






'''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()
UpperCamelCase__	=     logging.get_logger(__name__)




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:      Any          =       MobileNetVaConfig(layer_norm_eps=0.001							)

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

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

   if model_name.startswith("deeplabv3_"							):
      lowercase_		:      Dict          =       8
      lowercase_		:      Tuple          =       21
      lowercase_		:      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".
      lowercase_		:      Union[str, Any]          =       1001
      lowercase_		:      List[str]          =       "imagenet-1k-id2label.json"

   lowercase_		:      Optional[Any]          =       "huggingface/label-files"
   lowercase_		:      Any          =       json.load(open(hf_hub_download(_UpperCamelCase  ,						_UpperCamelCase  ,						repo_type="dataset"							)  ,						"r"							)							)

   if config.num_labels == 1001:
      lowercase_		:      str          =       {int(_UpperCamelCase							) + 1: v for k, v in idalabel.items()}
      lowercase_		:      List[str]          =       "background"
   else:
      lowercase_		:      Optional[int]          =       {int(_UpperCamelCase							): v for k, v in idalabel.items()}

   lowercase_		:      Dict          =       idalabel
   lowercase_		:      Tuple          =       {v: k for k, v in idalabel.items()}

   return config




def 						__SCREAMING_SNAKE_CASE   (					):


   """simple docstring"""




   lowercase_		:      Any          =       "http://images.cocodataset.org/val2017/000000039769.jpg"
   lowercase_		:      Optional[Any]          =       Image.open(requests.get(_UpperCamelCase  ,						stream=_UpperCamelCase							).raw							)
   return im




@torch.no_grad()
def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase=False							):


   """simple docstring"""




   lowercase_		:      int          =       get_mobilenet_va_config(_UpperCamelCase							)

   # Load 🤗 model
   if model_name.startswith("deeplabv3_"							):
      lowercase_		:      List[Any]          =       MobileNetVaForSemanticSegmentation(_UpperCamelCase							).eval()
   else:
      lowercase_		:      str          =       MobileNetVaForImageClassification(_UpperCamelCase							).eval()

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

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

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

      if model_name == "deeplabv3_mobilenet_v2_1.0_513":
         lowercase_		:      Tuple          =       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]  ,						_UpperCamelCase  ,						atol=1e-4							)
   else:
      assert logits.shape == (1, 1001)

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

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

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

   if push_to_hub:
      print("Pushing to the hub..."							)
      lowercase_		:      Optional[int]          =       "google/" + model_name
      image_processor.push_to_hub(_UpperCamelCase							)
      model.push_to_hub(_UpperCamelCase							)


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
     )












'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_mbartaa import MBartaaTokenizer
else:
 UpperCamelCase__	=     None


UpperCamelCase__	=     logging.get_logger(__name__)

UpperCamelCase__	=     {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__	=     {
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

UpperCamelCase__	=     {
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
UpperCamelCase__	=     ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']


class     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				List[str]						    =      VOCAB_FILES_NAMES
    __lowerCamelCase:				int						    =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase:				List[str]						    =      PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase:				List[str]						    =      ['input_ids', 'attention_mask']
    __lowerCamelCase:				str						    =      MBartaaTokenizer

    __lowerCamelCase:				List[int]						    =      []
    __lowerCamelCase:				List[int]						    =      []




    def __init__(     self					:    Optional[Any]					,       a					:    Any=None					,       a					:    List[Any]=None					,       a					:    Optional[int]=None					,       a					:    Dict=None					,       a					:    int="</s>"					,       a					:    Optional[Any]="</s>"					,       a					:    List[Any]="<s>"					,       a					:    Any="<unk>"					,       a					:    Optional[Any]="<pad>"					,       a					:    List[str]="<mask>"					,       **a					:    Optional[Any]					,       ):






       '''simple docstring'''






       lowercase_		:      int          =       AddedToken(a					,       lstrip=a					,       rstrip=a     ) if isinstance(a					,       a     ) else mask_token

       lowercase_		:      List[Any]          =       kwargs.get("additional_special_tokens"					,       []     )
       kwargs["additional_special_tokens"] += [
           code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
       ]

       super().__init__(
           a					,       src_lang=a					,       tgt_lang=a					,       tokenizer_file=a					,       eos_token=a					,       sep_token=a					,       cls_token=a					,       unk_token=a					,       pad_token=a					,       mask_token=a					,       **a					,       )

       lowercase_		:      Any          =       vocab_file
       lowercase_		:      int          =       False if not self.vocab_file else True

       lowercase_		:      Optional[Any]          =       {
           lang_code: self.convert_tokens_to_ids(a     ) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       lowercase_		:      int          =       src_lang if src_lang is not None else "en_XX"
       lowercase_		:      Tuple          =       tgt_lang
       lowercase_		:      List[Any]          =       self.lang_code_to_id[self._src_lang]
       self.set_src_lang_special_tokens(self._src_lang     )




    @property
    def 			lowerCAmelCase__			(     self					:    List[Any]     ):






       '''simple docstring'''






       return self._src_lang




    @src_lang.setter
    def 			lowerCAmelCase__			(     self					:    Union[str, Any]					,       a					:    str     ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       new_src_lang
       self.set_src_lang_special_tokens(self._src_lang     )




    def 			lowerCAmelCase__			(     self					:    int					,       a					:    List[int]					,       a					:    Optional[List[int]] = None     ):






       '''simple docstring'''






       if token_ids_a is None:
          return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




    def 			lowerCAmelCase__			(     self					:    Any					,       a					:    List[str]					,       a					:    str = "en_XX"					,       a					:    Optional[List[str]] = None					,       a					:    str = "ro_RO"					,       **a					:    Union[str, Any]					,       ):






       '''simple docstring'''






       lowercase_		:      Dict          =       src_lang
       lowercase_		:      Dict          =       tgt_lang
       return super().prepare_seqaseq_batch(a					,       a					,       **a     )




    def 			lowerCAmelCase__			(     self					:    Dict     ):






       '''simple docstring'''






       return self.set_src_lang_special_tokens(self.src_lang     )




    def 			lowerCAmelCase__			(     self					:    int     ):






       '''simple docstring'''






       return self.set_tgt_lang_special_tokens(self.tgt_lang     )




    def 			lowerCAmelCase__			(     self					:    str					,       a					:    str     ):






       '''simple docstring'''






       lowercase_		:      Optional[int]          =       self.convert_tokens_to_ids(a     )
       lowercase_		:      Optional[int]          =       [self.cur_lang_code_id]
       lowercase_		:      List[Any]          =       [self.eos_token_id]

       lowercase_		:      Optional[Any]          =       self.convert_ids_to_tokens(self.prefix_tokens     )
       lowercase_		:      Union[str, Any]          =       self.convert_ids_to_tokens(self.suffix_tokens     )

       lowercase_		:      Optional[Any]          =       processors.TemplateProcessing(
           single=prefix_tokens_str + ["$A"] + suffix_tokens_str					,       pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str					,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str					,       self.prefix_tokens + self.suffix_tokens     )     )					,       )




    def 			lowerCAmelCase__			(     self					:    Tuple					,       a					:    str     ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       self.convert_tokens_to_ids(a     )
       lowercase_		:      str          =       [self.cur_lang_code_id]
       lowercase_		:      List[str]          =       [self.eos_token_id]

       lowercase_		:      Dict          =       self.convert_ids_to_tokens(self.prefix_tokens     )
       lowercase_		:      Dict          =       self.convert_ids_to_tokens(self.suffix_tokens     )

       lowercase_		:      int          =       processors.TemplateProcessing(
           single=prefix_tokens_str + ["$A"] + suffix_tokens_str					,       pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str					,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str					,       self.prefix_tokens + self.suffix_tokens     )     )					,       )




    def 			lowerCAmelCase__			(     self					:    Any					,       a					:    int					,       a					:    str					,       a					:    Optional[str]					,       a					:    Optional[str]					,       **a					:    str     ):






       '''simple docstring'''






       if src_lang is None or tgt_lang is None:
          raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"     )
       lowercase_		:      Any          =       src_lang
       lowercase_		:      int          =       self(a					,       add_special_tokens=a					,       return_tensors=a					,       **a     )
       lowercase_		:      Any          =       self.convert_tokens_to_ids(a     )
       lowercase_		:      Optional[Any]          =       tgt_lang_id
       return inputs





    def 			lowerCAmelCase__			(     self					:    Any					,       a					:    str					,       a					:    Optional[str] = None     ):






       '''simple docstring'''






       if not self.can_save_slow_tokenizer:
          raise ValueError(
              "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
              "tokenizer."     )

       if not os.path.isdir(a     ):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
          return
       lowercase_		:      Any          =       os.path.join(
           a					,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

       if os.path.abspath(self.vocab_file     ) != os.path.abspath(a     ):
          copyfile(self.vocab_file					,       a     )

       return (out_vocab_file,)




