )

      UpperCAmelCase      					=  torch.Size([1, 4_8_0, 6_4_0] )

      assert predicted_depth.shape == expected_shape
      assert torch.allclose(predicted_depth[0, :3, :3]	,    UpperCamelCase__	,    atol=1e-4 )
      print("""Looks ok!""" )

   # finally, push to hub if required
   if push_to_hub:
      logger.info("""Pushing model and image processor to the hub...""" )
      model.push_to_hub(
          repo_path_or_name=Path(UpperCamelCase__	,    UpperCamelCase__ )	,    organization="""nielsr"""	,    commit_message="""Add model"""	,    use_temp_dir=UpperCamelCase__	,    )
      image_processor.push_to_hub(
          repo_path_or_name=Path(UpperCamelCase__	,    UpperCamelCase__ )	,    organization="""nielsr"""	,    commit_message="""Add image processor"""	,    use_temp_dir=UpperCamelCase__	,    )


if __name__ == "__main__":
 __a	 =      argparse.ArgumentParser()

 parser.add_argument(
     """--checkpoint_path""",
     default=None,
     type=str,
     help="""Path 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."""
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
 )
 parser.add_argument(
     """--model_name""",
     default="""glpn-kitti""",
     type=str,
     help="""Name of the model in case you're pushing to the hub.""",
 )
 __a	 =      parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase  =logging.get_logger(__name__)

lowerCamelCase  ={
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	'''wav2vec2'''






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE=3_2    ,    __SCREAMING_SNAKE_CASE=7_6_8    ,    __SCREAMING_SNAKE_CASE=1_2    ,    __SCREAMING_SNAKE_CASE=1_2    ,    __SCREAMING_SNAKE_CASE=3_0_7_2    ,    __SCREAMING_SNAKE_CASE="gelu"    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=0.0    ,    __SCREAMING_SNAKE_CASE=0.0    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=0.02    ,    __SCREAMING_SNAKE_CASE=1e-5    ,    __SCREAMING_SNAKE_CASE="group"    ,    __SCREAMING_SNAKE_CASE="gelu"    ,    __SCREAMING_SNAKE_CASE=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)    ,    __SCREAMING_SNAKE_CASE=(5, 2, 2, 2, 2, 2, 2)    ,    __SCREAMING_SNAKE_CASE=(1_0, 3, 3, 3, 3, 2, 2)    ,    __SCREAMING_SNAKE_CASE=False    ,    __SCREAMING_SNAKE_CASE=1_2_8    ,    __SCREAMING_SNAKE_CASE=1_6    ,    __SCREAMING_SNAKE_CASE=False    ,    __SCREAMING_SNAKE_CASE=True    ,    __SCREAMING_SNAKE_CASE=0.05    ,    __SCREAMING_SNAKE_CASE=1_0    ,    __SCREAMING_SNAKE_CASE=2    ,    __SCREAMING_SNAKE_CASE=0.0    ,    __SCREAMING_SNAKE_CASE=1_0    ,    __SCREAMING_SNAKE_CASE=0    ,    __SCREAMING_SNAKE_CASE=3_2_0    ,    __SCREAMING_SNAKE_CASE=2    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=1_0_0    ,    __SCREAMING_SNAKE_CASE=2_5_6    ,    __SCREAMING_SNAKE_CASE=2_5_6    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE="sum"    ,    __SCREAMING_SNAKE_CASE=False    ,    __SCREAMING_SNAKE_CASE=False    ,    __SCREAMING_SNAKE_CASE=2_5_6    ,    __SCREAMING_SNAKE_CASE=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)    ,    __SCREAMING_SNAKE_CASE=(5, 3, 3, 1, 1)    ,    __SCREAMING_SNAKE_CASE=(1, 2, 3, 1, 1)    ,    __SCREAMING_SNAKE_CASE=5_1_2    ,    __SCREAMING_SNAKE_CASE=0    ,    __SCREAMING_SNAKE_CASE=1    ,    __SCREAMING_SNAKE_CASE=2    ,    __SCREAMING_SNAKE_CASE=False    ,    __SCREAMING_SNAKE_CASE=3    ,    __SCREAMING_SNAKE_CASE=2    ,    __SCREAMING_SNAKE_CASE=3    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE=None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> str:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE    ,    pad_token_id=__SCREAMING_SNAKE_CASE    ,    bos_token_id=__SCREAMING_SNAKE_CASE    ,    eos_token_id=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : str											=					hidden_size
           UpperCamelCase__     : List[Any]											=					feat_extract_norm
           UpperCamelCase__     : Union[str, Any]											=					feat_extract_activation
           UpperCamelCase__     : Union[str, Any]											=					list(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Dict											=					list(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Dict											=					list(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Tuple											=					conv_bias
           UpperCamelCase__     : Optional[Any]											=					num_conv_pos_embeddings
           UpperCamelCase__     : Tuple											=					num_conv_pos_embedding_groups
           UpperCamelCase__     : Tuple											=					len(self.conv_dim	)
           UpperCamelCase__     : Optional[Any]											=					num_hidden_layers
           UpperCamelCase__     : Union[str, Any]											=					intermediate_size
           UpperCamelCase__     : Union[str, Any]											=					hidden_act
           UpperCamelCase__     : List[Any]											=					num_attention_heads
           UpperCamelCase__     : Optional[int]											=					hidden_dropout
           UpperCamelCase__     : Tuple											=					attention_dropout
           UpperCamelCase__     : List[Any]											=					activation_dropout
           UpperCamelCase__     : Optional[int]											=					feat_proj_dropout
           UpperCamelCase__     : int											=					final_dropout
           UpperCamelCase__     : str											=					layerdrop
           UpperCamelCase__     : Dict											=					layer_norm_eps
           UpperCamelCase__     : str											=					initializer_range
           UpperCamelCase__     : List[Any]											=					vocab_size
           UpperCamelCase__     : Dict											=					do_stable_layer_norm
           UpperCamelCase__     : Dict											=					use_weighted_layer_sum

           if (
               (len(self.conv_stride	) != self.num_feat_extract_layers)
               or (len(self.conv_kernel	) != self.num_feat_extract_layers)
               or (len(self.conv_dim	) != self.num_feat_extract_layers)
           ):
                 raise ValueError(
                     '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                     ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                     F''' {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,'''
                     F''' `len(config.conv_kernel) = {len(self.conv_kernel	)}`.'''	)

           # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
           UpperCamelCase__     : Tuple											=					apply_spec_augment
           UpperCamelCase__     : str											=					mask_time_prob
           UpperCamelCase__     : Tuple											=					mask_time_length
           UpperCamelCase__     : Optional[Any]											=					mask_time_min_masks
           UpperCamelCase__     : str											=					mask_feature_prob
           UpperCamelCase__     : List[str]											=					mask_feature_length
           UpperCamelCase__     : Optional[Any]											=					mask_feature_min_masks

           # parameters for pretraining with codevector quantized representations
           UpperCamelCase__     : Dict											=					num_codevectors_per_group
           UpperCamelCase__     : Optional[int]											=					num_codevector_groups
           UpperCamelCase__     : Dict											=					contrastive_logits_temperature
           UpperCamelCase__     : List[Any]											=					feat_quantizer_dropout
           UpperCamelCase__     : List[Any]											=					num_negatives
           UpperCamelCase__     : Tuple											=					codevector_dim
           UpperCamelCase__     : List[str]											=					proj_codevector_dim
           UpperCamelCase__     : Tuple											=					diversity_loss_weight

           # ctc loss
           UpperCamelCase__     : List[str]											=					ctc_loss_reduction
           UpperCamelCase__     : Optional[Any]											=					ctc_zero_infinity

           # adapter
           UpperCamelCase__     : List[Any]											=					add_adapter
           UpperCamelCase__     : Any											=					adapter_kernel_size
           UpperCamelCase__     : Tuple											=					adapter_stride
           UpperCamelCase__     : Tuple											=					num_adapter_layers
           UpperCamelCase__     : Optional[Any]											=					output_hidden_size or hidden_size
           UpperCamelCase__     : Union[str, Any]											=					adapter_attn_dim

           # SequenceClassification-specific parameter. Feel free to ignore for other classes.
           UpperCamelCase__     : List[str]											=					classifier_proj_size

           # XVector-specific parameters. Feel free to ignore for other classes.
           UpperCamelCase__     : Union[str, Any]											=					list(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[str]											=					list(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					list(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : int											=					xvector_output_dim






     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           return functools.reduce(operator.mul    ,    self.conv_stride    ,    1	)


