

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

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


A_      =							logging.get_logger(__name__)

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

A_      =							{
    "vocab_file": {
        "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json",
    },
    "merges_file": {"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"},
}

A_      =							{
    "microsoft/biogpt": 10_24,
}





def 				A_					(	snake_case	):
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   set()
  SCREAMING_SNAKE_CASE:Any   =   word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)	)
    SCREAMING_SNAKE_CASE:int   =   char
  return pairs





class 							_snake_case	(    lowerCAmelCase__  ):
   _A    :       Optional[Any]      				=   VOCAB_FILES_NAMES
   _A    :       Dict      				=   PRETRAINED_VOCAB_FILES_MAP
   _A    :       int      				=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _A    :       Union[str, Any]      				=   ["input_ids", "attention_mask"]
   def __init__(	self     : Any       ,SCREAMING_SNAKE_CASE__     : List[Any]       ,SCREAMING_SNAKE_CASE__     : Optional[Any]       ,SCREAMING_SNAKE_CASE__     : List[Any]="<unk>"       ,SCREAMING_SNAKE_CASE__     : str="<s>"       ,SCREAMING_SNAKE_CASE__     : List[str]="</s>"       ,SCREAMING_SNAKE_CASE__     : Tuple="</s>"       ,SCREAMING_SNAKE_CASE__     : Tuple="<pad>"       ,**SCREAMING_SNAKE_CASE__     : Optional[Any]       ,):
     super().__init__(
         bos_token=_UpperCAmelCase       ,eos_token=_UpperCAmelCase       ,sep_token=_UpperCAmelCase       ,unk_token=_UpperCAmelCase       ,pad_token=_UpperCAmelCase       ,**_UpperCAmelCase       ,)

     try:
       import sacremoses
     except ImportError:
       raise ImportError(
           "You need to install sacremoses to use BioGptTokenizer. "
           "See https://pypi.org/project/sacremoses/ for installation."							)

     SCREAMING_SNAKE_CASE:List[str]   =   "en"
     SCREAMING_SNAKE_CASE:List[Any]   =   sacremoses
     # cache of sm.MosesTokenizer instance
     SCREAMING_SNAKE_CASE:List[Any]   =   {}
     SCREAMING_SNAKE_CASE:List[Any]   =   {}
     with open(_UpperCAmelCase       ,encoding="utf-8"							) as vocab_handle:
       SCREAMING_SNAKE_CASE:Any   =   json.load(_UpperCAmelCase							)
     SCREAMING_SNAKE_CASE:Tuple   =   {v: k for k, v in self.encoder.items()}
     with open(_UpperCAmelCase       ,encoding="utf-8"							) as merges_handle:
       SCREAMING_SNAKE_CASE:Optional[Any]   =   merges_handle.read().split("\n"							)[:-1]
     SCREAMING_SNAKE_CASE:Dict   =   [tuple(merge.split()[:2]							) for merge in merges]
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   dict(zip(_UpperCAmelCase       ,range(len(_UpperCAmelCase							)							)							)							)
     SCREAMING_SNAKE_CASE:List[Any]   =   {}
   @property
   def       __UpperCamelCase  (	self     : int							):
     return len(self.encoder							)
   def       __UpperCamelCase  (	self     : str							):
     return dict(self.encoder       ,**self.added_tokens_encoder							)
   def       __UpperCamelCase  (	self     : Tuple       ,SCREAMING_SNAKE_CASE__     : List[Any]       ,SCREAMING_SNAKE_CASE__     : Tuple							):


     if lang not in self.cache_moses_tokenizer:
       SCREAMING_SNAKE_CASE:Tuple   =   self.sm.MosesTokenizer(lang=_UpperCAmelCase							)
       SCREAMING_SNAKE_CASE:Tuple   =   moses_tokenizer
     return self.cache_moses_tokenizer[lang].tokenize(
         _UpperCAmelCase       ,aggressive_dash_splits=_UpperCAmelCase       ,return_str=_UpperCAmelCase       ,escape=_UpperCAmelCase							)
   def       __UpperCamelCase  (	self     : Optional[int]       ,SCREAMING_SNAKE_CASE__     : int       ,SCREAMING_SNAKE_CASE__     : Dict							):


     if lang not in self.cache_moses_detokenizer:
       SCREAMING_SNAKE_CASE:int   =   self.sm.MosesDetokenizer(lang=_UpperCAmelCase							)
       SCREAMING_SNAKE_CASE:Optional[Any]   =   moses_detokenizer
     return self.cache_moses_detokenizer[lang].detokenize(_UpperCAmelCase							)
   def       __UpperCamelCase  (	self     : Tuple       ,SCREAMING_SNAKE_CASE__     : int							):
     SCREAMING_SNAKE_CASE:Any   =   tuple(token[:-1]							) + (token[-1] + "</w>",)
     if token in self.cache:
       return self.cache[token]
     SCREAMING_SNAKE_CASE:Tuple   =   get_pairs(_UpperCAmelCase							)

     if not pairs:
       return token + "</w>"

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

         if word[i] == first and i < len(_UpperCAmelCase							) - 1 and word[i + 1] == second:
           new_word.append(first + second							)
           i += 2
         else:
           new_word.append(word[i]							)
           i += 1
       SCREAMING_SNAKE_CASE:Dict   =   tuple(_UpperCAmelCase							)
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   new_word
       if len(_UpperCAmelCase							) == 1:
         break
       else:
         SCREAMING_SNAKE_CASE:List[str]   =   get_pairs(_UpperCAmelCase							)
     SCREAMING_SNAKE_CASE:List[str]   =   " ".join(_UpperCAmelCase							)
     if word == "\n  </w>":
       SCREAMING_SNAKE_CASE:Union[str, Any]   =   "\n</w>"
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   word
     return word
   def       __UpperCamelCase  (	self     : List[str]       ,SCREAMING_SNAKE_CASE__     : List[str]       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]=False							):


     if bypass_tokenizer:
       SCREAMING_SNAKE_CASE:List[str]   =   text.split()
     else:
       SCREAMING_SNAKE_CASE:str   =   self.moses_tokenize(_UpperCAmelCase       ,self.lang							)

     SCREAMING_SNAKE_CASE:Any   =   []
     for token in text:
       if token:
         split_tokens.extend(list(self.bpe(_UpperCAmelCase							).split(" "							)							)							)

     return split_tokens
   def       __UpperCamelCase  (	self     : str       ,SCREAMING_SNAKE_CASE__     : Any							):
     return self.encoder.get(_UpperCAmelCase       ,self.encoder.get(self.unk_token							)							)
   def       __UpperCamelCase  (	self     : Dict       ,SCREAMING_SNAKE_CASE__     : List[Any]							):
     return self.decoder.get(_UpperCAmelCase       ,self.unk_token							)
   def       __UpperCamelCase  (	self     : int       ,SCREAMING_SNAKE_CASE__     : Tuple							):
     SCREAMING_SNAKE_CASE:str   =   [t.replace(" "       ,""							).replace("</w>"       ," "							) for t in tokens]
     SCREAMING_SNAKE_CASE:int   =   "".join(_UpperCAmelCase							).split()
     # detokenize
     SCREAMING_SNAKE_CASE:Dict   =   self.moses_detokenize(_UpperCAmelCase       ,self.lang							)
     return text
   def       __UpperCamelCase  (	self     : Dict       ,SCREAMING_SNAKE_CASE__     : List[int]       ,SCREAMING_SNAKE_CASE__     : Optional[List[int]] = None							):


     if token_ids_a is None:
       return [self.sep_token_id] + token_ids_a
     SCREAMING_SNAKE_CASE:List[str]   =   [self.sep_token_id]
     return sep + token_ids_a + sep + token_ids_a
   def       __UpperCamelCase  (	self     : List[str]       ,SCREAMING_SNAKE_CASE__     : List[int]       ,SCREAMING_SNAKE_CASE__     : Optional[List[int]] = None       ,SCREAMING_SNAKE_CASE__     : bool = False							):


     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_UpperCAmelCase       ,token_ids_a=_UpperCAmelCase       ,already_has_special_tokens=_UpperCAmelCase							)
     # no bos used in fairseq
     if token_ids_a is not None:
       return [1] + ([0] * len(_UpperCAmelCase							)) + [1] + ([0] * len(_UpperCAmelCase							))
     return [1] + ([0] * len(_UpperCAmelCase							))
   def       __UpperCamelCase  (	self     : List[str]       ,SCREAMING_SNAKE_CASE__     : List[int]       ,SCREAMING_SNAKE_CASE__     : Optional[List[int]] = None							):
     SCREAMING_SNAKE_CASE:Any   =   [self.sep_token_id]

     # no bos used in fairseq
     if token_ids_a is None:
       return len(token_ids_a + sep							) * [0]
     return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]
   def       __UpperCamelCase  (	self     : int       ,SCREAMING_SNAKE_CASE__     : str       ,SCREAMING_SNAKE_CASE__     : Optional[str] = None							):


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

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

     SCREAMING_SNAKE_CASE:Optional[Any]   =   0
     with open(_UpperCAmelCase       ,"w"       ,encoding="utf-8"							) as writer:
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,key=lambda SCREAMING_SNAKE_CASE__							: 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!"							)
           SCREAMING_SNAKE_CASE:List[Any]   =   token_index
         writer.write(" ".join(_UpperCAmelCase							) + "\n"							)
         index += 1

     return vocab_file, merge_file
   def __getstate__(	self     : Dict							):
     SCREAMING_SNAKE_CASE:List[str]   =   self.__dict__.copy()
     SCREAMING_SNAKE_CASE:int   =   None
     return state







   def __setstate__(	self     : Optional[Any]       ,SCREAMING_SNAKE_CASE__     : List[Any]							):
     SCREAMING_SNAKE_CASE:List[Any]   =   d

     try:
       import sacremoses
     except ImportError:
       raise ImportError(
           "You need to install sacremoses to use XLMTokenizer. "
           "See https://pypi.org/project/sacremoses/ for installation."							)

     SCREAMING_SNAKE_CASE:Optional[int]   =   sacremoses









'''simple docstring'''

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()
A_      =							logging.get_logger(__name__)





def 				A_					(	snake_case	):
  SCREAMING_SNAKE_CASE:Optional[int]   =   MobileNetVaConfig(layer_norm_eps=0.001	)

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

  SCREAMING_SNAKE_CASE:Any   =   re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$" ,    snake_case	)
  if matches:
    SCREAMING_SNAKE_CASE:str   =   float(matches[1]	)
    SCREAMING_SNAKE_CASE:Optional[int]   =   int(matches[2]	)

  if model_name.startswith("deeplabv3_"	):
    SCREAMING_SNAKE_CASE:str   =   8
    SCREAMING_SNAKE_CASE:Optional[int]   =   21
    SCREAMING_SNAKE_CASE:Dict   =   "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".
    SCREAMING_SNAKE_CASE:Optional[int]   =   1001
    SCREAMING_SNAKE_CASE:Tuple   =   "imagenet-1k-id2label.json"

  SCREAMING_SNAKE_CASE:List[str]   =   "huggingface/label-files"
  SCREAMING_SNAKE_CASE:List[Any]   =   json.load(open(hf_hub_download(snake_case ,    snake_case ,    repo_type="dataset"	) ,    "r"	)	)

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

  SCREAMING_SNAKE_CASE:str   =   idalabel
  SCREAMING_SNAKE_CASE:int   =   {v: k for k, v in idalabel.items()}

  return config





def 				A_					(	):
  SCREAMING_SNAKE_CASE:Optional[Any]   =   "http://images.cocodataset.org/val2017/000000039769.jpg"
  SCREAMING_SNAKE_CASE:str   =   Image.open(requests.get(snake_case ,    stream=snake_case	).raw	)
  return im





@torch.no_grad()
def 				A_					(	snake_case ,    snake_case ,    snake_case ,    snake_case=False	):
  SCREAMING_SNAKE_CASE:Any   =   get_mobilenet_va_config(snake_case	)

  # Load 🤗 model
  if model_name.startswith("deeplabv3_"	):
    SCREAMING_SNAKE_CASE:Tuple   =   MobileNetVaForSemanticSegmentation(snake_case	).eval()
  else:
    SCREAMING_SNAKE_CASE:List[str]   =   MobileNetVaForImageClassification(snake_case	).eval()

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

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

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

    if model_name == "deeplabv3_mobilenet_v2_1.0_513":
      SCREAMING_SNAKE_CASE:int   =   torch.tensor(
          [
              [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
              [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
              [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
          ]	)

    else:
      raise ValueError(F'''Unknown model name: {model_name}'''	)

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

    if model_name == "mobilenet_v2_1.4_224":
      SCREAMING_SNAKE_CASE:List[str]   =   torch.tensor([0.0181, -1.0015, 0.4688]	)
    elif model_name == "mobilenet_v2_1.0_224":
      SCREAMING_SNAKE_CASE:Optional[int]   =   torch.tensor([0.2445, -1.1993, 0.1905]	)
    elif model_name == "mobilenet_v2_0.75_160":
      SCREAMING_SNAKE_CASE:Optional[Any]   =   torch.tensor([0.2482, 0.4136, 0.6669]	)
    elif model_name == "mobilenet_v2_0.35_96":
      SCREAMING_SNAKE_CASE:int   =   torch.tensor([0.1451, -0.4624, 0.7192]	)
    else:
      SCREAMING_SNAKE_CASE:Optional[Any]   =   None

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

  Path(snake_case	).mkdir(exist_ok=snake_case	)
  print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''	)
  model.save_pretrained(snake_case	)
  print(F'''Saving image processor to {pytorch_dump_folder_path}'''	)
  image_processor.save_pretrained(snake_case	)

  if push_to_hub:
    print("Pushing to the hub..."	)
    SCREAMING_SNAKE_CASE:Tuple   =   "google/" + model_name
    image_processor.push_to_hub(snake_case	)
    model.push_to_hub(snake_case	)


if __name__ == "__main__":
    A_      =							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."
    )

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






