
import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel


class 			lowercase_		:


       def __init__(    self						,       __A						,       __A=99						,       __A=13						,       __A=7						,       __A=9						,       __A=True						,       __A=True						,       __A=False						,       __A=32						,       __A=5						,       __A=4						,       __A=37						,       __A=8						,       __A=0.1						,       __A=0.002						,       __A=1						,       __A=0						,       __A=0						,       __A=None						,       __A=None						,       )						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=parent
              SCREAMING_SNAKE_CASE_							:				List[str]		=batch_size
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=encoder_seq_length
              SCREAMING_SNAKE_CASE_							:				str		=decoder_seq_length
              # For common tests
              SCREAMING_SNAKE_CASE_							:				Dict		=self.decoder_seq_length
              SCREAMING_SNAKE_CASE_							:				List[Any]		=is_training
              SCREAMING_SNAKE_CASE_							:				Dict		=use_attention_mask
              SCREAMING_SNAKE_CASE_							:				Dict		=use_labels
              SCREAMING_SNAKE_CASE_							:				List[Any]		=vocab_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=hidden_size
              SCREAMING_SNAKE_CASE_							:				List[Any]		=num_hidden_layers
              SCREAMING_SNAKE_CASE_							:				List[Any]		=num_attention_heads
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=d_ff
              SCREAMING_SNAKE_CASE_							:				Dict		=relative_attention_num_buckets
              SCREAMING_SNAKE_CASE_							:				Any		=dropout_rate
              SCREAMING_SNAKE_CASE_							:				Dict		=initializer_factor
              SCREAMING_SNAKE_CASE_							:				int		=eos_token_id
              SCREAMING_SNAKE_CASE_							:				Tuple		=pad_token_id
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=decoder_start_token_id
              SCREAMING_SNAKE_CASE_							:				Dict		=None
              SCREAMING_SNAKE_CASE_							:				Tuple		=decoder_layers


       def 	_snake_case				(    self				)						->    Tuple:
              return TaConfig.from_pretrained('''google/umt5-base'''				)


       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A=None						,       __A=None						,       __A=None						,       __A=None						,       __A=None						,       )						->    Union[str, Any]:
              if attention_mask is None:
                     SCREAMING_SNAKE_CASE_							:				int		=input_ids.ne(config.pad_token_id				)
              if decoder_attention_mask is None:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=decoder_input_ids.ne(config.pad_token_id				)
              if head_mask is None:
                     SCREAMING_SNAKE_CASE_							:				str		=torch.ones(config.num_hidden_layers						,       config.num_attention_heads						,       device=__A				)
              if decoder_head_mask is None:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=torch.ones(config.num_decoder_layers						,       config.num_attention_heads						,       device=__A				)
              if cross_attn_head_mask is None:
                     SCREAMING_SNAKE_CASE_							:				Any		=torch.ones(
                         config.num_decoder_layers						,       config.num_attention_heads						,       device=__A				)
              return {
                  "input_ids": input_ids,
                  "decoder_input_ids": decoder_input_ids,
                  "attention_mask": attention_mask,
                  "decoder_attention_mask": decoder_attention_mask,
                  "head_mask": head_mask,
                  "decoder_head_mask": decoder_head_mask,
                  "cross_attn_head_mask": cross_attn_head_mask,
              }


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Tuple		=ids_tensor([self.batch_size, self.encoder_seq_length]						,       self.vocab_size				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=ids_tensor([self.batch_size, self.decoder_seq_length]						,       self.vocab_size				)

              # we need to clamp the input ids here to avoid having pad token in between
              # this is because for NllbMoe the position_ids are prepared such that
              # all pad tokens have pos id = 2 and rest are between 2..seq_length
              # and the seq_length here is seq_length - num_pad_tokens
              # but when using past, there is no way of knowing if the past input ids had
              # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
              # position_ids being off by num_pad_tokens in past input
              SCREAMING_SNAKE_CASE_							:				Any		=input_ids.clamp(self.pad_token_id + 1				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=decoder_input_ids.clamp(self.pad_token_id + 1				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=self.get_config()
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=config.num_attention_heads
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.prepare_inputs_dict(__A						,       __A						,       __A				)
              return config, input_dict


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.prepare_config_and_inputs()
              return config, inputs_dict


       def 	_snake_case				(    self				)						->    Optional[int]:
              return TaConfig(
                  vocab_size=166						,       d_model=self.hidden_size						,       d_ff=self.d_ff						,       d_kv=self.hidden_size // self.num_attention_heads						,       num_layers=self.num_hidden_layers						,       num_decoder_layers=self.decoder_layers						,       num_heads=self.num_attention_heads						,       relative_attention_num_buckets=self.relative_attention_num_buckets						,       dropout_rate=self.dropout_rate						,       initializer_factor=self.initializer_factor						,       eos_token_id=self.eos_token_id						,       bos_token_id=self.pad_token_id						,       pad_token_id=self.pad_token_id						,       decoder_start_token_id=self.decoder_start_token_id						,       )


       def 	_snake_case				(    self				)						->    int:
              return TaConfig(
                  vocab_size=self.vocab_size						,       d_model=self.hidden_size						,       d_ff=self.d_ff						,       d_kv=self.hidden_size // self.num_attention_heads						,       num_layers=self.num_hidden_layers						,       num_decoder_layers=self.decoder_layers						,       num_heads=self.num_attention_heads						,       relative_attention_num_buckets=self.relative_attention_num_buckets						,       dropout_rate=self.dropout_rate						,       initializer_factor=self.initializer_factor						,       eos_token_id=self.eos_token_id						,       bos_token_id=self.pad_token_id						,       pad_token_id=self.pad_token_id						,       decoder_start_token_id=self.decoder_start_token_id						,       )


       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       )						->    Dict:
              SCREAMING_SNAKE_CASE_							:				List[str]		=UMTaModel(config=__A				)
              model.to(__A				)
              model.eval()
              SCREAMING_SNAKE_CASE_							:				str		=model(
                  input_ids=__A						,       decoder_input_ids=__A						,       attention_mask=__A						,       decoder_attention_mask=__A						,       )
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model(input_ids=__A						,       decoder_input_ids=__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=result.last_hidden_state
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=result.past_key_values
              SCREAMING_SNAKE_CASE_							:				List[Any]		=result.encoder_last_hidden_state

              self.parent.assertEqual(encoder_output.size()						,       (self.batch_size, self.encoder_seq_length, self.hidden_size)				)
              self.parent.assertEqual(decoder_output.size()						,       (self.batch_size, self.decoder_seq_length, self.hidden_size)				)
              # There should be `num_layers` key value embeddings stored in decoder_past
              self.parent.assertEqual(len(__A				)						,       config.num_layers				)
              # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
              self.parent.assertEqual(len(decoder_past[0]				)						,       4				)


       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A						,       __A						,       )						->    str:
              SCREAMING_SNAKE_CASE_							:				str		=UMTaModel(config=__A				).get_decoder().to(__A				).eval()
              # first forward pass
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model(__A						,       use_cache=__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model(__A				)
              SCREAMING_SNAKE_CASE_							:				Any		=model(__A						,       use_cache=__A				)

              self.parent.assertTrue(len(__A				) == len(__A				)				)
              self.parent.assertTrue(len(__A				) == len(__A				) + 1				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=outputs.to_tuple()

              # create hypothetical next token and extent to next_input_ids
              SCREAMING_SNAKE_CASE_							:				int		=ids_tensor((self.batch_size, 1)						,       config.vocab_size				)

              # append to next input_ids and
              SCREAMING_SNAKE_CASE_							:				Any		=torch.cat([input_ids, next_tokens]						,       dim=-1				)

              SCREAMING_SNAKE_CASE_							:				List[Any]		=model(__A				)['''last_hidden_state''']
              SCREAMING_SNAKE_CASE_							:				List[str]		=model(__A						,       past_key_values=__A				)['''last_hidden_state''']

              # select random slice
              SCREAMING_SNAKE_CASE_							:				Any		=ids_tensor((1,)						,       output_from_past.shape[-1]				).item()
              SCREAMING_SNAKE_CASE_							:				str		=output_from_no_past[:, -1, random_slice_idx].detach()
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=output_from_past[:, 0, random_slice_idx].detach()

              # test that outputs are equal for slice
              self.parent.assertTrue(torch.allclose(__A						,       __A						,       atol=1e-3				)				)





       def 	_snake_case				(    self						,       __A						,       __A						,       )						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Tuple		=UMTaModel(config=__A				).to(__A				).half().eval()
              SCREAMING_SNAKE_CASE_							:				List[str]		=model(**__A				)['''last_hidden_state''']
              self.parent.assertFalse(torch.isnan(__A				).any().item()				)






@require_torch
class 			lowercase_		(      A  ,      A  ,      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						(
           (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
       )
       __lowerCamelCase				       =						(UMTaForConditionalGeneration,) if is_torch_available() else ()
       __lowerCamelCase				       =						(
           {
               "conversational": UMTaForConditionalGeneration,
               "feature-extraction": UMTaModel,
               "summarization": UMTaForConditionalGeneration,
               "text2text-generation": UMTaForConditionalGeneration,
               "translation": UMTaForConditionalGeneration,
               "question-answering": UMTaForQuestionAnswering,
           }
           if is_torch_available()
           else {}
       )
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						True
       # The small UMT5 model needs higher percentages for CPU/MP tests
       __lowerCamelCase				       =						[0.8, 0.9]


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=UMTaModelTester(self				)


       @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''				)
       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.model_tester.prepare_config_and_inputs()
              SCREAMING_SNAKE_CASE_							:				Any		=UMTaModel(config_and_inputs[0]				).to(__A				)
              with tempfile.TemporaryDirectory() as tmpdirname:
                     torch.onnx.export(
                         __A						,       (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])						,       F'{tmpdirname}/t5_test.onnx'						,       export_params=__A						,       opset_version=9						,       input_names=['''input_ids''', '''decoder_input_ids''']						,       )


       @unittest.skipIf(torch_device == '''cpu'''						,       '''Cant do half precision'''				)
       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model_fpaa_forward(*__A				)


       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				List[str]		=['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.model_tester.prepare_config_and_inputs()
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=config_and_inputs[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=UMTaForConditionalGeneration(__A				).eval()
              model.to(__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		={
                  '''head_mask''': torch.zeros(config.num_layers						,       config.num_heads						,       device=__A				),
                  '''decoder_head_mask''': torch.zeros(config.num_decoder_layers						,       config.num_heads						,       device=__A				),
                  '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers						,       config.num_heads						,       device=__A				),
              }

              for attn_name, (name, mask) in zip(__A						,       head_masking.items()				):
                     SCREAMING_SNAKE_CASE_							:				Tuple		={name: mask}
                     # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
                     if name == "head_mask":
                            SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.ones(
                                config.num_decoder_layers						,       config.num_heads						,       device=__A				)

                     SCREAMING_SNAKE_CASE_							:				str		=model.generate(
                         config_and_inputs[1]['''input_ids''']						,       num_beams=1						,       max_length=3						,       output_attentions=__A						,       return_dict_in_generate=__A						,       **__A						,       )
                     # We check the state of decoder_attentions and cross_attentions just from the last step
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
                     self.assertEqual(sum([w.sum().item() for w in attn_weights]				)						,       0.0				)





       @unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''				)
       def 	_snake_case				(    self				)						->    int:
              pass






@require_torch
@require_sentencepiece
@require_tokenizers
class 			lowercase_		(      unittest.TestCase       ):


       @slow
       @unittest.skip(
           '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''				)
       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''						,       return_dict=__A				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=AutoTokenizer.from_pretrained('''google/umt5-small'''						,       use_fast=__A						,       legacy=__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=[
                  '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
                  '''No se como puedo <extra_id_0>.''',
                  '''This is the reason why we <extra_id_0> them.''',
                  '''The <extra_id_0> walks in <extra_id_1>, seats''',
                  '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
              ]
              SCREAMING_SNAKE_CASE_							:				List[str]		=tokenizer(__A						,       return_tensors='''pt'''						,       padding=__A				).input_ids
              # fmt: off
              SCREAMING_SNAKE_CASE_							:				int		=torch.tensor(
                  [
                      [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                      [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                      [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                      [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                      [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
                  ]				)
              # fmt: on
              torch.testing.assert_allclose(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				str		=model.generate(input_ids.to(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=[
                  '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
                  '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
                  '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
                  '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
                  '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
              ]
              SCREAMING_SNAKE_CASE_							:				int		=tokenizer.batch_decode(__A				)
              self.assertEqual(__A						,       __A				)


import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						MvpTokenizer
       __lowerCamelCase				       =						MvpTokenizerFast
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						filter_roberta_detectors


       def 	_snake_case				(    self				)						->    int:
              super().setUp()
              SCREAMING_SNAKE_CASE_							:				List[str]		=[
                  '''l''',
                  '''o''',
                  '''w''',
                  '''e''',
                  '''r''',
                  '''s''',
                  '''t''',
                  '''i''',
                  '''d''',
                  '''n''',
                  '''\u0120''',
                  '''\u0120l''',
                  '''\u0120n''',
                  '''\u0120lo''',
                  '''\u0120low''',
                  '''er''',
                  '''\u0120lowest''',
                  '''\u0120newer''',
                  '''\u0120wider''',
                  '''<unk>''',
              ]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=dict(zip(__A						,       range(len(__A				)				)				)				)
              SCREAMING_SNAKE_CASE_							:				int		=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
              SCREAMING_SNAKE_CASE_							:				Dict		={'''unk_token''': '''<unk>'''}

              SCREAMING_SNAKE_CASE_							:				Any		=os.path.join(self.tmpdirname						,       VOCAB_FILES_NAMES['''vocab_file''']				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=os.path.join(self.tmpdirname						,       VOCAB_FILES_NAMES['''merges_file''']				)
              with open(self.vocab_file						,       '''w'''						,       encoding='''utf-8'''				) as fp:
                     fp.write(json.dumps(__A				) + '''\n'''				)
              with open(self.merges_file						,       '''w'''						,       encoding='''utf-8'''				) as fp:
                     fp.write('''\n'''.join(__A				)				)


       def 	_snake_case				(    self						,       **__A				)						->    int:
              kwargs.update(self.special_tokens_map				)
              return self.tokenizer_class.from_pretrained(self.tmpdirname						,       **__A				)


       def 	_snake_case				(    self						,       **__A				)						->    Any:
              kwargs.update(self.special_tokens_map				)
              return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,       **__A				)


       def 	_snake_case				(    self						,       __A				)						->    Any:
              return "lower newer", "lower newer"


       @cached_property
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''				)


       @cached_property
       def 	_snake_case				(    self				)						->    Optional[int]:
              return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''				)


       @require_torch
       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				str		=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
              SCREAMING_SNAKE_CASE_							:				Tuple		=[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=tokenizer(__A						,       max_length=len(__A				)						,       padding=__A						,       return_tensors='''pt'''				)
                     self.assertIsInstance(__A						,       __A				)

                     self.assertEqual((2, 9)						,       batch.input_ids.shape				)
                     self.assertEqual((2, 9)						,       batch.attention_mask.shape				)
                     SCREAMING_SNAKE_CASE_							:				Tuple		=batch.input_ids.tolist()[0]
                     self.assertListEqual(__A						,       __A				)
                     # Test that special tokens are reset


       @require_torch
       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer(__A						,       padding=__A						,       return_tensors='''pt'''				)
                     # check if input_ids are returned and no labels
                     self.assertIn('''input_ids'''						,       __A				)
                     self.assertIn('''attention_mask'''						,       __A				)
                     self.assertNotIn('''labels'''						,       __A				)
                     self.assertNotIn('''decoder_attention_mask'''						,       __A				)


       @require_torch
       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Any		=[
                  '''Summary of the text.''',
                  '''Another summary.''',
              ]
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=tokenizer(text_target=__A						,       max_length=32						,       padding='''max_length'''						,       return_tensors='''pt'''				)
                     self.assertEqual(32						,       targets['''input_ids'''].shape[1]				)


       @require_torch
       def 	_snake_case				(    self				)						->    str:
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer(
                         ['''I am a small frog''' * 1_024, '''I am a small frog''']						,       padding=__A						,       truncation=__A						,       return_tensors='''pt'''				)
                     self.assertIsInstance(__A						,       __A				)
                     self.assertEqual(batch.input_ids.shape						,       (2, 1_024)				)


       @require_torch
       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				str		=['''A long paragraph for summarization.''']
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=[
                  '''Summary of the text.''',
              ]
              for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer(__A						,       text_target=__A						,       return_tensors='''pt'''				)
                     SCREAMING_SNAKE_CASE_							:				Tuple		=inputs['''input_ids''']
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=inputs['''labels''']
                     self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()				)
                     self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()				)
                     self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()				)
                     self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()				)


       def 	_snake_case				(    self				)						->    Any:
              pass





       def 	_snake_case				(    self				)						->    Optional[Any]:
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
                            SCREAMING_SNAKE_CASE_							:				Dict		=self.rust_tokenizer_class.from_pretrained(__A						,       **__A				)
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.tokenizer_class.from_pretrained(__A						,       **__A				)
                            SCREAMING_SNAKE_CASE_							:				List[Any]		='''A, <mask> AllenNLP sentence.'''
                            SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer_r.encode_plus(__A						,       add_special_tokens=__A						,       return_token_type_ids=__A				)
                            SCREAMING_SNAKE_CASE_							:				Any		=tokenizer_p.encode_plus(__A						,       add_special_tokens=__A						,       return_token_type_ids=__A				)

                            # token_type_ids should put 0 everywhere
                            self.assertEqual(sum(tokens_r['''token_type_ids''']				)						,       sum(tokens_p['''token_type_ids''']				)				)

                            # attention_mask should put 1 everywhere, so sum over length should be 1
                            self.assertEqual(
                                sum(tokens_r['''attention_mask''']				) / len(tokens_r['''attention_mask''']				)						,       sum(tokens_p['''attention_mask''']				) / len(tokens_p['''attention_mask''']				)						,       )

                            SCREAMING_SNAKE_CASE_							:				Any		=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']				)
                            SCREAMING_SNAKE_CASE_							:				List[str]		=tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']				)

                            # Rust correctly handles the space before the mask while python doesnt
                            self.assertSequenceEqual(tokens_p['''input_ids''']						,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)
                            self.assertSequenceEqual(tokens_r['''input_ids''']						,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]				)

                            self.assertSequenceEqual(
                                __A						,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)
                            self.assertSequenceEqual(
                                __A						,       ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)
