




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   A(						UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						None
 UpperCamelCase						       =						BloomTokenizerFast
 UpperCamelCase						       =						BloomTokenizerFast
 UpperCamelCase						       =						True
 UpperCamelCase						       =						False
 UpperCamelCase						       =						'''tokenizer_file'''
 UpperCamelCase						       =						{'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}

 def     a__			(    self :  Optional[Any]  )       ->     Union[str, Any]:

       """simple docstring"""






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

 def     a__			(    self :  List[str]						,							**A_ :  List[Any]  )       ->     Tuple:

       """simple docstring"""






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

 def     a__			(    self :  str  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   self.get_rust_tokenizer()

       lowerCamelCase_							=   ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
       lowerCamelCase_							=   [[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

       lowerCamelCase_							=   tokenizer.batch_encode_plus(A_  )['input_ids']
       self.assertListEqual(A_						,							A_  )

       lowerCamelCase_							=   tokenizer.batch_decode(A_  )
       self.assertListEqual(A_						,							A_  )

 def     a__			(    self :  Union[str, Any]						,							A_ :  Dict=6  )       ->     List[str]:

       """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(A_						,							**A_  )
                   # 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(A_						,							max_length=A_  )
                         tokenizer_r.encode_plus(A_						,							max_length=A_  )

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

                   lowerCamelCase_							=   None  # Hotfixing padding = None
                   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     a__			(    self :  int  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   self.get_rust_tokenizer()
       lowerCamelCase_							=   load_dataset('xnli'						,							'all_languages'						,							split='test'						,							streaming=A_  )

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

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





 def     a__			(    self :  Optional[Any]  )       ->     Dict:

       """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  )









from ..utils import is_flax_available, is_torch_available


if is_torch_available():
 from .autoencoder_kl import AutoencoderKL
 from .controlnet import ControlNetModel
 from .dual_transformer_ad import DualTransformeraDModel
 from .modeling_utils import ModelMixin
 from .prior_transformer import PriorTransformer
 from .ta_film_transformer import TaFilmDecoder
 from .transformer_ad import TransformeraDModel
 from .unet_ad import UNetaDModel
 from .unet_ad import UNetaDModel
 from .unet_ad_condition import UNetaDConditionModel
 from .unet_ad_condition import UNetaDConditionModel
 from .vq_model import VQModel

if is_flax_available():
 from .controlnet_flax import FlaxControlNetModel
 from .unet_ad_condition_flax import FlaxUNetaDConditionModel
 from .vae_flax import FlaxAutoencoderKL



