'''simple docstring'''
import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length



def 			__lowerCamelCase							(   _UpperCamelCase	:	Tuple       ):


      '''simple docstring'''


      UpperCAmelCase_      =	int(SCREAMING_SNAKE_CASE_       )
      UpperCAmelCase_   ,		UpperCAmelCase_   ,		UpperCAmelCase_      =	t // 3600, (t // 60) % 60, t % 60
      return F"""{h}:{m:02d}:{s:02d}""" if h != 0 else F"""{m:02d}:{s:02d}"""



def 			__lowerCamelCase							(   _UpperCamelCase	:	Optional[int]				,     _UpperCamelCase	:	int				,     _UpperCamelCase	:	Union[str, Any]				,     _UpperCamelCase	:	List[str]				,     _UpperCamelCase	:	List[str]=300       ):


      '''simple docstring'''


      return F"""\n    <div>\n      {prefix}\n      <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>\n      {label}\n    </div>\n    """



def 			__lowerCamelCase							(   _UpperCamelCase	:	int       ):


      '''simple docstring'''


      UpperCAmelCase_      =	'''<table border="1" class="dataframe">\n'''
      html_code += """  <thead>\n <tr style="text-align: left;">\n"""
      for i in items[0]:
            html_code += F"""      <th>{i}</th>\n"""
      html_code += "    </tr>\n  </thead>\n  <tbody>\n"
      for line in items[1:]:
            html_code += "    <tr>\n"
            for elt in line:
                  UpperCAmelCase_      =	F"""{elt:.6f}""" if isinstance(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_       ) else str(SCREAMING_SNAKE_CASE_       )
                  html_code += F"""      <td>{elt}</td>\n"""
            html_code += "    </tr>\n"
      html_code += "  </tbody>\n</table><p>"
      return html_code







class lowerCamelCase				:




  '''simple docstring'''



  lowerCAmelCase__      				=						5
  lowerCAmelCase__      				=						0.2






  def __init__(   self      :							Tuple	,				UpperCAmelCase__      :							int	,				UpperCAmelCase__      :							Optional[str] = None	,				UpperCAmelCase__      :							bool = True	,				UpperCAmelCase__      :							Optional["NotebookTrainingTracker"] = None	,				UpperCAmelCase__      :							int = 300	,				)					->Dict:
        UpperCAmelCase_      =	total
        UpperCAmelCase_      =	'''''' if prefix is None else prefix
        UpperCAmelCase_      =	leave
        UpperCAmelCase_      =	parent
        UpperCAmelCase_      =	width
        UpperCAmelCase_      =	None
        UpperCAmelCase_      =	None
        UpperCAmelCase_      =	None






  def 			lowerCAmelCase__			(   self      :							Optional[int]	,				UpperCAmelCase__      :							int	,				UpperCAmelCase__      :							bool = False	,				UpperCAmelCase__      :							str = None	)					->Dict:
        UpperCAmelCase_      =	value
        if comment is not None:
              UpperCAmelCase_      =	comment
        if self.last_value is None:
              UpperCAmelCase_      =	UpperCAmelCase_      =	time.time()
              UpperCAmelCase_      =	UpperCAmelCase_      =	value
              UpperCAmelCase_      =	UpperCAmelCase_      =	None
              UpperCAmelCase_      =	self.warmup
              UpperCAmelCase_      =	1
              self.update_bar(_lowercase	)
        elif value <= self.last_value and not force_update:
              return
        elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for	,				self.total	):
              if self.first_calls > 0:
                    self.first_calls -= 1
              UpperCAmelCase_      =	time.time()
              UpperCAmelCase_      =	current_time - self.start_time
              # We could have value = self.start_value if the update is called twixe with the same start value.
              if value > self.start_value:
                    UpperCAmelCase_      =	self.elapsed_time / (value - self.start_value)
              else:
                    UpperCAmelCase_      =	None
              if value >= self.total:
                    UpperCAmelCase_      =	self.total
                    UpperCAmelCase_      =	None
                    if not self.leave:
                          self.close()
              elif self.average_time_per_item is not None:
                    UpperCAmelCase_      =	self.average_time_per_item * (self.total - value)
              self.update_bar(_lowercase	)
              UpperCAmelCase_      =	value
              UpperCAmelCase_      =	current_time
              if self.average_time_per_item is None:
                    UpperCAmelCase_      =	1
              else:
                    UpperCAmelCase_      =	max(int(self.update_every / self.average_time_per_item	)	,				1	)






  def 			lowerCAmelCase__			(   self      :							str	,				UpperCAmelCase__      :							List[Any]	,				UpperCAmelCase__      :							Optional[int]=None	)					->str:
        UpperCAmelCase_      =	''' ''' * (len(str(self.total	)	) - len(str(_lowercase	)	)) + str(_lowercase	)
        if self.elapsed_time is None:
              UpperCAmelCase_      =	f"""[{spaced_value}/{self.total} : < :"""
        elif self.predicted_remaining is None:
              UpperCAmelCase_      =	f"""[{spaced_value}/{self.total} {format_time(self.elapsed_time	)}"""
        else:
              UpperCAmelCase_      =	(
                  f"""[{spaced_value}/{self.total} {format_time(self.elapsed_time	)} <"""
                  f""" {format_time(self.predicted_remaining	)}"""
              )
              self.label += f""", {1/self.average_time_per_item:.2f} it/s"""
        self.label += "]" if self.comment is None or len(self.comment	) == 0 else f""", {self.comment}]"""
        self.display()






  def 			lowerCAmelCase__			(   self      :							Optional[int]	)					->Dict:
        UpperCAmelCase_      =	html_progress_bar(self.value	,				self.total	,				self.prefix	,				self.label	,				self.width	)
        if self.parent is not None:
              # If this is a child bar, the parent will take care of the display.
              self.parent.display()
              return
        if self.output is None:
              UpperCAmelCase_      =	disp.display(disp.HTML(self.html_code	)	,				display_id=_lowercase	)
        else:
              self.output.update(disp.HTML(self.html_code	)	)






  def 			lowerCAmelCase__			(   self      :							List[Any]	)					->Optional[Any]:
        if self.parent is None and self.output is not None:
              self.output.update(disp.HTML(''''''	)	)







class lowerCamelCase				(						lowerCamelCase       ):




  '''simple docstring'''



  def __init__(   self      :							int	,				UpperCAmelCase__      :							Union[str, Any]	,				UpperCAmelCase__      :							str=None	)					->str:
        super().__init__(_lowercase	)
        UpperCAmelCase_      =	None if column_names is None else [column_names]
        UpperCAmelCase_      =	None






  def 			lowerCAmelCase__			(   self      :							Dict	)					->List[Any]:
        UpperCAmelCase_      =	html_progress_bar(self.value	,				self.total	,				self.prefix	,				self.label	,				self.width	)
        if self.inner_table is not None:
              self.html_code += text_to_html_table(self.inner_table	)
        if self.child_bar is not None:
              self.html_code += self.child_bar.html_code
        if self.output is None:
              UpperCAmelCase_      =	disp.display(disp.HTML(self.html_code	)	,				display_id=_lowercase	)
        else:
              self.output.update(disp.HTML(self.html_code	)	)






  def 			lowerCAmelCase__			(   self      :							str	,				UpperCAmelCase__      :							int	)					->Optional[int]:
        if self.inner_table is None:
              UpperCAmelCase_      =	[list(values.keys()	), list(values.values()	)]
        else:
              UpperCAmelCase_      =	self.inner_table[0]
              if len(self.inner_table	) == 1:
                    # We give a chance to update the column names at the first iteration
                    for key in values.keys():
                          if key not in columns:
                                columns.append(_lowercase	)
                    UpperCAmelCase_      =	columns
              self.inner_table.append([values[c] for c in columns]	)






  def 			lowerCAmelCase__			(   self      :							List[Any]	,				UpperCAmelCase__      :							Union[str, Any]	,				UpperCAmelCase__      :							str=None	,				UpperCAmelCase__      :							Optional[int]=300	)					->Dict:
        UpperCAmelCase_      =	NotebookProgressBar(_lowercase	,				prefix=_lowercase	,				parent=self	,				width=_lowercase	)
        return self.child_bar






  def 			lowerCAmelCase__			(   self      :							Any	)					->int:
        UpperCAmelCase_      =	None
        self.display()







class lowerCamelCase				(						lowerCamelCase       ):




  '''simple docstring'''



  def __init__(   self      :							Dict	)					->Tuple:
        UpperCAmelCase_      =	None
        UpperCAmelCase_      =	None
        UpperCAmelCase_      =	False






  def 			lowerCAmelCase__			(   self      :							int	,				UpperCAmelCase__      :							str	,				UpperCAmelCase__      :							str	,				UpperCAmelCase__      :							str	,				**UpperCAmelCase__      :							List[Any]	)					->List[Any]:
        UpperCAmelCase_      =	'''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
        UpperCAmelCase_      =	0
        UpperCAmelCase_      =	0
        UpperCAmelCase_      =	[self.first_column] + ['''Training Loss''']
        if args.evaluation_strategy != IntervalStrategy.NO:
              column_names.append('''Validation Loss'''	)
        UpperCAmelCase_      =	NotebookTrainingTracker(state.max_steps	,				_lowercase	)






  def 			lowerCAmelCase__			(   self      :							Tuple	,				UpperCAmelCase__      :							Union[str, Any]	,				UpperCAmelCase__      :							List[Any]	,				UpperCAmelCase__      :							List[str]	,				**UpperCAmelCase__      :							Union[str, Any]	)					->str:
        UpperCAmelCase_      =	int(state.epoch	) if int(state.epoch	) == state.epoch else f"""{state.epoch:.2f}"""
        self.training_tracker.update(
            state.global_step + 1	,				comment=f"""Epoch {epoch}/{state.num_train_epochs}"""	,				force_update=self._force_next_update	,				)
        UpperCAmelCase_      =	False






  def 			lowerCAmelCase__			(   self      :							Union[str, Any]	,				UpperCAmelCase__      :							Any	,				UpperCAmelCase__      :							int	,				UpperCAmelCase__      :							Optional[int]	,				UpperCAmelCase__      :							List[Any]=None	,				**UpperCAmelCase__      :							Any	)					->Union[str, Any]:
        if not has_length(_lowercase	):
              return
        if self.prediction_bar is None:
              if self.training_tracker is not None:
                    UpperCAmelCase_      =	self.training_tracker.add_child(len(_lowercase	)	)
              else:
                    UpperCAmelCase_      =	NotebookProgressBar(len(_lowercase	)	)
              self.prediction_bar.update(1	)
        else:
              self.prediction_bar.update(self.prediction_bar.value + 1	)






  def 			lowerCAmelCase__			(   self      :							List[Any]	,				UpperCAmelCase__      :							List[str]	,				UpperCAmelCase__      :							Any	,				UpperCAmelCase__      :							Union[str, Any]	,				**UpperCAmelCase__      :							Optional[int]	)					->Optional[Any]:
        if self.prediction_bar is not None:
              self.prediction_bar.close()
        UpperCAmelCase_      =	None






  def 			lowerCAmelCase__			(   self      :							Dict	,				UpperCAmelCase__      :							Dict	,				UpperCAmelCase__      :							str	,				UpperCAmelCase__      :							str	,				UpperCAmelCase__      :							Optional[Any]=None	,				**UpperCAmelCase__      :							Dict	)					->Optional[int]:
        # Only for when there is no evaluation
        if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
              UpperCAmelCase_      =	{'''Training Loss''': logs['''loss''']}
              # First column is necessarily Step sine we're not in epoch eval strategy
              UpperCAmelCase_      =	state.global_step
              self.training_tracker.write_line(_lowercase	)






  def 			lowerCAmelCase__			(   self      :							List[Any]	,				UpperCAmelCase__      :							Any	,				UpperCAmelCase__      :							Optional[int]	,				UpperCAmelCase__      :							str	,				UpperCAmelCase__      :							Optional[Any]=None	,				**UpperCAmelCase__      :							Union[str, Any]	)					->Union[str, Any]:
        if self.training_tracker is not None:
              UpperCAmelCase_      =	{'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
              for log in reversed(state.log_history	):
                    if "loss" in log:
                          UpperCAmelCase_      =	log['''loss''']
                          break

              if self.first_column == "Epoch":
                    UpperCAmelCase_      =	int(state.epoch	)
              else:
                    UpperCAmelCase_      =	state.global_step
              UpperCAmelCase_      =	'''eval'''
              for k in metrics:
                    if k.endswith('''_loss'''	):
                          UpperCAmelCase_      =	re.sub(r'''\_loss$'''	,				''''''	,				_lowercase	)
              UpperCAmelCase_      =	metrics.pop('''total_flos'''	,				_lowercase	)
              UpperCAmelCase_      =	metrics.pop('''epoch'''	,				_lowercase	)
              UpperCAmelCase_      =	metrics.pop(f"""{metric_key_prefix}_runtime"""	,				_lowercase	)
              UpperCAmelCase_      =	metrics.pop(f"""{metric_key_prefix}_samples_per_second"""	,				_lowercase	)
              UpperCAmelCase_      =	metrics.pop(f"""{metric_key_prefix}_steps_per_second"""	,				_lowercase	)
              UpperCAmelCase_      =	metrics.pop(f"""{metric_key_prefix}_jit_compilation_time"""	,				_lowercase	)
              for k, v in metrics.items():
                    if k == f"""{metric_key_prefix}_loss""":
                          UpperCAmelCase_      =	v
                    else:
                          UpperCAmelCase_      =	k.split('''_'''	)
                          UpperCAmelCase_      =	''' '''.join([part.capitalize() for part in splits[1:]]	)
                          UpperCAmelCase_      =	v
              self.training_tracker.write_line(_lowercase	)
              self.training_tracker.remove_child()
              UpperCAmelCase_      =	None
              # Evaluation takes a long time so we should force the next update.
              UpperCAmelCase_      =	True






  def 			lowerCAmelCase__			(   self      :							Tuple	,				UpperCAmelCase__      :							Dict	,				UpperCAmelCase__      :							str	,				UpperCAmelCase__      :							List[str]	,				**UpperCAmelCase__      :							str	)					->Optional[int]:
        self.training_tracker.update(
            state.global_step	,				comment=f"""Epoch {int(state.epoch	)}/{state.num_train_epochs}"""	,				force_update=_lowercase	)
        UpperCAmelCase_      =	None











'''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


_SCREAMING_SNAKE_CASE			=						get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		ReformerTokenizer
					_UpperCamelCase							=		ReformerTokenizerFast
					_UpperCamelCase							=		True
					_UpperCamelCase							=		False
					_UpperCamelCase							=		True



					def     __snake_case						(	self     :						Optional[int])    ->							int:
								super().setUp()

								A_									= ReformerTokenizer(_lowercase      ,       keep_accents=_lowercase)
								tokenizer.save_pretrained(self.tmpdirname)



					def     __snake_case						(	self     :						List[Any])    ->							str:
								A_									= '<s>'
								A_									= 1

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



					def     __snake_case						(	self     :						Any)    ->							Optional[Any]:
								A_									= 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(_lowercase)      ,       1_000)



					def     __snake_case						(	self     :						int)    ->							Optional[int]:
								self.assertEqual(self.get_tokenizer().vocab_size      ,       1_000)



					def     __snake_case						(	self     :						List[Any])    ->							Any:
								if not self.test_rust_tokenizer:
											return

								A_									= self.get_tokenizer()
								A_									= self.get_rust_tokenizer()

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

								A_									= tokenizer.tokenize(_lowercase)
								A_									= rust_tokenizer.tokenize(_lowercase)
								self.assertListEqual(_lowercase      ,       _lowercase)

								A_									= tokenizer.encode(_lowercase      ,       add_special_tokens=_lowercase)
								A_									= rust_tokenizer.encode(_lowercase      ,       add_special_tokens=_lowercase)
								self.assertListEqual(_lowercase      ,       _lowercase)

								A_									= self.get_rust_tokenizer()
								A_									= tokenizer.encode(_lowercase)
								A_									= rust_tokenizer.encode(_lowercase)
								self.assertListEqual(_lowercase      ,       _lowercase)



					def     __snake_case						(	self     :						Optional[int]      ,       _lowercase     :						Optional[int]=15)    ->							str:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
														A_									= self.rust_tokenizer_class.from_pretrained(_lowercase      ,       **_lowercase)

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

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

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

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

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

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



					def     __snake_case						(	self     :						Optional[int])    ->							Optional[int]:
								pass



					def     __snake_case						(	self     :						Tuple)    ->							Optional[Any]:
								A_									= ReformerTokenizer(_lowercase      ,       keep_accents=_lowercase)

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

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

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

								A_									= tokenizer.convert_ids_to_tokens(_lowercase)
								self.assertListEqual(
								    _lowercase      ,       [
								        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     __snake_case						(	self     :						str)    ->							Any:
								return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')



					@slow
					def     __snake_case						(	self     :						Tuple)    ->							Union[str, Any]:
								A_									= 'Hello World!'
								A_									= [126, 32, 262, 152, 38, 72, 287]

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



					@slow
					def     __snake_case						(	self     :						Optional[int])    ->							int:
								A_									= (
								    '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'
								)
								A_									= [
								    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(_lowercase      ,       self.big_tokenizer.encode(_lowercase))



					@require_torch
					@slow
					def     __snake_case						(	self     :						List[str])    ->							Tuple:
								import torch

								from transformers import ReformerConfig, ReformerModel

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

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

								# 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(**_lowercase)
											model(**_lowercase)



					@slow
					def     __snake_case						(	self     :						str)    ->							Union[str, Any]:
								# fmt: off
								A_									= {'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
								A_									= [
								    'This is a very simple sentence.',
								    'The quick brown fox jumps over the lazy dog.',
								]

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




