

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


__A			:					Any						=			logging.get_logger(__name__)

__A			:					List[Any]						=			'▁'

__A			:					List[str]						=			{'vocab_file': 'sentencepiece.bpe.model'}

__A			:					Union[str, Any]						=			{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
        ),
    }
}

__A			:					Union[str, Any]						=			{
    'facebook/nllb-200-distilled-600M': 1_0_2_4,
}

# fmt: off
__A			:					Optional[int]						=			['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      ["input_ids", "attention_mask"]

   lowerCamelCase__				=      []
   lowerCamelCase__				=      []





   def __init__(       self					:			str     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Optional[Any]="<s>"     , __lowerCamelCase					:			List[str]="</s>"     , __lowerCamelCase					:			str="</s>"     , __lowerCamelCase					:			str="<s>"     , __lowerCamelCase					:			Union[str, Any]="<unk>"     , __lowerCamelCase					:			Tuple="<pad>"     , __lowerCamelCase					:			Optional[Any]="<mask>"     , __lowerCamelCase					:			Any=None     , __lowerCamelCase					:			List[Any]=None     , __lowerCamelCase					:			List[str]=None     , __lowerCamelCase					:			Optional[Dict[str, Any]] = None     , __lowerCamelCase					:			Tuple=None     , __lowerCamelCase					:			Any=False     , **__lowerCamelCase					:			List[str]     , ):
       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else mask_token

       SCREAMING_SNAKE_CASE										=   {} if sp_model_kwargs is None else sp_model_kwargs
       SCREAMING_SNAKE_CASE										=   legacy_behaviour

       super().__init__(
           bos_token=__lowerCamelCase     , eos_token=__lowerCamelCase     , unk_token=__lowerCamelCase     , sep_token=__lowerCamelCase     , cls_token=__lowerCamelCase     , pad_token=__lowerCamelCase     , mask_token=__lowerCamelCase     , tokenizer_file=__lowerCamelCase     , src_lang=__lowerCamelCase     , tgt_lang=__lowerCamelCase     , additional_special_tokens=__lowerCamelCase     , sp_model_kwargs=self.sp_model_kwargs     , legacy_behaviour=__lowerCamelCase     , **__lowerCamelCase     , )

       SCREAMING_SNAKE_CASE										=   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
       self.sp_model.Load(str(__lowerCamelCase			)			)
       SCREAMING_SNAKE_CASE										=   vocab_file
       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
       # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
       # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

       # Mimic fairseq token-to-id alignment for the first 4 token
       SCREAMING_SNAKE_CASE										=   {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       SCREAMING_SNAKE_CASE										=   1

       SCREAMING_SNAKE_CASE										=   len(self.sp_model			)
       SCREAMING_SNAKE_CASE										=   {
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__lowerCamelCase			)
       }
       SCREAMING_SNAKE_CASE										=   {v: k for k, v in self.lang_code_to_id.items()}
       SCREAMING_SNAKE_CASE										=   len(self.sp_model			) + len(self.lang_code_to_id			) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id			)
       SCREAMING_SNAKE_CASE										=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       SCREAMING_SNAKE_CASE										=   list(self.lang_code_to_id.keys()			)

       if additional_special_tokens is not None:
           # Only add those special tokens if they are not already there.
           self._additional_special_tokens.extend(
               [t for t in additional_special_tokens if t not in self._additional_special_tokens]			)

       SCREAMING_SNAKE_CASE										=   src_lang if src_lang is not None else "eng_Latn"
       SCREAMING_SNAKE_CASE										=   self.lang_code_to_id[self._src_lang]
       SCREAMING_SNAKE_CASE										=   tgt_lang
       self.set_src_lang_special_tokens(self._src_lang			)





   def __getstate__(       self					:			Dict			):
       SCREAMING_SNAKE_CASE										=   self.__dict__.copy()
       SCREAMING_SNAKE_CASE										=   None
       SCREAMING_SNAKE_CASE										=   self.sp_model.serialized_model_proto()
       return state





   def __setstate__(       self					:			Tuple     , __lowerCamelCase					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   d

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

       SCREAMING_SNAKE_CASE										=   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)





   @property
   def      _snake_case		(       self					:			Any			):
       return len(self.sp_model			) + len(self.lang_code_to_id			) + self.fairseq_offset + 1  # Plus 1 for the mask token





   @property
   def      _snake_case		(       self					:			Optional[int]			):
       return self._src_lang





   @src_lang.setter
   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   new_src_lang
       self.set_src_lang_special_tokens(self._src_lang			)





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None     , __lowerCamelCase					:			bool = False			):

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

       SCREAMING_SNAKE_CASE										=   [1] * len(self.prefix_tokens			)
       SCREAMING_SNAKE_CASE										=   [1] * len(self.suffix_tokens			)
       if token_ids_a is None:
           return prefix_ones + ([0] * len(__lowerCamelCase			)) + suffix_ones
       return prefix_ones + ([0] * len(__lowerCamelCase			)) + ([0] * len(__lowerCamelCase			)) + suffix_ones





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None			):
       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      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None			):
       SCREAMING_SNAKE_CASE										=   [self.sep_token_id]
       SCREAMING_SNAKE_CASE										=   [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 + sep + token_ids_a + sep			) * [0]





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





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   {self.convert_ids_to_tokens(__lowerCamelCase			): i for i in range(self.vocab_size			)}
       vocab.update(self.added_tokens_encoder			)
       return vocab





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			str			):
       return self.sp_model.encode(__lowerCamelCase     , out_type=__lowerCamelCase			)





   def      _snake_case		(       self					:			List[str]     , __lowerCamelCase					:			str			):
       if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
       SCREAMING_SNAKE_CASE										=   self.sp_model.PieceToId(__lowerCamelCase			)

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			Dict			):
       if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset			)





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			Any			):
       SCREAMING_SNAKE_CASE										=   "".join(__lowerCamelCase			).replace(__lowerCamelCase     , " "			).strip()
       return out_string





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[str] = None			):
       if not os.path.isdir(__lowerCamelCase			):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
           return
       SCREAMING_SNAKE_CASE										=   os.path.join(
           __lowerCamelCase     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

       if os.path.abspath(self.vocab_file			) != os.path.abspath(__lowerCamelCase			) and os.path.isfile(self.vocab_file			):
           copyfile(self.vocab_file     , __lowerCamelCase			)
       elif not os.path.isfile(self.vocab_file			):
           with open(__lowerCamelCase     , "wb"			) as fi:
               SCREAMING_SNAKE_CASE										=   self.sp_model.serialized_model_proto()
               fi.write(__lowerCamelCase			)

       return (out_vocab_file,)





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			str = "eng_Latn"     , __lowerCamelCase					:			Optional[List[str]] = None     , __lowerCamelCase					:			str = "fra_Latn"     , **__lowerCamelCase					:			Optional[int]     , ):
       SCREAMING_SNAKE_CASE										=   src_lang
       SCREAMING_SNAKE_CASE										=   tgt_lang
       return super().prepare_seqaseq_batch(__lowerCamelCase     , __lowerCamelCase     , **__lowerCamelCase			)





   def      _snake_case		(       self					:			Any			):
       return self.set_src_lang_special_tokens(self.src_lang			)





   def      _snake_case		(       self					:			Optional[Any]			):
       return self.set_tgt_lang_special_tokens(self.tgt_lang			)





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   self.lang_code_to_id[src_lang]
       if self.legacy_behaviour:
           SCREAMING_SNAKE_CASE										=   []
           SCREAMING_SNAKE_CASE										=   [self.eos_token_id, self.cur_lang_code]
       else:
           SCREAMING_SNAKE_CASE										=   [self.cur_lang_code]
           SCREAMING_SNAKE_CASE										=   [self.eos_token_id]





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   self.lang_code_to_id[lang]
       if self.legacy_behaviour:
           SCREAMING_SNAKE_CASE										=   []
           SCREAMING_SNAKE_CASE										=   [self.eos_token_id, self.cur_lang_code]
       else:
           SCREAMING_SNAKE_CASE										=   [self.cur_lang_code]
           SCREAMING_SNAKE_CASE										=   [self.eos_token_id]






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,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_   :      str			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     MobileNetVaConfig(layer_norm_eps=0.001							)

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

		__lowercase											=     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'						,   snake_case							)
		if matches:
				__lowercase											=     float(matches[1]							)
				__lowercase											=     int(matches[2]							)

		# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
		# the usual 1000. The first class (index 0) is "background".
		__lowercase											=     1_001
		__lowercase											=     'imagenet-1k-id2label.json'
		__lowercase											=     'huggingface/label-files'
		__lowercase											=     json.load(open(hf_hub_download(snake_case						,   snake_case						,   repo_type='dataset'							)						,   'r'							)							)
		__lowercase											=     {int(snake_case							) + 1: v for k, v in idalabel.items()}
		__lowercase											=     'background'
		__lowercase											=     idalabel
		__lowercase											=     {v: k for k, v in idalabel.items()}

		return config



def  SCREAMING_SNAKE_CASE				(      )		->    List[Any]:
		__lowercase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		return im



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     get_mobilenet_va_config(snake_case							)

		# Load 🤗 model
		__lowercase											=     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 MobileNetV1ImageProcessor
		__lowercase											=     MobileNetVaImageProcessor(
		    crop_size={'width': config.image_size, 'height': config.image_size}						,   size={'shortest_edge': config.image_size + 32}						,   )
		__lowercase											=     image_processor(images=prepare_img()						,   return_tensors='pt'							)
		__lowercase											=     model(**snake_case							)
		__lowercase											=     outputs.logits

		assert logits.shape == (1, 1_001)

		if model_name == "mobilenet_v1_1.0_224":
				__lowercase											=     torch.tensor([-4.1739, -1.1233, 3.1205]							)
		elif model_name == "mobilenet_v1_0.75_192":
				__lowercase											=     torch.tensor([-3.9440, -2.3141, -0.3333]							)
		else:
				__lowercase											=     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...'							)
				__lowercase											=     'google/' + model_name
				image_processor.push_to_hub(snake_case							)
				model.push_to_hub(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''mobilenet_v1_1.0_224''',
		    type=str,
		    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<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.'''
		)

		SCREAMING_SNAKE_CASE_   :      int			=   parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)




