




"""simple docstring"""






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 lowerCAmelCase__			(  lowercase,			unittest.TestCase		):






   '''simple docstring'''



   lowerCamelCase__   =    OpenAIGPTTokenizer
   lowerCamelCase__   =    OpenAIGPTTokenizerFast
   lowerCamelCase__   =    True
   lowerCamelCase__   =    False
   def       A_					(						self						):
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       _lowerCamelCase  :    List[Any]					=					[
           '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  :    str					=					dict(zip(UpperCamelCase__						,   range(len(UpperCamelCase__						)						)						)						)
       _lowerCamelCase  :    Any					=					['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

       _lowerCamelCase  :    List[str]					=					os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES['vocab_file']						)
       _lowerCamelCase  :    str					=					os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES['merges_file']						)
       with open(self.vocab_file						,   'w'						) as fp:
           fp.write(json.dumps(UpperCamelCase__						)						)
       with open(self.merges_file						,   'w'						) as fp:
           fp.write('\n'.join(UpperCamelCase__						)						)
   def       A_					(						self						,   lowercase						):
       return "lower newer", "lower newer"
   def       A_					(						self						):
       _lowerCamelCase  :    Optional[int]					=					OpenAIGPTTokenizer(self.vocab_file						,   self.merges_file						)

       _lowerCamelCase  :    Any					=					'lower'
       _lowerCamelCase  :    Tuple					=					['low', 'er</w>']
       _lowerCamelCase  :    Union[str, Any]					=					tokenizer.tokenize(UpperCamelCase__						)
       self.assertListEqual(UpperCamelCase__						,   UpperCamelCase__						)

       _lowerCamelCase  :    Optional[Any]					=					tokens + ['<unk>']
       _lowerCamelCase  :    Optional[int]					=					[14, 15, 20]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__						)						,   UpperCamelCase__						)
   def       A_					(						self						,   lowercase=15						):





       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
               _lowerCamelCase  :    Any					=					self.rust_tokenizer_class.from_pretrained(UpperCamelCase__						,   **UpperCamelCase__						)

               # Simple input
               _lowerCamelCase  :    str					=					'This is a simple input'
               _lowerCamelCase  :    int					=					['This is a simple input 1', 'This is a simple input 2']
               _lowerCamelCase  :    List[Any]					=					('This is a simple input', 'This is a pair')
               _lowerCamelCase  :    Any					=					[
                   ('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(UpperCamelCase__						,   tokenizer_r.encode						,   UpperCamelCase__						,   max_length=UpperCamelCase__						,   padding='max_length'						)

               # Simple input
               self.assertRaises(UpperCamelCase__						,   tokenizer_r.encode_plus						,   UpperCamelCase__						,   max_length=UpperCamelCase__						,   padding='max_length'						)

               # Simple input
               self.assertRaises(
                   UpperCamelCase__						,   tokenizer_r.batch_encode_plus						,   UpperCamelCase__						,   max_length=UpperCamelCase__						,   padding='max_length'						,   )

               # Pair input
               self.assertRaises(UpperCamelCase__						,   tokenizer_r.encode						,   UpperCamelCase__						,   max_length=UpperCamelCase__						,   padding='max_length'						)

               # Pair input
               self.assertRaises(UpperCamelCase__						,   tokenizer_r.encode_plus						,   UpperCamelCase__						,   max_length=UpperCamelCase__						,   padding='max_length'						)

               # Pair input
               self.assertRaises(
                   UpperCamelCase__						,   tokenizer_r.batch_encode_plus						,   UpperCamelCase__						,   max_length=UpperCamelCase__						,   padding='max_length'						,   )






   def       A_					(						self						):
       pass






@require_ftfy
@require_spacy
@require_tokenizers
class lowerCAmelCase__			(  lowercase		):






   '''simple docstring'''



   pass





"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available






@dataclass
class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    42


try:
			if not (is_transformers_available() and is_torch_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
			from .pipeline_text_to_video_synth import TextToVideoSDPipeline
			from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
			from .pipeline_text_to_video_zero import TextToVideoZeroPipeline