


import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin
class 	_lowercase				(							A__						,      unittest.TestCase	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      Optional[Any]      =		SpeechaTextaTokenizer
				SCREAMING_SNAKE_CASE__    :      List[Any]      =		False


				def 		__magic_name__(	self      :str     )		->      Optional[Any]:
										super().setUp()

										__SCREAMING_SNAKE_CASE						:					Dict		      =		'''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''     )
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		['''he re</w> 123''', '''here a 1456''']
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		dict(zip(lowerCAmelCase__      ,      range(len(lowerCAmelCase__     )     )     )     )

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		{'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		tempfile.mkdtemp()
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['''vocab_file''']     )
										__SCREAMING_SNAKE_CASE						:					int		      =		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(lowerCAmelCase__     ) + '''\n'''     )

										with open(self.merges_file      ,      '''w'''     ) as fp:
																fp.write('''\n'''.join(lowerCAmelCase__     )     )


				def 		__magic_name__(	self      :Dict     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		list(self.get_tokenizer().get_vocab().keys()     )

										self.assertEqual(vocab_keys[0]      ,      '''<s>'''     )
										self.assertEqual(vocab_keys[1]      ,      '''<pad>'''     )
										self.assertEqual(vocab_keys[-1]      ,      '''vocab'''     )
										self.assertEqual(len(lowerCAmelCase__     )      ,      14     )


				def 		__magic_name__(	self      :Tuple     )		->      List[str]:
										self.assertEqual(self.get_tokenizer().vocab_size      ,      14     )


				def 		__magic_name__(	self      :int     )		->      List[Any]:
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		SpeechaTextaTokenizer.from_pretrained(self.tmpdirname     )

										# make sure @@ is correctly concatenated
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		tokenizer.decode(lowerCAmelCase__     )

										self.assertTrue(output_string == '''herecouple words ofthe'''     )


				def 		__magic_name__(	self      :Tuple     )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		SpeechaTextaTokenizer.from_pretrained(self.tmpdirname     )

										with tempfile.TemporaryDirectory() as tmp_dirname:
																tokenizer.save_pretrained(lowerCAmelCase__     )
																os.remove(os.path.join(lowerCAmelCase__      ,      '''merges.txt'''     )     )

																# load tokenizer without merges file should not throw an error
																__SCREAMING_SNAKE_CASE						:					str		      =		SpeechaTextaTokenizer.from_pretrained(lowerCAmelCase__     )

										with tempfile.TemporaryDirectory() as tmp_dirname:
																# save tokenizer and load again
																tokenizer.save_pretrained(lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					Dict		      =		SpeechaTextaTokenizer.from_pretrained(lowerCAmelCase__     )

										self.assertIsNotNone(lowerCAmelCase__     )



				def 		__magic_name__(	self      :Dict     )		->      Optional[Any]:
										if not self.test_slow_tokenizer:
																return

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		inspect.signature(self.tokenizer_class.__init__     )
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.get_tokenizer()

										for parameter_name, parameter in signature.parameters.items():
																if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																						self.assertIn(lowerCAmelCase__      ,      tokenizer.init_kwargs     )




from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		c_float


				@property
				def 		__magic_name__(	self      :int     )		->      int:
										return sizeof(self.value     )




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__ = 0				):
						# < 0 is possible if using a dynamic axis
						if dimension <= 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		fixed_dimension

						dimension -= num_token_to_add
						return dimension


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						return num_parameters * dtype.size


def 	_UpperCamelCase							(				lowercase__				):
						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

						try:
												return AutoProcessor.from_pretrained(lowercase__				)
						except (ValueError, OSError, KeyError):
												__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Tuple		      =		None, None
												try:
																		__SCREAMING_SNAKE_CASE						:					List[Any]		      =		AutoTokenizer.from_pretrained(lowercase__				)
												except (OSError, KeyError):
																		pass
												try:
																		__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		AutoFeatureExtractor.from_pretrained(lowercase__				)
												except (OSError, KeyError):
																		pass

												if tokenizer is not None and feature_extractor is not None:
																		raise ValueError(
																		    F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
												elif tokenizer is None and feature_extractor is None:
																		return None
												elif tokenizer is not None:
																		return tokenizer
												else:
																		return feature_extractor
