
import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

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


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_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

lowerCAmelCase__							=				{"facebook/blenderbot_small-90M": 512}



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = set()
		SCREAMING_SNAKE_CASE_						 = word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)    )
				SCREAMING_SNAKE_CASE_						 = char

		SCREAMING_SNAKE_CASE_						 = set(__a    )
		return pairs





class  snake_case     (							UpperCAmelCase_   ):
							UpperCAmelCase__						     =      VOCAB_FILES_NAMES
							UpperCAmelCase__						     =      PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__						     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__						     =      ['input_ids', 'attention_mask']

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_="__start__"       ,       SCREAMING_SNAKE_CASE_="__end__"       ,       SCREAMING_SNAKE_CASE_="__unk__"       ,       SCREAMING_SNAKE_CASE_="__null__"       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(unk_token=_lowercase       ,       bos_token=_lowercase       ,       eos_token=_lowercase       ,       pad_token=_lowercase       ,       **_lowercase    )

									with open(_lowercase       ,       encoding='''utf-8'''    ) as vocab_handle:
											SCREAMING_SNAKE_CASE_						 = json.load(_lowercase    )
									SCREAMING_SNAKE_CASE_						 = {v: k for k, v in self.encoder.items()}
									with open(_lowercase       ,       encoding='''utf-8'''    ) as merges_handle:
											SCREAMING_SNAKE_CASE_						 = merges_handle.read().split('''\n'''    )[1:-1]
									SCREAMING_SNAKE_CASE_						 = [tuple(merge.split()    ) for merge in merges]
									SCREAMING_SNAKE_CASE_						 = dict(zip(_lowercase       ,       range(len(_lowercase    )    )    )    )
									SCREAMING_SNAKE_CASE_						 = {}

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return len(self.encoder    )

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if token in self.cache:
											return self.cache[token]
									SCREAMING_SNAKE_CASE_						 = re.sub('''([.,!?()])'''       ,       R''' \1'''       ,       _lowercase    )
									SCREAMING_SNAKE_CASE_						 = re.sub('''(\')'''       ,       R''' \1 '''       ,       _lowercase    )
									SCREAMING_SNAKE_CASE_						 = re.sub(R'''\s{2,}'''       ,       ''' '''       ,       _lowercase    )
									if "\n" in token:
											SCREAMING_SNAKE_CASE_						 = token.replace('''\n'''       ,       ''' __newln__'''    )

									SCREAMING_SNAKE_CASE_						 = token.split(''' '''    )
									SCREAMING_SNAKE_CASE_						 = []
									for token in tokens:
											if not len(_lowercase    ):
													continue

											SCREAMING_SNAKE_CASE_						 = token.lower()
											SCREAMING_SNAKE_CASE_						 = tuple(_lowercase    )
											SCREAMING_SNAKE_CASE_						 = tuple(list(word[:-1]    ) + [word[-1] + '''</w>''']    )
											SCREAMING_SNAKE_CASE_						 = get_pairs(_lowercase    )

											if not pairs:
													words.append(_lowercase    )
													continue

											while True:
													SCREAMING_SNAKE_CASE_						 = min(_lowercase       ,       key=lambda SCREAMING_SNAKE_CASE_    : self.bpe_ranks.get(_lowercase       ,       float('''inf'''    )    )    )
													if bigram not in self.bpe_ranks:
															break
													SCREAMING_SNAKE_CASE_						 = bigram
													SCREAMING_SNAKE_CASE_						 = []
													SCREAMING_SNAKE_CASE_						 = 0

													while i < len(_lowercase    ):
															try:
																	SCREAMING_SNAKE_CASE_						 = word.index(_lowercase       ,       _lowercase    )
																	new_word.extend(word[i:j]    )
																	SCREAMING_SNAKE_CASE_						 = j
															except ValueError:
																	new_word.extend(word[i:]    )
																	break

															if word[i] == first and i < len(_lowercase    ) - 1 and word[i + 1] == second:
																	new_word.append(first + second    )
																	i += 2
															else:
																	new_word.append(word[i]    )
																	i += 1
													SCREAMING_SNAKE_CASE_						 = tuple(_lowercase    )
													SCREAMING_SNAKE_CASE_						 = new_word
													if len(_lowercase    ) == 1:
															break
													else:
															SCREAMING_SNAKE_CASE_						 = get_pairs(_lowercase    )
											SCREAMING_SNAKE_CASE_						 = '@@ '.join(_lowercase    )
											SCREAMING_SNAKE_CASE_						 = word[:-4]

											SCREAMING_SNAKE_CASE_						 = word
											words.append(_lowercase    )
									return " ".join(_lowercase    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = []

									SCREAMING_SNAKE_CASE_						 = re.findall(R'''\S+\n?'''       ,       _lowercase    )

									for token in words:
											split_tokens.extend(list(self.bpe(_lowercase    ).split(''' '''    )    )    )
									return split_tokens

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = token.lower()
									return self.encoder.get(_lowercase       ,       self.encoder.get(self.unk_token    )    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return self.decoder.get(_lowercase       ,       self.unk_token    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = ' '.join(_lowercase    ).replace('''@@ '''       ,       ''''''    ).strip()
									return out_string







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = None    ):

									"""simple docstring"""







									if not os.path.isdir(_lowercase    ):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
											return
									SCREAMING_SNAKE_CASE_						 = os.path.join(
									    _lowercase       ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(
									    _lowercase       ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']    )

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

									SCREAMING_SNAKE_CASE_						 = 0
									with open(_lowercase       ,       '''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 SCREAMING_SNAKE_CASE_    : 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!'''    )
															SCREAMING_SNAKE_CASE_						 = token_index
													writer.write(''' '''.join(_lowercase    ) + '''\n'''    )
													index += 1

									return vocab_file, merge_file

"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None







class  snake_case     (							__lowercase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=1       ,       SCREAMING_SNAKE_CASE_=0       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=5_12       ,       SCREAMING_SNAKE_CASE_="cls"       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=True       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,       bos_token_id=SCREAMING_SNAKE_CASE_       ,       eos_token_id=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = project_dim
									SCREAMING_SNAKE_CASE_						 = pooler_fn
									SCREAMING_SNAKE_CASE_						 = learn_encoder
									SCREAMING_SNAKE_CASE_						 = use_attention_mask







class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      [R'''pooler''', R'''logit_scale''']
							UpperCAmelCase__						     =      [R'''position_ids''', R'''predictions.decoder.bias''']
							UpperCAmelCase__						     =      '''roberta'''
							UpperCAmelCase__						     =      RobertaSeriesConfig

							def __init__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									super().__init__(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = XLMRobertaModel(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = nn.Linear(config.hidden_size       ,       config.project_dim    )
									SCREAMING_SNAKE_CASE_						 = getattr(SCREAMING_SNAKE_CASE_       ,       '''has_pre_transformation'''       ,       SCREAMING_SNAKE_CASE_    )
									if self.has_pre_transformation:
											SCREAMING_SNAKE_CASE_						 = nn.Linear(config.hidden_size       ,       config.project_dim    )
											SCREAMING_SNAKE_CASE_						 = nn.LayerNorm(config.hidden_size       ,       eps=config.layer_norm_eps    )
									self.post_init()







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_						 = self.base_model(
									    input_ids=SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_       ,       token_type_ids=SCREAMING_SNAKE_CASE_       ,       position_ids=SCREAMING_SNAKE_CASE_       ,       head_mask=SCREAMING_SNAKE_CASE_       ,       inputs_embeds=SCREAMING_SNAKE_CASE_       ,       encoder_hidden_states=SCREAMING_SNAKE_CASE_       ,       encoder_attention_mask=SCREAMING_SNAKE_CASE_       ,       output_attentions=SCREAMING_SNAKE_CASE_       ,       output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,       return_dict=SCREAMING_SNAKE_CASE_       ,       )

									if self.has_pre_transformation:
											SCREAMING_SNAKE_CASE_						 = outputs['''hidden_states'''][-2]
											SCREAMING_SNAKE_CASE_						 = self.pre_LN(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = self.transformation_pre(SCREAMING_SNAKE_CASE_    )

											return TransformationModelOutput(
											    projection_state=SCREAMING_SNAKE_CASE_       ,       last_hidden_state=outputs.last_hidden_state       ,       hidden_states=outputs.hidden_states       ,       attentions=outputs.attentions       ,       )
									else:
											SCREAMING_SNAKE_CASE_						 = self.transformation(outputs.last_hidden_state    )
											return TransformationModelOutput(
											    projection_state=SCREAMING_SNAKE_CASE_       ,       last_hidden_state=outputs.last_hidden_state       ,       hidden_states=outputs.hidden_states       ,       attentions=outputs.attentions       ,       )