


"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


snake_case__						:   int			 = logging.get_logger(__name__)

snake_case__						:   List[str]			 = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case__						:   int			 = {
    '''vocab_file''': {
        '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt''',
        '''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt''',
        '''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/vocab.txt''',
        '''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/vocab.txt''',
        '''bert-base-multilingual-uncased''': (
            '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'''
        ),
        '''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt''',
        '''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt''',
        '''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt''',
        '''bert-large-uncased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'''
        ),
        '''bert-large-cased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'''
        ),
        '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'''
        ),
        '''bert-large-cased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'''
        ),
        '''bert-base-cased-finetuned-mrpc''': (
            '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'''
        ),
        '''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt''',
        '''bert-base-german-dbmdz-uncased''': (
            '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'''
        ),
        '''TurkuNLP/bert-base-finnish-cased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'''
        ),
        '''TurkuNLP/bert-base-finnish-uncased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'''
        ),
        '''wietsedv/bert-base-dutch-cased''': (
            '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json''',
        '''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json''',
        '''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json''',
        '''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json''',
        '''bert-base-multilingual-uncased''': (
            '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'''
        ),
        '''bert-base-multilingual-cased''': (
            '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'''
        ),
        '''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json''',
        '''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json''',
        '''bert-large-uncased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'''
        ),
        '''bert-large-cased-whole-word-masking''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'''
        ),
        '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'''
        ),
        '''bert-large-cased-whole-word-masking-finetuned-squad''': (
            '''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'''
        ),
        '''bert-base-cased-finetuned-mrpc''': (
            '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'''
        ),
        '''bert-base-german-dbmdz-cased''': (
            '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'''
        ),
        '''bert-base-german-dbmdz-uncased''': (
            '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'''
        ),
        '''TurkuNLP/bert-base-finnish-cased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'''
        ),
        '''TurkuNLP/bert-base-finnish-uncased-v1''': (
            '''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'''
        ),
        '''wietsedv/bert-base-dutch-cased''': (
            '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case__						:   Any			 = {
    '''bert-base-uncased''': 512,
    '''bert-large-uncased''': 512,
    '''bert-base-cased''': 512,
    '''bert-large-cased''': 512,
    '''bert-base-multilingual-uncased''': 512,
    '''bert-base-multilingual-cased''': 512,
    '''bert-base-chinese''': 512,
    '''bert-base-german-cased''': 512,
    '''bert-large-uncased-whole-word-masking''': 512,
    '''bert-large-cased-whole-word-masking''': 512,
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': 512,
    '''bert-large-cased-whole-word-masking-finetuned-squad''': 512,
    '''bert-base-cased-finetuned-mrpc''': 512,
    '''bert-base-german-dbmdz-cased''': 512,
    '''bert-base-german-dbmdz-uncased''': 512,
    '''TurkuNLP/bert-base-finnish-cased-v1''': 512,
    '''TurkuNLP/bert-base-finnish-uncased-v1''': 512,
    '''wietsedv/bert-base-dutch-cased''': 512,
}

snake_case__						:   Tuple			 = {
    '''bert-base-uncased''': {'''do_lower_case''': True},
    '''bert-large-uncased''': {'''do_lower_case''': True},
    '''bert-base-cased''': {'''do_lower_case''': False},
    '''bert-large-cased''': {'''do_lower_case''': False},
    '''bert-base-multilingual-uncased''': {'''do_lower_case''': True},
    '''bert-base-multilingual-cased''': {'''do_lower_case''': False},
    '''bert-base-chinese''': {'''do_lower_case''': False},
    '''bert-base-german-cased''': {'''do_lower_case''': False},
    '''bert-large-uncased-whole-word-masking''': {'''do_lower_case''': True},
    '''bert-large-cased-whole-word-masking''': {'''do_lower_case''': False},
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': {'''do_lower_case''': True},
    '''bert-large-cased-whole-word-masking-finetuned-squad''': {'''do_lower_case''': False},
    '''bert-base-cased-finetuned-mrpc''': {'''do_lower_case''': False},
    '''bert-base-german-dbmdz-cased''': {'''do_lower_case''': False},
    '''bert-base-german-dbmdz-uncased''': {'''do_lower_case''': True},
    '''TurkuNLP/bert-base-finnish-cased-v1''': {'''do_lower_case''': False},
    '''TurkuNLP/bert-base-finnish-uncased-v1''': {'''do_lower_case''': True},
    '''wietsedv/bert-base-dutch-cased''': {'''do_lower_case''': False},
}



class 						snake_case_(				a__	):
     __UpperCamelCase     =    VOCAB_FILES_NAMES
     __UpperCamelCase     =    PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase     =    PRETRAINED_INIT_CONFIGURATION
     __UpperCamelCase     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase     =    BertTokenizer






     def __init__(		self     :    Any    , UpperCamelCase_     :    List[str]=None    , UpperCamelCase_     :    Tuple=None    , UpperCamelCase_     :    Optional[int]=True    , UpperCamelCase_     :    Tuple="[UNK]"    , UpperCamelCase_     :    List[Any]="[SEP]"    , UpperCamelCase_     :    List[str]="[PAD]"    , UpperCamelCase_     :    List[Any]="[CLS]"    , UpperCamelCase_     :    str="[MASK]"    , UpperCamelCase_     :    List[Any]=True    , UpperCamelCase_     :    Tuple=None    , **UpperCamelCase_     :    Tuple    , ):
           super().__init__(
               UpperCamelCase_    , tokenizer_file=UpperCamelCase_    , do_lower_case=UpperCamelCase_    , unk_token=UpperCamelCase_    , sep_token=UpperCamelCase_    , pad_token=UpperCamelCase_    , cls_token=UpperCamelCase_    , mask_token=UpperCamelCase_    , tokenize_chinese_chars=UpperCamelCase_    , strip_accents=UpperCamelCase_    , **UpperCamelCase_    , )

           lowerCAmelCase    :		Tuple				=      json.loads(self.backend_tokenizer.normalizer.__getstate__() )
           if (
               normalizer_state.get('''lowercase'''    , UpperCamelCase_ ) != do_lower_case
               or normalizer_state.get('''strip_accents'''    , UpperCamelCase_ ) != strip_accents
               or normalizer_state.get('''handle_chinese_chars'''    , UpperCamelCase_ ) != tokenize_chinese_chars
           ):
                 lowerCAmelCase    :		Optional[Any]				=      getattr(UpperCamelCase_    , normalizer_state.pop('''type''' ) )
                 lowerCAmelCase    :		List[Any]				=      do_lower_case
                 lowerCAmelCase    :		Optional[Any]				=      strip_accents
                 lowerCAmelCase    :		Tuple				=      tokenize_chinese_chars
                 lowerCAmelCase    :		int				=      normalizer_class(**UpperCamelCase_ )

           lowerCAmelCase    :		Tuple				=      do_lower_case






     def    lowerCamelCase__   (		self     :    Dict    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    int=None ):
           lowerCAmelCase    :		Dict				=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                 output += token_ids_a + [self.sep_token_id]

           return output






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    List[int]    , UpperCamelCase_     :    Optional[List[int]] = None ):
           lowerCAmelCase    :		int				=      [self.sep_token_id]
           lowerCAmelCase    :		List[Any]				=      [self.cls_token_id]
           if token_ids_a is None:
                 return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Optional[str] = None ):
           lowerCAmelCase    :		str				=      self._tokenizer.model.save(UpperCamelCase_    , name=UpperCamelCase_ )
           return tuple(UpperCamelCase_ )






"""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()
snake_case__						:   int			 = logging.get_logger(__name__)


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

      return new_state_dict


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


def    _snake_case  (  ):
      lowerCAmelCase    :		int				=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase    :		str				=      Image.open(requests.get(_snake_case       ,      stream=_snake_case	).raw	)

      return image


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

      # load image processor (only resize + rescale)
      lowerCAmelCase    :		Union[str, Any]				=      GLPNImageProcessor()

      # prepare image
      lowerCAmelCase    :		Tuple				=      prepare_img()
      lowerCAmelCase    :		Dict				=      image_processor(images=_snake_case       ,      return_tensors='''pt'''	).pixel_values

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

      # load original state dict
      lowerCAmelCase    :		List[str]				=      torch.load(_snake_case       ,      map_location=torch.device('''cpu'''	)	)

      # rename keys
      lowerCAmelCase    :		Tuple				=      rename_keys(_snake_case	)

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

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

      # forward pass
      lowerCAmelCase    :		Union[str, Any]				=      model(_snake_case	)
      lowerCAmelCase    :		int				=      outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  lowerCAmelCase    :		str				=      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:
                  lowerCAmelCase    :		str				=      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}'''	)

            lowerCAmelCase    :		List[Any]				=      torch.Size([1, 480, 640]	)

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


if __name__ == "__main__":
 snake_case__						:   Tuple			 = 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__						:   List[str]			 = parser.parse_args()
 convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)


