

'''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   UpperCamelCase__   (       unittest.TestCase       ):


		"""simple docstring"""

		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Any        =       "<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		:							List[str]        =       dict(zip(snake_case__   ,      range(len(snake_case__      )      )      )      )

							UpperCAmelCase		:							Tuple        =       {
							    "pad_token": "<pad>",
							    "unk_token": "<unk>",
							    "bos_token": "<s>",
							    "eos_token": "</s>",
							}
							UpperCAmelCase		:							Dict        =       {
							    "feature_size": 1,
							    "padding_value": 0.0,
							    "sampling_rate": 1_6_0_0_0,
							    "return_attention_mask": False,
							    "do_normalize": True,
							}

							UpperCAmelCase		:							str        =       tempfile.mkdtemp()
							UpperCAmelCase		:							Optional[Any]        =       os.path.join(self.tmpdirname   ,      VOCAB_FILES_NAMES["vocab_file"]      )
							UpperCAmelCase		:							Optional[int]        =       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 				A_	(			self   ,      **snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[int]        =       self.add_kwargs_tokens_map.copy()
							kwargs.update(snake_case__      )
							return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname   ,      **snake_case__      )







		def 				A_	(			self   ,      **snake_case      ):


							'''simple docstring'''

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







		def 				A_	(			self      ):


							'''simple docstring'''

							shutil.rmtree(self.tmpdirname      )







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[Any]        =       self.get_tokenizer()
							UpperCAmelCase		:							Union[str, Any]        =       self.get_feature_extractor()

							UpperCAmelCase		:							int        =       WavaVecaProcessor(tokenizer=snake_case__   ,      feature_extractor=snake_case__      )

							processor.save_pretrained(self.tmpdirname      )
							UpperCAmelCase		:							Any        =       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 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							int        =       WavaVecaProcessor(tokenizer=self.get_tokenizer()   ,      feature_extractor=self.get_feature_extractor()      )
							processor.save_pretrained(self.tmpdirname      )

							UpperCAmelCase		:							Union[str, Any]        =       self.get_tokenizer(bos_token="(BOS)"   ,      eos_token="(EOS)"      )
							UpperCAmelCase		:							List[str]        =       self.get_feature_extractor(do_normalize=snake_case__   ,      padding_value=1.0      )

							UpperCAmelCase		:							Any        =       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 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Any        =       self.get_feature_extractor()
							UpperCAmelCase		:							Tuple        =       self.get_tokenizer()

							UpperCAmelCase		:							Optional[int]        =       WavaVecaProcessor(tokenizer=snake_case__   ,      feature_extractor=snake_case__      )

							UpperCAmelCase		:							Dict        =       floats_list((3, 1_0_0_0)      )

							UpperCAmelCase		:							Union[str, Any]        =       feature_extractor(snake_case__   ,      return_tensors="np"      )
							UpperCAmelCase		:							int        =       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 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       self.get_feature_extractor()
							UpperCAmelCase		:							Any        =       self.get_tokenizer()

							UpperCAmelCase		:							List[Any]        =       WavaVecaProcessor(tokenizer=snake_case__   ,      feature_extractor=snake_case__      )

							UpperCAmelCase		:							int        =       "This is a test string"

							UpperCAmelCase		:							List[str]        =       processor(text=snake_case__      )

							UpperCAmelCase		:							List[str]        =       tokenizer(snake_case__      )

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







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[Any]        =       self.get_feature_extractor()
							UpperCAmelCase		:							Union[str, Any]        =       self.get_tokenizer()

							UpperCAmelCase		:							Optional[Any]        =       WavaVecaProcessor(tokenizer=snake_case__   ,      feature_extractor=snake_case__      )

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

							UpperCAmelCase		:							Dict        =       processor.batch_decode(snake_case__      )
							UpperCAmelCase		:							int        =       tokenizer.batch_decode(snake_case__      )

							self.assertListEqual(snake_case__   ,      snake_case__      )







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Any        =       self.get_feature_extractor()
							UpperCAmelCase		:							Tuple        =       self.get_tokenizer()

							UpperCAmelCase		:							Dict        =       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"   ,      )



'''simple docstring'''

from ..utils import DummyObject, requires_backends







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     List[Any]       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

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







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

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







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

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







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     int       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

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







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

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







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

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