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

import sentencepiece as spm

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


_UpperCAmelCase					: Tuple      =       logging.get_logger(__name__)

_UpperCAmelCase					: Optional[int]      =       """▁"""

_UpperCAmelCase					: int      =       {"""vocab_file""": """sentencepiece.bpe.model"""}

_UpperCAmelCase					: int      =       {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

_UpperCAmelCase					: Tuple      =       {
    """facebook/xglm-564M""": 2_048,
}


class 				lowercase					(		UpperCAmelCase__   ):
		__lowercase       :       str	     =				VOCAB_FILES_NAMES
		__lowercase       :       Tuple	     =				PRETRAINED_VOCAB_FILES_MAP
		__lowercase       :       Any	     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase       :       Union[str, Any]	     =				["input_ids", "attention_mask"]



		def __init__( self				,      A_				,      A_="<s>"				,      A_="</s>"				,      A_="</s>"				,      A_="<s>"				,      A_="<unk>"				,      A_="<pad>"				,      A_ = None				,      **A_				,      )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     {} if sp_model_kwargs is None else sp_model_kwargs

				# Compatibility with the original tokenizer
				UpperCamelCase		=     7
				UpperCamelCase		=     [F'''<madeupword{i}>''' for i in range(self.num_madeup_words       )]

				UpperCamelCase		=     kwargs.get('additional_special_tokens'				,      []       )
				kwargs["additional_special_tokens"] += [
				    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    bos_token=_a				,      eos_token=_a				,      unk_token=_a				,      sep_token=_a				,      cls_token=_a				,      pad_token=_a				,      sp_model_kwargs=self.sp_model_kwargs				,      **_a				,      )

				UpperCamelCase		=     spm.SentencePieceProcessor(**self.sp_model_kwargs       )
				self.sp_model.Load(str(_a       )       )
				UpperCamelCase		=     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'

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

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

				UpperCamelCase		=     len(self.sp_model       )
				UpperCamelCase		=     {F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words       )}
				self.fairseq_tokens_to_ids.update(_a       )

				UpperCamelCase		=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}



		def __getstate__( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.__dict__.copy()
				UpperCamelCase		=     None
				UpperCamelCase		=     self.sp_model.serialized_model_proto()
				return state



		def __setstate__( self				,      A_       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     d

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

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



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					Dict:
				"""simple docstring"""

				if token_ids_a is None:
						return [self.sep_token_id] + token_ids_a
				UpperCamelCase		=     [self.sep_token_id]
				return sep + token_ids_a + sep + sep + token_ids_a



		def __UpperCamelCase ( self				,      A_				,      A_ = None				,      A_ = False       )  ->					Optional[Any]:
				"""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       )

				if token_ids_a is None:
						return [1] + ([0] * len(_a       ))
				return [1] + ([0] * len(_a       )) + [1, 1] + ([0] * len(_a       ))



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     [self.sep_token_id]

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



		@property
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				return len(self.sp_model       ) + self.fairseq_offset + self.num_madeup_words



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     {self.convert_ids_to_tokens(_a       ): i for i in range(self.vocab_size       )}
				vocab.update(self.added_tokens_encoder       )
				return vocab



		def __UpperCamelCase ( self				,      A_       )  ->					str:
				"""simple docstring"""
				return self.sp_model.encode(_a				,      out_type=_a       )



		def __UpperCamelCase ( self				,      A_       )  ->					Dict:
				"""simple docstring"""
				if token in self.fairseq_tokens_to_ids:
						return self.fairseq_tokens_to_ids[token]
				UpperCamelCase		=     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 __UpperCamelCase ( self				,      A_       )  ->					Union[str, 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				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     ''.join(_a       ).replace(_a				,      ' '       ).strip()
				return out_string



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					List[Any]:
				"""simple docstring"""
				if not os.path.isdir(_a       ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
						return
				UpperCamelCase		=     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:
								UpperCamelCase		=     self.sp_model.serialized_model_proto()
								fi.write(_a       )

				return (out_vocab_file,)






"""simple docstring"""




import operator as op


lowerCAmelCase							:			Dict     							=							"""scaler.pt"""
lowerCAmelCase							:			Tuple     							=							"""pytorch_model"""
lowerCAmelCase							:			Union[str, Any]     							=							"""random_states"""
lowerCAmelCase							:			Union[str, Any]     							=							"""optimizer"""
lowerCAmelCase							:			Dict     							=							"""scheduler"""
lowerCAmelCase							:			int     							=							"""pytorch_model.bin"""
lowerCAmelCase							:			str     							=							"""pytorch_model.bin.index.json"""
lowerCAmelCase							:			Union[str, Any]     							=							"""model.safetensors"""
lowerCAmelCase							:			List[Any]     							=							"""model.safetensors.index.json"""
lowerCAmelCase							:			List[Any]     							=							"""1.10.2"""
lowerCAmelCase							:			Any     							=							"""py38"""
lowerCAmelCase							:			Optional[int]     							=							"""4.17.0"""
lowerCAmelCase							:			str     							=							["""ml.p3.16xlarge""", """ml.p3dn.24xlarge""", """ml.p4dn.24xlarge"""]
lowerCAmelCase							:			Tuple     							=							["""FULL_SHARD""", """SHARD_GRAD_OP""", """NO_SHARD""", """HYBRID_SHARD""", """HYBRID_SHARD_ZERO2"""]
lowerCAmelCase							:			List[Any]     							=							["""TRANSFORMER_BASED_WRAP""", """SIZE_BASED_WRAP""", """NO_WRAP"""]
lowerCAmelCase							:			List[str]     							=							["""BACKWARD_PRE""", """BACKWARD_POST""", """NO_PREFETCH"""]
lowerCAmelCase							:			List[str]     							=							["""FULL_STATE_DICT""", """LOCAL_STATE_DICT""", """SHARDED_STATE_DICT"""]
lowerCAmelCase							:			Any     							=							"""2.0.1"""
lowerCAmelCase							:			List[Any]     							=							["""pdsh""", """standard""", """openmpi""", """mvapich"""]
lowerCAmelCase							:			Union[str, Any]     							=							["""default""", """reduce-overhead""", """max-autotune"""]

lowerCAmelCase							:			Optional[int]     							=							{""">""": op.gt, """>=""": op.ge, """==""": op.eq, """!=""": op.ne, """<=""": op.le, """<""": op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
lowerCAmelCase							:			Union[str, Any]     							=							[
    """nnodes""",
    """nproc_per_node""",
    """rdzv_backend""",
    """rdzv_endpoint""",
    """rdzv_id""",
    """rdzv_conf""",
    """standalone""",
    """max_restarts""",
    """monitor_interval""",
    """start_method""",
    """role""",
    """module""",
    """m""",
    """no_python""",
    """run_path""",
    """log_dir""",
    """r""",
    """redirects""",
    """t""",
    """tee""",
    """node_rank""",
    """master_addr""",
    """master_port""",
]

lowerCAmelCase							:			List[str]     							=							["""DEEPSPEED""", """MULTI_GPU""", """FSDP""", """MEGATRON_LM"""]
lowerCAmelCase							:			Optional[Any]     							=							["""DEEPSPEED""", """MULTI_XPU""", """FSDP"""]



