import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer



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



							def __init__(						self				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    lowercase_ = None    ):
													super().__init__()
													_snake_case						:					List[str]    =   pad_token_id
													_snake_case						:					List[str]    =   max_length
													_snake_case						:					Dict    =   vocab
													_snake_case						:					List[str]    =   merges
													_snake_case						:					List[str]    =   BytePairTokenizer(_a				,    _a				,    sequence_length=_a    )



							@classmethod
							def 	UpperCamelCase	(						cls				,    lowercase_				,    *lowercase_				,    **lowercase_    ):
													_snake_case						:					Dict    =   [" ".join(_a    ) for m in tokenizer.bpe_ranks.keys()]
													_snake_case						:					str    =   tokenizer.get_vocab()
													return cls(_a				,    _a				,    *_a				,    **_a    )



							@classmethod
							def 	UpperCamelCase	(						cls				,    lowercase_				,    *lowercase_				,    **lowercase_    ):
													_snake_case						:					List[Any]    =   GPTaTokenizer.from_pretrained(_a				,    *_a				,    **_a    )
													return cls.from_tokenizer(_a				,    *_a				,    **_a    )



							@classmethod
							def 	UpperCamelCase	(						cls				,    lowercase_    ):
													return cls(**_a    )



							def 	UpperCamelCase	(						self    ):
													return {
													    "vocab": self.vocab,
													    "merges": self.merges,
													    "max_length": self.max_length,
													    "pad_token_id": self.pad_token_id,
													}



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):
													_snake_case						:					Dict    =   self.tf_tokenizer(_a    )
													_snake_case						:					str    =   tf.ones_like(_a    )

													if self.pad_token_id is not None:
																			# pad the tokens up to max length
																			_snake_case						:					Any    =   max_length if max_length is not None else self.max_length

																			if max_length is not None:
																									_snake_case    ,_snake_case						:					Any    =   pad_model_inputs(
																									    _a				,    max_seq_length=_a				,    pad_value=self.pad_token_id    )

													return {"attention_mask": attention_mask, "input_ids": input_ids}
from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass



@dataclass
class        lowercase_   :
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42




class        lowercase_   :



							def __init__(						self				,    lowercase_    ):
													_snake_case						:					list[list[Edge]]    =   [[] for _ in range(lowercase_    )]
													_snake_case						:					Union[str, Any]    =   size



							def __getitem__(						self				,    lowercase_    ):
													return iter(self._graph[vertex]    )



							@property
							def 	UpperCamelCase	(						self    ):
													return self._size



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_    ):
													if weight not in (0, 1):
																			raise ValueError("Edge weight must be either 0 or 1."    )

													if to_vertex < 0 or to_vertex >= self.size:
																			raise ValueError("Vertex indexes must be in [0; size)."    )

													self._graph[from_vertex].append(Edge(lowercase_				,    lowercase_    )    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Optional[int]    =   deque([start_vertex]    )
													_snake_case						:					list[int | None]    =   [None] * self.size
													_snake_case						:					Tuple    =   0

													while queue:
																			_snake_case						:					List[Any]    =   queue.popleft()
																			_snake_case						:					Tuple    =   distances[current_vertex]
																			if current_distance is None:
																									continue

																			for edge in self[current_vertex]:
																									_snake_case						:					Dict    =   current_distance + edge.weight
																									_snake_case						:					str    =   distances[edge.destination_vertex]
																									if (
																									    isinstance(lowercase_				,    lowercase_    )
																									    and new_distance >= dest_vertex_distance
																									):
																															continue
																									_snake_case						:					List[Any]    =   new_distance
																									if edge.weight == 0:
																															queue.appendleft(edge.destination_vertex    )
																									else:
																															queue.append(edge.destination_vertex    )

													if distances[finish_vertex] is None:
																			raise ValueError("No path from start_vertex to finish_vertex."    )

													return distances[finish_vertex]


if __name__ == "__main__":
				import doctest

				doctest.testmod()