


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()
__lowerCAmelCase		       =							logging.get_logger(__name__)




def    snake_case_     (  snake_case     )  ->				int:
       lowercase__:   List[Any]  			=   OrderedDict()
       for key, value in state_dict.items():
              if key.startswith('module.encoder'     ):
                     lowercase__:   Optional[int]  			=   key.replace('module.encoder'			,							'glpn.encoder'     )
              if key.startswith('module.decoder'     ):
                     lowercase__:   str  			=   key.replace('module.decoder'			,							'decoder.stages'     )
              if "patch_embed" in key:
                     # replace for example patch_embed1 by patch_embeddings.0
                     lowercase__:   Any  			=   key[key.find('patch_embed'     ) + len('patch_embed'     )]
                     lowercase__:   str  			=   key.replace(f'patch_embed{idx}'			,							f'patch_embeddings.{int(A__     )-1}'     )
              if "norm" in key:
                     lowercase__:   Dict  			=   key.replace('norm'			,							'layer_norm'     )
              if "glpn.encoder.layer_norm" in key:
                     # replace for example layer_norm1 by layer_norm.0
                     lowercase__:   List[str]  			=   key[key.find('glpn.encoder.layer_norm'     ) + len('glpn.encoder.layer_norm'     )]
                     lowercase__:   Optional[int]  			=   key.replace(f'layer_norm{idx}'			,							f'layer_norm.{int(A__     )-1}'     )
              if "layer_norm1" in key:
                     lowercase__:   Any  			=   key.replace('layer_norm1'			,							'layer_norm_1'     )
              if "layer_norm2" in key:
                     lowercase__:   List[str]  			=   key.replace('layer_norm2'			,							'layer_norm_2'     )
              if "block" in key:
                     # replace for example block1 by block.0
                     lowercase__:   str  			=   key[key.find('block'     ) + len('block'     )]
                     lowercase__:   Dict  			=   key.replace(f'block{idx}'			,							f'block.{int(A__     )-1}'     )
              if "attn.q" in key:
                     lowercase__:   List[Any]  			=   key.replace('attn.q'			,							'attention.self.query'     )
              if "attn.proj" in key:
                     lowercase__:   int  			=   key.replace('attn.proj'			,							'attention.output.dense'     )
              if "attn" in key:
                     lowercase__:   str  			=   key.replace('attn'			,							'attention.self'     )
              if "fc1" in key:
                     lowercase__:   List[str]  			=   key.replace('fc1'			,							'dense1'     )
              if "fc2" in key:
                     lowercase__:   Tuple  			=   key.replace('fc2'			,							'dense2'     )
              if "linear_pred" in key:
                     lowercase__:   Tuple  			=   key.replace('linear_pred'			,							'classifier'     )
              if "linear_fuse" in key:
                     lowercase__:   int  			=   key.replace('linear_fuse.conv'			,							'linear_fuse'     )
                     lowercase__:   Union[str, Any]  			=   key.replace('linear_fuse.bn'			,							'batch_norm'     )
              if "linear_c" in key:
                     # replace for example linear_c4 by linear_c.3
                     lowercase__:   Optional[int]  			=   key[key.find('linear_c'     ) + len('linear_c'     )]
                     lowercase__:   str  			=   key.replace(f'linear_c{idx}'			,							f'linear_c.{int(A__     )-1}'     )
              if "bot_conv" in key:
                     lowercase__:   int  			=   key.replace('bot_conv'			,							'0.convolution'     )
              if "skip_conv1" in key:
                     lowercase__:   Optional[Any]  			=   key.replace('skip_conv1'			,							'1.convolution'     )
              if "skip_conv2" in key:
                     lowercase__:   Dict  			=   key.replace('skip_conv2'			,							'2.convolution'     )
              if "fusion1" in key:
                     lowercase__:   Optional[Any]  			=   key.replace('fusion1'			,							'1.fusion'     )
              if "fusion2" in key:
                     lowercase__:   Any  			=   key.replace('fusion2'			,							'2.fusion'     )
              if "fusion3" in key:
                     lowercase__:   List[str]  			=   key.replace('fusion3'			,							'3.fusion'     )
              if "fusion" in key and "conv" in key:
                     lowercase__:   List[Any]  			=   key.replace('conv'			,							'convolutional_layer'     )
              if key.startswith('module.last_layer_depth'     ):
                     lowercase__:   Optional[int]  			=   key.replace('module.last_layer_depth'			,							'head.head'     )
              lowercase__:   Union[str, Any]  			=   value

       return new_state_dict




def    snake_case_     (  snake_case			,							snake_case     )  ->				List[Any]:



       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)
                     lowercase__:   List[Any]  			=   state_dict.pop(f'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'     )
                     lowercase__:   str  			=   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
                     lowercase__:   int  			=   kv_weight[
                         : config.hidden_sizes[i], :
                     ]
                     lowercase__:   Optional[Any]  			=   kv_bias[: config.hidden_sizes[i]]
                     lowercase__:   Dict  			=   kv_weight[
                         config.hidden_sizes[i] :, :
                     ]
                     lowercase__:   Optional[int]  			=   kv_bias[config.hidden_sizes[i] :]




def    snake_case_     (  )  ->				List[str]:
       lowercase__:   Tuple  			=   'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase__:   List[str]  			=   Image.open(requests.get(A__			,							stream=A__     ).raw     )

       return image




@torch.no_grad()
def    snake_case_     (  snake_case			,							snake_case			,							snake_case=False			,							snake_case=None     )  ->				List[str]:
       lowercase__:   Optional[int]  			=   GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12]			,							decoder_hidden_size=64			,							depths=[3, 8, 27, 3]     )

       # load image processor (only resize + rescale)
       lowercase__:   List[Any]  			=   GLPNImageProcessor()

       # prepare image
       lowercase__:   Any  			=   prepare_img()
       lowercase__:   Optional[Any]  			=   image_processor(images=A__			,							return_tensors='pt'     ).pixel_values

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

       # load original state dict
       lowercase__:   Optional[int]  			=   torch.load(A__			,							map_location=torch.device('cpu'     )     )

       # rename keys
       lowercase__:   Any  			=   rename_keys(A__     )

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

       # create HuggingFace model and load state dict
       lowercase__:   str  			=   GLPNForDepthEstimation(A__     )
       model.load_state_dict(A__     )
       model.eval()

       # forward pass
       lowercase__:   int  			=   model(A__     )
       lowercase__:   int  			=   outputs.predicted_depth

       # verify output
       if model_name is not None:
              if "nyu" in model_name:
                     lowercase__:   str  			=   torch.tensor(
                         [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]     )
              elif "kitti" in model_name:
                     lowercase__:   List[Any]  			=   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]]     )
              else:
                     raise ValueError(f'Unknown model name: {model_name}'     )

              lowercase__:   Dict  			=   torch.Size([1, 4_80, 6_40]     )

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


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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_									=   {
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['BloomTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BloomForCausalLM',
      'BloomModel',
      'BloomPreTrainedModel',
      'BloomForSequenceClassification',
      'BloomForTokenClassification',
      'BloomForQuestionAnswering',
  ]

if TYPE_CHECKING:
  from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bloom_fast import BloomTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bloom import (
        BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
        BloomForCausalLM,
        BloomForQuestionAnswering,
        BloomForSequenceClassification,
        BloomForTokenClassification,
        BloomModel,
        BloomPreTrainedModel,
    )

else:
  import sys

  lowercase_									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


