
import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class      lowerCAmelCase_						(							UpperCAmelCase__							,							unittest.TestCase				):



							'''simple docstring'''





							_snake_case		     =						BertweetTokenizer
							_snake_case		     =						False




							def  A__		(				self							)   ->       Any:
													super().setUp()

													# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
													__lowerCAmelCase        =       ["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
													__lowerCAmelCase        =       dict(zip(snake_case_  ,							range(len(snake_case_							)							)							)							)
													__lowerCAmelCase        =       ["""#version: 0.2""", """a m</w>"""]
													__lowerCAmelCase        =       {"""unk_token""": """<unk>"""}

													__lowerCAmelCase        =       os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES["""vocab_file"""]							)
													__lowerCAmelCase        =       os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES["""merges_file"""]							)
													with open(self.vocab_file  ,							"""w"""  ,							encoding="""utf-8"""							) as fp:
																			for token in vocab_tokens:
																									fp.write(f"""{token} {vocab_tokens[token]}\n"""							)
													with open(self.merges_file  ,							"""w"""  ,							encoding="""utf-8"""							) as fp:
																			fp.write("""\n""".join(snake_case_							)							)




							def  A__		(				self  ,							**snake_case_							)   ->       str:
													kwargs.update(self.special_tokens_map							)
													return BertweetTokenizer.from_pretrained(self.tmpdirname  ,							**snake_case_							)




							def  A__		(				self  ,							snake_case_							)   ->       str:
													__lowerCAmelCase        =       """I am VinAI Research"""
													__lowerCAmelCase        =       """I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
													return input_text, output_text




							def  A__		(				self							)   ->       str:
													__lowerCAmelCase        =       BertweetTokenizer(self.vocab_file  ,							self.merges_file  ,							**self.special_tokens_map							)
													__lowerCAmelCase        =       """I am VinAI Research"""
													__lowerCAmelCase        =       """I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
													__lowerCAmelCase        =       tokenizer.tokenize(snake_case_							)
													self.assertListEqual(snake_case_  ,							snake_case_							)

													__lowerCAmelCase        =       tokens + [tokenizer.unk_token]

													__lowerCAmelCase        =       [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_							)  ,							snake_case_							)


"""simple docstring"""
import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
			import torch
class      lowerCAmelCase_						(							unittest.TestCase							,							A__				):



							'''simple docstring'''





							def  A__		(				self							)   ->       Any:
													__lowerCAmelCase        =       load_tool("""speech-to-text"""							)
													self.tool.setup()




							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       self.tool(torch.ones(3_000							)							)
													self.assertEqual(snake_case_  ,							""" you"""							)




							def  A__		(				self							)   ->       Optional[int]:
													__lowerCAmelCase        =       self.tool(audio=torch.ones(3_000							)							)
													self.assertEqual(snake_case_  ,							""" you"""							)
