
"""simple docstring"""
import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str,						**lowerCamelCase__							:							Optional[Any]				)  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = AutoConfig.from_pretrained(lowerCamelCase__,						**lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = AutoModelForSeqaSeqLM.from_config(lowerCamelCase__				)
  model.save_pretrained(lowerCamelCase__				)
  AutoTokenizer.from_pretrained(lowerCamelCase__				).save_pretrained(lowerCamelCase__				)
  return model


if __name__ == "__main__":
      fire.Fire(save_randomly_initialized_version)





"""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 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       unittest.TestCase  ):
     A__      =			None
     A__      =			BloomTokenizerFast
     A__      =			BloomTokenizerFast
     A__      =			True
     A__      =			False
     A__      =			"""tokenizer_file"""
     A__      =			{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       super().setUp()
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = BloomTokenizerFast.from_pretrained("bigscience/tokenizer"				)
       tokenizer.save_pretrained(self.tmpdirname				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     **snake_case__				):




       """simple docstring"""


       kwargs.update(self.special_tokens_map				)
       return BloomTokenizerFast.from_pretrained(self.tmpdirname  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = self.get_rust_tokenizer()

       _SCREAMING_SNAKE_CASE   :				Tuple							    = ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = [[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tokenizer.batch_encode_plus(snake_case__				)["input_ids"]
       self.assertListEqual(snake_case__  ,     snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Tuple							    = tokenizer.batch_decode(snake_case__				)
       self.assertListEqual(snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=6				):




       """simple docstring"""


       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
           _SCREAMING_SNAKE_CASE   :				Tuple							    = self.rust_tokenizer_class.from_pretrained(snake_case__  ,     **snake_case__				)
           # tokenizer_r.pad_token = None # Hotfixing padding = None
           # Simple input
           _SCREAMING_SNAKE_CASE   :				str							    = "This is a simple input"
           _SCREAMING_SNAKE_CASE   :				Tuple							    = ["This is a simple input 1", "This is a simple input 2"]
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = ("This is a simple input", "This is a pair")
           _SCREAMING_SNAKE_CASE   :				List[str]							    = [
               ("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"				)

           _SCREAMING_SNAKE_CASE   :				Tuple							    = 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 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.get_rust_tokenizer()
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = load_dataset("xnli"  ,     "all_languages"  ,     split="test"  ,     streaming=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Tuple							    = next(iter(snake_case__				)				)["premise"]  # pick up one data
       _SCREAMING_SNAKE_CASE   :				int							    = list(sample_data.values()				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = list(map(tokenizer.encode  ,     snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				int							    = [tokenizer.decode(snake_case__  ,     clean_up_tokenization_spaces=snake_case__				) for x in output_tokens]
       self.assertListEqual(snake_case__  ,     snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       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				)



