






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

    return new_state_dict


def   _A   (				SCREAMING_SNAKE_CASE__  :	Dict     ,  SCREAMING_SNAKE_CASE__  :	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       :Optional[int]								=   state_dict.pop(F'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''       )
            UpperCamelCase       :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
            UpperCamelCase       :Dict								=   kv_weight[
                : config.hidden_sizes[i], :
            ]
            UpperCamelCase       :Optional[Any]								=   kv_bias[: config.hidden_sizes[i]]
            UpperCamelCase       :str								=   kv_weight[
                config.hidden_sizes[i] :, :
            ]
            UpperCamelCase       :Tuple								=   kv_bias[config.hidden_sizes[i] :]


def   _A   (				):
    UpperCamelCase       :Optional[int]								=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase       :int								=   Image.open(requests.get(UpperCamelCase__     ,  stream=UpperCamelCase__       ).raw       )

    return image


@torch.no_grad()
def   _A   (				SCREAMING_SNAKE_CASE__  :	Union[str, Any]     ,  SCREAMING_SNAKE_CASE__  :	Union[str, Any]     ,  SCREAMING_SNAKE_CASE__  :	Dict=False     ,  SCREAMING_SNAKE_CASE__  :	Optional[int]=None       ):
    UpperCamelCase       :Union[str, Any]								=   GLPNConfig(hidden_sizes=[64, 128, 320, 512]     ,  decoder_hidden_size=64     ,  depths=[3, 8, 27, 3]       )

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

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

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

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

    # rename keys
    UpperCamelCase       :Tuple								=   rename_keys(UpperCamelCase__       )

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

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

    # forward pass
    UpperCamelCase       :List[str]								=   model(UpperCamelCase__       )
    UpperCamelCase       :Dict								=   outputs.predicted_depth

    # verify output
    if model_name is not None:
        if "nyu" in model_name:
            UpperCamelCase       :int								=   torch.tensor(
                [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]       )
        elif "kitti" in model_name:
            UpperCamelCase       :List[Any]								=   torch.tensor(
                [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]       )
        else:
            raise ValueError(F'''Unknown model name: {model_name}'''       )

        UpperCamelCase       :Union[str, Any]								=   torch.Size([1, 480, 640]       )

        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__":
 __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)










"""simple docstring"""
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		Optional[Any]							):
    # Load configuration defined in the metadata file
    with open(UpperCamelCase__							) as metadata_file:
        _UpperCAmelCase							:  Dict											=    json.load(UpperCamelCase__							)
    _UpperCAmelCase							:  List[Any]											=    LukeConfig(use_entity_aware_attention=UpperCamelCase__     ,       **metadata['''model_config''']							)

    # Load in the weights from the checkpoint_path
    _UpperCAmelCase							:  List[Any]											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)

    # Load the entity vocab file
    _UpperCAmelCase							:  Optional[int]											=    load_entity_vocab(UpperCamelCase__							)

    _UpperCAmelCase							:  Optional[int]											=    RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

    # Add special tokens to the token vocabulary for downstream tasks
    _UpperCAmelCase							:  int											=    AddedToken('''<ent>'''     ,       lstrip=UpperCamelCase__     ,       rstrip=UpperCamelCase__							)
    _UpperCAmelCase							:  Optional[Any]											=    AddedToken('''<ent2>'''     ,       lstrip=UpperCamelCase__     ,       rstrip=UpperCamelCase__							)
    tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}							)
    config.vocab_size += 2

    print(F'Saving tokenizer to {pytorch_dump_folder_path}'							)
    tokenizer.save_pretrained(UpperCamelCase__							)
    with open(os.path.join(UpperCamelCase__     ,       LukeTokenizer.vocab_files_names['''entity_vocab_file''']							)     ,       '''w'''							) as f:
        json.dump(UpperCamelCase__     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Any											=    LukeTokenizer.from_pretrained(UpperCamelCase__							)

    # Initialize the embeddings of the special tokens
    _UpperCAmelCase							:  str											=    state_dict['''embeddings.word_embeddings.weight''']
    _UpperCAmelCase							:  Dict											=    word_emb[tokenizer.convert_tokens_to_ids(['''@''']							)[0]].unsqueeze(0							)
    _UpperCAmelCase							:  Union[str, Any]											=    word_emb[tokenizer.convert_tokens_to_ids(['''#''']							)[0]].unsqueeze(0							)
    _UpperCAmelCase							:  Tuple											=    torch.cat([word_emb, ent_emb, enta_emb]							)

    # Initialize the query layers of the entity-aware self-attention mechanism
    for layer_index in range(config.num_hidden_layers							):
        for matrix_name in ["query.weight", "query.bias"]:
            _UpperCAmelCase							:  List[Any]											=    F'encoder.layer.{layer_index}.attention.self.'
            _UpperCAmelCase							:  Optional[Any]											=    state_dict[prefix + matrix_name]
            _UpperCAmelCase							:  Tuple											=    state_dict[prefix + matrix_name]
            _UpperCAmelCase							:  str											=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
    _UpperCAmelCase							:  Any											=    state_dict['''entity_embeddings.entity_embeddings.weight''']
    _UpperCAmelCase							:  Dict											=    entity_emb[entity_vocab['''[MASK]''']]

    _UpperCAmelCase							:  Optional[int]											=    LukeModel(config=UpperCamelCase__							).eval()

    _UpperCAmelCase							,				_UpperCAmelCase							:  int											=    model.load_state_dict(UpperCamelCase__     ,       strict=UpperCamelCase__							)
    if not (len(UpperCamelCase__							) == 1 and missing_keys[0] == "embeddings.position_ids"):
        raise ValueError(F'Missing keys {", ".join(UpperCamelCase__							)}. Expected only missing embeddings.position_ids'							)
    if not (all(key.startswith('''entity_predictions'''							) or key.startswith('''lm_head'''							) for key in unexpected_keys							)):
        raise ValueError(
            '''Unexpected keys'''
            F' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"							) or key.startswith("lm_head"							))]							)}'							)

    # Check outputs
    _UpperCAmelCase							:  Optional[int]											=    LukeTokenizer.from_pretrained(UpperCamelCase__     ,       task='''entity_classification'''							)

    _UpperCAmelCase							:  List[str]											=    (
        '''Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'''
        ''' new world number one avoid a humiliating second- round exit at Wimbledon .'''
    )
    _UpperCAmelCase							:  Dict											=    (39, 42)
    _UpperCAmelCase							:  Any											=    tokenizer(UpperCamelCase__     ,       entity_spans=[span]     ,       add_prefix_space=UpperCamelCase__     ,       return_tensors='''pt'''							)

    _UpperCAmelCase							:  List[Any]											=    model(**UpperCamelCase__							)

    # Verify word hidden states
    if model_size == "large":
        _UpperCAmelCase							:  str											=    torch.Size((1, 42, 1024)							)
        _UpperCAmelCase							:  Union[str, Any]											=    torch.tensor(
            [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]							)
    else:  # base
        _UpperCAmelCase							:  Optional[Any]											=    torch.Size((1, 42, 768)							)
        _UpperCAmelCase							:  str											=    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]							)

    if not (outputs.last_hidden_state.shape == expected_shape):
        raise ValueError(
            F'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'							)
    if not torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,       UpperCamelCase__     ,       atol=1E-4							):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        _UpperCAmelCase							:  int											=    torch.Size((1, 1, 1024)							)
        _UpperCAmelCase							:  str											=    torch.tensor([[0.0466, -0.0106, -0.0179]]							)
    else:  # base
        _UpperCAmelCase							:  List[str]											=    torch.Size((1, 1, 768)							)
        _UpperCAmelCase							:  List[Any]											=    torch.tensor([[0.1457, 0.1044, 0.0174]]							)

    if not (outputs.entity_last_hidden_state.shape != expected_shape):
        raise ValueError(
            F'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
            F' {expected_shape}'							)
    if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]     ,       UpperCamelCase__     ,       atol=1E-4							):
        raise ValueError

    # Finally, save our PyTorch model and tokenizer
    print('''Saving PyTorch model to {}'''.format(UpperCamelCase__							)							)
    model.save_pretrained(UpperCamelCase__							)





def  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]							):
    _UpperCAmelCase							:  Any											=    {}
    with open(UpperCamelCase__     ,       '''r'''     ,       encoding='''utf-8'''							) as f:
        for index, line in enumerate(UpperCamelCase__							):
            _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    line.rstrip().split('''\t'''							)
            _UpperCAmelCase							:  Tuple											=    index

    return entity_vocab


if __name__ == "__main__":
  _lowerCAmelCase    :List[Any]     =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
  parser.add_argument(
      '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
  )
  parser.add_argument(
      '--entity_vocab_path',
      default=None,
      type=str,
      help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
  )
  parser.add_argument(
      '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
  )
  _lowerCAmelCase    :Any     =					parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )


