

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

import sentencepiece as spm

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


lowerCAmelCase__			=		logging.get_logger(__name__)

lowerCAmelCase__			=		{"""vocab_file""": """spiece.model"""}

lowerCAmelCase__			=		{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

lowerCAmelCase__			=		{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
lowerCAmelCase__			=		0
lowerCAmelCase__			=		1
lowerCAmelCase__			=		2
lowerCAmelCase__			=		3
lowerCAmelCase__			=		4







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  VOCAB_FILES_NAMES
		a__					       =  PRETRAINED_VOCAB_FILES_MAP
		a__					       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a__					       =  "left"


		def __init__(				self	,       __snake_case	,       __snake_case=False	,       __snake_case=True	,       __snake_case=False	,       __snake_case="<s>"	,       __snake_case="</s>"	,       __snake_case="<unk>"	,       __snake_case="<sep>"	,       __snake_case="<pad>"	,       __snake_case="<cls>"	,       __snake_case="<mask>"	,       __snake_case=["<eop>", "<eod>"]	,       __snake_case = None	,       **__snake_case	,       ):
			# Mask token behave like a normal word, i.e. include the space before it
			_UpperCamelCase   :		str			=						AddedToken(__snake_case	,       lstrip=__snake_case	,       rstrip=__snake_case) if isinstance(__snake_case	,       __snake_case) else mask_token

			_UpperCamelCase   :		Optional[Any]			=						{} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    do_lower_case=__snake_case	,       remove_space=__snake_case	,       keep_accents=__snake_case	,       bos_token=__snake_case	,       eos_token=__snake_case	,       unk_token=__snake_case	,       sep_token=__snake_case	,       pad_token=__snake_case	,       cls_token=__snake_case	,       mask_token=__snake_case	,       additional_special_tokens=__snake_case	,       sp_model_kwargs=self.sp_model_kwargs	,       **__snake_case	,       )

			_UpperCamelCase   :		Any			=						3

			_UpperCamelCase   :		List[Any]			=						do_lower_case
			_UpperCamelCase   :		int			=						remove_space
			_UpperCamelCase   :		Union[str, Any]			=						keep_accents
			_UpperCamelCase   :		List[str]			=						vocab_file

			_UpperCamelCase   :		Tuple			=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
			self.sp_model.Load(__snake_case)


		@property
		def 		A__					(				self):
			return len(self.sp_model)


		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						{self.convert_ids_to_tokens(__snake_case): i for i in range(self.vocab_size)}
			vocab.update(self.added_tokens_encoder)
			return vocab


		def __getstate__(				self):
			_UpperCamelCase   :		Dict			=						self.__dict__.copy()
			_UpperCamelCase   :		str			=						None
			return state


		def __setstate__(				self	,       __snake_case):
			_UpperCamelCase   :		str			=						d

			# for backward compatibility
			if not hasattr(self	,       'sp_model_kwargs'):
				_UpperCamelCase   :		Optional[Any]			=						{}

			_UpperCamelCase   :		Tuple			=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
			self.sp_model.Load(self.vocab_file)


		def 		A__					(				self	,       __snake_case):
			if self.remove_space:
				_UpperCamelCase   :		Any			=						' '.join(inputs.strip().split())
			else:
				_UpperCamelCase   :		Optional[Any]			=						inputs
			_UpperCamelCase   :		str			=						outputs.replace('``'	,       '"').replace('\'\''	,       '"')

			if not self.keep_accents:
				_UpperCamelCase   :		List[str]			=						unicodedata.normalize('NFKD'	,       __snake_case)
				_UpperCamelCase   :		Optional[int]			=						''.join([c for c in outputs if not unicodedata.combining(__snake_case)])
			if self.do_lower_case:
				_UpperCamelCase   :		Dict			=						outputs.lower()

			return outputs


		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		Any			=						self.preprocess_text(__snake_case)
			_UpperCamelCase   :		Dict			=						self.sp_model.encode(__snake_case	,       out_type=__snake_case)
			_UpperCamelCase   :		List[Any]			=						[]
			for piece in pieces:
				if len(__snake_case) > 1 and piece[-1] == str(',') and piece[-2].isdigit():
					_UpperCamelCase   :		List[Any]			=						self.sp_model.EncodeAsPieces(piece[:-1].replace(__snake_case	,       ''))
					if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
						if len(cur_pieces[0]) == 1:
							_UpperCamelCase   :		int			=						cur_pieces[1:]
						else:
							_UpperCamelCase   :		Optional[Any]			=						cur_pieces[0][1:]
					cur_pieces.append(piece[-1])
					new_pieces.extend(__snake_case)
				else:
					new_pieces.append(__snake_case)

			return new_pieces


		def 		A__					(				self	,       __snake_case):
			return self.sp_model.PieceToId(__snake_case)


		def 		A__					(				self	,       __snake_case):
			return self.sp_model.IdToPiece(__snake_case)


		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		Optional[Any]			=						''.join(__snake_case).replace(__snake_case	,       ' ').strip()
			return out_string


		def 		A__					(				self	,       __snake_case	,       __snake_case = False	,       __snake_case = None	,       __snake_case = True	,       **__snake_case	,       ):
			_UpperCamelCase   :		List[Any]			=						kwargs.pop('use_source_tokenizer'	,       __snake_case)

			_UpperCamelCase   :		Union[str, Any]			=						self.convert_ids_to_tokens(__snake_case	,       skip_special_tokens=__snake_case)

			# To avoid mixing byte-level and unicode for byte-level BPT
			# we need to build string separately for added tokens and byte-level tokens
			# cf. https://github.com/huggingface/transformers/issues/1133
			_UpperCamelCase   :		Optional[int]			=						[]
			_UpperCamelCase   :		Dict			=						[]
			for token in filtered_tokens:
				if skip_special_tokens and token in self.all_special_ids:
					continue
				if token in self.added_tokens_encoder:
					if current_sub_text:
						sub_texts.append(self.convert_tokens_to_string(__snake_case))
						_UpperCamelCase   :		List[Any]			=						[]
					sub_texts.append(__snake_case)
				else:
					current_sub_text.append(__snake_case)
			if current_sub_text:
				sub_texts.append(self.convert_tokens_to_string(__snake_case))

			# Mimic the behavior of the Rust tokenizer:
			# By default, there are no spaces between special tokens
			_UpperCamelCase   :		Optional[Any]			=						''.join(__snake_case)

			_UpperCamelCase   :		Any			=						(
			    clean_up_tokenization_spaces
			    if clean_up_tokenization_spaces is not None
			    else self.clean_up_tokenization_spaces
			)
			if clean_up_tokenization_spaces:
				_UpperCamelCase   :		int			=						self.clean_up_tokenization(__snake_case)
				return clean_text
			else:
				return text


		def 		A__					(				self	,       __snake_case	,       __snake_case = None):
			_UpperCamelCase   :		Optional[Any]			=						[self.sep_token_id]
			_UpperCamelCase   :		Optional[int]			=						[self.cls_token_id]
			if token_ids_a is None:
				return token_ids_a + sep + cls
			return token_ids_a + sep + token_ids_a + sep + cls


		def 		A__					(				self	,       __snake_case	,       __snake_case = None	,       __snake_case = False):

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

			if token_ids_a is not None:
				return ([0] * len(__snake_case)) + [1] + ([0] * len(__snake_case)) + [1, 1]
			return ([0] * len(__snake_case)) + [1, 1]


		def 		A__					(				self	,       __snake_case	,       __snake_case = None):
			_UpperCamelCase   :		Optional[Any]			=						[self.sep_token_id]
			_UpperCamelCase   :		Any			=						[2]

			if token_ids_a is None:
				return len(token_ids_a + sep) * [0] + cls_segment_id
			return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id






		def 		A__					(				self	,       __snake_case	,       __snake_case = None):
			if not os.path.isdir(__snake_case):
				logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
				return
			_UpperCamelCase   :		Tuple			=						os.path.join(
			    __snake_case	,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

			if os.path.abspath(self.vocab_file) != os.path.abspath(__snake_case) and os.path.isfile(self.vocab_file):
				copyfile(self.vocab_file	,       __snake_case)
			elif not os.path.isfile(self.vocab_file):
				with open(__snake_case	,       'wb') as fi:
					_UpperCamelCase   :		List[str]			=						self.sp_model.serialized_model_proto()
					fi.write(__snake_case)

			return (out_vocab_file,)







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


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    """facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "vit_mae"


		def __init__(				self	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.0	,       __snake_case=0.0	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=2_24	,       __snake_case=16	,       __snake_case=3	,       __snake_case=True	,       __snake_case=16	,       __snake_case=5_12	,       __snake_case=8	,       __snake_case=20_48	,       __snake_case=0.7_5	,       __snake_case=False	,       **__snake_case	,       ):
			super().__init__(**__snake_case)

			_UpperCamelCase   :		Optional[int]			=						hidden_size
			_UpperCamelCase   :		Optional[int]			=						num_hidden_layers
			_UpperCamelCase   :		Tuple			=						num_attention_heads
			_UpperCamelCase   :		List[str]			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_act
			_UpperCamelCase   :		List[str]			=						hidden_dropout_prob
			_UpperCamelCase   :		List[Any]			=						attention_probs_dropout_prob
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		Any			=						layer_norm_eps
			_UpperCamelCase   :		int			=						image_size
			_UpperCamelCase   :		Any			=						patch_size
			_UpperCamelCase   :		List[Any]			=						num_channels
			_UpperCamelCase   :		Union[str, Any]			=						qkv_bias
			_UpperCamelCase   :		str			=						decoder_num_attention_heads
			_UpperCamelCase   :		Union[str, Any]			=						decoder_hidden_size
			_UpperCamelCase   :		Union[str, Any]			=						decoder_num_hidden_layers
			_UpperCamelCase   :		Any			=						decoder_intermediate_size
			_UpperCamelCase   :		int			=						mask_ratio
			_UpperCamelCase   :		List[Any]			=						norm_pix_loss




