
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


lowerCamelCase__          =		logging.get_logger(__name__)

lowerCamelCase__          =		"""▁"""

lowerCamelCase__          =		{"""vocab_file""": """sentencepiece.bpe.model"""}

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

lowerCamelCase__          =		{
    """facebook/xglm-564M""": 2048,
}







class      A__							(     __magic_name__	):
				lowercase			    =  VOCAB_FILES_NAMES
				lowercase			    =  PRETRAINED_VOCAB_FILES_MAP
				lowercase			    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase			    =  ['input_ids', 'attention_mask']






				def __init__(						self					:      Dict    ,     a					:      Dict    ,     a					:      int="<s>"    ,     a					:      str="</s>"    ,     a					:      Tuple="</s>"    ,     a					:      str="<s>"    ,     a					:      List[Any]="<unk>"    ,     a					:      str="<pad>"    ,     a					:      Optional[Dict[str, Any]] = None    ,     **a					:      Union[str, Any]    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		{} if sp_model_kwargs is None else sp_model_kwargs

							# Compatibility with the original tokenizer
							lowerCAmelCase__      :      List[str]         =		7
							lowerCAmelCase__      :      List[str]         =		[f'''<madeupword{i}>''' for i in range(self.num_madeup_words   )]

							lowerCAmelCase__      :      Tuple         =		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    ,     )

							lowerCAmelCase__      :      Union[str, Any]         =		spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(a   )   )
							lowerCAmelCase__      :      str         =		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
							lowerCAmelCase__      :      List[str]         =		1

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

							lowerCAmelCase__      :      List[Any]         =		len(self.sp_model   )
							lowerCAmelCase__      :      Any         =		{f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words   )}
							self.fairseq_tokens_to_ids.update(a   )

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






				def __getstate__(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		self.__dict__.copy()
							lowerCAmelCase__      :      Tuple         =		None
							lowerCAmelCase__      :      Union[str, Any]         =		self.sp_model.serialized_model_proto()
							return state






				def __setstate__(						self					:      List[Any]    ,     a					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		d

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

							lowerCAmelCase__      :      List[Any]         =		spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )






				def 							_lowerCamelCase		(						self					:      List[Any]    ,     a					:      List[int]    ,     a					:      Optional[List[int]] = None   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      List[Any]    ,     a					:      List[int]    ,     a					:      Optional[List[int]] = None    ,     a					:      bool = 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   )

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






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      List[int]    ,     a					:      Optional[List[int]] = None   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		[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 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							return len(self.sp_model   ) + self.fairseq_offset + self.num_madeup_words






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		{self.convert_ids_to_tokens(a   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab






				def 							_lowerCamelCase		(						self					:      List[Any]    ,     a					:      str   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      Tuple   ):






							'''simple docstring'''







							if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
							lowerCAmelCase__      :      List[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 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      Dict   ):






							'''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 							_lowerCamelCase		(						self					:      int    ,     a					:      Dict   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		''.join(a   ).replace(a    ,     ' '   ).strip()
							return out_string






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      str    ,     a					:      Optional[str] = None   ):






							'''simple docstring'''







							if not os.path.isdir(a   ):
										logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
										return
							lowerCAmelCase__      :      List[str]         =		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__      :      List[str]         =		self.sp_model.serialized_model_proto()
													fi.write(a   )

							return (out_vocab_file,)

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCamelCase__          =		logging.get_logger(__name__)

lowerCamelCase__          =		{
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}







class      A__							(     __magic_name__	):
				lowercase			    =  'gptj'
				lowercase			    =  {
				    'max_position_embeddings': 'n_positions',
				    'hidden_size': 'n_embd',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}






				def __init__(						self					:      Optional[Any]    ,     a					:      Dict=50_400    ,     a					:      Union[str, Any]=2_048    ,     a					:      List[str]=4_096    ,     a					:      Any=28    ,     a					:      Optional[Any]=16    ,     a					:      Optional[Any]=64    ,     a					:      int=None    ,     a					:      Any="gelu_new"    ,     a					:      Union[str, Any]=0.0    ,     a					:      List[Any]=0.0    ,     a					:      List[Any]=0.0    ,     a					:      Optional[Any]=1E-5    ,     a					:      Optional[int]=0.0_2    ,     a					:      int=True    ,     a					:      str=50_256    ,     a					:      str=50_256    ,     a					:      Any=False    ,     **a					:      Dict    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		vocab_size
							lowerCAmelCase__      :      List[Any]         =		n_positions
							lowerCAmelCase__      :      str         =		n_embd
							lowerCAmelCase__      :      str         =		n_layer
							lowerCAmelCase__      :      str         =		n_head
							lowerCAmelCase__      :      Dict         =		n_inner
							lowerCAmelCase__      :      Union[str, Any]         =		rotary_dim
							lowerCAmelCase__      :      Optional[int]         =		activation_function
							lowerCAmelCase__      :      Any         =		resid_pdrop
							lowerCAmelCase__      :      int         =		embd_pdrop
							lowerCAmelCase__      :      int         =		attn_pdrop
							lowerCAmelCase__      :      List[Any]         =		layer_norm_epsilon
							lowerCAmelCase__      :      str         =		initializer_range
							lowerCAmelCase__      :      Dict         =		use_cache

							lowerCAmelCase__      :      str         =		bos_token_id
							lowerCAmelCase__      :      int         =		eos_token_id

							super().__init__(
							    bos_token_id=a    ,     eos_token_id=a    ,     tie_word_embeddings=a    ,     **a   )







class      A__							(     __magic_name__	):






				def __init__(						self					:      str    ,     a					:      PretrainedConfig    ,     a					:      str = "default"    ,     a					:      List[PatchingSpec] = None    ,     a					:      bool = False    ,     ):






							'''simple docstring'''







							super().__init__(a    ,     task=a    ,     patching_specs=a    ,     use_past=a   )
							if not getattr(self._config    ,     'pad_token_id'    ,     a   ):
										# TODO: how to do that better?
										lowerCAmelCase__      :      int         =		0






				@property
				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
							if self.use_past:
										self.fill_with_past_key_values_(a    ,     direction='inputs'   )
										lowerCAmelCase__      :      Optional[Any]         =		{0: 'batch', 1: 'past_sequence + sequence'}
							else:
										lowerCAmelCase__      :      Tuple         =		{0: 'batch', 1: 'sequence'}

							return common_inputs






				@property
				def 							_lowerCamelCase		(						self					:      Union[str, Any]   ):






							'''simple docstring'''







							return self._config.n_layer






				@property
				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							return self._config.n_head






				def 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      PreTrainedTokenizer    ,     a					:      int = -1    ,     a					:      int = -1    ,     a					:      bool = False    ,     a					:      Optional[TensorType] = None    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		super(a    ,     self   ).generate_dummy_inputs(
							    a    ,     batch_size=a    ,     seq_length=a    ,     is_pair=a    ,     framework=a   )

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase__      :      Optional[int]         =		OrderedDict({'input_ids': common_inputs['input_ids']}   )

							# Need to add the past_keys
							if self.use_past:
										if not is_torch_available():
													raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
										else:
													import torch

													lowerCAmelCase__      ,				lowerCAmelCase__      :      int         =		common_inputs['input_ids'].shape
													# Not using the same length for past_key_values
													lowerCAmelCase__      :      Optional[int]         =		seqlen + 2
													lowerCAmelCase__      :      Tuple         =		(
													    batch,
													    self.num_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // self.num_attention_heads,
													)
													lowerCAmelCase__      :      Tuple         =		[
													    (torch.zeros(a   ), torch.zeros(a   )) for _ in range(self.num_layers   )
													]

							lowerCAmelCase__      :      Any         =		common_inputs['attention_mask']
							if self.use_past:
										lowerCAmelCase__      :      List[str]         =		ordered_inputs['attention_mask'].dtype
										lowerCAmelCase__      :      Optional[Any]         =		torch.cat(
										    [ordered_inputs['attention_mask'], torch.ones(a    ,     a    ,     dtype=a   )]    ,     dim=1   )

							return ordered_inputs






				@property
				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							return 13