"""simple docstring"""
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()
lowercase__					       =	logging.get_logger(__name__)







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

  return new_state_dict







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->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)
      a__:     str										=	state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'   )
      a__:     Tuple										=	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
      a__:     Tuple										=	kv_weight[
          : config.hidden_sizes[i], :
      ]
      a__:     List[Any]										=	kv_bias[: config.hidden_sizes[i]]
      a__:     Dict										=	kv_weight[
          config.hidden_sizes[i] :, :
      ]
      a__:     str										=	kv_bias[config.hidden_sizes[i] :]







def 						__a   (	)	->str:
  a__:     Optional[Any]										=	'http://images.cocodataset.org/val2017/000000039769.jpg'
  a__:     Union[str, Any]										=	Image.open(requests.get(_SCREAMING_SNAKE_CASE		,     stream=_SCREAMING_SNAKE_CASE   ).raw   )

  return image







@torch.no_grad()
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=False		,     _SCREAMING_SNAKE_CASE=None   )	->List[str]:
  a__:     Optional[int]										=	GLPNConfig(hidden_sizes=[64, 128, 320, 512]		,     decoder_hidden_size=64		,     depths=[3, 8, 27, 3]   )

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

  # prepare image
  a__:     Dict										=	prepare_img()
  a__:     Union[str, Any]										=	image_processor(images=_SCREAMING_SNAKE_CASE		,     return_tensors='pt'   ).pixel_values

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

  # load original state dict
  a__:     Optional[Any]										=	torch.load(_SCREAMING_SNAKE_CASE		,     map_location=torch.device('cpu'   )   )

  # rename keys
  a__:     List[str]										=	rename_keys(_SCREAMING_SNAKE_CASE   )

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

  # create HuggingFace model and load state dict
  a__:     Optional[int]										=	GLPNForDepthEstimation(_SCREAMING_SNAKE_CASE   )
  model.load_state_dict(_SCREAMING_SNAKE_CASE   )
  model.eval()

  # forward pass
  a__:     int										=	model(_SCREAMING_SNAKE_CASE   )
  a__:     Tuple										=	outputs.predicted_depth

  # verify output
  if model_name is not None:
    if "nyu" in model_name:
      a__:     Dict										=	torch.tensor(
          [[4.4_147, 4.0_873, 4.0_673], [3.7_890, 3.2_881, 3.1_525], [3.7_674, 3.5_423, 3.4_913]]   )
    elif "kitti" in model_name:
      a__:     List[Any]										=	torch.tensor(
          [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]   )
    else:
      raise ValueError(F'Unknown model name: {model_name}'   )

    a__:     Optional[int]										=	torch.Size([1, 480, 640]   )

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


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



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'edbeeching/decision-transformer-gym-hopper-medium': (
        'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""decision_transformer"""
      a__        =		["""past_key_values"""]
      a__        =		{
          """max_position_embeddings""": """n_positions""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }



      def __init__(						self    ,			lowercase=17    ,			lowercase=4    ,			lowercase=1_28    ,			lowercase=40_96    ,			lowercase=True    ,			lowercase=1    ,			lowercase=10_24    ,			lowercase=3    ,			lowercase=1    ,			lowercase=None    ,			lowercase="relu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=1e-5    ,			lowercase=0.02    ,			lowercase=True    ,			lowercase=True    ,			lowercase=5_02_56    ,			lowercase=5_02_56    ,			lowercase=False    ,			lowercase=False    ,			**lowercase    ,			)	->			Tuple:





        '''simple docstring'''





        a__:     List[str]										=	state_dim
        a__:     int										=	act_dim
        a__:     List[Any]										=	hidden_size
        a__:     List[str]										=	max_ep_len
        a__:     List[Any]										=	action_tanh
        a__:     Optional[Any]										=	vocab_size
        a__:     Tuple										=	n_positions
        a__:     Dict										=	n_layer
        a__:     Optional[int]										=	n_head
        a__:     Optional[int]										=	n_inner
        a__:     Any										=	activation_function
        a__:     Union[str, Any]										=	resid_pdrop
        a__:     Any										=	embd_pdrop
        a__:     Any										=	attn_pdrop
        a__:     List[Any]										=	layer_norm_epsilon
        a__:     Optional[Any]										=	initializer_range
        a__:     Any										=	scale_attn_weights
        a__:     Dict										=	use_cache
        a__:     Optional[int]										=	scale_attn_by_inverse_layer_idx
        a__:     List[str]										=	reorder_and_upcast_attn

        a__:     Any										=	bos_token_id
        a__:     int										=	eos_token_id

        super().__init__(bos_token_id=lowercase    ,			eos_token_id=lowercase    ,			**lowercase)


