

import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Any      =OpenAIGPTTokenizer
 _lowercase     :					Any      =OpenAIGPTTokenizerFast
 _lowercase     :					List[str]      =True
 _lowercase     :					Tuple      =False

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _lowerCAmelCase   :					Optional[int]        =    [
          "l",
          "o",
          "w",
          "e",
          "r",
          "s",
          "t",
          "i",
          "d",
          "n",
          "w</w>",
          "r</w>",
          "t</w>",
          "lo",
          "low",
          "er</w>",
          "low</w>",
          "lowest</w>",
          "newer</w>",
          "wider</w>",
          "<unk>",
      ]
      _lowerCAmelCase   :					Union[str, Any]        =    dict(zip(A_     ,						range(len(A_       )       )       )       )
      _lowerCAmelCase   :					List[Any]        =    ["#version: 0.2", "l o", "lo w", "e r</w>", ""]

      _lowerCAmelCase   :					Union[str, Any]        =    os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES["vocab_file"]       )
      _lowerCAmelCase   :					List[Any]        =    os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES["merges_file"]       )
      with open(self.vocab_file     ,						"w"       ) as fp:
           fp.write(json.dumps(A_       )       )
      with open(self.merges_file     ,						"w"       ) as fp:
           fp.write("\n".join(A_       )       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Any       ):






      '''simple docstring'''




      return "lower newer", "lower newer"

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    OpenAIGPTTokenizer(self.vocab_file     ,						self.merges_file       )

      _lowerCAmelCase   :					int        =    "lower"
      _lowerCAmelCase   :					Tuple        =    ["low", "er</w>"]
      _lowerCAmelCase   :					Dict        =    tokenizer.tokenize(A_       )
      self.assertListEqual(A_     ,						A_       )

      _lowerCAmelCase   :					List[Any]        =    tokens + ["<unk>"]
      _lowerCAmelCase   :					int        =    [1_4, 1_5, 2_0]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A_       )     ,						A_       )

 def    __magic_name__       (		self :	str     ,						A_ :	Any=1_5       ):






      '''simple docstring'''




      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                _lowerCAmelCase   :					Union[str, Any]        =    self.rust_tokenizer_class.from_pretrained(A_     ,						**A_       )

                # Simple input
                _lowerCAmelCase   :					int        =    "This is a simple input"
                _lowerCAmelCase   :					Dict        =    ["This is a simple input 1", "This is a simple input 2"]
                _lowerCAmelCase   :					Tuple        =    ("This is a simple input", "This is a pair")
                _lowerCAmelCase   :					Dict        =    [
                    ("This is a simple input 1", "This is a simple input 2"),
                    ("This is a simple pair 1", "This is a simple pair 2"),
                ]

                # Simple input tests
                self.assertRaises(A_     ,						tokenizer_r.encode     ,						A_     ,						max_length=A_     ,						padding="max_length"       )

                # Simple input
                self.assertRaises(A_     ,						tokenizer_r.encode_plus     ,						A_     ,						max_length=A_     ,						padding="max_length"       )

                # Simple input
                self.assertRaises(
                    A_     ,						tokenizer_r.batch_encode_plus     ,						A_     ,						max_length=A_     ,						padding="max_length"     ,						)

                # Pair input
                self.assertRaises(A_     ,						tokenizer_r.encode     ,						A_     ,						max_length=A_     ,						padding="max_length"       )

                # Pair input
                self.assertRaises(A_     ,						tokenizer_r.encode_plus     ,						A_     ,						max_length=A_     ,						padding="max_length"       )

                # Pair input
                self.assertRaises(
                    A_     ,						tokenizer_r.batch_encode_plus     ,						A_     ,						max_length=A_     ,						padding="max_length"     ,						)


 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      pass





@require_ftfy
@require_spacy
@require_tokenizers
class A__    ( A ):



 """simple docstring"""







 pass









# 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

__UpperCAmelCase    				=     TypeVar('T')





class A__    ( Generic[T] ):



 """simple docstring"""







 def __init__(		self :	Optional[int]     ,						A_ :	bool = True       ):






      '''simple docstring'''




      _lowerCAmelCase   :					dict[T, list[T]]        =    {}  # dictionary of lists
      _lowerCAmelCase   :					List[Any]        =    directed

 def    __magic_name__       (		self :	Any     ,						A_ :	T     ,						A_ :	T       ):






      '''simple docstring'''




      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(A_       )
                self.adj_list[destination_vertex].append(A_       )
           # 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(A_       )
                _lowerCAmelCase   :					List[Any]        =    [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(A_       )
                _lowerCAmelCase   :					List[str]        =    [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:
                _lowerCAmelCase   :					List[str]        =    [destination_vertex]
                _lowerCAmelCase   :					Dict        =    [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(A_       )
           # 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(A_       )
                _lowerCAmelCase   :					Tuple        =    []
           # 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:
                _lowerCAmelCase   :					Dict        =    [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:
                _lowerCAmelCase   :					Any        =    [destination_vertex]
                _lowerCAmelCase   :					Union[str, Any]        =    []

      return self


 def __repr__(		self :	List[str]       ):






      '''simple docstring'''




      return pformat(self.adj_list       )






