





import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation

_UpperCamelCase       = logging.get_logger(__name__)

_UpperCamelCase       = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

_UpperCamelCase       = {
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

_UpperCamelCase       = {
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

_UpperCamelCase       = {
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}







def       lowerCAmelCase__(	) ->    Optional[int]:
  __snake_case   :				List[str]        =       (
      list(range(ord("!"				)   ,     ord("~"				) + 1				)				) + list(range(ord("¡"				)   ,     ord("¬"				) + 1				)				) + list(range(ord("®"				)   ,     ord("ÿ"				) + 1				)				)
  )
  __snake_case   :				List[str]        =       bs[:]
  __snake_case   :				Dict        =       0
  for b in range(2**8				):
    if b not in bs:
      bs.append(lowercase				)
      cs.append(2**8 + n				)
      n += 1
  __snake_case   :				Tuple        =       [chr(lowercase				) for n in cs]
  return dict(zip(lowercase   ,     lowercase				)				)







def       lowerCAmelCase__(	lowercase       :    Union[str, Any]				) ->    Dict:
  __snake_case   :				str        =       set()
  __snake_case   :				str        =       word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)				)
    __snake_case   :				Optional[Any]        =       char
  return pairs




class 				_lowerCamelCase (    UpperCamelCase__    ):



      """simple docstring"""






      UpperCAmelCase_						:      Any       =VOCAB_FILES_NAMES
      UpperCAmelCase_						:      Union[str, Any]       =PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_						:      List[Any]       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_						:      Any       =["""input_ids""", """attention_mask""", """token_type_ids"""]







      def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase="replace"				,						UpperCAmelCase="[CLS]"				,						UpperCAmelCase="[SEP]"				,						UpperCAmelCase="[SEP]"				,						UpperCAmelCase="[CLS]"				,						UpperCAmelCase="[UNK]"				,						UpperCAmelCase="[PAD]"				,						UpperCAmelCase="[MASK]"				,						UpperCAmelCase=False				,						UpperCAmelCase=False				,						**UpperCAmelCase				,						)       ->   Any:






        '''simple docstring'''






        __snake_case   :				List[str]        =       AddedToken(lowerCAmelCase__				,						lstrip=lowerCAmelCase__				,						rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__				,						lowerCAmelCase__   ) else bos_token
        __snake_case   :				List[str]        =       AddedToken(lowerCAmelCase__				,						lstrip=lowerCAmelCase__				,						rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__				,						lowerCAmelCase__   ) else eos_token
        __snake_case   :				Union[str, Any]        =       AddedToken(lowerCAmelCase__				,						lstrip=lowerCAmelCase__				,						rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__				,						lowerCAmelCase__   ) else sep_token
        __snake_case   :				List[str]        =       AddedToken(lowerCAmelCase__				,						lstrip=lowerCAmelCase__				,						rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__				,						lowerCAmelCase__   ) else cls_token
        __snake_case   :				int        =       AddedToken(lowerCAmelCase__				,						lstrip=lowerCAmelCase__				,						rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__				,						lowerCAmelCase__   ) else unk_token
        __snake_case   :				Optional[Any]        =       AddedToken(lowerCAmelCase__				,						lstrip=lowerCAmelCase__				,						rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__				,						lowerCAmelCase__   ) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        __snake_case   :				Dict        =       AddedToken(lowerCAmelCase__				,						lstrip=lowerCAmelCase__				,						rstrip=lowerCAmelCase__   ) if isinstance(lowerCAmelCase__				,						lowerCAmelCase__   ) else mask_token

        super().__init__(
            errors=lowerCAmelCase__				,						bos_token=lowerCAmelCase__				,						eos_token=lowerCAmelCase__				,						unk_token=lowerCAmelCase__				,						sep_token=lowerCAmelCase__				,						cls_token=lowerCAmelCase__				,						pad_token=lowerCAmelCase__				,						mask_token=lowerCAmelCase__				,						add_prefix_space=lowerCAmelCase__				,						add_bos_token=lowerCAmelCase__				,						**lowerCAmelCase__				,						)
        __snake_case   :				Optional[int]        =       add_bos_token

        with open(lowerCAmelCase__				,						encoding="utf-8"   ) as vocab_handle:
          __snake_case   :				List[Any]        =       json.load(lowerCAmelCase__   )
        __snake_case   :				List[str]        =       {v: k for k, v in self.encoder.items()}
        __snake_case   :				List[Any]        =       errors  # how to handle errors in decoding
        __snake_case   :				Optional[int]        =       bytes_to_unicode()
        __snake_case   :				Dict        =       {v: k for k, v in self.byte_encoder.items()}
        with open(lowerCAmelCase__				,						encoding="utf-8"   ) as merges_handle:
          __snake_case   :				Any        =       merges_handle.read().split("\n"   )[1:-1]
        __snake_case   :				Union[str, Any]        =       [tuple(merge.split()   ) for merge in bpe_merges]
        __snake_case   :				Optional[Any]        =       dict(zip(lowerCAmelCase__				,						range(len(lowerCAmelCase__   )   )   )   )
        __snake_case   :				List[str]        =       {}
        __snake_case   :				List[str]        =       add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        __snake_case   :				Optional[int]        =       re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"   )







      @property
      # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
      def 						UpperCAmelCase		( self   )       ->   Any:






        '''simple docstring'''






        return len(self.encoder   )







      def 						UpperCAmelCase		( self   )       ->   Optional[int]:






        '''simple docstring'''






        return dict(self.encoder				,						**self.added_tokens_encoder   )







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   List[str]:






        '''simple docstring'''






        if token in self.cache:
          return self.cache[token]
        __snake_case   :				Optional[Any]        =       tuple(lowerCAmelCase__   )
        __snake_case   :				str        =       get_pairs(lowerCAmelCase__   )

        if not pairs:
          return token

        while True:
          __snake_case   :				List[str]        =       min(lowerCAmelCase__				,						key=lambda UpperCAmelCase   : self.bpe_ranks.get(lowerCAmelCase__				,						float("inf"   )   )   )
          if bigram not in self.bpe_ranks:
            break
          __snake_case   :				Optional[int]        =       bigram
          __snake_case   :				Tuple        =       []
          __snake_case   :				Optional[int]        =       0
          while i < len(lowerCAmelCase__   ):
            try:
              __snake_case   :				Optional[Any]        =       word.index(lowerCAmelCase__				,						lowerCAmelCase__   )
            except ValueError:
              new_word.extend(word[i:]   )
              break
            else:
              new_word.extend(word[i:j]   )
              __snake_case   :				Optional[int]        =       j

            if word[i] == first and i < len(lowerCAmelCase__   ) - 1 and word[i + 1] == second:
              new_word.append(first + second   )
              i += 2
            else:
              new_word.append(word[i]   )
              i += 1
          __snake_case   :				Optional[int]        =       tuple(lowerCAmelCase__   )
          __snake_case   :				Any        =       new_word
          if len(lowerCAmelCase__   ) == 1:
            break
          else:
            __snake_case   :				Optional[int]        =       get_pairs(lowerCAmelCase__   )
        __snake_case   :				Union[str, Any]        =       " ".join(lowerCAmelCase__   )
        __snake_case   :				Union[str, Any]        =       word
        return word







      def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = None   )       ->   List[int]:






        '''simple docstring'''






        if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __snake_case   :				Union[str, Any]        =       [self.cls_token_id]
        __snake_case   :				List[Any]        =       [self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep







      def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = False   )       ->   List[int]:






        '''simple docstring'''






        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=lowerCAmelCase__				,						token_ids_a=lowerCAmelCase__				,						already_has_special_tokens=lowerCAmelCase__   )

        if token_ids_a is None:
          return [1] + ([0] * len(lowerCAmelCase__   )) + [1]
        return [1] + ([0] * len(lowerCAmelCase__   )) + [1] + ([0] * len(lowerCAmelCase__   )) + [1]







      def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = None   )       ->   List[int]:






        '''simple docstring'''






        __snake_case   :				Dict        =       [self.sep_token_id]
        __snake_case   :				int        =       [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 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Optional[int]:






        '''simple docstring'''






        __snake_case   :				Tuple        =       []
        for token in re.findall(self.pat				,						lowerCAmelCase__   ):
          __snake_case   :				Any        =       "".join(
              self.byte_encoder[b] for b in token.encode("utf-8"   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__   ).split(" "   )   )
        return bpe_tokens







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   int:






        '''simple docstring'''






        return self.encoder.get(lowerCAmelCase__				,						self.encoder.get(self.unk_token   )   )







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   int:






        '''simple docstring'''






        return self.decoder.get(lowerCAmelCase__   )







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:






        '''simple docstring'''






        __snake_case   :				Optional[int]        =       "".join(lowerCAmelCase__   )
        __snake_case   :				Tuple        =       bytearray([self.byte_decoder[c] for c in text]   ).decode("utf-8"				,						errors=self.errors   )
        return text







      def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = None   )       ->   Tuple[str]:






        '''simple docstring'''






        if not os.path.isdir(lowerCAmelCase__   ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
          return
        __snake_case   :				List[str]        =       os.path.join(
            lowerCAmelCase__				,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )
        __snake_case   :				str        =       os.path.join(
            lowerCAmelCase__				,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]   )

        with open(lowerCAmelCase__				,						"w"				,						encoding="utf-8"   ) as f:
          f.write(json.dumps(self.encoder				,						indent=2				,						sort_keys=lowerCAmelCase__				,						ensure_ascii=lowerCAmelCase__   ) + "\n"   )

        __snake_case   :				List[str]        =       0
        with open(lowerCAmelCase__				,						"w"				,						encoding="utf-8"   ) as writer:
          writer.write("#version: 0.2\n"   )
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,						key=lambda UpperCAmelCase   : kv[1]   ):
            if index != token_index:
              logger.warning(
                  F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                  " Please check that the tokenizer is not corrupted!"   )
              __snake_case   :				Union[str, Any]        =       token_index
            writer.write(" ".join(lowerCAmelCase__   ) + "\n"   )
            index += 1

        return vocab_file, merge_file







      def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase=False				,						**UpperCAmelCase   )       ->   Dict:






        '''simple docstring'''






        __snake_case   :				List[Any]        =       kwargs.pop("add_prefix_space"				,						self.add_prefix_space   )
        if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__   ) > 0 and not text[0].isspace()):
          __snake_case   :				Tuple        =       " " + text
        return (text, kwargs)







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   List[int]:






        '''simple docstring'''






        __snake_case   :				Dict        =       []
        for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(lowerCAmelCase__				,						add_special_tokens=lowerCAmelCase__   ) + [self.eos_token_id]   )
        if len(lowerCAmelCase__   ) > self.model_max_length:
          __snake_case   :				Tuple        =       input_ids[-self.model_max_length :]
        return input_ids








import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase       = logging.get_logger(__name__)







def       lowerCAmelCase__(	lowercase       :    Dict				) ->    Union[str, Any]:
  __snake_case   :				List[str]        =       MobileNetVaConfig(layer_norm_eps=0.0_0_1				)

  if "quant" in model_name:
    raise ValueError("Quantized models are not supported."				)

  __snake_case   :				Dict        =       re.match(R"^.*mobilenet_v2_([^_]*)_([^_]*)$"   ,     lowercase				)
  if matches:
    __snake_case   :				Tuple        =       float(matches[1]				)
    __snake_case   :				List[Any]        =       int(matches[2]				)

  if model_name.startswith("deeplabv3_"				):
    __snake_case   :				Any        =       8
    __snake_case   :				Any        =       21
    __snake_case   :				Optional[Any]        =       "pascal-voc-id2label.json"
  else:
    # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
    # of the usual 1000. The first class (index 0) is "background".
    __snake_case   :				Optional[Any]        =       1001
    __snake_case   :				str        =       "imagenet-1k-id2label.json"

  __snake_case   :				Any        =       "huggingface/label-files"
  __snake_case   :				str        =       json.load(open(hf_hub_download(lowercase   ,     lowercase   ,     repo_type="dataset"				)   ,     "r"				)				)

  if config.num_labels == 1001:
    __snake_case   :				Optional[Any]        =       {int(lowercase				) + 1: v for k, v in idalabel.items()}
    __snake_case   :				Any        =       "background"
  else:
    __snake_case   :				Optional[Any]        =       {int(lowercase				): v for k, v in idalabel.items()}

  __snake_case   :				Any        =       idalabel
  __snake_case   :				List[str]        =       {v: k for k, v in idalabel.items()}

  return config







def       lowerCAmelCase__(	) ->    Optional[Any]:
  __snake_case   :				List[Any]        =       "http://images.cocodataset.org/val2017/000000039769.jpg"
  __snake_case   :				Union[str, Any]        =       Image.open(requests.get(lowercase   ,     stream=lowercase				).raw				)
  return im







@torch.no_grad()
def       lowerCAmelCase__(	lowercase       :    str   ,     lowercase       :    List[str]   ,     lowercase       :    int   ,     lowercase       :    List[str]=False				) ->    str:
  __snake_case   :				Dict        =       get_mobilenet_va_config(lowercase				)

  # Load 🤗 model
  if model_name.startswith("deeplabv3_"				):
    __snake_case   :				str        =       MobileNetVaForSemanticSegmentation(lowercase				).eval()
  else:
    __snake_case   :				Any        =       MobileNetVaForImageClassification(lowercase				).eval()

  # Load weights from TensorFlow checkpoint
  load_tf_weights_in_mobilenet_va(lowercase   ,     lowercase   ,     lowercase				)

  # Check outputs on an image, prepared by MobileNetV2ImageProcessor
  __snake_case   :				List[str]        =       MobileNetVaImageProcessor(
      crop_size={"width": config.image_size, "height": config.image_size}   ,     size={"shortest_edge": config.image_size + 32}   ,     )
  __snake_case   :				Any        =       image_processor(images=prepare_img()   ,     return_tensors="pt"				)
  __snake_case   :				Dict        =       model(**lowercase				)
  __snake_case   :				List[Any]        =       outputs.logits

  if model_name.startswith("deeplabv3_"				):
    assert logits.shape == (1, 21, 65, 65)

    if model_name == "deeplabv3_mobilenet_v2_1.0_513":
      __snake_case   :				Any        =       torch.tensor(
          [
              [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
              [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
              [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
          ]				)

    else:
      raise ValueError(f"""Unknown model name: {model_name}"""				)

    assert torch.allclose(logits[0, :3, :3, :3]   ,     lowercase   ,     atol=1E-4				)
  else:
    assert logits.shape == (1, 1001)

    if model_name == "mobilenet_v2_1.4_224":
      __snake_case   :				List[str]        =       torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]				)
    elif model_name == "mobilenet_v2_1.0_224":
      __snake_case   :				str        =       torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]				)
    elif model_name == "mobilenet_v2_0.75_160":
      __snake_case   :				Dict        =       torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]				)
    elif model_name == "mobilenet_v2_0.35_96":
      __snake_case   :				List[Any]        =       torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]				)
    else:
      __snake_case   :				int        =       None

    if expected_logits is not None:
      assert torch.allclose(logits[0, :3]   ,     lowercase   ,     atol=1E-4				)

  Path(lowercase				).mkdir(exist_ok=lowercase				)
  print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
  model.save_pretrained(lowercase				)
  print(f"""Saving image processor to {pytorch_dump_folder_path}"""				)
  image_processor.save_pretrained(lowercase				)

  if push_to_hub:
    print("Pushing to the hub..."				)
    __snake_case   :				Optional[Any]        =       "google/" + model_name
    image_processor.push_to_hub(lowercase				)
    model.push_to_hub(lowercase				)


if __name__ == "__main__":
     _UpperCamelCase       = argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--model_name''',
         default='''mobilenet_v2_1.0_224''',
         type=str,
         help='''Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<depth>_<size>\'.''',
     )
     parser.add_argument(
         '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
     )
     parser.add_argument(
         '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
     )

     _UpperCamelCase       = parser.parse_args()
     convert_movilevit_checkpoint(
         args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )

