





"""simple docstring"""





import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   "▁"

SCREAMING_SNAKE_CASE__					=   {"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}

SCREAMING_SNAKE_CASE__					=   {
    "sentencepiece_model_file": "sentencepiece.bpe.model",
    "vocab_file": "vocab.txt",
}

SCREAMING_SNAKE_CASE__					=   {
    "vocab_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
    },
    "sentencepiece_model_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
    },
}

SCREAMING_SNAKE_CASE__					=   {
    "ernie-m-base": 514,
    "ernie-m-large": 514,
}

SCREAMING_SNAKE_CASE__					=   {
    "ernie-m-base": {"do_lower_case": False},
    "ernie-m-large": {"do_lower_case": False},
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      ["input_ids"]

      _SCREAMING_SNAKE_CASE					=      VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_INIT_CONFIGURATION
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE					=      RESOURCE_FILES_NAMES





      def __init__(       self      ,						lowercase      ,						lowercase=None      ,						lowercase=False      ,						lowercase="utf8"      ,						lowercase="[UNK]"      ,						lowercase="[SEP]"      ,						lowercase="[PAD]"      ,						lowercase="[CLS]"      ,						lowercase="[MASK]"      ,						lowercase = None      ,						**lowercase      ,						)		->     None:
            # Mask token behave like a normal word, i.e. include the space before it and
            # is included in the raw text, there should be a match in a non-normalized sentence.

            lowerCAmelCase  						=       {} if sp_model_kwargs is None else sp_model_kwargs
            super().__init__(
                do_lower_case=lowercase      ,						unk_token=lowercase      ,						sep_token=lowercase      ,						pad_token=lowercase      ,						cls_token=lowercase      ,						mask_token=lowercase      ,						vocab_file=lowercase      ,						encoding=lowercase      ,						sp_model_kwargs=self.sp_model_kwargs      ,						**lowercase      ,						)
            lowerCAmelCase  						=       do_lower_case
            lowerCAmelCase  						=       sentencepiece_model_ckpt
            lowerCAmelCase  						=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(lowercase )

            # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
            if vocab_file is not None:
                  lowerCAmelCase  						=       self.load_vocab(filepath=lowercase )
            else:
                  lowerCAmelCase  						=       {self.sp_model.id_to_piece(lowercase ): id for id in range(self.sp_model.get_piece_size() )}
            lowerCAmelCase  						=       {v: k for k, v in self.vocab.items()}





      def     _snake_case			(       self      ,						lowercase )		->     str:
            if text is None:
                  return None

            lowerCAmelCase  						=       self.tokenize(lowercase )
            lowerCAmelCase						,		lowerCAmelCase  						=       """""", []

            for i, ch in enumerate(lowercase ):
                  if ch in self.SP_CHAR_MAPPING:
                        lowerCAmelCase  						=       self.SP_CHAR_MAPPING.get(lowercase )
                  else:
                        lowerCAmelCase  						=       unicodedata.normalize("""NFKC"""      ,						lowercase )
                  if self.is_whitespace(lowercase ):
                        continue
                  normalized_text += ch
                  char_mapping.extend([i] * len(lowercase ) )

            lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       normalized_text, [], 0

            if self.do_lower_case:
                  lowerCAmelCase  						=       text.lower()

            for token in split_tokens:
                  if token[:1] == "▁":
                        lowerCAmelCase  						=       token[1:]
                  lowerCAmelCase  						=       text[offset:].index(lowercase ) + offset
                  lowerCAmelCase  						=       start + len(lowercase )

                  token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
                  lowerCAmelCase  						=       end
            return token_mapping





      @property
      def     _snake_case			(       self )		->     str:
            return len(self.vocab )





      def     _snake_case			(       self )		->     str:
            return dict(self.vocab      ,						**self.added_tokens_encoder )





      def __getstate__(       self )		->     List[str]:
            lowerCAmelCase  						=       self.__dict__.copy()
            lowerCAmelCase  						=       None
            return state





      def __setstate__(       self      ,						lowercase )		->     int:
            lowerCAmelCase  						=       d

            # for backward compatibility
            if not hasattr(self      ,						"""sp_model_kwargs""" ):
                  lowerCAmelCase  						=       {}

            lowerCAmelCase  						=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(self.sentencepiece_model_ckpt )





      def     _snake_case			(       self      ,						lowercase )		->     List[str]:
            return "".join((self.SP_CHAR_MAPPING.get(lowercase      ,						lowercase ) for c in text) )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=False      ,						lowercase=64      ,						lowercase=0.1 )		->     List[Any]:

            if self.sp_model_kwargs.get("""enable_sampling""" ) is True:
                  lowerCAmelCase  						=       True
            if self.sp_model_kwargs.get("""alpha""" ) is not None:
                  lowerCAmelCase  						=       self.sp_model_kwargs.get("""alpha""" )
            if self.sp_model_kwargs.get("""nbest_size""" ) is not None:
                  lowerCAmelCase  						=       self.sp_model_kwargs.get("""nbest_size""" )

            if not enable_sampling:
                  lowerCAmelCase  						=       self.sp_model.EncodeAsPieces(lowercase )
            else:
                  lowerCAmelCase  						=       self.sp_model.SampleEncodeAsPieces(lowercase      ,						lowercase      ,						lowercase )
            lowerCAmelCase  						=       []
            for pi, piece in enumerate(lowercase ):
                  if piece == SPIECE_UNDERLINE:
                        if not pieces[pi + 1].startswith(lowercase ) and pi != 0:
                              new_pieces.append(lowercase )
                              continue
                        else:
                              continue
                  lowerCAmelCase  						=       0
                  for i, chunk in enumerate(lowercase ):
                        if chunk == SPIECE_UNDERLINE:
                              continue
                        if self.is_ch_char(lowercase ) or self.is_punct(lowercase ):
                              if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                    new_pieces.append(piece[lst_i:i] )
                              new_pieces.append(lowercase )
                              lowerCAmelCase  						=       i + 1
                        elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                              if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                    new_pieces.append(piece[lst_i:i] )
                              lowerCAmelCase  						=       i
                        elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                              if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                                    new_pieces.append(piece[lst_i:i] )
                              lowerCAmelCase  						=       i
                  if len(lowercase ) > lst_i:
                        new_pieces.append(piece[lst_i:] )
            return new_pieces





      def     _snake_case			(       self      ,						lowercase )		->     Union[str, Any]:
            lowerCAmelCase  						=       """""".join(lowercase ).replace(lowercase      ,						""" """ ).strip()
            return out_string





      def     _snake_case			(       self      ,						lowercase )		->     Any:
            lowerCAmelCase  						=       self.convert_ids_to_tokens(lowercase )
            lowerCAmelCase  						=       """""".join(lowercase ).replace(lowercase      ,						""" """ ).strip()
            return out_string





      def     _snake_case			(       self      ,						lowercase )		->     Union[str, Any]:
            return self.vocab.get(lowercase      ,						self.vocab.get(self.unk_token ) )





      def     _snake_case			(       self      ,						lowercase )		->     List[Any]:
            return self.reverse_vocab.get(lowercase      ,						self.unk_token )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=None )		->     Union[str, Any]:
            if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            lowerCAmelCase  						=       [self.cls_token_id]
            lowerCAmelCase  						=       [self.sep_token_id]
            return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=None )		->     Any:
            if offset_mapping_a is None:
                  return [(0, 0)] + offset_mapping_a + [(0, 0)]

            return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=None      ,						lowercase=False )		->     Any:

            if already_has_special_tokens:
                  if token_ids_a is not None:
                        raise ValueError(
                            """You should not supply a second sequence if the provided sequence of """
                            """ids is already formatted with special tokens for the model.""" )
                  return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

            if token_ids_a is not None:
                  return [1] + ([0] * len(lowercase )) + [1, 1] + ([0] * len(lowercase )) + [1]
            return [1] + ([0] * len(lowercase )) + [1]





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     List[int]:
            # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
            if token_ids_a is None:
                  # [CLS] X [SEP]
                  return (len(lowercase ) + 2) * [0]

            # [CLS] A [SEP] [SEP] B [SEP]
            return [0] * (len(lowercase ) + 1) + [1] * (len(lowercase ) + 3)





      def     _snake_case			(       self      ,						lowercase )		->     str:
            if "\u4e00" <= char <= "\u9fff":
                  return True
            return False





      def     _snake_case			(       self      ,						lowercase )		->     Union[str, Any]:
            if ("a" <= char <= "z") or ("A" <= char <= "Z"):
                  return True
            return False





      def     _snake_case			(       self      ,						lowercase )		->     Optional[int]:
            if char in ",;:.?!~，；：。？！《》【】":
                  return True
            return False





      def     _snake_case			(       self      ,						lowercase )		->     str:
            if char == " " or char == "\t" or char == "\n" or char == "\r":
                  return True
            if len(lowercase ) == 1:
                  lowerCAmelCase  						=       unicodedata.category(lowercase )
                  if cat == "Zs":
                        return True
            return False





      def     _snake_case			(       self      ,						lowercase )		->     Dict:
            lowerCAmelCase  						=       {}
            with io.open(lowercase      ,						"""r"""      ,						encoding="""utf-8""" ) as f:
                  for index, line in enumerate(lowercase ):
                        lowerCAmelCase  						=       line.rstrip("""\n""" )
                        lowerCAmelCase  						=       int(lowercase )

            return token_to_idx





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     Tuple[str]:
            lowerCAmelCase  						=       0
            if os.path.isdir(lowercase ):
                  lowerCAmelCase  						=       os.path.join(
                      lowercase      ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
            else:
                  lowerCAmelCase  						=       (filename_prefix + """-""" if filename_prefix else """""") + save_directory
            with open(lowercase      ,						"""w"""      ,						encoding="""utf-8""" ) as writer:
                  for token, token_index in sorted(self.vocab.items()      ,						key=lambda lowercase : kv[1] ):
                        if index != token_index:
                              logger.warning(
                                  f'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
                                  """ Please check that the vocabulary is not corrupted!""" )
                              lowerCAmelCase  						=       token_index
                        writer.write(token + """\n""" )
                        index += 1

            lowerCAmelCase  						=       os.path.join(lowercase      ,						"""sentencepiece.bpe.model""" )
            with open(lowercase      ,						"""wb""" ) as fi:
                  lowerCAmelCase  						=       self.sp_model.serialized_model_proto()
                  fi.write(lowercase )

            return (vocab_file,)













"""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()
SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




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

      return new_state_dict


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						List[str]			):







      '''simple docstring'''




      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  						=       state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'			)
                  lowerCAmelCase  						=       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  						=       kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  lowerCAmelCase  						=       kv_bias[: config.hidden_sizes[i]]
                  lowerCAmelCase  						=       kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  lowerCAmelCase  						=       kv_bias[config.hidden_sizes[i] :]


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowerCAmelCase  						=       Image.open(requests.get(SCREAMING_SNAKE_CASE							,						stream=SCREAMING_SNAKE_CASE			).raw			)

      return image


@torch.no_grad()
def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=False							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]=None			):







      '''simple docstring'''




      lowerCAmelCase  						=       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)
      lowerCAmelCase  						=       GLPNImageProcessor()

      # prepare image
      lowerCAmelCase  						=       prepare_img()
      lowerCAmelCase  						=       image_processor(images=SCREAMING_SNAKE_CASE							,						return_tensors="""pt"""			).pixel_values

      logger.info("""Converting model..."""			)

      # load original state dict
      lowerCAmelCase  						=       torch.load(SCREAMING_SNAKE_CASE							,						map_location=torch.device("""cpu"""			)			)

      # rename keys
      lowerCAmelCase  						=       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
      lowerCAmelCase  						=       GLPNForDepthEstimation(SCREAMING_SNAKE_CASE			)
      model.load_state_dict(SCREAMING_SNAKE_CASE			)
      model.eval()

      # forward pass
      lowerCAmelCase  						=       model(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       outputs.predicted_depth

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

            lowerCAmelCase  						=       torch.Size([1, 4_80, 6_40]			)

            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__":
       SCREAMING_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.",
       )
       SCREAMING_SNAKE_CASE__					=   parser.parse_args()
       convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






