





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__A							:			Optional[int]         =			logging.get_logger(__name__)

__A							:			int         =			{
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			'deberta-v2'




     def __init__(    self				,     snake_case_=12_8100				,     snake_case_=1536				,     snake_case_=24				,     snake_case_=24				,     snake_case_=6144				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=512				,     snake_case_=0				,     snake_case_=0.02				,     snake_case_=1E-7				,     snake_case_=False				,     snake_case_=-1				,     snake_case_=0				,     snake_case_=True				,     snake_case_=None				,     snake_case_=0				,     snake_case_="gelu"				,     **snake_case_				,     ):
         super().__init__(**snake_case_							)

         _A     =							hidden_size
         _A     =							num_hidden_layers
         _A     =							num_attention_heads
         _A     =							intermediate_size
         _A     =							hidden_act
         _A     =							hidden_dropout_prob
         _A     =							attention_probs_dropout_prob
         _A     =							max_position_embeddings
         _A     =							type_vocab_size
         _A     =							initializer_range
         _A     =							relative_attention
         _A     =							max_relative_positions
         _A     =							pad_token_id
         _A     =							position_biased_input

         # Backwards compatibility
         if type(snake_case_							) == str:
             _A     =							[x.strip() for x in pos_att_type.lower().split('|'							)]

         _A     =							pos_att_type
         _A     =							vocab_size
         _A     =							layer_norm_eps

         _A     =							kwargs.get('pooler_hidden_size'				,     snake_case_							)
         _A     =							pooler_dropout
         _A     =							pooler_hidden_act







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         if self.task == "multiple-choice":
             _A     =							{0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
             _A     =							{0: 'batch', 1: 'sequence'}
         if self._config.type_vocab_size > 0:
             return OrderedDict(
                 [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]							)
         else:
             return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]							)




     @property
     def        lowerCAmelCase__		(    self							):
         return 12




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = -1				,     snake_case_ = -1				,     snake_case_ = -1				,     snake_case_ = False				,     snake_case_ = None				,     snake_case_ = 3				,     snake_case_ = 40				,     snake_case_ = 40				,     snake_case_ = None				,     ):
         _A     =							super().generate_dummy_inputs(preprocessor=snake_case_				,     framework=snake_case_							)
         if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
             del dummy_inputs["token_type_ids"]
         return dummy_inputs







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()
_snake_case      = logging.get_logger(__name__)

def  __lowerCamelCase					( _lowercase			)       ->      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(_lowercase			)-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(_lowercase			)-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(_lowercase			)-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(_lowercase			)-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  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Union[str, 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  __lowerCamelCase					( )       ->      Any:
     UpperCamelCase						    =					'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCamelCase						    =					Image.open(requests.get(_lowercase						,     stream=_lowercase			).raw			)

     return image

@torch.no_grad()
def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase=False						,     _lowercase=None			)       ->      Tuple:
     UpperCamelCase						    =					GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,     decoder_hidden_size=64						,     depths=[3, 8, 27, 3]			)

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

     # prepare image
     UpperCamelCase						    =					prepare_img()
     UpperCamelCase						    =					image_processor(images=_lowercase						,     return_tensors='pt'			).pixel_values

     logger.info('Converting model...'			)

     # load original state dict
     UpperCamelCase						    =					torch.load(_lowercase						,     map_location=torch.device('cpu'			)			)

     # rename keys
     UpperCamelCase						    =					rename_keys(_lowercase			)

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

     # create HuggingFace model and load state dict
     UpperCamelCase						    =					GLPNForDepthEstimation(_lowercase			)
     model.load_state_dict(_lowercase			)
     model.eval()

     # forward pass
     UpperCamelCase						    =					model(_lowercase			)
     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, 480, 640]			)

          assert predicted_depth.shape == expected_shape
          assert torch.allclose(predicted_depth[0, :3, :3]						,     _lowercase						,     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(_lowercase						,     _lowercase			)						,     organization='nielsr'						,     commit_message='Add model'						,     use_temp_dir=_lowercase						,     )
          image_processor.push_to_hub(
              repo_path_or_name=Path(_lowercase						,     _lowercase			)						,     organization='nielsr'						,     commit_message='Add image processor'						,     use_temp_dir=_lowercase						,     )


if __name__ == "__main__":
 _snake_case      = 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.''',
 )
 _snake_case      = parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


