




"""simple docstring"""

import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 							lowercase_			(	__lowerCAmelCase     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Tuple     =      None
 UpperCAmelCase       :	int     =      BloomTokenizerFast
 UpperCAmelCase       :	Union[str, Any]     =      BloomTokenizerFast
 UpperCAmelCase       :	Optional[Any]     =      True
 UpperCAmelCase       :	Any     =      False
 UpperCAmelCase       :	str     =      '''tokenizer_file'''
 UpperCAmelCase       :	Tuple     =      {'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}

 def      lowerCAmelCase_				(					self			:			Tuple						):
       super().setUp()
       _A     =  BloomTokenizerFast.from_pretrained('bigscience/tokenizer'						)
       tokenizer.save_pretrained(self.tmpdirname						)

 def      lowerCAmelCase_				(					self			:			Optional[int]       ,				**_UpperCAmelCase			:			Optional[Any]						):
       kwargs.update(self.special_tokens_map						)
       return BloomTokenizerFast.from_pretrained(self.tmpdirname       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			int						):
       _A     =  self.get_rust_tokenizer()

       _A     =  ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
       _A     =  [[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

       _A     =  tokenizer.batch_encode_plus(_UpperCAmelCase						)['input_ids']
       self.assertListEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)

       _A     =  tokenizer.batch_decode(_UpperCAmelCase						)
       self.assertListEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			Tuple=6						):
       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(_UpperCAmelCase       ,				**_UpperCAmelCase						)
                   # tokenizer_r.pad_token = None # Hotfixing padding = None
                   # 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
                   try:
                         tokenizer_r.encode(_UpperCAmelCase       ,				max_length=_UpperCAmelCase						)
                         tokenizer_r.encode_plus(_UpperCAmelCase       ,				max_length=_UpperCAmelCase						)

                         tokenizer_r.batch_encode_plus(_UpperCAmelCase       ,				max_length=_UpperCAmelCase						)
                         tokenizer_r.encode(_UpperCAmelCase       ,				max_length=_UpperCAmelCase						)
                         tokenizer_r.batch_encode_plus(_UpperCAmelCase       ,				max_length=_UpperCAmelCase						)
                   except ValueError:
                         self.fail('Bloom Tokenizer should be able to deal with padding'						)

                   _A     =  None  # Hotfixing padding = None
                   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			:			Union[str, Any]						):
       _A     =  self.get_rust_tokenizer()
       _A     =  load_dataset('xnli'       ,				'all_languages'       ,				split='test'       ,				streaming=_UpperCAmelCase						)

       _A     =  next(iter(_UpperCAmelCase						)						)['premise']  # pick up one data
       _A     =  list(sample_data.values()						)

       _A     =  list(map(tokenizer.encode       ,				_UpperCAmelCase						)						)
       _A     =  [tokenizer.decode(_UpperCAmelCase       ,				clean_up_tokenization_spaces=_UpperCAmelCase						) for x in output_tokens]
       self.assertListEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)



 def      lowerCAmelCase_				(					self			:			int						):
       # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
       # any sequence length constraints. This test of the parent class will fail since it relies on the
       # maximum sequence length of the positoonal embeddings.
       self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map						)       ,				1						)
       self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()						)[0]						)       ,				1						)







"""simple docstring"""

from datetime import datetime

import matplotlib.pyplot as plt
import torch






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      for param in module.parameters():
            _A     =  False






def 		_snake_case	(   )       ->   Tuple:






      '''simple docstring'''


      _A     =  'cuda' if torch.cuda.is_available() else 'cpu'
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            _A     =  'mps'
      if device == "mps":
            print(
                'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                ' with generations.'				)
      return device






def 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  plt.imshow(_snake_case				)
      fig.axes.get_xaxis().set_visible(_snake_case				)
      fig.axes.get_yaxis().set_visible(_snake_case				)
      plt.show()







def 		_snake_case	(   )       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  datetime.now()
      _A     =  current_time.strftime('%H:%M:%S'				)
      return timestamp

