
import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin






class        SCREAMING_SNAKE_CASE__    (							__lowerCAmelCase   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  BlenderbotSmallTokenizer
							_snake_case     		=  False



							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													super().setUp()

													__UpperCamelCase       =			["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
													__UpperCamelCase       =			dict(zip(_UpperCamelCase     ,	range(len(_UpperCamelCase			)			)			)			)

													__UpperCamelCase       =			["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
													__UpperCamelCase       =			{"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

													__UpperCamelCase       =			os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES['''vocab_file''']			)
													__UpperCamelCase       =			os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES['''merges_file''']			)
													with open(self.vocab_file     ,	'''w'''     ,	encoding='''utf-8'''			) as fp:
																			fp.write(json.dumps(_UpperCamelCase			) + '''\n'''			)
													with open(self.merges_file     ,	'''w'''     ,	encoding='''utf-8'''			) as fp:
																			fp.write('''\n'''.join(_UpperCamelCase			)			)



							def     A__		(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Optional[int]:





													'''simple docstring'''






													kwargs.update(self.special_tokens_map			)
													return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname     ,	**_UpperCamelCase			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			"""adapt act apte"""
													__UpperCamelCase       =			"""adapt act apte"""
													return input_text, output_text



							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			BlenderbotSmallTokenizer(self.vocab_file     ,	self.merges_file     ,	**self.special_tokens_map			)
													__UpperCamelCase       =			"""adapt act apte"""
													__UpperCamelCase       =			["""adapt""", """act""", """ap@@""", """te"""]
													__UpperCamelCase       =			tokenizer.tokenize(_UpperCamelCase			)
													self.assertListEqual(_UpperCamelCase     ,	_UpperCamelCase			)

													__UpperCamelCase       =			[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

													__UpperCamelCase       =			[0, 1, 2, 3, 4, 5]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase			)     ,	_UpperCamelCase			)



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''			)
													assert tok('''sam'''			).input_ids == [1384]
													__UpperCamelCase       =			"""I am a small frog."""
													__UpperCamelCase       =			tok([src_text]     ,	padding=_UpperCamelCase     ,	truncation=_UpperCamelCase			)["""input_ids"""]
													__UpperCamelCase       =			tok.batch_decode(_UpperCamelCase     ,	skip_special_tokens=_UpperCamelCase     ,	clean_up_tokenization_spaces=_UpperCamelCase			)[0]
													assert src_text != decoded  # I wish it did!
													assert decoded == "i am a small frog ."



							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''			)
													__UpperCamelCase       =			"""I am a small frog ."""
													__UpperCamelCase       =			"""."""
													__UpperCamelCase       =			tok(_UpperCamelCase			)["""input_ids"""]
													__UpperCamelCase       =			tok(_UpperCamelCase			)["""input_ids"""]

													assert encoded[-1] == encoded_dot[0]






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def 	A_ (							snake_case							:   str ,      snake_case							:   str ,      **snake_case							:   List[str] )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			AutoConfig.from_pretrained(snake_case ,      **snake_case )
						__UpperCamelCase       =			AutoModelForSeqaSeqLM.from_config(snake_case )
						model.save_pretrained(snake_case )
						AutoTokenizer.from_pretrained(snake_case ).save_pretrained(snake_case )
						return model


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




