
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

lowerCamelCase__          =		logging.get_logger(__name__)


lowerCamelCase__          =		{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowerCamelCase__          =		{
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

lowerCamelCase__          =		{"""facebook/blenderbot-3B""": 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def  lowerCAmelCase__			(  )				->		List[str]:
			lowerCAmelCase__      :      List[str]         =		(
			    list(range(ord('!'     )       ,							ord('~'     ) + 1     )     ) + list(range(ord('¡'     )       ,							ord('¬'     ) + 1     )     ) + list(range(ord('®'     )       ,							ord('ÿ'     ) + 1     )     )
			)
			lowerCAmelCase__      :      Any         =		bs[:]
			lowerCAmelCase__      :      Optional[int]         =		0
			for b in range(2**8     ):
						if b not in bs:
									bs.append(SCREAMING_SNAKE_CASE_     )
									cs.append(2**8 + n     )
									n += 1
			lowerCAmelCase__      :      str         =		[chr(SCREAMING_SNAKE_CASE_     ) for n in cs]
			return dict(zip(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		List[Any]:
			lowerCAmelCase__      :      Optional[Any]         =		set()
			lowerCAmelCase__      :      Any         =		word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)     )
						lowerCAmelCase__      :      List[Any]         =		char
			return pairs







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					:      int    ,     a					:      Optional[int]    ,     a					:      Any    ,     a					:      int="replace"    ,     a					:      List[Any]="<s>"    ,     a					:      Optional[int]="</s>"    ,     a					:      Any="</s>"    ,     a					:      int="<s>"    ,     a					:      Optional[Any]="<unk>"    ,     a					:      Tuple="<pad>"    ,     a					:      Union[str, Any]="<mask>"    ,     a					:      str=False    ,     **a					:      Any    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[int]         =		AddedToken(a    ,     lstrip=a    ,     rstrip=a   ) if isinstance(a    ,     a   ) else bos_token
							lowerCAmelCase__      :      Optional[int]         =		AddedToken(a    ,     lstrip=a    ,     rstrip=a   ) if isinstance(a    ,     a   ) else eos_token
							lowerCAmelCase__      :      int         =		AddedToken(a    ,     lstrip=a    ,     rstrip=a   ) if isinstance(a    ,     a   ) else sep_token
							lowerCAmelCase__      :      Dict         =		AddedToken(a    ,     lstrip=a    ,     rstrip=a   ) if isinstance(a    ,     a   ) else cls_token
							lowerCAmelCase__      :      List[Any]         =		AddedToken(a    ,     lstrip=a    ,     rstrip=a   ) if isinstance(a    ,     a   ) else unk_token
							lowerCAmelCase__      :      List[Any]         =		AddedToken(a    ,     lstrip=a    ,     rstrip=a   ) if isinstance(a    ,     a   ) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							lowerCAmelCase__      :      List[Any]         =		AddedToken(a    ,     lstrip=a    ,     rstrip=a   ) if isinstance(a    ,     a   ) else mask_token

							super().__init__(
							    errors=a    ,     bos_token=a    ,     eos_token=a    ,     unk_token=a    ,     sep_token=a    ,     cls_token=a    ,     pad_token=a    ,     mask_token=a    ,     add_prefix_space=a    ,     **a    ,     )

							with open(a    ,     encoding='utf-8'   ) as vocab_handle:
										lowerCAmelCase__      :      Union[str, Any]         =		json.load(a   )
							lowerCAmelCase__      :      int         =		{v: k for k, v in self.encoder.items()}
							lowerCAmelCase__      :      Optional[int]         =		errors  # how to handle errors in decoding
							lowerCAmelCase__      :      Any         =		bytes_to_unicode()
							lowerCAmelCase__      :      Any         =		{v: k for k, v in self.byte_encoder.items()}
							with open(a    ,     encoding='utf-8'   ) as merges_handle:
										lowerCAmelCase__      :      Union[str, Any]         =		merges_handle.read().split('\n'   )[1:-1]
							lowerCAmelCase__      :      Optional[Any]         =		[tuple(merge.split()   ) for merge in bpe_merges]
							lowerCAmelCase__      :      Any         =		dict(zip(a    ,     range(len(a   )   )   )   )
							lowerCAmelCase__      :      List[str]         =		{}
							lowerCAmelCase__      :      Any         =		add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							lowerCAmelCase__      :      List[Any]         =		re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'   )






				@property
				# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							return len(self.encoder   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							return dict(self.encoder    ,     **self.added_tokens_encoder   )






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






							'''simple docstring'''







							if token in self.cache:
										return self.cache[token]
							lowerCAmelCase__      :      Tuple         =		tuple(a   )
							lowerCAmelCase__      :      List[str]         =		get_pairs(a   )

							if not pairs:
										return token

							while True:
										lowerCAmelCase__      :      Any         =		min(a    ,     key=lambda a   : self.bpe_ranks.get(a    ,     float('inf'   )   )   )
										if bigram not in self.bpe_ranks:
													break
										lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[int]         =		bigram
										lowerCAmelCase__      :      Optional[Any]         =		[]
										lowerCAmelCase__      :      int         =		0
										while i < len(a   ):
													try:
																lowerCAmelCase__      :      List[str]         =		word.index(a    ,     a   )
													except ValueError:
																new_word.extend(word[i:]   )
																break
													else:
																new_word.extend(word[i:j]   )
																lowerCAmelCase__      :      int         =		j

													if word[i] == first and i < len(a   ) - 1 and word[i + 1] == second:
																new_word.append(first + second   )
																i += 2
													else:
																new_word.append(word[i]   )
																i += 1
										lowerCAmelCase__      :      List[str]         =		tuple(a   )
										lowerCAmelCase__      :      Tuple         =		new_word
										if len(a   ) == 1:
													break
										else:
													lowerCAmelCase__      :      Union[str, Any]         =		get_pairs(a   )
							lowerCAmelCase__      :      Any         =		' '.join(a   )
							lowerCAmelCase__      :      Dict         =		word
							return word






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      str         =		[]
							for token in re.findall(self.pat    ,     a   ):
										lowerCAmelCase__      :      Optional[Any]         =		''.join(
										    self.byte_encoder[b] for b in token.encode('utf-8'   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
										bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a   ).split(' '   )   )
							return bpe_tokens






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






							'''simple docstring'''







							return self.encoder.get(a    ,     self.encoder.get(self.unk_token   )   )






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      Dict   ):






							'''simple docstring'''







							return self.decoder.get(a   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      str   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		''.join(a   )
							lowerCAmelCase__      :      Union[str, Any]         =		bytearray([self.byte_decoder[c] for c in text]   ).decode('utf-8'    ,     errors=self.errors   )
							return text






				def 							_lowerCamelCase		(						self					:      List[Any]    ,     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__      :      Any         =		os.path.join(
							    a    ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )
							lowerCAmelCase__      :      Optional[int]         =		os.path.join(
							    a    ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']   )

							with open(a    ,     'w'    ,     encoding='utf-8'   ) as f:
										f.write(json.dumps(self.encoder    ,     indent=2    ,     sort_keys=a    ,     ensure_ascii=a   ) + '\n'   )

							lowerCAmelCase__      :      Dict         =		0
							with open(a    ,     'w'    ,     encoding='utf-8'   ) as writer:
										writer.write('#version: 0.2\n'   )
										for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,     key=lambda a   : kv[1]   ):
													if index != token_index:
																logger.warning(
																    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																    ' Please check that the tokenizer is not corrupted!'   )
																lowerCAmelCase__      :      str         =		token_index
													writer.write(' '.join(a   ) + '\n'   )
													index += 1

							return vocab_file, merge_file






				def 							_lowerCamelCase		(						self					:      List[str]    ,     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   )) + [1]
							return [1] + ([0] * len(a   )) + [1, 1] + ([0] * len(a   )) + [1]






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






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		[self.sep_token_id]
							lowerCAmelCase__      :      Dict         =		[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 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      Any    ,     a					:      Optional[Any]=False    ,     **a					:      Dict   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		kwargs.pop('add_prefix_space'    ,     self.add_prefix_space   )
							if (is_split_into_words or add_prefix_space) and (len(a   ) > 0 and not text[0].isspace()):
										lowerCAmelCase__      :      Dict         =		' ' + text
							return (text, kwargs)






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






							'''simple docstring'''







							return token_ids_a + [self.eos_token_id]






				def 							_lowerCamelCase		(						self					:      str    ,     a					:      "Conversation"   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		[]
							for is_user, text in conversation.iter_texts():
										if is_user:
													# We need to space prefix as it's being done within blenderbot
													inputs.append(' ' + text   )
										else:
													# Generated responses should contain them already.
													inputs.append(a   )

							lowerCAmelCase__      :      List[Any]         =		'  '.join(a   )
							lowerCAmelCase__      :      int         =		self.encode(a   )
							if len(a   ) > self.model_max_length:
										lowerCAmelCase__      :      Optional[int]         =		input_ids[-self.model_max_length :]
										logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''   )
							return input_ids

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