'''simple docstring'''

import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors





@require_tokenizers
class 							lowercase						(						__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=MvpTokenizer
   lowerCamelCase_    					=MvpTokenizerFast
   lowerCamelCase_    					=True
   lowerCamelCase_    					=filter_roberta_detectors


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

         lowercase_     =      os.path.join(self.tmpdirname   ,		VOCAB_FILES_NAMES["vocab_file"])
         lowercase_     =      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 __UpperCAmelCase       (   self				:	Optional[Any]   ,		**__lowerCAmelCase				:	Any)      ->					str:
         kwargs.update(self.special_tokens_map)
         return self.tokenizer_class.from_pretrained(self.tmpdirname   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		**__lowerCAmelCase				:	Any)      ->					List[Any]:
         kwargs.update(self.special_tokens_map)
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	Dict)      ->					Optional[Any]:
         return "lower newer", "lower newer"


   @cached_property
   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					str:
         return MvpTokenizer.from_pretrained("RUCAIBox/mvp")


   @cached_property
   def __UpperCAmelCase       (   self				:	Tuple)      ->					Dict:
         return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp")


   @require_torch
   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Optional[Any]:
         lowercase_     =      ["A long paragraph for summarization.", "Another paragraph for summarization."]
         lowercase_     =      [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     =      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)
               lowercase_     =      batch.input_ids.tolist()[0]
               self.assertListEqual(__lowerCAmelCase   ,		__lowerCAmelCase)
               # Test that special tokens are reset


   @require_torch
   def __UpperCAmelCase       (   self				:	List[Any])      ->					Dict:
         lowercase_     =      ["A long paragraph for summarization.", "Another paragraph for summarization."]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     =      tokenizer(__lowerCAmelCase   ,		padding=__lowerCAmelCase   ,		return_tensors="pt")
               # check if input_ids are returned and no labels
               self.assertIn("input_ids"   ,		__lowerCAmelCase)
               self.assertIn("attention_mask"   ,		__lowerCAmelCase)
               self.assertNotIn("labels"   ,		__lowerCAmelCase)
               self.assertNotIn("decoder_attention_mask"   ,		__lowerCAmelCase)


   @require_torch
   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Union[str, Any]:
         lowercase_     =      [
             "Summary of the text.",
             "Another summary.",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     =      tokenizer(text_target=__lowerCAmelCase   ,		max_length=32   ,		padding="max_length"   ,		return_tensors="pt")
               self.assertEqual(32   ,		targets["input_ids"].shape[1])


   @require_torch
   def __UpperCAmelCase       (   self				:	Dict)      ->					List[Any]:
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     =      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, 1024))


   @require_torch
   def __UpperCAmelCase       (   self				:	str)      ->					List[str]:
         lowercase_     =      ["A long paragraph for summarization."]
         lowercase_     =      [
             "Summary of the text.",
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowercase_     =      tokenizer(__lowerCAmelCase   ,		text_target=__lowerCAmelCase   ,		return_tensors="pt")
               lowercase_     =      inputs["input_ids"]
               lowercase_     =      inputs["labels"]
               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())


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Any:
         pass


   def __UpperCAmelCase       (   self				:	List[Any])      ->					Any:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                     lowercase_     =      self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase   ,		**__lowerCAmelCase)
                     lowercase_     =      self.tokenizer_class.from_pretrained(__lowerCAmelCase   ,		**__lowerCAmelCase)
                     lowercase_     =      "A, <mask> AllenNLP sentence."
                     lowercase_     =      tokenizer_r.encode_plus(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase   ,		return_token_type_ids=__lowerCAmelCase)
                     lowercase_     =      tokenizer_p.encode_plus(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase   ,		return_token_type_ids=__lowerCAmelCase)

                     # token_type_ids should put 0 everywhere
                     self.assertEqual(sum(tokens_r["token_type_ids"])   ,		sum(tokens_p["token_type_ids"]))

                     # attention_mask should put 1 everywhere, so sum over length should be 1
                     self.assertEqual(
                         sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"])   ,		sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"])   ,		)

                     lowercase_     =      tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
                     lowercase_     =      tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

                     # Rust correctly handles the space before the mask while python doesnt
                     self.assertSequenceEqual(tokens_p["input_ids"]   ,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
                     self.assertSequenceEqual(tokens_r["input_ids"]   ,		[0, 250, 6, 5_0264, 3823, 487, 2_1992, 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>"])







'''simple docstring'''

# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowerCAmelCase_ : Dict						  =      TypeVar("T")





class 							lowercase						(						Generic[T] ):


   def __init__(   self				:	int   ,		__lowerCAmelCase				:	bool = True)      ->					None:
         lowercase_     =      {}  # dictionary of lists
         lowercase_     =      directed


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	T   ,		__lowerCAmelCase				:	T)      ->					GraphAdjacencyList[T]:

         if not self.directed:  # For undirected graphs
               # if both source vertex and destination vertex are both present in the
               # adjacency list, add destination vertex to source vertex list of adjacent
               # vertices and add source vertex to destination vertex list of adjacent
               # vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     self.adj_list[destination_vertex].append(__lowerCAmelCase)
               # if only source vertex is present in adjacency list, add destination vertex
               # to source vertex list of adjacent vertices, then create a new vertex with
               # destination vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif source_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     lowercase_     =      [source_vertex]
               # if only destination vertex is present in adjacency list, add source vertex
               # to destination vertex list of adjacent vertices, then create a new vertex
               # with source vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif destination_vertex in self.adj_list:
                     self.adj_list[destination_vertex].append(__lowerCAmelCase)
                     lowercase_     =      [destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and assign a list
               # containing the destination vertex as it's first adjacent vertex also
               # create a new vertex with destination vertex as key and assign a list
               # containing the source vertex as it's first adjacent vertex.
               else:
                     lowercase_     =      [destination_vertex]
                     lowercase_     =      [source_vertex]
         else:  # For directed graphs
               # if both source vertex and destination vertex are present in adjacency
               # list, add destination vertex to source vertex list of adjacent vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
               # if only source vertex is present in adjacency list, add destination
               # vertex to source vertex list of adjacent vertices and create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               elif source_vertex in self.adj_list:
                     self.adj_list[source_vertex].append(__lowerCAmelCase)
                     lowercase_     =      []
               # if only destination vertex is present in adjacency list, create a new
               # vertex with source vertex as key and assign a list containing destination
               # vertex as first adjacent vertex
               elif destination_vertex in self.adj_list:
                     lowercase_     =      [destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and a list containing
               # destination vertex as it's first adjacent vertex. Then create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               else:
                     lowercase_     =      [destination_vertex]
                     lowercase_     =      []

         return self


   def __repr__(   self				:	str)      ->					str:
         return pformat(self.adj_list)






