



import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list


class    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= "<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" "     )
									UpperCamelCase_			= dict(zip(snake_case__  ,			range(len(snake_case__     )     )     )     )

									UpperCamelCase_			= {
									    "pad_token": "<pad>",
									    "unk_token": "<unk>",
									    "bos_token": "<s>",
									    "eos_token": "</s>",
									}
									UpperCamelCase_			= {
									    "feature_size": 1,
									    "padding_value": 0.0,
									    "sampling_rate": 1_6000,
									    "return_attention_mask": False,
									    "do_normalize": True,
									}

									UpperCamelCase_			= tempfile.mkdtemp()
									UpperCamelCase_			= os.path.join(self.tmpdirname  ,			VOCAB_FILES_NAMES["vocab_file"]     )
									UpperCamelCase_			= os.path.join(self.tmpdirname  ,			snake_case__     )
									with open(self.vocab_file  ,			"w"  ,			encoding="utf-8"     ) as fp:
														fp.write(json.dumps(snake_case__     ) + "\n"     )

									with open(self.feature_extraction_file  ,			"w"  ,			encoding="utf-8"     ) as fp:
														fp.write(json.dumps(snake_case__     ) + "\n"     )

				def    _lowerCamelCase       (			self  ,			**snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= self.add_kwargs_tokens_map.copy()
									kwargs.update(snake_case__     )
									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname  ,			**snake_case__     )

				def    _lowerCamelCase       (			self  ,			**snake_case__     ):

									'''simple docstring'''


									return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname  ,			**snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									shutil.rmtree(self.tmpdirname     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.get_tokenizer()
									UpperCamelCase_			= self.get_feature_extractor()

									UpperCamelCase_			= WavaVecaProcessor(tokenizer=snake_case__  ,			feature_extractor=snake_case__     )

									processor.save_pretrained(self.tmpdirname     )
									UpperCamelCase_			= WavaVecaProcessor.from_pretrained(self.tmpdirname     )

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

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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= WavaVecaProcessor(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=snake_case__  ,			padding_value=1.0     )

									UpperCamelCase_			= WavaVecaProcessor.from_pretrained(
									    self.tmpdirname  ,			bos_token="(BOS)"  ,			eos_token="(EOS)"  ,			do_normalize=snake_case__  ,			padding_value=1.0     )

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

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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.get_feature_extractor()
									UpperCamelCase_			= self.get_tokenizer()

									UpperCamelCase_			= WavaVecaProcessor(tokenizer=snake_case__  ,			feature_extractor=snake_case__     )

									UpperCamelCase_			= floats_list((3, 1000)     )

									UpperCamelCase_			= feature_extractor(snake_case__  ,			return_tensors="np"     )
									UpperCamelCase_			= processor(snake_case__  ,			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     ):

									'''simple docstring'''


									UpperCamelCase_			= self.get_feature_extractor()
									UpperCamelCase_			= self.get_tokenizer()

									UpperCamelCase_			= WavaVecaProcessor(tokenizer=snake_case__  ,			feature_extractor=snake_case__     )

									UpperCamelCase_			= "This is a test string"

									UpperCamelCase_			= processor(text=snake_case__     )

									UpperCamelCase_			= tokenizer(snake_case__     )

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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.get_feature_extractor()
									UpperCamelCase_			= self.get_tokenizer()

									UpperCamelCase_			= WavaVecaProcessor(tokenizer=snake_case__  ,			feature_extractor=snake_case__     )

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

									UpperCamelCase_			= processor.batch_decode(snake_case__     )
									UpperCamelCase_			= tokenizer.batch_decode(snake_case__     )

									self.assertListEqual(snake_case__  ,			snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.get_feature_extractor()
									UpperCamelCase_			= self.get_tokenizer()

									UpperCamelCase_			= WavaVecaProcessor(tokenizer=snake_case__  ,			feature_extractor=snake_case__     )

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





UpperCAmelCase :				Optional[int]							="""
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
"""

UpperCAmelCase :				List[Any]							=[{"""type""": """code""", """content""": INSTALL_CONTENT}]
