






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"microsoft/speecht5_tts"
					a_     =		(
					    "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."
					)
					a_     =		"text_reader"
					a_     =		SpeechTaProcessor
					a_     =		SpeechTaForTextToSpeech
					a_     =		SpeechTaHifiGan

					a_     =		["text"]
					a_     =		["audio"]



					def 	_lowercase					( self					:  List[str]	):
												if self.post_processor is None:
																			snake_case__			:				Optional[Any]       =  "microsoft/speecht5_hifigan"
												super().setup()



					def 	_lowercase					( self					:  Any							,		__A					:  Any							,		__A					:  List[str]=None	):
												snake_case__			:				Dict       =  self.pre_processor(text=__A							,		return_tensors="pt"							,		truncation=__A	)

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

																			snake_case__			:				Union[str, Any]       =  load_dataset("Matthijs/cmu-arctic-xvectors"							,		split="validation"	)
																			snake_case__			:				Optional[Any]       =  torch.tensor(embeddings_dataset[7_3_0_5]["xvector"]	).unsqueeze(0	)

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



					def 	_lowercase					( self					:  List[str]							,		__A					:  List[Any]	):
												with torch.no_grad():
																			return self.model.generate_speech(**__A	)






					def 	_lowercase					( self					:  Optional[int]							,		__A					:  int	):
												with torch.no_grad():
																			return self.post_processor(__A	).cpu().detach()











import os

import pytest
from attr import dataclass


__lowerCamelCase :      Any										=				"""us-east-1"""  # defaults region



@dataclass
class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					a_     =		42
					a_     =		"arn:aws:iam::558105141721:role/sagemaker_execution_role"
					a_     =		{
					    "task_name": "mnli",
					    "per_device_train_batch_size": 1_6,
					    "per_device_eval_batch_size": 1_6,
					    "do_train": True,
					    "do_eval": True,
					    "do_predict": True,
					    "output_dir": "/opt/ml/model",
					    "overwrite_output_dir": True,
					    "max_steps": 5_0_0,
					    "save_steps": 5_5_0_0,
					}
					a_     =		{**hyperparameters, "max_steps": 1_0_0_0}



					@property
					def 	_lowercase					( self					:  List[Any]	):
						if self.framework == "pytorch":
							return [
							    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
							    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
							    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
							]
						else:
							return [
							    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
							    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
							    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
							]



					@property
					def 	_lowercase					( self					:  Any	):
						return f'''{self.framework}-transfromers-test'''



					@property
					def 	_lowercase					( self					:  Optional[Any]	):
						return f'''./tests/sagemaker/scripts/{self.framework}'''






					@property
					def 	_lowercase					( self					:  Union[str, Any]	):
						if self.framework == "pytorch":
							return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
						else:
							return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"







@pytest.fixture(scope="class"       )
def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]       ):
	snake_case__			:				Optional[int]       =  SageMakerTestEnvironment(framework=request.cls.framework       )



