




'''simple docstring'''

import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


a       :						Tuple								=		logging.getLogger()



@unittest.skip("""Temporarily disable the doc tests."""	)
@require_torch
@require_tf
@slow
class 		SCREAMING_SNAKE_CASE__  (			unittest.TestCase	):





			def 							A (      self						:    List[str]      ,		a_						:    Path      ,		a_						:    Union[str, None] = None      ,		a_						:    Union[List[str], None] = None      ,		a_						:    Union[str, List[str], None] = None      ,		a_						:    bool = True      ,		):



						"""simple docstring"""




						__snake_case			=		[file for file in os.listdir(a_							) if os.path.isfile(os.path.join(a_      ,		a_							)							)]

						if identifier is not None:
									__snake_case			=		[file for file in files if identifier in file]

						if n_identifier is not None:
									if isinstance(a_      ,		a_							):
												for n_ in n_identifier:
															__snake_case			=		[file for file in files if n_ not in file]
									else:
												__snake_case			=		[file for file in files if n_identifier not in file]

						__snake_case			=		ignore_files or []
						ignore_files.append("__init__.py"							)
						__snake_case			=		[file for file in files if file not in ignore_files]

						for file in files:
									# Open all files
									print("Testing"      ,		a_							)

									if only_modules:
												__snake_case			=		file.split("."							)[0]
												try:
															__snake_case			=		getattr(a_      ,		a_							)
															__snake_case			=		doctest.DocTestSuite(a_							)
															__snake_case			=		unittest.TextTestRunner().run(a_							)
															self.assertIs(len(result.failures							)      ,		0							)
												except AttributeError:
															logger.info(f'''{module_identifier} is not a module.'''							)
									else:
												__snake_case			=		doctest.testfile(str(".." / directory / file							)      ,		optionflags=doctest.ELLIPSIS							)
												self.assertIs(result.failed      ,		0							)





			def 							A (      self						:    int							):



						"""simple docstring"""




						__snake_case			=		Path("src/transformers"							)
						__snake_case			=		"modeling"
						__snake_case			=		[
						    "modeling_ctrl.py",
						    "modeling_tf_ctrl.py",
						]
						self.analyze_directory(a_      ,		identifier=a_      ,		ignore_files=a_							)





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		Path("src/transformers"							)
						__snake_case			=		"tokenization"
						self.analyze_directory(a_      ,		identifier=a_							)





			def 							A (      self						:    Any							):



						"""simple docstring"""




						__snake_case			=		Path("src/transformers"							)
						__snake_case			=		"configuration"
						self.analyze_directory(a_      ,		identifier=a_							)





			def 							A (      self						:    List[Any]							):



						"""simple docstring"""




						__snake_case			=		Path("src/transformers"							)
						__snake_case			=		["configuration", "modeling", "tokenization"]
						self.analyze_directory(a_      ,		n_identifier=a_							)






			def 							A (      self						:    Optional[int]							):



						"""simple docstring"""




						__snake_case			=		Path("docs/source"							)
						__snake_case			=		["favicon.ico"]
						self.analyze_directory(a_      ,		ignore_files=a_      ,		only_modules=a_							)










"""simple docstring"""







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 					A_(unittest.TestCase      ):







			"""simple docstring"""



			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    'laion/clap-htsat-unfused'
						_lowerCamelCase					:					Optional[Any]	       =    tempfile.mkdtemp()
			def    _lowerCAmelCase		(	self				,   **A							):
						return RobertaTokenizer.from_pretrained(self.checkpoint				,   **A							)
			def    _lowerCAmelCase		(	self				,   **A							):
						return ClapFeatureExtractor.from_pretrained(self.checkpoint				,   **A							)
			def    _lowerCAmelCase		(	self							):
						shutil.rmtree(self.tmpdirname							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[Any]	       =    self.get_tokenizer()
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_feature_extractor()

						_lowerCamelCase					:					List[str]	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						processor.save_pretrained(self.tmpdirname							)
						_lowerCamelCase					:					str	       =    ClapProcessor.from_pretrained(self.tmpdirname							)

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

						self.assertEqual(processor.feature_extractor.to_json_string()				,   feature_extractor.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor				,   A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    ClapProcessor(tokenizer=self.get_tokenizer()				,   feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						_lowerCamelCase					:					Tuple	       =    self.get_tokenizer(bos_token='(BOS)'				,   eos_token='(EOS)'							)
						_lowerCamelCase					:					str	       =    self.get_feature_extractor(do_normalize=A				,   padding_value=1.0							)

						_lowerCamelCase					:					List[Any]	       =    ClapProcessor.from_pretrained(
						    self.tmpdirname				,   bos_token='(BOS)'				,   eos_token='(EOS)'				,   do_normalize=A				,   padding_value=1.0							)

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

						self.assertEqual(processor.feature_extractor.to_json_string()				,   feature_extractor_add_kwargs.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor				,   A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[int]	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Optional[int]	       =    self.get_tokenizer()

						_lowerCamelCase					:					Any	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						_lowerCamelCase					:					int	       =    floats_list((3, 1000)							)

						_lowerCamelCase					:					str	       =    feature_extractor(A				,   return_tensors='np'							)
						_lowerCamelCase					:					List[str]	       =    processor(audios=A				,   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							):
						_lowerCamelCase					:					Any	       =    self.get_feature_extractor()
						_lowerCamelCase					:					str	       =    self.get_tokenizer()

						_lowerCamelCase					:					List[Any]	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						_lowerCamelCase					:					List[str]	       =    'This is a test string'

						_lowerCamelCase					:					Any	       =    processor(text=A							)

						_lowerCamelCase					:					Optional[int]	       =    tokenizer(A							)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]				,   encoded_processor[key]							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_tokenizer()

						_lowerCamelCase					:					Optional[int]	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						_lowerCamelCase					:					Dict	       =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						_lowerCamelCase					:					int	       =    processor.batch_decode(A							)
						_lowerCamelCase					:					int	       =    tokenizer.batch_decode(A							)

						self.assertListEqual(A				,   A							)

			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Optional[Any]	       =    self.get_tokenizer()

						_lowerCamelCase					:					int	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

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





