

"""simple docstring"""






import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("0.8.3"):
 raise Exception("requires gluonnlp == 0.8.3")

if version.parse(mx.__version__) != version.parse("1.5.0"):
 raise Exception("requires mxnet == 1.5.0")

logging.set_verbosity_info()
__lowerCamelCase										=		logging.get_logger(__name__)

__lowerCamelCase										=		"The Nymphenburg Palace is a beautiful palace in Munich!"





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      List[str]:
 __magic_name__     =      {
     '''attention_cell''': '''multi_head''',
     '''num_layers''': 4,
     '''units''': 1024,
     '''hidden_size''': 768,
     '''max_length''': 512,
     '''num_heads''': 8,
     '''scaled''': True,
     '''dropout''': 0.1,
     '''use_residual''': True,
     '''embed_size''': 1024,
     '''embed_dropout''': 0.1,
     '''word_embed''': None,
     '''layer_norm_eps''': 1e-5,
     '''token_type_vocab_size''': 2,
 }

 __magic_name__     =      bort_4_8_768_1024_hparams

 # Let's construct the original Bort model here
 # Taken from official BERT implementation, see:
 # https://github.com/alexa/bort/blob/master/bort/bort.py
 __magic_name__     =      BERTEncoder(
     attention_cell=predefined_args['''attention_cell''']      ,   num_layers=predefined_args['''num_layers''']      ,   units=predefined_args['''units''']      ,   hidden_size=predefined_args['''hidden_size''']      ,   max_length=predefined_args['''max_length''']      ,   num_heads=predefined_args['''num_heads''']      ,   scaled=predefined_args['''scaled''']      ,   dropout=predefined_args['''dropout''']      ,   output_attention=__UpperCamelCase      ,   output_all_encodings=__UpperCamelCase      ,   use_residual=predefined_args['''use_residual''']      ,   activation=predefined_args.get('''activation'''      ,   '''gelu'''						)      ,   layer_norm_eps=predefined_args.get('''layer_norm_eps'''      ,   __UpperCamelCase						)      ,   )

 # Vocab information needs to be fetched first
 # It's the same as RoBERTa, so RobertaTokenizer can be used later
 __magic_name__     =      '''openwebtext_ccnews_stories_books_cased'''

 # Specify download folder to Gluonnlp's vocab
 __magic_name__     =      os.path.join(get_home_dir()      ,   '''models'''						)
 __magic_name__     =      _load_vocab(__UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   cls=__UpperCamelCase						)

 __magic_name__     =      nlp.model.BERTModel(
     __UpperCamelCase      ,   len(__UpperCamelCase						)      ,   units=predefined_args['''units''']      ,   embed_size=predefined_args['''embed_size''']      ,   embed_dropout=predefined_args['''embed_dropout''']      ,   word_embed=predefined_args['''word_embed''']      ,   use_pooler=__UpperCamelCase      ,   use_token_type_embed=__UpperCamelCase      ,   token_type_vocab_size=predefined_args['''token_type_vocab_size''']      ,   use_classifier=__UpperCamelCase      ,   use_decoder=__UpperCamelCase      ,   )

 original_bort.load_parameters(__UpperCamelCase      ,   cast_dtype=__UpperCamelCase      ,   ignore_extra=__UpperCamelCase						)
 __magic_name__     =      original_bort._collect_params_with_prefix()

 # Build our config 🤗
 __magic_name__     =      {
     '''architectures''': ['''BertForMaskedLM'''],
     '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
     '''hidden_act''': '''gelu''',
     '''hidden_dropout_prob''': predefined_args['''dropout'''],
     '''hidden_size''': predefined_args['''embed_size'''],
     '''initializer_range''': 0.02,
     '''intermediate_size''': predefined_args['''hidden_size'''],
     '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
     '''max_position_embeddings''': predefined_args['''max_length'''],
     '''model_type''': '''bort''',
     '''num_attention_heads''': predefined_args['''num_heads'''],
     '''num_hidden_layers''': predefined_args['''num_layers'''],
     '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
     '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
     '''vocab_size''': len(__UpperCamelCase						),
 }

 __magic_name__     =      BertConfig.from_dict(__UpperCamelCase						)
 __magic_name__     =      BertForMaskedLM(__UpperCamelCase						)
 hf_bort_model.eval()

 # Parameter mapping table (Gluonnlp to Transformers)
 # * denotes layer index
 #
 # | Gluon Parameter                                                | Transformers Parameter
 # | -------------------------------------------------------------- | ----------------------
 # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
 # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
 # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
 # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
 # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
 # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
 # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
 # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
 # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
 # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
 # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
 # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
 # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
 # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
 # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
 # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
 # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
 # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
 # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
 # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

 # Helper function to convert MXNET Arrays to PyTorch
 def to_torch(__UpperCamelCase						) -> nn.Parameter:
  return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()						)						)

 # Check param shapes and map new HF param back
 def check_and_map_params(__UpperCamelCase      ,   __UpperCamelCase						):
  __magic_name__     =      hf_param.shape

  __magic_name__     =      to_torch(params[gluon_param]						)
  __magic_name__     =      gluon_param.shape

  assert (
      shape_hf == shape_gluon
  ), f'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''

  return gluon_param

 __magic_name__     =      check_and_map_params(
     hf_bort_model.bert.embeddings.word_embeddings.weight      ,   '''word_embed.0.weight'''						)
 __magic_name__     =      check_and_map_params(
     hf_bort_model.bert.embeddings.position_embeddings.weight      ,   '''encoder.position_weight'''						)
 __magic_name__     =      check_and_map_params(
     hf_bort_model.bert.embeddings.LayerNorm.bias      ,   '''encoder.layer_norm.beta'''						)
 __magic_name__     =      check_and_map_params(
     hf_bort_model.bert.embeddings.LayerNorm.weight      ,   '''encoder.layer_norm.gamma'''						)

 # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
 __magic_name__     =      torch.zeros_like(
     hf_bort_model.bert.embeddings.token_type_embeddings.weight.data						)

 for i in range(hf_bort_config.num_hidden_layers						):
  __magic_name__     =      hf_bort_model.bert.encoder.layer[i]

  # self attention
  __magic_name__     =      layer.attention.self

  __magic_name__     =      check_and_map_params(
      self_attn.key.bias.data      ,   f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''						)

  __magic_name__     =      check_and_map_params(
      self_attn.key.weight.data      ,   f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''						)
  __magic_name__     =      check_and_map_params(
      self_attn.query.bias.data      ,   f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''						)
  __magic_name__     =      check_and_map_params(
      self_attn.query.weight.data      ,   f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''						)
  __magic_name__     =      check_and_map_params(
      self_attn.value.bias.data      ,   f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''						)
  __magic_name__     =      check_and_map_params(
      self_attn.value.weight.data      ,   f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''						)

  # self attention output
  __magic_name__     =      layer.attention.output

  __magic_name__     =      check_and_map_params(
      self_output.dense.bias      ,   f'''encoder.transformer_cells.{i}.proj.bias'''						)
  __magic_name__     =      check_and_map_params(
      self_output.dense.weight      ,   f'''encoder.transformer_cells.{i}.proj.weight'''						)
  __magic_name__     =      check_and_map_params(
      self_output.LayerNorm.bias      ,   f'''encoder.transformer_cells.{i}.layer_norm.beta'''						)
  __magic_name__     =      check_and_map_params(
      self_output.LayerNorm.weight      ,   f'''encoder.transformer_cells.{i}.layer_norm.gamma'''						)

  # intermediate
  __magic_name__     =      layer.intermediate

  __magic_name__     =      check_and_map_params(
      intermediate.dense.bias      ,   f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''						)
  __magic_name__     =      check_and_map_params(
      intermediate.dense.weight      ,   f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''						)

  # output
  __magic_name__     =      layer.output

  __magic_name__     =      check_and_map_params(
      bert_output.dense.bias      ,   f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''						)
  __magic_name__     =      check_and_map_params(
      bert_output.dense.weight      ,   f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''						)
  __magic_name__     =      check_and_map_params(
      bert_output.LayerNorm.bias      ,   f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''						)
  __magic_name__     =      check_and_map_params(
      bert_output.LayerNorm.weight      ,   f'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma'''						)

 # Save space and energy 🎄
 hf_bort_model.half()

 # Compare output of both models
 __magic_name__     =      RobertaTokenizer.from_pretrained('''roberta-base'''						)

 __magic_name__     =      tokenizer.encode_plus(__UpperCamelCase						)['''input_ids''']

 # Get gluon output
 __magic_name__     =      mx.nd.array([input_ids]						)
 __magic_name__     =      original_bort(inputs=__UpperCamelCase      ,   token_types=[]						)

 # Get Transformer output (save and reload model again)
 hf_bort_model.save_pretrained(__UpperCamelCase						)
 __magic_name__     =      BertModel.from_pretrained(__UpperCamelCase						)
 hf_bort_model.eval()

 __magic_name__     =      tokenizer.encode_plus(__UpperCamelCase      ,   return_tensors='''pt'''						)
 __magic_name__     =      hf_bort_model(**__UpperCamelCase						)[0]

 __magic_name__     =      output_gluon[0].asnumpy()
 __magic_name__     =      output_hf[0].detach().numpy()

 __magic_name__     =      np.max(np.abs(hf_layer - gluon_layer						)						).item()
 __magic_name__     =      np.allclose(__UpperCamelCase      ,   __UpperCamelCase      ,   atol=1e-3						)

 if success:
  print('''✔️ Both model do output the same tensors'''						)
 else:
  print('''❌ Both model do **NOT** output the same tensors'''						)
  print('''Absolute difference is:'''      ,   __UpperCamelCase						)


if __name__ == "__main__":
 __lowerCamelCase										=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--bort_checkpoint_path", default=None, type=str, required=True, help="Path the official Bort params file."
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 __lowerCamelCase										=		parser.parse_args()
 convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




"""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


__lowerCamelCase										=		get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class        _lowercase					(     __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       ReformerTokenizer
     _lowerCamelCase     =       ReformerTokenizerFast
     _lowerCamelCase     =       True
     _lowerCamelCase     =       False
     _lowerCamelCase     =       True







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

      __magic_name__     =      ReformerTokenizer(UpperCamelCase_ ,						keep_accents=UpperCamelCase_       )
      tokenizer.save_pretrained(self.tmpdirname       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      '''<s>'''
      __magic_name__     =      1

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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      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(UpperCamelCase_       ) ,						1000       )







     def 	lowerCAmelCase__    (      self       ):
      self.assertEqual(self.get_tokenizer().vocab_size ,						1000       )







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

      __magic_name__     =      self.get_tokenizer()
      __magic_name__     =      self.get_rust_tokenizer()

      __magic_name__     =      '''I was born in 92000, and this is falsé.'''

      __magic_name__     =      tokenizer.tokenize(UpperCamelCase_       )
      __magic_name__     =      rust_tokenizer.tokenize(UpperCamelCase_       )
      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )

      __magic_name__     =      tokenizer.encode(UpperCamelCase_ ,						add_special_tokens=UpperCamelCase_       )
      __magic_name__     =      rust_tokenizer.encode(UpperCamelCase_ ,						add_special_tokens=UpperCamelCase_       )
      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )

      __magic_name__     =      self.get_rust_tokenizer()
      __magic_name__     =      tokenizer.encode(UpperCamelCase_       )
      __magic_name__     =      rust_tokenizer.encode(UpperCamelCase_       )
      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_=15       ):
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
        __magic_name__     =      self.rust_tokenizer_class.from_pretrained(UpperCamelCase_ ,						**UpperCamelCase_       )

        # Simple input
        __magic_name__     =      '''This is a simple input'''
        __magic_name__     =      ['''This is a simple input 1''', '''This is a simple input 2''']
        __magic_name__     =      ('''This is a simple input''', '''This is a pair''')
        __magic_name__     =      [
            ('''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(UpperCamelCase_ ,						tokenizer_r.encode ,						UpperCamelCase_ ,						max_length=UpperCamelCase_ ,						padding='''max_length'''       )

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

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

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

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

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







     def 	lowerCAmelCase__    (      self       ):
      pass







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      ReformerTokenizer(UpperCamelCase_ ,						keep_accents=UpperCamelCase_       )

      __magic_name__     =      tokenizer.tokenize('''This is a test'''       )
      self.assertListEqual(UpperCamelCase_ ,						['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

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

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

      __magic_name__     =      tokenizer.convert_ids_to_tokens(UpperCamelCase_       )
      self.assertListEqual(
          UpperCamelCase_ ,						[
              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       ):
      return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment'''       )







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      '''Hello World!'''
      __magic_name__     =      [126, 32, 262, 152, 38, 72, 287]

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







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      (
          '''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'''
      )
      __magic_name__     =      [
          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(UpperCamelCase_ ,						self.big_tokenizer.encode(UpperCamelCase_       )       )







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

      from transformers import ReformerConfig, ReformerModel

      # Build sequence
      __magic_name__     =      list(self.big_tokenizer.get_vocab().keys()       )[:10]
      __magic_name__     =      ''' '''.join(UpperCamelCase_       )
      __magic_name__     =      self.big_tokenizer.encode_plus(UpperCamelCase_ ,						return_tensors='''pt'''       )
      __magic_name__     =      self.big_tokenizer.batch_encode_plus([sequence, sequence] ,						return_tensors='''pt'''       )

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

      # 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(**UpperCamelCase_       )
       model(**UpperCamelCase_       )







     @slow
     def 	lowerCAmelCase__    (      self       ):
      # fmt: off
      __magic_name__     =      {'''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
      __magic_name__     =      [
          '''This is a very simple sentence.''',
          '''The quick brown fox jumps over the lazy dog.''',
      ]

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

