


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


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     '''▁'''

_snake_case             =     {'''vocab_file''': '''sentencepiece.bpe.model'''}

_snake_case             =     {
    '''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'''
        ),
    }
}

_snake_case             =     {
    '''facebook/mbart-large-50-one-to-many-mmt''': 10_24,
}

# fmt: off
_snake_case             =     ['''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_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      List[str]     		=		VOCAB_FILES_NAMES
   __A :      Union[str, Any]     		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A :      Tuple     		=		PRETRAINED_VOCAB_FILES_MAP
   __A :      int     		=		["input_ids", "attention_mask"]

   __A :      List[int]     		=		[]
   __A :      List[int]     		=		[]






   def __init__(		self						,				__A						,				__A=None						,				__A=None						,				__A="</s>"						,				__A="</s>"						,				__A="<s>"						,				__A="<unk>"						,				__A="<pad>"						,				__A="<mask>"						,				__A = None						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					Any    =				AddedToken(__A						,				lstrip=__A						,				rstrip=__A ) if isinstance(__A						,				__A ) else mask_token

     lowerCamelCase :					Any    =				{} if sp_model_kwargs is None else sp_model_kwargs

     lowerCamelCase :					Union[str, 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__(
         src_lang=__A						,				tgt_lang=__A						,				eos_token=__A						,				unk_token=__A						,				sep_token=__A						,				cls_token=__A						,				pad_token=__A						,				mask_token=__A						,				sp_model_kwargs=self.sp_model_kwargs						,				**__A						,				)

     lowerCamelCase :					Tuple    =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.Load(str(__A ) )
     lowerCamelCase :					Tuple    =				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>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
     # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

     # Mimic fairseq token-to-id alignment for the first 4 token
     lowerCamelCase :					List[Any]    =				{"<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
     lowerCamelCase :					str    =				1

     lowerCamelCase :					Tuple    =				len(self.sp_model )
     lowerCamelCase :					Optional[int]    =				{
         code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__A )
     }
     lowerCamelCase :					str    =				{v: k for k, v in self.lang_code_to_id.items()}
     lowerCamelCase :					List[Any]    =				len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset

     self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
     lowerCamelCase :					Any    =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}

     lowerCamelCase :					str    =				src_lang if src_lang is not None else "en_XX"
     lowerCamelCase :					Optional[int]    =				self.lang_code_to_id[self._src_lang]
     lowerCamelCase :					Tuple    =				tgt_lang
     self.set_src_lang_special_tokens(self._src_lang )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     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 ):






     """simple docstring"""





     return self._src_lang






   @src_lang.setter
   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				new_src_lang
     self.set_src_lang_special_tokens(self._src_lang )






   def __getstate__(		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				self.__dict__.copy()
     lowerCamelCase :					Dict    =				None
     return state






   def __setstate__(		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				d

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

     lowerCamelCase :					Any    =				spm.SentencePieceProcessor(**self.sp_model_kwargs )
     self.sp_model.Load(self.vocab_file )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					str    =				{self.convert_ids_to_tokens(__A ): i for i in range(self.vocab_size )}
     vocab.update(self.added_tokens_encoder )
     return vocab






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     return self.sp_model.encode(__A						,				out_type=__A )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     lowerCamelCase :					Union[str, Any]    =				self.sp_model.PieceToId(__A )

     # 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						,				__A ):






     """simple docstring"""





     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						,				__A ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				[]
     lowerCamelCase :					Dict    =				""
     lowerCamelCase :					Tuple    =				False
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         if not prev_is_special:
           out_string += " "
         out_string += self.sp_model.decode(__A ) + token
         lowerCamelCase :					List[Any]    =				True
         lowerCamelCase :					int    =				[]
       else:
         current_sub_tokens.append(__A )
         lowerCamelCase :					Optional[Any]    =				False
     out_string += self.sp_model.decode(__A )
     return out_string.strip()






   def _snake_case    (		self						,				__A						,				__A = None ):






     """simple docstring"""





     if not os.path.isdir(__A ):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
       return
     lowerCamelCase :					int    =				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 ) and os.path.isfile(self.vocab_file ):
       copyfile(self.vocab_file						,				__A )
     elif not os.path.isfile(self.vocab_file ):
       with open(__A						,				"wb" ) as fi:
         lowerCamelCase :					Tuple    =				self.sp_model.serialized_model_proto()
         fi.write(__A )

     return (out_vocab_file,)






   def _snake_case    (		self						,				__A						,				__A = None						,				__A = False ):






     """simple docstring"""





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

     lowerCamelCase :					Union[str, Any]    =				[1] * len(self.prefix_tokens )
     lowerCamelCase :					List[str]    =				[1] * len(self.suffix_tokens )
     if token_ids_a is None:
       return prefix_ones + ([0] * len(__A )) + suffix_ones
     return prefix_ones + ([0] * len(__A )) + ([0] * len(__A )) + suffix_ones






   def _snake_case    (		self						,				__A						,				__A = 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 _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				**__A ):






     """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" )
     lowerCamelCase :					int    =				src_lang
     lowerCamelCase :					Any    =				self(__A						,				add_special_tokens=__A						,				return_tensors=__A						,				**__A )
     lowerCamelCase :					Union[str, Any]    =				self.convert_tokens_to_ids(__A )
     lowerCamelCase :					List[str]    =				tgt_lang_id
     return inputs






   def _snake_case    (		self						,				__A						,				__A = "en_XX"						,				__A = None						,				__A = "ro_RO"						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				src_lang
     lowerCamelCase :					Optional[Any]    =				tgt_lang
     return super().prepare_seqaseq_batch(__A						,				__A						,				**__A )






   def _snake_case    (		self ):






     """simple docstring"""





     return self.set_src_lang_special_tokens(self.src_lang )






   def _snake_case    (		self ):






     """simple docstring"""





     return self.set_tgt_lang_special_tokens(self.tgt_lang )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				self.lang_code_to_id[src_lang]
     lowerCamelCase :					Dict    =				[self.cur_lang_code_id]
     lowerCamelCase :					Optional[Any]    =				[self.eos_token_id]






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				self.lang_code_to_id[tgt_lang]
     lowerCamelCase :					Any    =				[self.cur_lang_code_id]
     lowerCamelCase :					List[str]    =				[self.eos_token_id]







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		"mobilenet_v2"






   def __init__(		self						,				__A=3						,				__A=224						,				__A=1.0						,				__A=8						,				__A=8						,				__A=6						,				__A=32						,				__A=True						,				__A=True						,				__A="relu6"						,				__A=True						,				__A=0.8						,				__A=0.02						,				__A=0.001						,				__A=255						,				**__A						,				):






     """simple docstring"""





     super().__init__(**__A )

     if depth_multiplier <= 0:
       raise ValueError("depth_multiplier must be greater than zero." )

     lowerCamelCase :					str    =				num_channels
     lowerCamelCase :					Any    =				image_size
     lowerCamelCase :					Union[str, Any]    =				depth_multiplier
     lowerCamelCase :					Tuple    =				depth_divisible_by
     lowerCamelCase :					Dict    =				min_depth
     lowerCamelCase :					Dict    =				expand_ratio
     lowerCamelCase :					Optional[Any]    =				output_stride
     lowerCamelCase :					int    =				first_layer_is_expansion
     lowerCamelCase :					Union[str, Any]    =				finegrained_output
     lowerCamelCase :					Optional[Any]    =				hidden_act
     lowerCamelCase :					Optional[Any]    =				tf_padding
     lowerCamelCase :					Optional[Any]    =				classifier_dropout_prob
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					str    =				layer_norm_eps
     lowerCamelCase :					Optional[Any]    =				semantic_loss_ignore_index



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Union[str, Any]     		=		version.parse("1.11"    )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return OrderedDict([("pixel_values", {0: "batch"})] )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     if self.task == "image-classification":
       return OrderedDict([("logits", {0: "batch"})] )
     else:
       return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return 1e-4



