


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


SCREAMING_SNAKE_CASE			: int				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: Tuple				   =					"▁"

SCREAMING_SNAKE_CASE			: Optional[Any]				   =					{"vocab_file": "sentencepiece.bpe.model"}

SCREAMING_SNAKE_CASE			: List[Any]				   =					{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

SCREAMING_SNAKE_CASE			: Tuple				   =					{
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
SCREAMING_SNAKE_CASE			: List[Any]				   =					["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"]






class _lowerCamelCase(		_a  ):
       lowercase_					:      Optional[int]       					=     VOCAB_FILES_NAMES
       lowercase_					:      Any       					=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_					:      Tuple       					=     PRETRAINED_VOCAB_FILES_MAP
       lowercase_					:      int       					=     ["""input_ids""", """attention_mask"""]

       lowercase_					:      List[int]       					=     []
       lowercase_					:      List[int]       					=     []
       def __init__(   self, lowerCamelCase, lowerCamelCase="<s>", lowerCamelCase="</s>", lowerCamelCase="</s>", lowerCamelCase="<s>", lowerCamelCase="<unk>", lowerCamelCase="<pad>", lowerCamelCase="<mask>", lowerCamelCase=None, lowerCamelCase=None, lowerCamelCase=None, lowerCamelCase = None, lowerCamelCase=None, **lowerCamelCase, )			->  int:




           """simple docstring"""

           _lowercase			:				Dict 						=						AddedToken(lowerCamelCase, lstrip=lowerCamelCase, rstrip=lowerCamelCase) if isinstance(lowerCamelCase, lowerCamelCase) else mask_token

           _lowercase			:				Tuple 						=						{} if sp_model_kwargs is None else sp_model_kwargs

           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, **lowerCamelCase, )

           _lowercase			:				Dict 						=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(str(lowerCamelCase))
           _lowercase			:				List[Any] 						=						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
           _lowercase			:				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
           _lowercase			:				Dict 						=						1

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

           self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
           _lowercase			:				Union[str, Any] 						=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
           _lowercase			:				List[Any] 						=						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])

           _lowercase			:				Union[str, Any] 						=						src_lang if src_lang is not None else 'en_XX'
           _lowercase			:				int 						=						self.lang_code_to_id[self._src_lang]
           _lowercase			:				str 						=						tgt_lang
           self.set_src_lang_special_tokens(self._src_lang)
       def __getstate__(   self)			->  str:




           """simple docstring"""

           _lowercase			:				List[str] 						=						self.__dict__.copy()
           _lowercase			:				Tuple 						=						None
           _lowercase			:				Dict 						=						self.sp_model.serialized_model_proto()
           return state
       def __setstate__(   self, lowerCamelCase)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						d

           # for backward compatibility
           if not hasattr(self, 'sp_model_kwargs'):
               _lowercase			:				Tuple 						=						{}

           _lowercase			:				List[str] 						=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
       @property
       def      UpperCamelCase	(   self)			->  List[Any]:




           """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      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           return self._src_lang
       @src_lang.setter
       def      UpperCamelCase	(   self, lowerCamelCase)			->  None:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						new_src_lang
           self.set_src_lang_special_tokens(self._src_lang)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = 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)

           _lowercase			:				List[Any] 						=						[1] * len(self.prefix_tokens)
           _lowercase			:				Optional[Any] 						=						[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      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  List[int]:




           """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      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  List[int]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						[self.sep_token_id]
           _lowercase			:				Union[str, Any] 						=						[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      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, **lowerCamelCase)			->  int:




           """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			:				List[Any] 						=						src_lang
           _lowercase			:				List[str] 						=						self(lowerCamelCase, add_special_tokens=lowerCamelCase, return_tensors=lowerCamelCase, **lowerCamelCase)
           _lowercase			:				int 						=						self.convert_tokens_to_ids(lowerCamelCase)
           _lowercase			:				Any 						=						tgt_lang_id
           return inputs
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						{self.convert_ids_to_tokens(lowerCamelCase): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab
       def      UpperCamelCase	(   self, lowerCamelCase)			->  List[str]:




           """simple docstring"""

           return self.sp_model.encode(lowerCamelCase, out_type=lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  str:




           """simple docstring"""

           if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
           _lowercase			:				Optional[Any] 						=						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      UpperCamelCase	(   self, lowerCamelCase)			->  List[Any]:




           """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      UpperCamelCase	(   self, lowerCamelCase)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						''.join(lowerCamelCase).replace(lowerCamelCase, ' ').strip()
           return out_string
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  Tuple[str]:




           """simple docstring"""

           if not os.path.isdir(lowerCamelCase):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
               return
           _lowercase			:				int 						=						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:
                   _lowercase			:				Dict 						=						self.sp_model.serialized_model_proto()
                   fi.write(lowerCamelCase)

           return (out_vocab_file,)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = "en_XX", lowerCamelCase = None, lowerCamelCase = "ro_RO", **lowerCamelCase, )			->  BatchEncoding:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						src_lang
           _lowercase			:				str 						=						tgt_lang
           return super().prepare_seqaseq_batch(lowerCamelCase, lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           return self.set_src_lang_special_tokens(self.src_lang)
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return self.set_tgt_lang_special_tokens(self.tgt_lang)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  None:




           """simple docstring"""

           _lowercase			:				List[str] 						=						self.lang_code_to_id[src_lang]
           _lowercase			:				str 						=						[]
           _lowercase			:				List[Any] 						=						[self.eos_token_id, self.cur_lang_code]




       def      UpperCamelCase	(   self, lowerCamelCase)			->  None:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						self.lang_code_to_id[lang]
           _lowercase			:				Union[str, Any] 						=						[]
           _lowercase			:				List[str] 						=						[self.eos_token_id, self.cur_lang_code]









'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''donut-swin'''

					SCREAMING_SNAKE_CASE__				=	{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}


					def __init__(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=2_24      ,						lowerCamelCase_				:			Union[str, Any]=4      ,						lowerCamelCase_				:			Optional[Any]=3      ,						lowerCamelCase_				:			Dict=96      ,						lowerCamelCase_				:			int=[2, 2, 6, 2]      ,						lowerCamelCase_				:			Optional[Any]=[3, 6, 12, 24]      ,						lowerCamelCase_				:			List[str]=7      ,						lowerCamelCase_				:			List[str]=4.0      ,						lowerCamelCase_				:			Optional[Any]=True      ,						lowerCamelCase_				:			List[str]=0.0      ,						lowerCamelCase_				:			Dict=0.0      ,						lowerCamelCase_				:			Dict=0.1      ,						lowerCamelCase_				:			Union[str, Any]="gelu"      ,						lowerCamelCase_				:			List[str]=False      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			Any=1e-5      ,						**lowerCamelCase_				:			Union[str, Any]      ,						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			image_size
							SCREAMING_SNAKE_CASE :				Dict		=			patch_size
							SCREAMING_SNAKE_CASE :				str		=			num_channels
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			embed_dim
							SCREAMING_SNAKE_CASE :				Optional[int]		=			depths
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			num_heads
							SCREAMING_SNAKE_CASE :				Optional[int]		=			window_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			mlp_ratio
							SCREAMING_SNAKE_CASE :				Dict		=			qkv_bias
							SCREAMING_SNAKE_CASE :				Optional[int]		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE :				Dict		=			drop_path_rate
							SCREAMING_SNAKE_CASE :				List[str]		=			hidden_act
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			use_absolute_embeddings
							SCREAMING_SNAKE_CASE :				Tuple		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							SCREAMING_SNAKE_CASE :				str		=			int(embed_dim * 2 ** (len(lowerCamelCase_						) - 1)						)



