



"""simple docstring"""






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


__UpperCAmelCase      :      Union[str, Any]   					=					get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')

@require_torch
class 	__lowerCAmelCase							(unittest.TestCase  ):


					'''simple docstring'''


					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Union[str, Any]								=						tempfile.mkdtemp()

										snake_case_						:List[str]								=						SpeechTaTokenizer(a   )
										tokenizer.save_pretrained(self.tmpdirname   )

										snake_case_						:Dict								=						{
										    "feature_size": 1,
										    "padding_value": 0.0,
										    "sampling_rate": 1_60_00,
										    "do_normalize": False,
										    "num_mel_bins": 80,
										    "hop_length": 16,
										    "win_length": 64,
										    "win_function": "hann_window",
										    "fmin": 80,
										    "fmax": 76_00,
										    "mel_floor": 1e-1_0,
										    "reduction_factor": 2,
										    "return_attention_mask": True,
										}

										snake_case_						:List[Any]								=						os.path.join(self.tmpdirname    ,       a   )
										with open(self.feature_extraction_file    ,       "w"    ,       encoding="utf-8"   ) as fp:
															fp.write(json.dumps(a   ) + "\n"   )







					def 		_a (      self    ,       **a   ):




										"""simple docstring"""





										return SpeechTaTokenizer.from_pretrained(self.tmpdirname    ,       **a   )







					def 		_a (      self    ,       **a   ):




										"""simple docstring"""





										return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname    ,       **a   )







					def 		_a (      self   ):




										"""simple docstring"""





										shutil.rmtree(self.tmpdirname   )







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Dict								=						self.get_tokenizer()
										snake_case_						:Optional[Any]								=						self.get_feature_extractor()

										snake_case_						:Dict								=						SpeechTaProcessor(tokenizer=a    ,       feature_extractor=a   )

										processor.save_pretrained(self.tmpdirname   )
										snake_case_						:Tuple								=						SpeechTaProcessor.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 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Tuple								=						SpeechTaProcessor(tokenizer=self.get_tokenizer()    ,       feature_extractor=self.get_feature_extractor()   )
										processor.save_pretrained(self.tmpdirname   )

										snake_case_						:List[Any]								=						self.get_tokenizer(bos_token="(BOS)"    ,       eos_token="(EOS)"   )
										snake_case_						:Tuple								=						self.get_feature_extractor(do_normalize=a    ,       padding_value=1.0   )

										snake_case_						:Dict								=						SpeechTaProcessor.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 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Any								=						self.get_feature_extractor()
										snake_case_						:str								=						self.get_tokenizer()

										snake_case_						:Optional[Any]								=						SpeechTaProcessor(tokenizer=a    ,       feature_extractor=a   )

										snake_case_						:Optional[int]								=						floats_list((3, 10_00)   )

										snake_case_						:List[Any]								=						feature_extractor(audio=a    ,       return_tensors="np"   )
										snake_case_						:Tuple								=						processor(audio=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 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Tuple								=						self.get_feature_extractor()
										snake_case_						:str								=						self.get_tokenizer()

										snake_case_						:str								=						SpeechTaProcessor(tokenizer=a    ,       feature_extractor=a   )

										snake_case_						:str								=						floats_list((3, 10_00)   )

										snake_case_						:List[str]								=						feature_extractor(audio_target=a    ,       return_tensors="np"   )
										snake_case_						:Tuple								=						processor(audio_target=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 		_a (      self   ):




										"""simple docstring"""





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

										snake_case_						:str								=						SpeechTaProcessor(tokenizer=a    ,       feature_extractor=a   )

										snake_case_						:int								=						"This is a test string"

										snake_case_						:List[str]								=						processor(text=a   )
										snake_case_						:Optional[int]								=						tokenizer(a   )

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







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Any								=						self.get_feature_extractor()
										snake_case_						:Dict								=						self.get_tokenizer()

										snake_case_						:Any								=						SpeechTaProcessor(tokenizer=a    ,       feature_extractor=a   )

										snake_case_						:Dict								=						"This is a test string"

										snake_case_						:List[Any]								=						processor(text_target=a   )
										snake_case_						:Any								=						tokenizer(a   )

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







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:str								=						self.get_feature_extractor()
										snake_case_						:int								=						self.get_tokenizer()

										snake_case_						:Optional[int]								=						SpeechTaProcessor(tokenizer=a    ,       feature_extractor=a   )

										snake_case_						:Optional[int]								=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										snake_case_						:Tuple								=						processor.batch_decode(a   )
										snake_case_						:Any								=						tokenizer.batch_decode(a   )

										self.assertListEqual(a    ,       a   )







					def 		_a (      self   ):




										"""simple docstring"""





										snake_case_						:Tuple								=						self.get_feature_extractor()
										snake_case_						:Tuple								=						self.get_tokenizer()

										snake_case_						:int								=						SpeechTaProcessor(tokenizer=a    ,       feature_extractor=a   )

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






"""simple docstring"""






def      A      (			_A,     _A,     _A,     _A,     _A,     _A,     ):




					"""simple docstring"""
					snake_case_						:List[Any]								=						list(_A							)
					snake_case_						:int								=						list(_A							)
					snake_case_						:List[Any]								=						len(_A							)
					snake_case_						:List[Any]								=						len(_A							)

					snake_case_						:Union[str, Any]								=						[
					    [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
					]
					snake_case_						:int								=						[
					    ["0" for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
					]

					for i in range(1,     len_source_seq + 1							):
										snake_case_						:List[str]								=						i * delete_cost
										snake_case_						:str								=						F'''D{source_seq[i - 1]:c}'''

					for i in range(1,     len_destination_seq + 1							):
										snake_case_						:int								=						i * insert_cost
										snake_case_						:Dict								=						F'''I{destination_seq[i - 1]:c}'''

					for i in range(1,     len_source_seq + 1							):
										for j in range(1,     len_destination_seq + 1							):
															if source_seq[i - 1] == destination_seq[j - 1]:
																				snake_case_						:List[Any]								=						costs[i - 1][j - 1] + copy_cost
																				snake_case_						:str								=						F'''C{source_seq[i - 1]:c}'''
															else:
																				snake_case_						:Any								=						costs[i - 1][j - 1] + replace_cost
																				snake_case_						:Any								=						F'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]							)

															if costs[i - 1][j] + delete_cost < costs[i][j]:
																				snake_case_						:str								=						costs[i - 1][j] + delete_cost
																				snake_case_						:Tuple								=						F'''D{source_seq[i - 1]:c}'''

															if costs[i][j - 1] + insert_cost < costs[i][j]:
																				snake_case_						:int								=						costs[i][j - 1] + insert_cost
																				snake_case_						:int								=						F'''I{destination_seq[j - 1]:c}'''

					return costs, ops
def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					if i == 0 and j == 0:
										return []
					else:
										if ops[i][j][0] == "C" or ops[i][j][0] == "R":
															snake_case_						:Optional[Any]								=						assemble_transformation(_A,     i - 1,     j - 1							)
															seq.append(ops[i][j]							)
															return seq
										elif ops[i][j][0] == "D":
															snake_case_						:Union[str, Any]								=						assemble_transformation(_A,     i - 1,     _A							)
															seq.append(ops[i][j]							)
															return seq
										else:
															snake_case_						:Optional[Any]								=						assemble_transformation(_A,     _A,     j - 1							)
															seq.append(ops[i][j]							)
															return seq


if __name__ == "__main__":
				__UpperCAmelCase					,	__UpperCAmelCase      :      List[Any]   					=					compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

				__UpperCAmelCase      :      List[Any]   					=					len(operations)
				__UpperCAmelCase      :      Dict   					=					len(operations[0])
				__UpperCAmelCase      :      List[str]   					=					assemble_transformation(operations, m - 1, n - 1)

				__UpperCAmelCase      :      Any   					=					list('Python')
				__UpperCAmelCase      :      Tuple   					=					0
				__UpperCAmelCase      :      Optional[int]   					=					0

				with open('min_cost.txt', 'w') as file:
								for op in sequence:
												print(''.join(string))

												if op[0] == "C":
																file.write('%-16s' % 'Copy %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost -= 1
												elif op[0] == "R":
																__UpperCAmelCase      :      Any   					=					op[2]

																file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
																file.write('\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 1
												elif op[0] == "D":
																string.pop(i)

																file.write('%-16s' % 'Delete %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 2
												else:
																string.insert(i, op[1])

																file.write('%-16s' % 'Insert %c' % op[1])
																file.write('\t\t\t' + ''.join(string))
																file.write('\r\n')

																cost += 2

												i += 1

								print(''.join(string))
								print('Cost: ', cost)

								file.write('\r\nMinimum cost: ' + str(cost))

