





'''simple docstring'''



import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_PRETRAINING_MAPPING,
     MegatronBertForCausalLM,
     MegatronBertForMaskedLM,
     MegatronBertForMultipleChoice,
     MegatronBertForNextSentencePrediction,
     MegatronBertForPreTraining,
     MegatronBertForQuestionAnswering,
     MegatronBertForSequenceClassification,
     MegatronBertForTokenClassification,
     MegatronBertModel,
 )


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=13						,   lowerCamelCase=7						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=99						,   lowerCamelCase=64						,   lowerCamelCase=32						,   lowerCamelCase=5						,   lowerCamelCase=4						,   lowerCamelCase=37						,   lowerCamelCase="gelu"						,   lowerCamelCase=0.1						,   lowerCamelCase=0.1						,   lowerCamelCase=512						,   lowerCamelCase=16						,   lowerCamelCase=2						,   lowerCamelCase=0.02						,   lowerCamelCase=3						,   lowerCamelCase=4						,   lowerCamelCase=None						,   ):
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				seq_length
       _snake_case     		=				is_training
       _snake_case     		=				use_input_mask
       _snake_case     		=				use_token_type_ids
       _snake_case     		=				use_labels
       _snake_case     		=				vocab_size
       _snake_case     		=				hidden_size
       _snake_case     		=				embedding_size
       _snake_case     		=				num_hidden_layers
       _snake_case     		=				num_attention_heads
       _snake_case     		=				intermediate_size
       _snake_case     		=				hidden_act
       _snake_case     		=				hidden_dropout_prob
       _snake_case     		=				attention_probs_dropout_prob
       _snake_case     		=				max_position_embeddings
       _snake_case     		=				type_vocab_size
       _snake_case     		=				type_sequence_label_size
       _snake_case     		=				initializer_range
       _snake_case     		=				num_labels
       _snake_case     		=				num_choices
       _snake_case     		=				scope

     def 	UpperCamelCase(					self	):
       _snake_case     		=				ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size	)

       _snake_case     		=				None
       if self.use_input_mask:
         _snake_case     		=				random_attention_mask([self.batch_size, self.seq_length]	)

       _snake_case     		=				None
       if self.use_token_type_ids:
         _snake_case     		=				ids_tensor([self.batch_size, self.seq_length]						,   self.type_vocab_size	)

       _snake_case     		=				None
       _snake_case     		=				None
       _snake_case     		=				None
       if self.use_labels:
         _snake_case     		=				ids_tensor([self.batch_size]						,   self.type_sequence_label_size	)
         _snake_case     		=				ids_tensor([self.batch_size, self.seq_length]						,   self.num_labels	)
         _snake_case     		=				ids_tensor([self.batch_size]						,   self.num_choices	)

       _snake_case     		=				self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

     def 	UpperCamelCase(					self	):
       return MegatronBertConfig(
           vocab_size=self.vocab_size						,   hidden_size=self.hidden_size						,   num_hidden_layers=self.num_hidden_layers						,   num_attention_heads=self.num_attention_heads						,   intermediate_size=self.intermediate_size						,   embedding_size=self.embedding_size						,   hidden_act=self.hidden_act						,   hidden_dropout_prob=self.hidden_dropout_prob						,   attention_probs_dropout_prob=self.attention_probs_dropout_prob						,   max_position_embeddings=self.max_position_embeddings						,   type_vocab_size=self.type_vocab_size						,   is_decoder=lowerCamelCase_						,   initializer_range=self.initializer_range						,   )

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				MegatronBertModel(config=lowerCamelCase_	)
       model.to(lowerCamelCase_	)
       model.eval()
       _snake_case     		=				model(lowerCamelCase_						,   attention_mask=lowerCamelCase_						,   token_type_ids=lowerCamelCase_	)
       _snake_case     		=				model(lowerCamelCase_						,   token_type_ids=lowerCamelCase_	)
       _snake_case     		=				model(lowerCamelCase_	)

       self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)	)
       self.parent.assertEqual(result.pooler_output.shape						,   (self.batch_size, self.hidden_size)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				MegatronBertForMaskedLM(config=lowerCamelCase_	)
       model.to(lowerCamelCase_	)
       model.eval()
       _snake_case     		=				model(lowerCamelCase_						,   attention_mask=lowerCamelCase_						,   token_type_ids=lowerCamelCase_						,   labels=lowerCamelCase_	)
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				MegatronBertForCausalLM(config=lowerCamelCase_	)
       model.to(lowerCamelCase_	)
       model.eval()
       _snake_case     		=				model(lowerCamelCase_						,   attention_mask=lowerCamelCase_						,   token_type_ids=lowerCamelCase_						,   labels=lowerCamelCase_	)
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				MegatronBertForNextSentencePrediction(config=lowerCamelCase_	)
       model.to(lowerCamelCase_	)
       model.eval()
       _snake_case     		=				model(
           lowerCamelCase_						,   attention_mask=lowerCamelCase_						,   token_type_ids=lowerCamelCase_						,   labels=lowerCamelCase_						,   )
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, 2)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				MegatronBertForPreTraining(config=lowerCamelCase_	)
       model.to(lowerCamelCase_	)
       model.eval()
       _snake_case     		=				model(
           lowerCamelCase_						,   attention_mask=lowerCamelCase_						,   token_type_ids=lowerCamelCase_						,   labels=lowerCamelCase_						,   next_sentence_label=lowerCamelCase_						,   )
       self.parent.assertEqual(result.prediction_logits.shape						,   (self.batch_size, self.seq_length, self.vocab_size)	)
       self.parent.assertEqual(result.seq_relationship_logits.shape						,   (self.batch_size, 2)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				MegatronBertForQuestionAnswering(config=lowerCamelCase_	)
       model.to(lowerCamelCase_	)
       model.eval()
       _snake_case     		=				model(
           lowerCamelCase_						,   attention_mask=lowerCamelCase_						,   token_type_ids=lowerCamelCase_						,   start_positions=lowerCamelCase_						,   end_positions=lowerCamelCase_						,   )
       self.parent.assertEqual(result.start_logits.shape						,   (self.batch_size, self.seq_length)	)
       self.parent.assertEqual(result.end_logits.shape						,   (self.batch_size, self.seq_length)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				self.num_labels
       _snake_case     		=				MegatronBertForSequenceClassification(lowerCamelCase_	)
       model.to(lowerCamelCase_	)
       model.eval()
       _snake_case     		=				model(lowerCamelCase_						,   attention_mask=lowerCamelCase_						,   token_type_ids=lowerCamelCase_						,   labels=lowerCamelCase_	)
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_labels)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				self.num_labels
       _snake_case     		=				MegatronBertForTokenClassification(config=lowerCamelCase_	)
       model.to(lowerCamelCase_	)
       model.eval()
       _snake_case     		=				model(lowerCamelCase_						,   attention_mask=lowerCamelCase_						,   token_type_ids=lowerCamelCase_						,   labels=lowerCamelCase_	)
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.seq_length, self.num_labels)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				self.num_choices
       _snake_case     		=				MegatronBertForMultipleChoice(config=lowerCamelCase_	)
       model.to(lowerCamelCase_	)
       model.eval()
       _snake_case     		=				input_ids.unsqueeze(1	).expand(-1						,   self.num_choices						,   -1	).contiguous()
       _snake_case     		=				token_type_ids.unsqueeze(1	).expand(-1						,   self.num_choices						,   -1	).contiguous()
       _snake_case     		=				input_mask.unsqueeze(1	).expand(-1						,   self.num_choices						,   -1	).contiguous()
       _snake_case     		=				model(
           lowerCamelCase_						,   attention_mask=lowerCamelCase_						,   token_type_ids=lowerCamelCase_						,   labels=lowerCamelCase_						,   )
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_choices)	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.prepare_config_and_inputs()
       (
           _snake_case					
       )     		=				config_and_inputs
       _snake_case     		=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict







@require_torch
class       __SCREAMING_SNAKE_CASE	(						_UpperCAmelCase					,    _UpperCAmelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   (
         (
             MegatronBertModel,
             MegatronBertForMaskedLM,
             MegatronBertForCausalLM,
             MegatronBertForMultipleChoice,
             MegatronBertForNextSentencePrediction,
             MegatronBertForPreTraining,
             MegatronBertForQuestionAnswering,
             MegatronBertForSequenceClassification,
             MegatronBertForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase__       :						str      =   (
         {
             '''feature-extraction''': MegatronBertModel,
             '''fill-mask''': MegatronBertForMaskedLM,
             '''question-answering''': MegatronBertForQuestionAnswering,
             '''text-classification''': MegatronBertForSequenceClassification,
             '''text-generation''': MegatronBertForCausalLM,
             '''token-classification''': MegatronBertForTokenClassification,
             '''zero-shot''': MegatronBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     UpperCAmelCase__       :						Any      =   True
     # test_resize_embeddings = False
     UpperCAmelCase__       :						Optional[int]      =   False

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=False	):
       _snake_case     		=				super()._prepare_for_class(lowerCamelCase_						,   lowerCamelCase_						,   return_labels=lowerCamelCase_	)

       if return_labels:
         if model_class in get_values(lowerCamelCase_	):
           _snake_case     		=				torch.zeros(
               (self.model_tester.batch_size, self.model_tester.seq_length)						,   dtype=torch.long						,   device=lowerCamelCase_	)
           _snake_case     		=				torch.zeros(
               self.model_tester.batch_size						,   dtype=torch.long						,   device=lowerCamelCase_	)
       return inputs_dict

     def 	UpperCamelCase(					self	):
       _snake_case     		=				MegatronBertModelTester(self	)
       _snake_case     		=				ConfigTester(self						,   config_class=lowerCamelCase_						,   hidden_size=37	)

     def 	UpperCamelCase(					self	):
       self.config_tester.run_common_tests()

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_model(*lowerCamelCase_	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_masked_lm(*lowerCamelCase_	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*lowerCamelCase_	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*lowerCamelCase_	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_pretraining(*lowerCamelCase_	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_question_answering(*lowerCamelCase_	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*lowerCamelCase_	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_megatron_bert_for_token_classification(*lowerCamelCase_	)







def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return torch.tensor(
      a_					,	dtype=torch.long					,	device=a_					,	)


__magic_name__   :						str    =			1e-4




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



     '''simple docstring'''






     @slow
     @unittest.skip("Model is not available."	)
     def 	UpperCamelCase(					self	):
       _snake_case     		=				'''nvidia/megatron-bert-uncased-345m'''
       if "MYDIR" in os.environ:
         _snake_case     		=				os.path.join(os.environ["MYDIR"]						,   lowerCamelCase_	)
       _snake_case     		=				MegatronBertModel.from_pretrained(lowerCamelCase_	)
       model.to(lowerCamelCase_	)
       model.half()
       _snake_case     		=				_long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]	)
       with torch.no_grad():
         _snake_case     		=				model(lowerCamelCase_	)[0]
       _snake_case     		=				torch.Size((1, 9, 1_024)	)
       self.assertEqual(output.shape						,   lowerCamelCase_	)

       _snake_case     		=				[-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
       for ii in range(3	):
         for jj in range(3	):
           _snake_case     		=				output[0, ii, jj]
           _snake_case     		=				expected[3 * ii + jj]
           _snake_case     		=				'''ii={} jj={} a={} b={}'''.format(lowerCamelCase_						,   lowerCamelCase_						,   lowerCamelCase_						,   lowerCamelCase_	)
           self.assertTrue(math.isclose(lowerCamelCase_						,   lowerCamelCase_						,   rel_tol=lowerCamelCase_						,   abs_tol=lowerCamelCase_	)						,   msg=lowerCamelCase_	)







'''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


__magic_name__   :						Optional[Any]    =			get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
@require_tokenizers
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Any      =   ReformerTokenizer
     UpperCAmelCase__       :						List[str]      =   ReformerTokenizerFast
     UpperCAmelCase__       :						Any      =   True
     UpperCAmelCase__       :						Union[str, Any]      =   False
     UpperCAmelCase__       :						Optional[Any]      =   True

     def 	UpperCamelCase(					self	):
       super().setUp()

       _snake_case     		=				ReformerTokenizer(lowerCamelCase						,   keep_accents=lowerCamelCase	)
       tokenizer.save_pretrained(self.tmpdirname	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				"<s>"
       _snake_case     		=				1

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				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(lowerCamelCase	)						,   1_000	)

     def 	UpperCamelCase(					self	):
       self.assertEqual(self.get_tokenizer().vocab_size						,   1_000	)

     def 	UpperCamelCase(					self	):
       if not self.test_rust_tokenizer:
         return

       _snake_case     		=				self.get_tokenizer()
       _snake_case     		=				self.get_rust_tokenizer()

       _snake_case     		=				"I was born in 92000, and this is falsé."

       _snake_case     		=				tokenizer.tokenize(lowerCamelCase	)
       _snake_case     		=				rust_tokenizer.tokenize(lowerCamelCase	)
       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)

       _snake_case     		=				tokenizer.encode(lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
       _snake_case     		=				rust_tokenizer.encode(lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)

       _snake_case     		=				self.get_rust_tokenizer()
       _snake_case     		=				tokenizer.encode(lowerCamelCase	)
       _snake_case     		=				rust_tokenizer.encode(lowerCamelCase	)
       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase=15	):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(lowerCamelCase						,   **lowerCamelCase	)

           # Simple input
           _snake_case     		=				"This is a simple input"
           _snake_case     		=				["This is a simple input 1", "This is a simple input 2"]
           _snake_case     		=				("This is a simple input", "This is a pair")
           _snake_case     		=				[
               ("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(lowerCamelCase						,   tokenizer_r.encode						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"	)

           # Simple input
           self.assertRaises(lowerCamelCase						,   tokenizer_r.encode_plus						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"	)

           # Simple input
           self.assertRaises(
               lowerCamelCase						,   tokenizer_r.batch_encode_plus						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"						,   )

           # Pair input
           self.assertRaises(lowerCamelCase						,   tokenizer_r.encode						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"	)

           # Pair input
           self.assertRaises(lowerCamelCase						,   tokenizer_r.encode_plus						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"	)

           # Pair input
           self.assertRaises(
               lowerCamelCase						,   tokenizer_r.batch_encode_plus						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"						,   )

     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       _snake_case     		=				ReformerTokenizer(lowerCamelCase						,   keep_accents=lowerCamelCase	)

       _snake_case     		=				tokenizer.tokenize("This is a test"	)
       self.assertListEqual(lowerCamelCase						,   ["▁This", "▁is", "▁a", "▁t", "est"]	)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(lowerCamelCase	)						,   [285, 46, 10, 170, 382]						,   )

       _snake_case     		=				tokenizer.tokenize("I was born in 92000, and this is falsé."	)
       self.assertListEqual(
           lowerCamelCase						,   [
               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",
               ".",
           ]						,   )
       _snake_case     		=				tokenizer.convert_tokens_to_ids(lowerCamelCase	)
       self.assertListEqual(
           lowerCamelCase						,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,   )

       _snake_case     		=				tokenizer.convert_ids_to_tokens(lowerCamelCase	)
       self.assertListEqual(
           lowerCamelCase						,   [
               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 	UpperCamelCase(					self	):
       return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"	)

     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				"Hello World!"
       _snake_case     		=				[126, 32, 262, 152, 38, 72, 287]

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

     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				(
           "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"
       )
       _snake_case     		=				[
           108,
           265,
           24,
           111,
           4,
           258,
           156,
           35,
           28,
           275,
           3,
           259,
           297,
           260,
           84,
           4,
           35,
           110,
           44,
           8,
           259,
           91,
           268,
           21,
           11,
           209,
           274,
           109,
           266,
           277,
           117,
           86,
           93,
           315,
           258,
           278,
           258,
           277,
           258,
           0,
           258,
           288,
           258,
           319,
           258,
           0,
           258,
           0,
           258,
           0,
           258,
           0,
           258,
           287,
           258,
           315,
           258,
           289,
           258,
           278,
           99,
           269,
           266,
           262,
           8,
           259,
           241,
           4,
           217,
           230,
           268,
           266,
           55,
           168,
           106,
           75,
           193,
           266,
           223,
           27,
           49,
           26,
           282,
           25,
           264,
           299,
           19,
           26,
           0,
           258,
           277,
           117,
           86,
           93,
           176,
           183,
           270,
           11,
           262,
           42,
           61,
           265,
       ]

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

     @require_torch
     @slow
     def 	UpperCamelCase(					self	):
       import torch

       from transformers import ReformerConfig, ReformerModel

       # Build sequence
       _snake_case     		=				list(self.big_tokenizer.get_vocab().keys()	)[:10]
       _snake_case     		=				" ".join(lowerCamelCase	)
       _snake_case     		=				self.big_tokenizer.encode_plus(lowerCamelCase						,   return_tensors="pt"	)
       _snake_case     		=				self.big_tokenizer.batch_encode_plus([sequence, sequence]						,   return_tensors="pt"	)

       _snake_case     		=				ReformerConfig()
       # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
       _snake_case     		=				encoded_sequence["input_ids"].shape
       _snake_case     		=				ReformerModel(lowerCamelCase	)

       # 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(**lowerCamelCase	)
         model(**lowerCamelCase	)






     @slow
     def 	UpperCamelCase(					self	):
       # fmt: off
       _snake_case     		=				{"input_ids": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], "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
       _snake_case     		=				[
           "This is a very simple sentence.",
           "The quick brown fox jumps over the lazy dog.",
       ]

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


