
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


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



      """simple docstring"""







      @slow
      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Dict	    =       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''				,       return_dict=_snake_case				).to(_snake_case				)
          __lowercase		:			Optional[int]	    =       AutoTokenizer.from_pretrained('''google/mt5-small'''				)

          __lowercase		:			List[Any]	    =       tokenizer('''Hello there'''				,       return_tensors='''pt'''				).input_ids
          __lowercase		:			Dict	    =       tokenizer('''Hi I am'''				,       return_tensors='''pt'''				).input_ids

          __lowercase		:			str	    =       model(input_ids.to(_snake_case				)				,       labels=labels.to(_snake_case				)				).loss
          __lowercase		:			List[Any]	    =       -(labels.shape[-1] * loss.item())

          __lowercase		:			Optional[int]	    =       -84.91_27
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 1E-4				)


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 						__lowerCAmelCase	(	lowerCAmelCase_ , unittest.TestCase      ):



      """simple docstring"""







      A__	:			Dict        =    None
      A__	:			Any        =    BloomTokenizerFast
      A__	:			Optional[Any]        =    BloomTokenizerFast
      A__	:			List[Any]        =    True
      A__	:			int        =    False
      A__	:			Any        =    '''tokenizer_file'''
      A__	:			Optional[int]        =    {'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




      def 		snake_case_			(      self		:       Any				):
          super().setUp()
          __lowercase		:			int	    =       BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''				)
          tokenizer.save_pretrained(self.tmpdirname				)




      def 		snake_case_			(      self		:       Dict				,       **_snake_case		:       Any				):
          kwargs.update(self.special_tokens_map				)
          return BloomTokenizerFast.from_pretrained(self.tmpdirname				,       **_snake_case				)




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Dict	    =       self.get_rust_tokenizer()

          __lowercase		:			Optional[int]	    =       ['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
          __lowercase		:			Union[str, Any]	    =       [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

          __lowercase		:			Optional[int]	    =       tokenizer.batch_encode_plus(_snake_case				)['''input_ids''']
          self.assertListEqual(_snake_case				,       _snake_case				)

          __lowercase		:			List[Any]	    =       tokenizer.batch_decode(_snake_case				)
          self.assertListEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       str				,       _snake_case		:       int=6				):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
                  __lowercase		:			Dict	    =       self.rust_tokenizer_class.from_pretrained(_snake_case				,       **_snake_case				)
                  # tokenizer_r.pad_token = None # Hotfixing padding = None
                  # Simple input
                  __lowercase		:			List[Any]	    =       '''This is a simple input'''
                  __lowercase		:			List[str]	    =       ['''This is a simple input 1''', '''This is a simple input 2''']
                  __lowercase		:			Optional[Any]	    =       ('''This is a simple input''', '''This is a pair''')
                  __lowercase		:			Any	    =       [
                      ('''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(_snake_case				,       max_length=_snake_case				)
                      tokenizer_r.encode_plus(_snake_case				,       max_length=_snake_case				)

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

                  __lowercase		:			Dict	    =       None  # Hotfixing padding = None
                  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 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			List[Any]	    =       self.get_rust_tokenizer()
          __lowercase		:			int	    =       load_dataset('''xnli'''				,       '''all_languages'''				,       split='''test'''				,       streaming=_snake_case				)

          __lowercase		:			Optional[Any]	    =       next(iter(_snake_case				)				)['''premise''']  # pick up one data
          __lowercase		:			List[str]	    =       list(sample_data.values()				)

          __lowercase		:			List[str]	    =       list(map(tokenizer.encode				,       _snake_case				)				)
          __lowercase		:			Tuple	    =       [tokenizer.decode(_snake_case				,       clean_up_tokenization_spaces=_snake_case				) for x in output_tokens]
          self.assertListEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          # 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				)


