


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

import regex as re

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


_snake_case	:						Dict         =							logging.get_logger(__name__)


_snake_case	:						Union[str, Any]         =							{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

_snake_case	:						Union[str, Any]         =							{
    "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"
        )
    },
}

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







def 							lowerCAmelCase_	(   __lowerCamelCase      ):
							__snake_case						:     Tuple									=  set()
							__snake_case						:     str									=  word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)      )
														__snake_case						:     Optional[int]									=  char

							__snake_case						:     Dict									=  set(__lowerCamelCase      )
							return pairs

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Tuple	    =    VOCAB_FILES_NAMES
							__UpperCAmelCase       :				int	    =    PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase       :				Optional[int]	    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase       :				Union[str, Any]	    =    ["input_ids", "attention_mask"]

							def __init__(				self			:    Optional[int]       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    Dict="__start__"       ,      lowerCamelCase			:    int="__end__"       ,      lowerCamelCase			:    Optional[Any]="__unk__"       ,      lowerCamelCase			:    Union[str, Any]="__null__"       ,      **lowerCamelCase			:    Dict       ,      )      ->       Tuple:
														super().__init__(unk_token=lowerCamelCase       ,      bos_token=lowerCamelCase       ,      eos_token=lowerCamelCase       ,      pad_token=lowerCamelCase       ,      **lowerCamelCase							)

														with open(lowerCamelCase       ,      encoding="utf-8"							) as vocab_handle:
																					__snake_case						:     Optional[int]									=  json.load(lowerCamelCase							)
														__snake_case						:     int									=  {v: k for k, v in self.encoder.items()}
														with open(lowerCamelCase       ,      encoding="utf-8"							) as merges_handle:
																					__snake_case						:     Union[str, Any]									=  merges_handle.read().split("\n"							)[1:-1]
														__snake_case						:     str									=  [tuple(merge.split()							) for merge in merges]
														__snake_case						:     Optional[int]									=  dict(zip(lowerCamelCase       ,      range(len(lowerCamelCase							)							)							)							)
														__snake_case						:     int									=  {}

							@property
							def        __snake_case   (				self			:    List[str]							)      ->       int:
														return len(self.encoder							)

							def        __snake_case   (				self			:    int							)      ->       Dict:
														return dict(self.encoder       ,      **self.added_tokens_encoder							)

							def        __snake_case   (				self			:    str       ,      lowerCamelCase			:    str							)      ->       str:
														if token in self.cache:
																					return self.cache[token]
														__snake_case						:     str									=  re.sub("([.,!?()])"       ,      R" \1"       ,      lowerCamelCase							)
														__snake_case						:     Union[str, Any]									=  re.sub("(')"       ,      R" \1 "       ,      lowerCamelCase							)
														__snake_case						:     Optional[int]									=  re.sub(R"\s{2,}"       ,      " "       ,      lowerCamelCase							)
														if "\n" in token:
																					__snake_case						:     Tuple									=  token.replace("\n"       ,      " __newln__"							)

														__snake_case						:     Optional[Any]									=  token.split(" "							)
														__snake_case						:     Dict									=  []
														for token in tokens:
																					if not len(lowerCamelCase							):
																												continue

																					__snake_case						:     List[Any]									=  token.lower()
																					__snake_case						:     Optional[Any]									=  tuple(lowerCamelCase							)
																					__snake_case						:     str									=  tuple(list(word[:-1]							) + [word[-1] + "</w>"]							)
																					__snake_case						:     Optional[Any]									=  get_pairs(lowerCamelCase							)

																					if not pairs:
																												words.append(lowerCamelCase							)
																												continue

																					while True:
																												__snake_case						:     Optional[Any]									=  min(lowerCamelCase       ,      key=lambda lowerCamelCase							: self.bpe_ranks.get(lowerCamelCase       ,      float("inf"							)							)							)
																												if bigram not in self.bpe_ranks:
																																			break
																												__snake_case					,     __snake_case						:     Dict									=  bigram
																												__snake_case						:     int									=  []
																												__snake_case						:     Optional[int]									=  0

																												while i < len(lowerCamelCase							):
																																			try:
																																										__snake_case						:     Optional[int]									=  word.index(lowerCamelCase       ,      lowerCamelCase							)
																																										new_word.extend(word[i:j]							)
																																										__snake_case						:     Optional[int]									=  j
																																			except ValueError:
																																										new_word.extend(word[i:]							)
																																										break

																																			if word[i] == first and i < len(lowerCamelCase							) - 1 and word[i + 1] == second:
																																										new_word.append(first + second							)
																																										i += 2
																																			else:
																																										new_word.append(word[i]							)
																																										i += 1
																												__snake_case						:     List[Any]									=  tuple(lowerCamelCase							)
																												__snake_case						:     Union[str, Any]									=  new_word
																												if len(lowerCamelCase							) == 1:
																																			break
																												else:
																																			__snake_case						:     Tuple									=  get_pairs(lowerCamelCase							)
																					__snake_case						:     List[Any]									=  "@@ ".join(lowerCamelCase							)
																					__snake_case						:     List[str]									=  word[:-4]

																					__snake_case						:     List[Any]									=  word
																					words.append(lowerCamelCase							)
														return " ".join(lowerCamelCase							)

							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    str							)      ->       List[str]:
														__snake_case						:     int									=  []

														__snake_case						:     Optional[int]									=  re.findall(R"\S+\n?"       ,      lowerCamelCase							)

														for token in words:
																					split_tokens.extend(list(self.bpe(lowerCamelCase							).split(" "							)							)							)
														return split_tokens

							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    str							)      ->       int:
														__snake_case						:     str									=  token.lower()
														return self.encoder.get(lowerCamelCase       ,      self.encoder.get(self.unk_token							)							)

							def        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    int							)      ->       str:
														return self.decoder.get(lowerCamelCase       ,      self.unk_token							)

							def        __snake_case   (				self			:    Dict       ,      lowerCamelCase			:    List[str]							)      ->       str:
														__snake_case						:     List[str]									=  " ".join(lowerCamelCase							).replace("@@ "       ,      ""							).strip()
														return out_string






							def        __snake_case   (				self			:    Tuple       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    Optional[str] = None							)      ->       Tuple[str]:
														if not os.path.isdir(lowerCamelCase							):
																					logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																					return
														__snake_case						:     int									=  os.path.join(
														    lowerCamelCase       ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)
														__snake_case						:     Tuple									=  os.path.join(
														    lowerCamelCase       ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]							)

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

														__snake_case						:     Tuple									=  0
														with open(lowerCamelCase       ,      "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 lowerCamelCase							: 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!"							)
																																			__snake_case						:     Dict									=  token_index
																												writer.write(" ".join(lowerCamelCase							) + "\n"							)
																												index += 1

														return vocab_file, merge_file




import tensorflow as tf

from ...tf_utils import shape_list



class        _a		(tf.keras.layers.Layer							):


					'''simple docstring'''






					def __init__(					self       ,			A__       ,			A__       ,			A__       ,			A__       ,			A__=1       ,			A__=False       ,			**A__     ):
						super().__init__(**A__     )

						A__				:       Tuple	  =						vocab_size
						A__				:       List[str]	  =						d_embed
						A__				:       int	  =						d_proj

						A__				:       Tuple	  =						cutoffs + [vocab_size]
						A__				:       Any	  =						[0] + self.cutoffs
						A__				:       str	  =						div_val

						A__				:       int	  =						self.cutoffs[0]
						A__				:       List[str]	  =						len(self.cutoffs     ) - 1
						A__				:       Optional[int]	  =						self.shortlist_size + self.n_clusters
						A__				:       List[Any]	  =						keep_order

						A__				:       Optional[int]	  =						[]
						A__				:       Optional[Any]	  =						[]






					def     __A     (					self       ,			A__     ):
						if self.n_clusters > 0:
							A__				:       List[str]	  =						self.add_weight(
							    shape=(self.n_clusters, self.d_embed)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name="""cluster_weight"""     )
							A__				:       Union[str, Any]	  =						self.add_weight(
							    shape=(self.n_clusters,)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name="""cluster_bias"""     )

						if self.div_val == 1:
							for i in range(len(self.cutoffs     )     ):
								if self.d_proj != self.d_embed:
									A__				:       List[str]	  =						self.add_weight(
									    shape=(self.d_embed, self.d_proj)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_projs_._{i}"""       ,			)
									self.out_projs.append(A__     )
								else:
									self.out_projs.append(A__     )
								A__				:       int	  =						self.add_weight(
								    shape=(self.vocab_size, self.d_embed)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_layers_._{i}_._weight"""       ,			)
								A__				:       str	  =						self.add_weight(
								    shape=(self.vocab_size,)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_layers_._{i}_._bias"""       ,			)
								self.out_layers.append((weight, bias)     )
						else:
							for i in range(len(self.cutoffs     )     ):
								A__  ,  A__				:       Optional[int]	  =						self.cutoff_ends[i], self.cutoff_ends[i + 1]
								A__				:       Optional[int]	  =						self.d_embed // (self.div_val**i)

								A__				:       Optional[Any]	  =						self.add_weight(
								    shape=(d_emb_i, self.d_proj)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_projs_._{i}"""     )
								self.out_projs.append(A__     )
								A__				:       List[str]	  =						self.add_weight(
								    shape=(r_idx - l_idx, d_emb_i)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_layers_._{i}_._weight"""       ,			)
								A__				:       Optional[int]	  =						self.add_weight(
								    shape=(r_idx - l_idx,)       ,			initializer="""zeros"""       ,			trainable=A__       ,			name=F"""out_layers_._{i}_._bias"""       ,			)
								self.out_layers.append((weight, bias)     )
						super().build(A__     )






					@staticmethod
					def     __A     (					A__       ,			A__       ,			A__       ,			A__=None     ):
						A__				:       Tuple	  =						x
						if proj is not None:
							A__				:       Tuple	  =						tf.einsum("""ibd,ed->ibe"""       ,			A__       ,			A__     )
						return tf.einsum("""ibd,nd->ibn"""       ,			A__       ,			A__     ) + b






					@staticmethod
					def     __A     (					A__       ,			A__     ):
						A__				:       Optional[Any]	  =						shape_list(A__     )
						A__				:       Optional[Any]	  =						tf.range(lp_size[0]       ,			dtype=target.dtype     )
						A__				:       List[str]	  =						tf.stack([r, target]       ,			1     )
						return tf.gather_nd(A__       ,			A__     )






					def     __A     (					self       ,			A__       ,			A__       ,			A__=True       ,			A__=False     ):
						A__				:       str	  =						0
						if self.n_clusters == 0:
							A__				:       List[str]	  =						self._logit(A__       ,			self.out_layers[0][0]       ,			self.out_layers[0][1]       ,			self.out_projs[0]     )
							if target is not None:
								A__				:       Union[str, Any]	  =						tf.nn.sparse_softmax_cross_entropy_with_logits(labels=A__       ,			logits=A__     )
							A__				:       int	  =						tf.nn.log_softmax(A__       ,			axis=-1     )
						else:
							A__				:       Any	  =						shape_list(A__     )
							A__				:       int	  =						[]
							A__				:       Union[str, Any]	  =						tf.zeros(hidden_sizes[:2]     )
							for i in range(len(self.cutoffs     )     ):
								A__  ,  A__				:       Union[str, Any]	  =						self.cutoff_ends[i], self.cutoff_ends[i + 1]
								if target is not None:
									A__				:       Any	  =						(target >= l_idx) & (target < r_idx)
									A__				:       Optional[Any]	  =						tf.where(A__     )
									A__				:       int	  =						tf.boolean_mask(A__       ,			A__     ) - l_idx

								if self.div_val == 1:
									A__				:       Any	  =						self.out_layers[0][0][l_idx:r_idx]
									A__				:       List[Any]	  =						self.out_layers[0][1][l_idx:r_idx]
								else:
									A__				:       Optional[int]	  =						self.out_layers[i][0]
									A__				:       Tuple	  =						self.out_layers[i][1]

								if i == 0:
									A__				:       Union[str, Any]	  =						tf.concat([cur_W, self.cluster_weight]       ,			0     )
									A__				:       Optional[int]	  =						tf.concat([cur_b, self.cluster_bias]       ,			0     )

									A__				:       Any	  =						self._logit(A__       ,			A__       ,			A__       ,			self.out_projs[0]     )
									A__				:       Optional[Any]	  =						tf.nn.log_softmax(A__     )
									out.append(head_logprob[..., : self.cutoffs[0]]     )
									if target is not None:
										A__				:       Optional[Any]	  =						tf.boolean_mask(A__       ,			A__     )
										A__				:       List[str]	  =						self._gather_logprob(A__       ,			A__     )
								else:
									A__				:       str	  =						self._logit(A__       ,			A__       ,			A__       ,			self.out_projs[i]     )
									A__				:       Dict	  =						tf.nn.log_softmax(A__     )
									A__				:       List[str]	  =						self.cutoffs[0] + i - 1  # No probability for the head cluster
									A__				:       Optional[Any]	  =						head_logprob[..., cluster_prob_idx, None] + tail_logprob
									out.append(A__     )
									if target is not None:
										A__				:       Any	  =						tf.boolean_mask(A__       ,			A__     )
										A__				:       List[Any]	  =						tf.boolean_mask(A__       ,			A__     )
										A__				:       List[Any]	  =						self._gather_logprob(A__       ,			A__     )
										cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
								if target is not None:
									loss += tf.scatter_nd(A__       ,			-cur_logprob       ,			shape_list(A__     )     )
							A__				:       List[str]	  =						tf.concat(A__       ,			axis=-1     )

						if target is not None:
							if return_mean:
								A__				:       List[Any]	  =						tf.reduce_mean(A__     )
							# Add the training-time loss value to the layer using `self.add_loss()`.
							self.add_loss(A__     )

							# Log the loss as a metric (we could log arbitrary metrics,
							# including different metrics for training and inference.
							self.add_metric(A__       ,			name=self.name       ,			aggregation="""mean""" if return_mean else """"""     )

						return out



