


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 			_lowercase (							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			LEDTokenizer
       A__											=			LEDTokenizerFast
       A__											=			True





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             super().setUp()
             lowerCamelCase__		:				Optional[int]    =  [
                 "l",
                 "o",
                 "w",
                 "e",
                 "r",
                 "s",
                 "t",
                 "i",
                 "d",
                 "n",
                 "\u0120",
                 "\u0120l",
                 "\u0120n",
                 "\u0120lo",
                 "\u0120low",
                 "er",
                 "\u0120lowest",
                 "\u0120newer",
                 "\u0120wider",
                 "<unk>",
             ]
             lowerCamelCase__		:				str    =  dict(zip(__lowerCamelCase					,      range(len(__lowerCamelCase       )       )       )       )
             lowerCamelCase__		:				Any    =  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
             lowerCamelCase__		:				Optional[int]    =  {"unk_token": "<unk>"}

             lowerCamelCase__		:				List[str]    =  os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["vocab_file"]       )
             lowerCamelCase__		:				List[str]    =  os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["merges_file"]       )
             with open(self.vocab_file					,      "w"					,      encoding="utf-8"       ) as fp:
                   fp.write(json.dumps(__lowerCamelCase       ) + "\n"       )
             with open(self.merges_file					,      "w"					,      encoding="utf-8"       ) as fp:
                   fp.write("\n".join(__lowerCamelCase       )       )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      **__lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             kwargs.update(self.special_tokens_map       )
             return self.tokenizer_class.from_pretrained(self.tmpdirname					,      **__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Tuple					,      **__lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



             kwargs.update(self.special_tokens_map       )
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,      **__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			str					,      __lowerCamelCase							:			Optional[int]       ):


             '''simple docstring'''



             return "lower newer", "lower newer"





       @cached_property
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             return LEDTokenizer.from_pretrained("allenai/led-base-16384"       )





       @cached_property
       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"       )





       @require_torch
       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
             lowerCamelCase__		:				Tuple    =  [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   lowerCamelCase__		:				List[str]    =  tokenizer(__lowerCamelCase					,      max_length=len(__lowerCamelCase       )					,      padding=__lowerCamelCase					,      return_tensors="pt"       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )

                   self.assertEqual((2, 9)					,      batch.input_ids.shape       )
                   self.assertEqual((2, 9)					,      batch.attention_mask.shape       )
                   lowerCamelCase__		:				Optional[Any]    =  batch.input_ids.tolist()[0]
                   self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase       )





       @require_torch
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[str]    =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   lowerCamelCase__		:				List[Any]    =  tokenizer(__lowerCamelCase					,      padding=__lowerCamelCase					,      return_tensors="pt"       )
                   self.assertIn("input_ids"					,      __lowerCamelCase       )
                   self.assertIn("attention_mask"					,      __lowerCamelCase       )
                   self.assertNotIn("labels"					,      __lowerCamelCase       )
                   self.assertNotIn("decoder_attention_mask"					,      __lowerCamelCase       )





       @require_torch
       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  [
                 "Summary of the text.",
                 "Another summary.",
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   lowerCamelCase__		:				Union[str, Any]    =  tokenizer(text_target=__lowerCamelCase					,      max_length=32					,      padding="max_length"					,      return_tensors="pt"       )
                   self.assertEqual(32					,      targets["input_ids"].shape[1]       )





       @require_torch
       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   lowerCamelCase__		:				Tuple    =  tokenizer(
                       ["I am a small frog" * 1024, "I am a small frog"]					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase					,      return_tensors="pt"       )
                   self.assertIsInstance(__lowerCamelCase					,      __lowerCamelCase       )
                   self.assertEqual(batch.input_ids.shape					,      (2, 5122)       )





       @require_torch
       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  ["A long paragraph for summarization."]
             lowerCamelCase__		:				List[Any]    =  [
                 "Summary of the text.",
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   lowerCamelCase__		:				Dict    =  tokenizer(__lowerCamelCase					,      return_tensors="pt"       )
                   lowerCamelCase__		:				List[str]    =  tokenizer(text_target=__lowerCamelCase					,      return_tensors="pt"       )
                   lowerCamelCase__		:				List[str]    =  inputs["input_ids"]
                   lowerCamelCase__		:				Dict    =  targets["input_ids"]
                   self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()       )
                   self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()       )
                   self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()       )
                   self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()       )





       @require_torch
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   lowerCamelCase__		:				List[str]    =  ["Summary of the text.", "Another summary."]
                   lowerCamelCase__		:				List[Any]    =  [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

                   lowerCamelCase__		:				int    =  tokenizer(__lowerCamelCase					,      padding=__lowerCamelCase       )
                   lowerCamelCase__		:				Union[str, Any]    =  [[0] * len(__lowerCamelCase       ) for x in encoded_output["input_ids"]]
                   lowerCamelCase__		:				List[Any]    =  tokenizer.pad(__lowerCamelCase       )
                   self.assertSequenceEqual(outputs["global_attention_mask"]					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
                         lowerCamelCase__		:				str    =  self.rust_tokenizer_class.from_pretrained(__lowerCamelCase					,      **__lowerCamelCase       )
                         lowerCamelCase__		:				Union[str, Any]    =  self.tokenizer_class.from_pretrained(__lowerCamelCase					,      **__lowerCamelCase       )
                         lowerCamelCase__		:				int    =  "A, <mask> AllenNLP sentence."
                         lowerCamelCase__		:				Any    =  tokenizer_r.encode_plus(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      return_token_type_ids=__lowerCamelCase       )
                         lowerCamelCase__		:				List[Any]    =  tokenizer_p.encode_plus(__lowerCamelCase					,      add_special_tokens=__lowerCamelCase					,      return_token_type_ids=__lowerCamelCase       )
                         self.assertEqual(sum(tokens_r["token_type_ids"]       )					,      sum(tokens_p["token_type_ids"]       )       )
                         self.assertEqual(
                             sum(tokens_r["attention_mask"]       ) / len(tokens_r["attention_mask"]       )					,      sum(tokens_p["attention_mask"]       ) / len(tokens_p["attention_mask"]       )					,      )

                         lowerCamelCase__		:				str    =  tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]       )
                         lowerCamelCase__		:				List[str]    =  tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]       )
                         self.assertSequenceEqual(tokens_p["input_ids"]					,      [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]       )
                         self.assertSequenceEqual(tokens_r["input_ids"]					,      [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]       )

                         self.assertSequenceEqual(
                             __lowerCamelCase					,      ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]       )
                         self.assertSequenceEqual(
                             __lowerCamelCase					,      ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]       )






from __future__ import annotations

A      :    Union[str, Any]		=					{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Tuple					,      __lowerCamelCase							:			dict[str, list[str]]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  graph
             # mapping node to its parent in resulting breadth first tree
             lowerCamelCase__		:				dict[str, str | None]    =  {}
             lowerCamelCase__		:				Dict    =  source_vertex





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  {self.source_vertex}
             lowerCamelCase__		:				Optional[int]    =  None
             lowerCamelCase__		:				Dict    =  [self.source_vertex]  # first in first out queue

             while queue:
                   lowerCamelCase__		:				Optional[int]    =  queue.pop(0       )
                   for adjacent_vertex in self.graph[vertex]:
                         if adjacent_vertex not in visited:
                               visited.add(__lowerCamelCase       )
                               lowerCamelCase__		:				List[str]    =  vertex
                               queue.append(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             if target_vertex == self.source_vertex:
                   return self.source_vertex

             lowerCamelCase__		:				Tuple    =  self.parent.get(__lowerCamelCase       )
             if target_vertex_parent is None:
                   lowerCamelCase__		:				Tuple    =  (
                       f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
                   )
                   raise ValueError(__lowerCamelCase       )

             return self.shortest_path(__lowerCamelCase       ) + f"->{target_vertex}"


if __name__ == "__main__":
    A      :    List[str]		=					Graph(graph, "G")
    g.breath_first_search()
    print(g.shortest_path("D"))
    print(g.shortest_path("G"))
    print(g.shortest_path("Foo"))


