





import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY





@is_pipeline_test
@require_torch_or_tf
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : List[str]      =  MODEL_FOR_CAUSAL_LM_MAPPING
       SCREAMING_SNAKE_CASE_      : List[Any]      =  TF_MODEL_FOR_CAUSAL_LM_MAPPING







       @require_torch
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Union[str, Any]:
         _lowercase							= pipeline(task='text-generation'						,model='sshleifer/tiny-ctrl'						,framework='pt'  )
         # Using `do_sample=False` to force deterministic output
         _lowercase							= text_generator('This is a test'						,do_sample=__A  )
         self.assertEqual(
             __A						,[
                 {
                     'generated_text': (
                         'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
                         ' oscope. FiliFili@@'
                     )
                 }
             ]						,)

         _lowercase							= text_generator(['This is a test', 'This is a second test']  )
         self.assertEqual(
             __A						,[
                 [
                     {
                         'generated_text': (
                             'This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'
                             ' oscope. FiliFili@@'
                         )
                     }
                 ],
                 [
                     {
                         'generated_text': (
                             'This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'
                             ' oscope. oscope. FiliFili@@'
                         )
                     }
                 ],
             ]						,)

         _lowercase							= text_generator('This is a test'						,do_sample=__A						,num_return_sequences=2						,return_tensors=__A  )
         self.assertEqual(
             __A						,[
                 {'generated_token_ids': ANY(__A  )},
                 {'generated_token_ids': ANY(__A  )},
             ]						,)
         _lowercase							= text_generator.model.config.eos_token_id
         _lowercase							= '<pad>'
         _lowercase							= text_generator(
             ['This is a test', 'This is a second test']						,do_sample=__A						,num_return_sequences=2						,batch_size=2						,return_tensors=__A						,)
         self.assertEqual(
             __A						,[
                 [
                     {'generated_token_ids': ANY(__A  )},
                     {'generated_token_ids': ANY(__A  )},
                 ],
                 [
                     {'generated_token_ids': ANY(__A  )},
                     {'generated_token_ids': ANY(__A  )},
                 ],
             ]						,)







       @require_tf
       def    __UpperCAmelCase (    self	:	int  )						->					int:
         _lowercase							= pipeline(task='text-generation'						,model='sshleifer/tiny-ctrl'						,framework='tf'  )

         # Using `do_sample=False` to force deterministic output
         _lowercase							= text_generator('This is a test'						,do_sample=__A  )
         self.assertEqual(
             __A						,[
                 {
                     'generated_text': (
                         'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
                         ' please,'
                     )
                 }
             ]						,)

         _lowercase							= text_generator(['This is a test', 'This is a second test']						,do_sample=__A  )
         self.assertEqual(
             __A						,[
                 [
                     {
                         'generated_text': (
                             'This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'
                             ' please,'
                         )
                     }
                 ],
                 [
                     {
                         'generated_text': (
                             'This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'
                             ' Cannes 閲閲Cannes Cannes Cannes 攵 please,'
                         )
                     }
                 ],
             ]						,)







       def    __UpperCAmelCase (    self	:	Any						,__A	:	int						,__A	:	Any						,__A	:	Dict  )						->					List[Any]:
         _lowercase							= TextGenerationPipeline(model=__A						,tokenizer=__A  )
         return text_generator, ["This is a test", "Another test"]







       def    __UpperCAmelCase (    self	:	Tuple  )						->					List[Any]:
         _lowercase							= 'Hello I believe in'
         _lowercase							= pipeline('text-generation'						,model='hf-internal-testing/tiny-random-gpt2'  )
         _lowercase							= text_generator(__A  )
         self.assertEqual(
             __A						,[{'generated_text': 'Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'}]						,)

         _lowercase							= text_generator(__A						,stop_sequence=' fe'  )
         self.assertEqual(__A						,[{'generated_text': 'Hello I believe in fe'}]  )







       def    __UpperCAmelCase (    self	:	str						,__A	:	List[str]						,__A	:	int  )						->					Dict:
         _lowercase							= text_generator.model
         _lowercase							= text_generator.tokenizer

         _lowercase							= text_generator('This is a test'  )
         self.assertEqual(__A						,[{'generated_text': ANY(__A  )}]  )
         self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'  )  )

         _lowercase							= text_generator('This is a test'						,return_full_text=__A  )
         self.assertEqual(__A						,[{'generated_text': ANY(__A  )}]  )
         self.assertNotIn('This is a test'						,outputs[0]['generated_text']  )

         _lowercase							= pipeline(task='text-generation'						,model=__A						,tokenizer=__A						,return_full_text=__A  )
         _lowercase							= text_generator('This is a test'  )
         self.assertEqual(__A						,[{'generated_text': ANY(__A  )}]  )
         self.assertNotIn('This is a test'						,outputs[0]['generated_text']  )

         _lowercase							= text_generator('This is a test'						,return_full_text=__A  )
         self.assertEqual(__A						,[{'generated_text': ANY(__A  )}]  )
         self.assertTrue(outputs[0]['generated_text'].startswith('This is a test'  )  )

         _lowercase							= text_generator(['This is great !', 'Something else']						,num_return_sequences=2						,do_sample=__A  )
         self.assertEqual(
             __A						,[
                 [{'generated_text': ANY(__A  )}, {'generated_text': ANY(__A  )}],
                 [{'generated_text': ANY(__A  )}, {'generated_text': ANY(__A  )}],
             ]						,)

         if text_generator.tokenizer.pad_token is not None:
           _lowercase							= text_generator(
               ['This is great !', 'Something else']						,num_return_sequences=2						,batch_size=2						,do_sample=__A  )
           self.assertEqual(
               __A						,[
                   [{'generated_text': ANY(__A  )}, {'generated_text': ANY(__A  )}],
                   [{'generated_text': ANY(__A  )}, {'generated_text': ANY(__A  )}],
               ]						,)

         with self.assertRaises(__A  ):
           _lowercase							= text_generator('test'						,return_full_text=__A						,return_text=__A  )
         with self.assertRaises(__A  ):
           _lowercase							= text_generator('test'						,return_full_text=__A						,return_tensors=__A  )
         with self.assertRaises(__A  ):
           _lowercase							= text_generator('test'						,return_text=__A						,return_tensors=__A  )

         # Empty prompt is slighly special
         # it requires BOS token to exist.
         # Special case for Pegasus which will always append EOS so will
         # work even without BOS.
         if (
             text_generator.tokenizer.bos_token_id is not None
             or "Pegasus" in tokenizer.__class__.__name__
             or "Git" in model.__class__.__name__
         ):
           _lowercase							= text_generator(''  )
           self.assertEqual(__A						,[{'generated_text': ANY(__A  )}]  )
         else:
           with self.assertRaises((ValueError, AssertionError)  ):
             _lowercase							= text_generator(''  )

         if text_generator.framework == "tf":
           # TF generation does not support max_new_tokens, and it's impossible
           # to control long generation with only max_length without
           # fancy calculation, dismissing tests for now.
           return
         # We don't care about infinite range models.
         # They already work.
         # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
         _lowercase							= ['RwkvForCausalLM', 'XGLMForCausalLM', 'GPTNeoXForCausalLM']
         if (
             tokenizer.model_max_length < 1_0000
             and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
         ):
           # Handling of large generations
           with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)  ):
             text_generator('This is a test' * 500						,max_new_tokens=20  )

           _lowercase							= text_generator('This is a test' * 500						,handle_long_generation='hole'						,max_new_tokens=20  )
           # Hole strategy cannot work
           with self.assertRaises(__A  ):
             text_generator(
                 'This is a test' * 500						,handle_long_generation='hole'						,max_new_tokens=tokenizer.model_max_length + 10						,)







       @require_torch
       @require_accelerate
       @require_torch_gpu
       def    __UpperCAmelCase (    self	:	str  )						->					Dict:
         import torch

         # Classic `model_kwargs`
         _lowercase							= pipeline(
             model='hf-internal-testing/tiny-random-bloom'						,model_kwargs={'device_map': 'auto', 'torch_dtype': torch.bfloataa}						,)
         self.assertEqual(pipe.model.device						,torch.device(0  )  )
         self.assertEqual(pipe.model.lm_head.weight.dtype						,torch.bfloataa  )
         _lowercase							= pipe('This is a test'  )
         self.assertEqual(
             __A						,[
                 {
                     'generated_text': (
                         'This is a test test test test test test test test test test test test test test test test'
                         ' test'
                     )
                 }
             ]						,)

         # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
         _lowercase							= pipeline(model='hf-internal-testing/tiny-random-bloom'						,device_map='auto'						,torch_dtype=torch.bfloataa  )
         self.assertEqual(pipe.model.device						,torch.device(0  )  )
         self.assertEqual(pipe.model.lm_head.weight.dtype						,torch.bfloataa  )
         _lowercase							= pipe('This is a test'  )
         self.assertEqual(
             __A						,[
                 {
                     'generated_text': (
                         'This is a test test test test test test test test test test test test test test test test'
                         ' test'
                     )
                 }
             ]						,)

         # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
         _lowercase							= pipeline(model='hf-internal-testing/tiny-random-bloom'						,device_map='auto'  )
         self.assertEqual(pipe.model.device						,torch.device(0  )  )
         self.assertEqual(pipe.model.lm_head.weight.dtype						,torch.floataa  )
         _lowercase							= pipe('This is a test'  )
         self.assertEqual(
             __A						,[
                 {
                     'generated_text': (
                         'This is a test test test test test test test test test test test test test test test test'
                         ' test'
                     )
                 }
             ]						,)







       @require_torch
       @require_torch_gpu
       def    __UpperCAmelCase (    self	:	Any  )						->					Optional[Any]:
         import torch

         _lowercase							= pipeline(model='hf-internal-testing/tiny-random-bloom'						,device=0						,torch_dtype=torch.floataa  )
         pipe('This is a test'  )







       @require_torch
       @require_accelerate
       @require_torch_gpu
       def    __UpperCAmelCase (    self	:	Any  )						->					Any:
         import torch

         _lowercase							= pipeline(model='hf-internal-testing/tiny-random-bloom'						,device_map='auto'						,torch_dtype=torch.floataa  )
         pipe('This is a test'						,do_sample=__A						,top_p=0.5  )







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Optional[int]:
         _lowercase							= 'Hello world'
         _lowercase							= pipeline('text-generation'						,model='hf-internal-testing/tiny-random-gpt2'  )
         if text_generator.model.framework == "tf":
           _lowercase							= logging.get_logger('transformers.generation.tf_utils'  )
         else:
           _lowercase							= logging.get_logger('transformers.generation.utils'  )
         _lowercase							= 'Both `max_new_tokens`'  # The beggining of the message to be checked in this test

         # Both are set by the user -> log warning
         with CaptureLogger(__A  ) as cl:
           _lowercase							= text_generator(__A						,max_length=10						,max_new_tokens=1  )
         self.assertIn(__A						,cl.out  )

         # The user only sets one -> no warning
         with CaptureLogger(__A  ) as cl:
           _lowercase							= text_generator(__A						,max_new_tokens=1  )
         self.assertNotIn(__A						,cl.out  )

         with CaptureLogger(__A  ) as cl:
           _lowercase							= text_generator(__A						,max_length=10  )
         self.assertNotIn(__A						,cl.out  )






import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case 				=       get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")





@require_sentencepiece
@require_tokenizers
class 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : int      =  PegasusTokenizer
       SCREAMING_SNAKE_CASE_      : Optional[int]      =  PegasusTokenizerFast
       SCREAMING_SNAKE_CASE_      : Any      =  True
       SCREAMING_SNAKE_CASE_      : Optional[int]      =  True







       def    __UpperCAmelCase (    self	:	List[str]  )						->					Any:
         super().setUp()

         # We have a SentencePiece fixture for testing
         _lowercase							= PegasusTokenizer(__A  )
         tokenizer.save_pretrained(self.tmpdirname  )







       @cached_property
       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					List[Any]:
         return PegasusTokenizer.from_pretrained('google/pegasus-large'  )







       def    __UpperCAmelCase (    self	:	Any						,**__A	:	int  )						->					PegasusTokenizer:
         return PegasusTokenizer.from_pretrained(self.tmpdirname						,**__A  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	int  )						->					List[str]:
         return ("This is a test", "This is a test")







       def    __UpperCAmelCase (    self	:	List[str]  )						->					Optional[Any]:
         _lowercase							= '</s>'
         _lowercase							= 1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A  )						,__A  )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A  )						,__A  )







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Optional[Any]:
         _lowercase							= list(self.get_tokenizer().get_vocab().keys()  )

         self.assertEqual(vocab_keys[0]						,'<pad>'  )
         self.assertEqual(vocab_keys[1]						,'</s>'  )
         self.assertEqual(vocab_keys[-1]						,'v'  )
         self.assertEqual(len(__A  )						,1103  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
         self.assertEqual(self.get_tokenizer().vocab_size						,1103  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					List[str]:
         _lowercase							= self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
         _lowercase							= self.tokenizer_class.from_pretrained(self.tmpdirname  )
         _lowercase							= (
             'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
             ' </s> <pad> <pad> <pad>'
         )
         _lowercase							= rust_tokenizer([raw_input_str]						,return_tensors=__A						,add_special_tokens=__A  ).input_ids[0]
         _lowercase							= py_tokenizer([raw_input_str]						,return_tensors=__A						,add_special_tokens=__A  ).input_ids[0]
         self.assertListEqual(__A						,__A  )







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					List[Any]:
         _lowercase							= self._large_tokenizer
         # <mask_1> masks whole sentence while <mask_2> masks single word
         _lowercase							= '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
         _lowercase							= [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
         _lowercase							= tokenizer([raw_input_str]						,return_tensors=__A  ).input_ids[0]
         self.assertListEqual(__A						,__A  )







       def    __UpperCAmelCase (    self	:	int  )						->					Union[str, Any]:
         _lowercase							= self._large_tokenizer
         # The tracebacks for the following asserts are **better** without messages or self.assertEqual
         assert tokenizer.vocab_size == 9_6103
         assert tokenizer.pad_token_id == 0
         assert tokenizer.eos_token_id == 1
         assert tokenizer.offset == 103
         assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
         assert tokenizer.unk_token == "<unk>"
         assert tokenizer.model_max_length == 1024
         _lowercase							= 'To ensure a smooth flow of bank resolutions.'
         _lowercase							= [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
         _lowercase							= tokenizer([raw_input_str]						,return_tensors=__A  ).input_ids[0]
         self.assertListEqual(__A						,__A  )
         assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]  ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







       @require_torch
       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					List[str]:
         _lowercase							= ['This is going to be way too long.' * 150, 'short example']
         _lowercase							= ['not super long but more than 5 tokens', 'tiny']
         _lowercase							= self._large_tokenizer(__A						,padding=__A						,truncation=__A						,return_tensors='pt'  )
         _lowercase							= self._large_tokenizer(
             text_target=__A						,max_length=5						,padding=__A						,truncation=__A						,return_tensors='pt'  )

         assert batch.input_ids.shape == (2, 1024)
         assert batch.attention_mask.shape == (2, 1024)
         assert targets["input_ids"].shape == (2, 5)
         assert len(__A  ) == 2  # input_ids, attention_mask.







       @slow
       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[int]:
         # fmt: off
         _lowercase							= {'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=__A						,model_name='google/bigbird-pegasus-large-arxiv'						,revision='ba85d0851d708441f91440d509690f1ab6353415'						,)





@require_sentencepiece
@require_tokenizers
class 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : int      =  PegasusTokenizer
       SCREAMING_SNAKE_CASE_      : int      =  PegasusTokenizerFast
       SCREAMING_SNAKE_CASE_      : str      =  True
       SCREAMING_SNAKE_CASE_      : List[Any]      =  True







       def    __UpperCAmelCase (    self	:	List[str]  )						->					Optional[int]:
         super().setUp()

         # We have a SentencePiece fixture for testing
         _lowercase							= PegasusTokenizer(__A						,offset=0						,mask_token_sent=__A						,mask_token='[MASK]'  )
         tokenizer.save_pretrained(self.tmpdirname  )







       @cached_property
       def    __UpperCAmelCase (    self	:	str  )						->					Optional[Any]:
         return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,**__A	:	Union[str, Any]  )						->					PegasusTokenizer:
         return PegasusTokenizer.from_pretrained(self.tmpdirname						,**__A  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	int  )						->					Tuple:
         return ("This is a test", "This is a test")







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Dict:
         _lowercase							= self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
         _lowercase							= self.tokenizer_class.from_pretrained(self.tmpdirname  )
         _lowercase							= (
             'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
             ' <pad> <pad> <pad>'
         )
         _lowercase							= rust_tokenizer([raw_input_str]						,return_tensors=__A						,add_special_tokens=__A  ).input_ids[0]
         _lowercase							= py_tokenizer([raw_input_str]						,return_tensors=__A						,add_special_tokens=__A  ).input_ids[0]
         self.assertListEqual(__A						,__A  )







       @require_torch
       def    __UpperCAmelCase (    self	:	List[str]  )						->					Dict:
         _lowercase							= ['This is going to be way too long.' * 1000, 'short example']
         _lowercase							= ['not super long but more than 5 tokens', 'tiny']
         _lowercase							= self._large_tokenizer(__A						,padding=__A						,truncation=__A						,return_tensors='pt'  )
         _lowercase							= self._large_tokenizer(
             text_target=__A						,max_length=5						,padding=__A						,truncation=__A						,return_tensors='pt'  )

         assert batch.input_ids.shape == (2, 4096)
         assert batch.attention_mask.shape == (2, 4096)
         assert targets["input_ids"].shape == (2, 5)
         assert len(__A  ) == 2  # input_ids, attention_mask.







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					int:
         _lowercase							= (
             'This is an example string that is used to test the original TF implementation against the HF'
             ' implementation'
         )

         _lowercase							= self._large_tokenizer(__A  ).input_ids

         self.assertListEqual(
             __A						,[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]						,)