

"""simple docstring"""

import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A						=			logging.get_logger(__name__)

_A						=			'▁'

_A						=			{'vocab_file': 'vocab.txt', 'sentencepiece_model_ckpt': 'sentencepiece.bpe.model'}

_A						=			{
    'sentencepiece_model_file': 'sentencepiece.bpe.model',
    'vocab_file': 'vocab.txt',
}

_A						=			{
    'vocab_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
    },
    'sentencepiece_model_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
    },
}

_A						=			{
    'ernie-m-base': 5_1_4,
    'ernie-m-large': 5_1_4,
}

_A						=			{
    'ernie-m-base': {'do_lower_case': False},
    'ernie-m-large': {'do_lower_case': False},
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     ['input_ids']

	lowercase_				=     VOCAB_FILES_NAMES
	lowercase_				=     PRETRAINED_INIT_CONFIGURATION
	lowercase_				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase_				=     PRETRAINED_VOCAB_FILES_MAP
	lowercase_				=     RESOURCE_FILES_NAMES




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			UpperCAmelCase_=False		,			UpperCAmelCase_="utf8"		,			UpperCAmelCase_="[UNK]"		,			UpperCAmelCase_="[SEP]"		,			UpperCAmelCase_="[PAD]"		,			UpperCAmelCase_="[CLS]"		,			UpperCAmelCase_="[MASK]"		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							None:
			# Mask token behave like a normal word, i.e. include the space before it and
			# is included in the raw text, there should be a match in a non-normalized sentence.

			lowerCamelCase					:      Optional[Any]          =   {} if sp_model_kwargs is None else sp_model_kwargs
			super().__init__(
			    do_lower_case=UpperCAmelCase_		,			unk_token=UpperCAmelCase_		,			sep_token=UpperCAmelCase_		,			pad_token=UpperCAmelCase_		,			cls_token=UpperCAmelCase_		,			mask_token=UpperCAmelCase_		,			vocab_file=UpperCAmelCase_		,			encoding=UpperCAmelCase_		,			sp_model_kwargs=self.sp_model_kwargs		,			**UpperCAmelCase_		,			)
			lowerCamelCase					:      int          =   do_lower_case
			lowerCamelCase					:      Union[str, Any]          =   sentencepiece_model_ckpt
			lowerCamelCase					:      str          =   spm.SentencePieceProcessor(**self.sp_model_kwargs )
			self.sp_model.Load(UpperCAmelCase_ )

			# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
			if vocab_file is not None:
					lowerCamelCase					:      Tuple          =   self.load_vocab(filepath=UpperCAmelCase_ )
			else:
					lowerCamelCase					:      Tuple          =   {self.sp_model.id_to_piece(UpperCAmelCase_ ): id for id in range(self.sp_model.get_piece_size() )}
			lowerCamelCase					:      Union[str, Any]          =   {v: k for k, v in self.vocab.items()}




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							int:
			if text is None:
					return None

			lowerCamelCase					:      Union[str, Any]          =   self.tokenize(UpperCAmelCase_ )
			lowerCamelCase					:      Tuple          =   '', []

			for i, ch in enumerate(UpperCAmelCase_ ):
					if ch in self.SP_CHAR_MAPPING:
							lowerCamelCase					:      Dict          =   self.SP_CHAR_MAPPING.get(UpperCAmelCase_ )
					else:
							lowerCamelCase					:      Tuple          =   unicodedata.normalize('NFKC'		,			UpperCAmelCase_ )
					if self.is_whitespace(UpperCAmelCase_ ):
							continue
					normalized_text += ch
					char_mapping.extend([i] * len(UpperCAmelCase_ ) )

			lowerCamelCase					:      int          =   normalized_text, [], 0

			if self.do_lower_case:
					lowerCamelCase					:      Tuple          =   text.lower()

			for token in split_tokens:
					if token[:1] == "▁":
							lowerCamelCase					:      Tuple          =   token[1:]
					lowerCamelCase					:      str          =   text[offset:].index(UpperCAmelCase_ ) + offset
					lowerCamelCase					:      List[Any]          =   start + len(UpperCAmelCase_ )

					token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
					lowerCamelCase					:      List[Any]          =   end
			return token_mapping




	@property
	def       _UpperCamelCase   (			self )		->							Optional[int]:
			return len(self.vocab )




	def       _UpperCamelCase   (			self )		->							List[Any]:
			return dict(self.vocab		,			**self.added_tokens_encoder )




	def __getstate__(			self )		->							List[Any]:
			lowerCamelCase					:      Dict          =   self.__dict__.copy()
			lowerCamelCase					:      Optional[Any]          =   None
			return state




	def __setstate__(			self		,			UpperCAmelCase_ )		->							List[str]:
			lowerCamelCase					:      Tuple          =   d

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

			lowerCamelCase					:      int          =   spm.SentencePieceProcessor(**self.sp_model_kwargs )
			self.sp_model.Load(self.sentencepiece_model_ckpt )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[int]:
			return "".join((self.SP_CHAR_MAPPING.get(UpperCAmelCase_		,			UpperCAmelCase_ ) for c in text) )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=False		,			UpperCAmelCase_=64		,			UpperCAmelCase_=0.1 )		->							Union[str, Any]:

			if self.sp_model_kwargs.get('enable_sampling' ) is True:
					lowerCamelCase					:      Optional[int]          =   True
			if self.sp_model_kwargs.get('alpha' ) is not None:
					lowerCamelCase					:      str          =   self.sp_model_kwargs.get('alpha' )
			if self.sp_model_kwargs.get('nbest_size' ) is not None:
					lowerCamelCase					:      Dict          =   self.sp_model_kwargs.get('nbest_size' )

			if not enable_sampling:
					lowerCamelCase					:      str          =   self.sp_model.EncodeAsPieces(UpperCAmelCase_ )
			else:
					lowerCamelCase					:      int          =   self.sp_model.SampleEncodeAsPieces(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )
			lowerCamelCase					:      Optional[int]          =   []
			for pi, piece in enumerate(UpperCAmelCase_ ):
					if piece == SPIECE_UNDERLINE:
							if not pieces[pi + 1].startswith(UpperCAmelCase_ ) and pi != 0:
									new_pieces.append(UpperCAmelCase_ )
									continue
							else:
									continue
					lowerCamelCase					:      List[Any]          =   0
					for i, chunk in enumerate(UpperCAmelCase_ ):
							if chunk == SPIECE_UNDERLINE:
									continue
							if self.is_ch_char(UpperCAmelCase_ ) or self.is_punct(UpperCAmelCase_ ):
									if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
											new_pieces.append(piece[lst_i:i] )
									new_pieces.append(UpperCAmelCase_ )
									lowerCamelCase					:      int          =   i + 1
							elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
									if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
											new_pieces.append(piece[lst_i:i] )
									lowerCamelCase					:      int          =   i
							elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
									if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
											new_pieces.append(piece[lst_i:i] )
									lowerCamelCase					:      str          =   i
					if len(UpperCAmelCase_ ) > lst_i:
							new_pieces.append(piece[lst_i:] )
			return new_pieces




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[str]:
			lowerCamelCase					:      int          =   ''.join(UpperCAmelCase_ ).replace(UpperCAmelCase_		,			' ' ).strip()
			return out_string




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Any:
			lowerCamelCase					:      Any          =   self.convert_ids_to_tokens(UpperCAmelCase_ )
			lowerCamelCase					:      Optional[Any]          =   ''.join(UpperCAmelCase_ ).replace(UpperCAmelCase_		,			' ' ).strip()
			return out_string




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Union[str, Any]:
			return self.vocab.get(UpperCAmelCase_		,			self.vocab.get(self.unk_token ) )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							int:
			return self.reverse_vocab.get(UpperCAmelCase_		,			self.unk_token )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							Optional[int]:
			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			lowerCamelCase					:      List[str]          =   [self.cls_token_id]
			lowerCamelCase					:      List[Any]          =   [self.sep_token_id]
			return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							int:
			if offset_mapping_a is None:
					return [(0, 0)] + offset_mapping_a + [(0, 0)]

			return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			UpperCAmelCase_=False )		->							List[str]:

			if already_has_special_tokens:
					if token_ids_a is not None:
							raise ValueError(
							    'You should not supply a second sequence if the provided sequence of '
							    'ids is already formatted with special tokens for the model.' )
					return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							List[int]:
			# called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
			if token_ids_a is None:
					# [CLS] X [SEP]
					return (len(UpperCAmelCase_ ) + 2) * [0]

			# [CLS] A [SEP] [SEP] B [SEP]
			return [0] * (len(UpperCAmelCase_ ) + 1) + [1] * (len(UpperCAmelCase_ ) + 3)




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[int]:
			if "\u4e00" <= char <= "\u9fff":
					return True
			return False




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[int]:
			if ("a" <= char <= "z") or ("A" <= char <= "Z"):
					return True
			return False




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							int:
			if char in ",;:.?!~，；：。？！《》【】":
					return True
			return False




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[Any]:
			if char == " " or char == "\t" or char == "\n" or char == "\r":
					return True
			if len(UpperCAmelCase_ ) == 1:
					lowerCamelCase					:      Dict          =   unicodedata.category(UpperCAmelCase_ )
					if cat == "Zs":
							return True
			return False




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[Any]:
			lowerCamelCase					:      Optional[Any]          =   {}
			with io.open(UpperCAmelCase_		,			'r'		,			encoding='utf-8' ) as f:
					for index, line in enumerate(UpperCAmelCase_ ):
							lowerCamelCase					:      List[str]          =   line.rstrip('\n' )
							lowerCamelCase					:      Any          =   int(UpperCAmelCase_ )

			return token_to_idx




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							Tuple[str]:
			lowerCamelCase					:      str          =   0
			if os.path.isdir(UpperCAmelCase_ ):
					lowerCamelCase					:      Optional[Any]          =   os.path.join(
					    UpperCAmelCase_		,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
			else:
					lowerCamelCase					:      Tuple          =   (filename_prefix + '-' if filename_prefix else '') + save_directory
			with open(UpperCAmelCase_		,			'w'		,			encoding='utf-8' ) as writer:
					for token, token_index in sorted(self.vocab.items()		,			key=lambda UpperCAmelCase_ : kv[1] ):
							if index != token_index:
									logger.warning(
									    F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
									    ' Please check that the vocabulary is not corrupted!' )
									lowerCamelCase					:      Optional[int]          =   token_index
							writer.write(token + '\n' )
							index += 1

			lowerCamelCase					:      Dict          =   os.path.join(UpperCAmelCase_		,			'sentencepiece.bpe.model' )
			with open(UpperCAmelCase_		,			'wb' ) as fi:
					lowerCamelCase					:      int          =   self.sp_model.serialized_model_proto()
					fi.write(UpperCAmelCase_ )

			return (vocab_file,)








"""simple docstring"""

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


_A						=			logging.get_logger(__name__)

_A						=			{
    'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
        'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'trajectory_transformer'
	lowercase_				=     ['past_key_values']
	lowercase_				=     {
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}




	def __init__(			self		,			UpperCAmelCase_=100		,			UpperCAmelCase_=5		,			UpperCAmelCase_=1		,			UpperCAmelCase_=1		,			UpperCAmelCase_=249		,			UpperCAmelCase_=6		,			UpperCAmelCase_=17		,			UpperCAmelCase_=25		,			UpperCAmelCase_=4		,			UpperCAmelCase_=4		,			UpperCAmelCase_=128		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.0006		,			UpperCAmelCase_=512		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=1E-1_2		,			UpperCAmelCase_=1		,			UpperCAmelCase_=True		,			UpperCAmelCase_=1		,			UpperCAmelCase_=50256		,			UpperCAmelCase_=50256		,			**UpperCAmelCase_		,			)		->							List[Any]:
			lowerCamelCase					:      int          =   vocab_size
			lowerCamelCase					:      List[str]          =   action_weight
			lowerCamelCase					:      List[Any]          =   reward_weight
			lowerCamelCase					:      List[str]          =   value_weight
			lowerCamelCase					:      Tuple          =   max_position_embeddings
			lowerCamelCase					:      List[str]          =   block_size
			lowerCamelCase					:      Any          =   action_dim
			lowerCamelCase					:      List[Any]          =   observation_dim
			lowerCamelCase					:      Any          =   transition_dim
			lowerCamelCase					:      int          =   learning_rate
			lowerCamelCase					:      Union[str, Any]          =   n_layer
			lowerCamelCase					:      Tuple          =   n_head
			lowerCamelCase					:      Any          =   n_embd
			lowerCamelCase					:      Union[str, Any]          =   embd_pdrop
			lowerCamelCase					:      Optional[int]          =   attn_pdrop
			lowerCamelCase					:      int          =   resid_pdrop
			lowerCamelCase					:      Optional[int]          =   initializer_range
			lowerCamelCase					:      List[str]          =   layer_norm_eps
			lowerCamelCase					:      Any          =   kaiming_initializer_range
			lowerCamelCase					:      str          =   use_cache
			super().__init__(pad_token_id=UpperCAmelCase_		,			bos_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_		,			**UpperCAmelCase_ )






