'''simple docstring'''

import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
	from datasets import load_dataset


class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		Optional[int]	=	'microsoft/speecht5_tts'
				__UpperCamelCase    :		Optional[int]	=	(
				    'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '
				    'text to read (in English) and returns a waveform object containing the sound.'
				)
				__UpperCamelCase    :		int	=	'text_reader'
				__UpperCamelCase    :		Tuple	=	SpeechTaProcessor
				__UpperCamelCase    :		str	=	SpeechTaForTextToSpeech
				__UpperCamelCase    :		Optional[Any]	=	SpeechTaHifiGan

				__UpperCamelCase    :		Dict	=	['text']
				__UpperCamelCase    :		Any	=	['audio']




				def 					_snake_case  (self		):
										if self.post_processor is None:
																__lowerCAmelCase							       =				'''microsoft/speecht5_hifigan'''
										super().setup()




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase=None		):
										__lowerCAmelCase							       =				self.pre_processor(text=__lowercase       ,			return_tensors='''pt'''       ,			truncation=__lowercase		)

										if speaker_embeddings is None:
																if not is_datasets_available():
																						raise ImportError('''Datasets needs to be installed if not passing speaker embeddings.'''		)

																__lowerCAmelCase							       =				load_dataset('''Matthijs/cmu-arctic-xvectors'''       ,			split='''validation'''		)
																__lowerCAmelCase							       =				torch.tensor(embeddings_dataset[73_05]['''xvector''']		).unsqueeze(0		)

										return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}




				def 					_snake_case  (self       ,			__lowercase		):
										with torch.no_grad():
																return self.model.generate_speech(**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										with torch.no_grad():
																return self.post_processor(__lowercase		).cpu().detach()





'''simple docstring'''

import json
import sys

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						with open(lowerCamelCase,				encoding='''utf-8''') as f:
												__lowerCAmelCase							       =				json.load(lowerCamelCase)

						__lowerCAmelCase							       =				['''<details>''', '''<summary>Show updated benchmarks!</summary>''', ''' ''']

						for benchmark_name in sorted(lowerCamelCase):
												__lowerCAmelCase							       =				results[benchmark_name]

												__lowerCAmelCase							       =				benchmark_name.split('''/''')[-1]
												output_md.append(F"""### Benchmark: {benchmark_file_name}""")

												__lowerCAmelCase							       =				'''| metric |'''
												__lowerCAmelCase							       =				'''|--------|'''
												__lowerCAmelCase							       =				'''| new / old (diff) |'''
												for metric_name in sorted(lowerCamelCase):
																		__lowerCAmelCase							       =				benchmark_res[metric_name]
																		__lowerCAmelCase							       =				metric_vals['''new''']
																		__lowerCAmelCase							       =				metric_vals.get('''old''',				lowerCamelCase)
																		__lowerCAmelCase							       =				metric_vals.get('''diff''',				lowerCamelCase)

																		__lowerCAmelCase							       =				F""" {new_val:f}""" if isinstance(lowerCamelCase,				(int, float)) else '''None'''

																		if old_val is not None:
																								val_str += F""" / {old_val:f}""" if isinstance(lowerCamelCase,				(int, float)) else "None"
																		if dif_val is not None:
																								val_str += F""" ({dif_val:f})""" if isinstance(lowerCamelCase,				(int, float)) else "None"

																		title += " " + metric_name + " |"
																		lines += "---|"
																		value += val_str + " |"

												output_md += [title, lines, value, " "]

						output_md.append('''</details>''')

						with open(lowerCamelCase,				'''w''',				encoding='''utf-8''') as f:
												f.writelines('''\n'''.join(lowerCamelCase))


if __name__ == "__main__":
	_UpperCAmelCase				: str							    =				sys.argv[1]
	_UpperCAmelCase				: Optional[Any]							    =				sys.argv[2]

	format_json_to_md(input_json_file, output_md_file)

