
'''simple docstring'''

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    A     (	unittest.TestCase    ):





	def 							__SCREAMING_SNAKE_CASE  (					self			:       str							):


				"""simple docstring"""

				lowerCAmelCase__								=							"<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(" "							)
				lowerCAmelCase__								=							dict(zip(__magic_name__      ,							range(len(__magic_name__							)							)							)							)

				lowerCAmelCase__								=							{
				    "pad_token": "<pad>",
				    "unk_token": "<unk>",
				    "bos_token": "<s>",
				    "eos_token": "</s>",
				}
				lowerCAmelCase__								=							{
				    "feature_size": 1,
				    "padding_value": 0.0,
				    "sampling_rate": 16000,
				    "return_attention_mask": False,
				    "do_normalize": True,
				}

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

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





	def 							__SCREAMING_SNAKE_CASE  (					self			:       int      ,							**__magic_name__			:       int							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.add_kwargs_tokens_map.copy()
				kwargs.update(__magic_name__							)
				return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,							**__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       str      ,							**__magic_name__			:       str							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""simple docstring"""

				shutil.rmtree(self.tmpdirname							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       str							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.get_tokenizer()
				lowerCAmelCase__								=							self.get_feature_extractor()

				lowerCAmelCase__								=							WavaVecaProcessor(tokenizer=__magic_name__      ,							feature_extractor=__magic_name__							)

				processor.save_pretrained(self.tmpdirname							)
				lowerCAmelCase__								=							WavaVecaProcessor.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 							__SCREAMING_SNAKE_CASE  (					self			:       int							):


				"""simple docstring"""

				lowerCAmelCase__								=							WavaVecaProcessor(tokenizer=self.get_tokenizer()      ,							feature_extractor=self.get_feature_extractor()							)
				processor.save_pretrained(self.tmpdirname							)

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

				lowerCAmelCase__								=							WavaVecaProcessor.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 							__SCREAMING_SNAKE_CASE  (					self			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.get_feature_extractor()
				lowerCAmelCase__								=							self.get_tokenizer()

				lowerCAmelCase__								=							WavaVecaProcessor(tokenizer=__magic_name__      ,							feature_extractor=__magic_name__							)

				lowerCAmelCase__								=							floats_list((3, 1000)							)

				lowerCAmelCase__								=							feature_extractor(__magic_name__      ,							return_tensors="np"							)
				lowerCAmelCase__								=							processor(__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 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.get_feature_extractor()
				lowerCAmelCase__								=							self.get_tokenizer()

				lowerCAmelCase__								=							WavaVecaProcessor(tokenizer=__magic_name__      ,							feature_extractor=__magic_name__							)

				lowerCAmelCase__								=							"This is a test string"

				lowerCAmelCase__								=							processor(text=__magic_name__							)

				lowerCAmelCase__								=							tokenizer(__magic_name__							)

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





	def 							__SCREAMING_SNAKE_CASE  (					self			:       int							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.get_feature_extractor()
				lowerCAmelCase__								=							self.get_tokenizer()

				lowerCAmelCase__								=							WavaVecaProcessor(tokenizer=__magic_name__      ,							feature_extractor=__magic_name__							)

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

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

				self.assertListEqual(__magic_name__      ,							__magic_name__							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__								=							self.get_feature_extractor()
				lowerCAmelCase__								=							self.get_tokenizer()

				lowerCAmelCase__								=							WavaVecaProcessor(tokenizer=__magic_name__      ,							feature_extractor=__magic_name__							)

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







'''simple docstring'''

from ..utils import DummyObject, requires_backends






class    A     (	metaclass=SCREAMING_SNAKE_CASE__    ):
	snake_case__      :Optional[Any]        =     ['torch', 'transformers', 'k_diffusion']





	def __init__(					self			:       Optional[Any]      ,							*__magic_name__			:       Optional[int]      ,							**__magic_name__			:       str							):


				"""simple docstring"""

				requires_backends(self      ,							["torch", "transformers", "k_diffusion"]							)





	@classmethod
	def 							__SCREAMING_SNAKE_CASE  (					cls			:       str      ,							*__magic_name__			:       Union[str, Any]      ,							**__magic_name__			:       int							):


				"""simple docstring"""

				requires_backends(cls      ,							["torch", "transformers", "k_diffusion"]							)







	@classmethod
	def 							__SCREAMING_SNAKE_CASE  (					cls			:       int      ,							*__magic_name__			:       Dict      ,							**__magic_name__			:       str							):


				"""simple docstring"""

				requires_backends(cls      ,							["torch", "transformers", "k_diffusion"]							)





