

'''simple docstring'''



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				_snake_case					(   a_     , unittest.TestCase			):
  SCREAMING_SNAKE_CASE			:       int				=     None
  SCREAMING_SNAKE_CASE			:       Optional[int]				=     BloomTokenizerFast
  SCREAMING_SNAKE_CASE			:       str				=     BloomTokenizerFast
  SCREAMING_SNAKE_CASE			:       int				=     True
  SCREAMING_SNAKE_CASE			:       Dict				=     False
  SCREAMING_SNAKE_CASE			:       Any				=     '''tokenizer_file'''
  SCREAMING_SNAKE_CASE			:       Optional[int]				=     {'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      super().setUp()
      lowerCAmelCase 						=							BloomTokenizerFast.from_pretrained('bigscience/tokenizer'	)
      tokenizer.save_pretrained(self.tmpdirname	)

  def   _SCREAMING_SNAKE_CASE				(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      kwargs.update(self.special_tokens_map	)
      return BloomTokenizerFast.from_pretrained(self.tmpdirname      , **_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.get_rust_tokenizer()

      lowerCAmelCase 						=							['The quick brown fox</s>', 'jumps over the lazy dog</s>']
      lowerCAmelCase 						=							[[21_75, 2_37_14, 7_31_73, 14_42_52, 2], [77, 13_26_19, 34_78, 3_68, 10_95_86, 3_54_33, 2]]

      lowerCAmelCase 						=							tokenizer.batch_encode_plus(_SCREAMING_SNAKE_CASE	)['input_ids']
      self.assertListEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							tokenizer.batch_decode(_SCREAMING_SNAKE_CASE	)
      self.assertListEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE=6	):





      '''simple docstring'''

      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'	):
              lowerCAmelCase 						=							self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)
              # tokenizer_r.pad_token = None # Hotfixing padding = None
              # Simple input
              lowerCAmelCase 						=							'This is a simple input'
              lowerCAmelCase 						=							['This is a simple input 1', 'This is a simple input 2']
              lowerCAmelCase 						=							('This is a simple input', 'This is a pair')
              lowerCAmelCase 						=							[
                  ('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
              try:
                  tokenizer_r.encode(_SCREAMING_SNAKE_CASE      , max_length=_SCREAMING_SNAKE_CASE	)
                  tokenizer_r.encode_plus(_SCREAMING_SNAKE_CASE      , max_length=_SCREAMING_SNAKE_CASE	)

                  tokenizer_r.batch_encode_plus(_SCREAMING_SNAKE_CASE      , max_length=_SCREAMING_SNAKE_CASE	)
                  tokenizer_r.encode(_SCREAMING_SNAKE_CASE      , max_length=_SCREAMING_SNAKE_CASE	)
                  tokenizer_r.batch_encode_plus(_SCREAMING_SNAKE_CASE      , max_length=_SCREAMING_SNAKE_CASE	)
              except ValueError:
                  self.fail('Bloom Tokenizer should be able to deal with padding'	)

              lowerCAmelCase 						=							None  # Hotfixing padding = None
              self.assertRaises(_SCREAMING_SNAKE_CASE      , tokenizer_r.encode      , _SCREAMING_SNAKE_CASE      , max_length=_SCREAMING_SNAKE_CASE      , padding='max_length'	)

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

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

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

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

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.get_rust_tokenizer()
      lowerCAmelCase 						=							load_dataset('xnli'      , 'all_languages'      , split='test'      , streaming=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							next(iter(_SCREAMING_SNAKE_CASE	)	)['premise']  # pick up one data
      lowerCAmelCase 						=							list(sample_data.values()	)

      lowerCAmelCase 						=							list(map(tokenizer.encode      , _SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							[tokenizer.decode(_SCREAMING_SNAKE_CASE      , clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE	) for x in output_tokens]
      self.assertListEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map	)      , 1	)
      self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()	)[0]	)      , 1	)








'''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 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Union[List[np.ndarray], torch.FloatTensor]


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





