


import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list




@require_torchaudio
@require_sentencepiece
class    UpperCAmelCase					(       unittest.TestCase    ):




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					"""laion/clap-htsat-unfused"""
						UpperCamelCase         =					tempfile.mkdtemp()




			def   lowerCamelCase_					(						self							:		int       ,       **__magic_name__							:		Dict						):







						"""simple docstring"""







						return RobertaTokenizer.from_pretrained(self.checkpoint       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						shutil.rmtree(self.tmpdirname						)




			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_tokenizer()
						UpperCamelCase         =					self.get_feature_extractor()

						UpperCamelCase         =					ClapProcessor(tokenizer=__magic_name__       ,       feature_extractor=__magic_name__						)

						processor.save_pretrained(self.tmpdirname						)
						UpperCamelCase         =					ClapProcessor.from_pretrained(self.tmpdirname						)

						self.assertEqual(processor.tokenizer.get_vocab()       ,       tokenizer.get_vocab()						)
						self.assertIsInstance(processor.tokenizer       ,       __magic_name__						)

						self.assertEqual(processor.feature_extractor.to_json_string()       ,       feature_extractor.to_json_string()						)
						self.assertIsInstance(processor.feature_extractor       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					ClapProcessor(tokenizer=self.get_tokenizer()       ,       feature_extractor=self.get_feature_extractor()						)
						processor.save_pretrained(self.tmpdirname						)

						UpperCamelCase         =					self.get_tokenizer(bos_token="""(BOS)"""       ,       eos_token="""(EOS)"""						)
						UpperCamelCase         =					self.get_feature_extractor(do_normalize=__magic_name__       ,       padding_value=1.0						)

						UpperCamelCase         =					ClapProcessor.from_pretrained(
						    self.tmpdirname       ,       bos_token="""(BOS)"""       ,       eos_token="""(EOS)"""       ,       do_normalize=__magic_name__       ,       padding_value=1.0						)

						self.assertEqual(processor.tokenizer.get_vocab()       ,       tokenizer_add_kwargs.get_vocab()						)
						self.assertIsInstance(processor.tokenizer       ,       __magic_name__						)

						self.assertEqual(processor.feature_extractor.to_json_string()       ,       feature_extractor_add_kwargs.to_json_string()						)
						self.assertIsInstance(processor.feature_extractor       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_feature_extractor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					ClapProcessor(tokenizer=__magic_name__       ,       feature_extractor=__magic_name__						)

						UpperCamelCase         =					floats_list((3, 1_0_0_0)						)

						UpperCamelCase         =					feature_extractor(__magic_name__       ,       return_tensors="""np"""						)
						UpperCamelCase         =					processor(audios=__magic_name__       ,       return_tensors="""np"""						)

						for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()       ,       input_processor[key].sum()       ,       delta=1e-2						)




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_feature_extractor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					ClapProcessor(tokenizer=__magic_name__       ,       feature_extractor=__magic_name__						)

						UpperCamelCase         =					"""This is a test string"""

						UpperCamelCase         =					processor(text=__magic_name__						)

						UpperCamelCase         =					tokenizer(__magic_name__						)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]       ,       encoded_processor[key]						)




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_feature_extractor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					ClapProcessor(tokenizer=__magic_name__       ,       feature_extractor=__magic_name__						)

						UpperCamelCase         =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						UpperCamelCase         =					processor.batch_decode(__magic_name__						)
						UpperCamelCase         =					tokenizer.batch_decode(__magic_name__						)

						self.assertListEqual(__magic_name__       ,       __magic_name__						)





			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						UpperCamelCase         =					self.get_feature_extractor()
						UpperCamelCase         =					self.get_tokenizer()

						UpperCamelCase         =					ClapProcessor(tokenizer=__magic_name__       ,       feature_extractor=__magic_name__						)

						self.assertListEqual(
						    processor.model_input_names[2:]       ,       feature_extractor.model_input_names       ,       msg="""`processor` and `feature_extractor` model input names do not match"""       ,       )








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class    UpperCAmelCase					(       __snake_case      , unittest.TestCase    ):
			lowercase						= XLMTokenizer
			lowercase						= False




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						UpperCamelCase         =					[
						    """l""",
						    """o""",
						    """w""",
						    """e""",
						    """r""",
						    """s""",
						    """t""",
						    """i""",
						    """d""",
						    """n""",
						    """w</w>""",
						    """r</w>""",
						    """t</w>""",
						    """lo""",
						    """low""",
						    """er</w>""",
						    """low</w>""",
						    """lowest</w>""",
						    """newer</w>""",
						    """wider</w>""",
						    """<unk>""",
						]
						UpperCamelCase         =					dict(zip(__magic_name__       ,       range(len(__magic_name__						)						)						)						)
						UpperCamelCase         =					["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

						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"""						) as fp:
									fp.write(json.dumps(__magic_name__						)						)
						with open(self.merges_file       ,       """w"""						) as fp:
									fp.write("""\n""".join(__magic_name__						)						)




			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					"""lower newer"""
						UpperCamelCase         =					"""lower newer"""
						return input_text, output_text




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMTokenizer(self.vocab_file       ,       self.merges_file						)

						UpperCamelCase         =					"""lower"""
						UpperCamelCase         =					["""low""", """er</w>"""]
						UpperCamelCase         =					tokenizer.tokenize(__magic_name__						)
						self.assertListEqual(__magic_name__       ,       __magic_name__						)

						UpperCamelCase         =					tokens + ["""<unk>"""]
						UpperCamelCase         =					[1_4, 1_5, 2_0]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__						)       ,       __magic_name__						)





			@slow
			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""						)

						UpperCamelCase         =					tokenizer.encode("""sequence builders"""       ,       add_special_tokens=__magic_name__						)
						UpperCamelCase         =					tokenizer.encode("""multi-sequence build"""       ,       add_special_tokens=__magic_name__						)

						UpperCamelCase         =					tokenizer.build_inputs_with_special_tokens(__magic_name__						)
						UpperCamelCase         =					tokenizer.build_inputs_with_special_tokens(__magic_name__       ,       __magic_name__						)

						assert encoded_sentence == [0] + text + [1]
						assert encoded_pair == [0] + text + [1] + text_a + [1]




