


'''simple docstring'''






import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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


lowercase							=get_tests_dir('fixtures/test_sentencepiece.model')






@require_sentencepiece
@require_tokenizers
class  __magic_name__							(		lowerCAmelCase ,unittest.TestCase    ):
       UpperCAmelCase										=ReformerTokenizer
       UpperCAmelCase										=ReformerTokenizerFast
       UpperCAmelCase										=True
       UpperCAmelCase										=False
       UpperCAmelCase										=True






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          super().setUp()

          _UpperCAmelCase       :       List[Any]							   =ReformerTokenizer(snake_case  ,							keep_accents=snake_case)
          tokenizer.save_pretrained(self.tmpdirname)






       def       lowerCAmelCase		(      self)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   ='<s>'
          _UpperCAmelCase       :       List[Any]							   =1

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






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =list(self.get_tokenizer().get_vocab().keys())

          self.assertEqual(vocab_keys[0]  ,							'<unk>')
          self.assertEqual(vocab_keys[1]  ,							'<s>')
          self.assertEqual(vocab_keys[-1]  ,							'j')
          self.assertEqual(len(snake_case)  ,							1_0_0_0)






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          self.assertEqual(self.get_tokenizer().vocab_size  ,							1_0_0_0)






       def       lowerCAmelCase		(      self)						->  List[Any]:




          '''simple docstring'''
          if not self.test_rust_tokenizer:
             return

          _UpperCAmelCase       :       Optional[Any]							   =self.get_tokenizer()
          _UpperCAmelCase       :       Union[str, Any]							   =self.get_rust_tokenizer()

          _UpperCAmelCase       :       Optional[int]							   ='I was born in 92000, and this is falsé.'

          _UpperCAmelCase       :       List[str]							   =tokenizer.tokenize(snake_case)
          _UpperCAmelCase       :       Tuple							   =rust_tokenizer.tokenize(snake_case)
          self.assertListEqual(snake_case  ,							snake_case)

          _UpperCAmelCase       :       Dict							   =tokenizer.encode(snake_case  ,							add_special_tokens=snake_case)
          _UpperCAmelCase       :       Any							   =rust_tokenizer.encode(snake_case  ,							add_special_tokens=snake_case)
          self.assertListEqual(snake_case  ,							snake_case)

          _UpperCAmelCase       :       Union[str, Any]							   =self.get_rust_tokenizer()
          _UpperCAmelCase       :       Tuple							   =tokenizer.encode(snake_case)
          _UpperCAmelCase       :       Any							   =rust_tokenizer.encode(snake_case)
          self.assertListEqual(snake_case  ,							snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case=1_5)						->  Dict:




          '''simple docstring'''
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                _UpperCAmelCase       :       Union[str, Any]							   =self.rust_tokenizer_class.from_pretrained(snake_case  ,							**snake_case)

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

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

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

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

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

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






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




          '''simple docstring'''
          pass






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       Any							   =ReformerTokenizer(snake_case  ,							keep_accents=snake_case)

          _UpperCAmelCase       :       Optional[Any]							   =tokenizer.tokenize('This is a test')
          self.assertListEqual(snake_case  ,							['▁This', '▁is', '▁a', '▁t', 'est'])

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(snake_case)  ,							[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]  ,							)

          _UpperCAmelCase       :       List[str]							   =tokenizer.tokenize('I was born in 92000, and this is falsé.')
          self.assertListEqual(
              snake_case  ,							[
                  SPIECE_UNDERLINE + 'I',
                  SPIECE_UNDERLINE + 'was',
                  SPIECE_UNDERLINE + 'b',
                  'or',
                  'n',
                  SPIECE_UNDERLINE + 'in',
                  SPIECE_UNDERLINE + '',
                  '9',
                  '2',
                  '0',
                  '0',
                  '0',
                  ',',
                  SPIECE_UNDERLINE + 'and',
                  SPIECE_UNDERLINE + 'this',
                  SPIECE_UNDERLINE + 'is',
                  SPIECE_UNDERLINE + 'f',
                  'al',
                  's',
                  'é',
                  '.',
              ]  ,							)
          _UpperCAmelCase       :       List[str]							   =tokenizer.convert_tokens_to_ids(snake_case)
          self.assertListEqual(
              snake_case  ,							[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]  ,							)

          _UpperCAmelCase       :       Dict							   =tokenizer.convert_ids_to_tokens(snake_case)
          self.assertListEqual(
              snake_case  ,							[
                  SPIECE_UNDERLINE + 'I',
                  SPIECE_UNDERLINE + 'was',
                  SPIECE_UNDERLINE + 'b',
                  'or',
                  'n',
                  SPIECE_UNDERLINE + 'in',
                  SPIECE_UNDERLINE + '',
                  '<unk>',
                  '2',
                  '0',
                  '0',
                  '0',
                  ',',
                  SPIECE_UNDERLINE + 'and',
                  SPIECE_UNDERLINE + 'this',
                  SPIECE_UNDERLINE + 'is',
                  SPIECE_UNDERLINE + 'f',
                  'al',
                  's',
                  '<unk>',
                  '.',
              ]  ,							)






       @cached_property
       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')






       @slow
       def       lowerCAmelCase		(      self)						->  List[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   ='Hello World!'
          _UpperCAmelCase       :       str							   =[1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

          self.assertListEqual(snake_case  ,							self.big_tokenizer.encode(snake_case))






       @slow
       def       lowerCAmelCase		(      self)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =(
              'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
              ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
          )
          _UpperCAmelCase       :       Union[str, Any]							   =[
              1_0_8,
              2_6_5,
              2_4,
              1_1_1,
              4,
              2_5_8,
              1_5_6,
              3_5,
              2_8,
              2_7_5,
              3,
              2_5_9,
              2_9_7,
              2_6_0,
              8_4,
              4,
              3_5,
              1_1_0,
              4_4,
              8,
              2_5_9,
              9_1,
              2_6_8,
              2_1,
              1_1,
              2_0_9,
              2_7_4,
              1_0_9,
              2_6_6,
              2_7_7,
              1_1_7,
              8_6,
              9_3,
              3_1_5,
              2_5_8,
              2_7_8,
              2_5_8,
              2_7_7,
              2_5_8,
              0,
              2_5_8,
              2_8_8,
              2_5_8,
              3_1_9,
              2_5_8,
              0,
              2_5_8,
              0,
              2_5_8,
              0,
              2_5_8,
              0,
              2_5_8,
              2_8_7,
              2_5_8,
              3_1_5,
              2_5_8,
              2_8_9,
              2_5_8,
              2_7_8,
              9_9,
              2_6_9,
              2_6_6,
              2_6_2,
              8,
              2_5_9,
              2_4_1,
              4,
              2_1_7,
              2_3_0,
              2_6_8,
              2_6_6,
              5_5,
              1_6_8,
              1_0_6,
              7_5,
              1_9_3,
              2_6_6,
              2_2_3,
              2_7,
              4_9,
              2_6,
              2_8_2,
              2_5,
              2_6_4,
              2_9_9,
              1_9,
              2_6,
              0,
              2_5_8,
              2_7_7,
              1_1_7,
              8_6,
              9_3,
              1_7_6,
              1_8_3,
              2_7_0,
              1_1,
              2_6_2,
              4_2,
              6_1,
              2_6_5,
          ]

          self.assertListEqual(snake_case  ,							self.big_tokenizer.encode(snake_case))






       @require_torch
       @slow
       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          import torch

          from transformers import ReformerConfig, ReformerModel

          # Build sequence
          _UpperCAmelCase       :       List[Any]							   =list(self.big_tokenizer.get_vocab().keys())[:1_0]
          _UpperCAmelCase       :       Union[str, Any]							   =' '.join(snake_case)
          _UpperCAmelCase       :       str							   =self.big_tokenizer.encode_plus(snake_case  ,							return_tensors='pt')
          _UpperCAmelCase       :       Union[str, Any]							   =self.big_tokenizer.batch_encode_plus([sequence, sequence]  ,							return_tensors='pt')

          _UpperCAmelCase       :       int							   =ReformerConfig()
          # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
          _UpperCAmelCase       :       Union[str, Any]							   =encoded_sequence['input_ids'].shape
          _UpperCAmelCase       :       str							   =ReformerModel(snake_case)

          # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
          assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

          with torch.no_grad():
             model(**snake_case)
             model(**snake_case)






       @slow
       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          # fmt: off
          _UpperCAmelCase       :       Union[str, Any]							   ={'input_ids': [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], '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]]}  # noqa: E501
          # fmt: on

          # This tokenizer does not know some characters like ")".
          # That is the reason why we use very simple texts here.
          # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
          _UpperCAmelCase       :       Union[str, Any]							   =[
              'This is a very simple sentence.',
              'The quick brown fox jumps over the lazy dog.',
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=snake_case  ,							model_name='google/reformer-crime-and-punishment'  ,							revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'  ,							padding=snake_case  ,							sequences=snake_case  ,							)





'''simple docstring'''






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowercase							=logging.get_logger(__name__)

lowercase							='T5Config'





def lowerCamelCase__      (						__lowerCamelCase	:							jnp.array	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =jnp.zeros_like(__lowerCamelCase							)
   _UpperCAmelCase       :       Union[str, Any]							   =shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]							)
   _UpperCAmelCase       :       List[Any]							   =shifted_input_ids.at[:, 0].set(__lowerCamelCase							)

   _UpperCAmelCase       :       str							   =jnp.where(shifted_input_ids == -1_0_0	,   __lowerCamelCase	,   __lowerCamelCase							)
   return shifted_input_ids






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="mt5"
       UpperCAmelCase										=MTaConfig

