import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
						from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

						from .test_feature_extraction_speechta import floats_list


lowerCAmelCase__  	=      get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")
@require_torch
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		_lowercase   =tempfile.mkdtemp()

		_lowercase   =SpeechTaTokenizer(lowerCAmelCase_       )
		tokenizer.save_pretrained(self.tmpdirname       )

		_lowercase   ={
		    "feature_size": 1,
		    "padding_value": 0.0,
		    "sampling_rate": 16000,
		    "do_normalize": False,
		    "num_mel_bins": 80,
		    "hop_length": 16,
		    "win_length": 64,
		    "win_function": "hann_window",
		    "fmin": 80,
		    "fmax": 7600,
		    "mel_floor": 1e-10,
		    "reduction_factor": 2,
		    "return_attention_mask": True,
		}

		_lowercase   =os.path.join(self.tmpdirname       ,  lowerCAmelCase_       )
		with open(self.feature_extraction_file       ,  "w"       ,  encoding="utf-8"       ) as fp:
			fp.write(json.dumps(lowerCAmelCase_       ) + "\n"       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		return SpeechTaTokenizer.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		shutil.rmtree(self.tmpdirname       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_tokenizer()
		_lowercase   =self.get_feature_extractor()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		processor.save_pretrained(self.tmpdirname       )
		_lowercase   =SpeechTaProcessor.from_pretrained(self.tmpdirname       )

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

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =SpeechTaProcessor(tokenizer=self.get_tokenizer()       ,  feature_extractor=self.get_feature_extractor()       )
		processor.save_pretrained(self.tmpdirname       )

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

		_lowercase   =SpeechTaProcessor.from_pretrained(
		    self.tmpdirname       ,  bos_token="(BOS)"       ,  eos_token="(EOS)"       ,  do_normalize=lowerCAmelCase_       ,  padding_value=1.0       )

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

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   =floats_list((3, 1000)       )

		_lowercase   =feature_extractor(audio=lowerCAmelCase_       ,  return_tensors="np"       )
		_lowercase   =processor(audio=lowerCAmelCase_       ,  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       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   =floats_list((3, 1000)       )

		_lowercase   =feature_extractor(audio_target=lowerCAmelCase_       ,  return_tensors="np"       )
		_lowercase   =processor(audio_target=lowerCAmelCase_       ,  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       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   ="This is a test string"

		_lowercase   =processor(text=lowerCAmelCase_       )
		_lowercase   =tokenizer(lowerCAmelCase_       )

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   ="This is a test string"

		_lowercase   =processor(text_target=lowerCAmelCase_       )
		_lowercase   =tokenizer(lowerCAmelCase_       )

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

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

		_lowercase   =processor.batch_decode(lowerCAmelCase_       )
		_lowercase   =tokenizer.batch_decode(lowerCAmelCase_       )

		self.assertListEqual(lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =SpeechTaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

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












__lowerCamelCase						= {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

__lowerCamelCase						= {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		list:







							"""simple docstring"""





							_a  :    List[Any]			=						True
							_a  :    Optional[Any]			=						[]

							for neighbour in graph[vert]:
														if not visited[neighbour]:
																					connected_verts += dfs(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )

							return [vert, *connected_verts]






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		list:







							"""simple docstring"""





							_a  :    Optional[int]			=						len(UpperCAmelCase     )
							_a  :    Optional[Any]			=						graph_size * [False]
							_a  :    Optional[int]			=						[]

							for i in range(UpperCAmelCase     ):
														if not visited[i]:
																					_a  :    List[str]			=						dfs(UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )
																					components_list.append(UpperCAmelCase     )

							return components_list


if __name__ == "__main__":
						import doctest

						doctest.testmod()