




import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				(CMStochasticIterativeScheduler,)
	_lowerCAmelCase							       =				10





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					A__  :	str         =							{
					    '''num_train_timesteps''': 201,
					    '''sigma_min''': 0.0_0_2,
					    '''sigma_max''': 80.0,
					}

					config.update(**UpperCamelCase__		)
					return config





	def   __snake_case				(			self		):
					A__  :	Tuple         =							10

					A__  :	Any         =							self.get_scheduler_config()
					A__  :	Tuple         =							self.scheduler_classes[0](**UpperCamelCase__		)

					scheduler.set_timesteps(UpperCamelCase__		)

					A__  :	Dict         =							scheduler.timesteps[0]
					A__  :	Dict         =							scheduler.timesteps[1]

					A__  :	List[str]         =							self.dummy_sample
					A__  :	str         =							0.1 * sample

					A__  :	Dict         =							scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		).prev_sample
					A__  :	int         =							scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		).prev_sample

					self.assertEqual(output_a.shape				,       sample.shape		)
					self.assertEqual(output_a.shape				,       output_a.shape		)





	def   __snake_case				(			self		):
					for timesteps in [10, 50, 100, 1000]:
									self.check_over_configs(num_train_timesteps=UpperCamelCase__		)





	def   __snake_case				(			self		):
					for clip_denoised in [True, False]:
									self.check_over_configs(clip_denoised=UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							self.scheduler_classes[0]
					A__  :	Dict         =							self.get_scheduler_config()
					A__  :	List[Any]         =							scheduler_class(**UpperCamelCase__		)

					A__  :	Optional[Any]         =							1
					scheduler.set_timesteps(UpperCamelCase__		)
					A__  :	Optional[int]         =							scheduler.timesteps

					A__  :	Tuple         =							torch.manual_seed(0		)

					A__  :	Optional[int]         =							self.dummy_model()
					A__  :	Dict         =							self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(UpperCamelCase__		):
									# 1. scale model input
									A__  :	Tuple         =							scheduler.scale_model_input(UpperCamelCase__				,       UpperCamelCase__		)

									# 2. predict noise residual
									A__  :	Dict         =							model(UpperCamelCase__				,       UpperCamelCase__		)

									# 3. predict previous sample x_t-1
									A__  :	Optional[int]         =							scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       generator=UpperCamelCase__		).prev_sample

									A__  :	Any         =							pred_prev_sample

					A__  :	Tuple         =							torch.sum(torch.abs(UpperCamelCase__		)		)
					A__  :	str         =							torch.mean(torch.abs(UpperCamelCase__		)		)

					assert abs(result_sum.item() - 192.7614		) < 1e-2
					assert abs(result_mean.item() - 0.2_5_1_0		) < 1e-3





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.scheduler_classes[0]
					A__  :	Any         =							self.get_scheduler_config()
					A__  :	str         =							scheduler_class(**UpperCamelCase__		)

					A__  :	Any         =							[106, 0]
					scheduler.set_timesteps(timesteps=UpperCamelCase__		)
					A__  :	int         =							scheduler.timesteps

					A__  :	Optional[int]         =							torch.manual_seed(0		)

					A__  :	int         =							self.dummy_model()
					A__  :	str         =							self.dummy_sample_deter * scheduler.init_noise_sigma

					for t in timesteps:
									# 1. scale model input
									A__  :	Any         =							scheduler.scale_model_input(UpperCamelCase__				,       UpperCamelCase__		)

									# 2. predict noise residual
									A__  :	Optional[Any]         =							model(UpperCamelCase__				,       UpperCamelCase__		)

									# 3. predict previous sample x_t-1
									A__  :	Union[str, Any]         =							scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       generator=UpperCamelCase__		).prev_sample

									A__  :	Tuple         =							pred_prev_sample

					A__  :	List[Any]         =							torch.sum(torch.abs(UpperCamelCase__		)		)
					A__  :	str         =							torch.mean(torch.abs(UpperCamelCase__		)		)

					assert abs(result_sum.item() - 347.6357		) < 1e-2
					assert abs(result_mean.item() - 0.4_5_2_7		) < 1e-3





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.scheduler_classes[0]
					A__  :	str         =							self.get_scheduler_config()
					A__  :	int         =							scheduler_class(**UpperCamelCase__		)

					A__  :	Optional[Any]         =							[39, 30, 12, 15, 0]

					with self.assertRaises(UpperCamelCase__				,       msg='''`timesteps` must be in descending order.'''		):
									scheduler.set_timesteps(timesteps=UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							self.scheduler_classes[0]
					A__  :	Tuple         =							self.get_scheduler_config()
					A__  :	Optional[int]         =							scheduler_class(**UpperCamelCase__		)

					A__  :	Optional[int]         =							[39, 30, 12, 1, 0]
					A__  :	Tuple         =							len(UpperCamelCase__		)

					with self.assertRaises(UpperCamelCase__				,       msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''		):
									scheduler.set_timesteps(num_inference_steps=UpperCamelCase__				,       timesteps=UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	List[str]         =							self.scheduler_classes[0]
					A__  :	int         =							self.get_scheduler_config()
					A__  :	Union[str, Any]         =							scheduler_class(**UpperCamelCase__		)

					A__  :	str         =							[scheduler.config.num_train_timesteps]

					with self.assertRaises(
					    UpperCamelCase__				,       msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,       ):
									scheduler.set_timesteps(timesteps=UpperCamelCase__		)

# 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  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"microsoft/speecht5_tts"
	_lowerCAmelCase							       =				(
	    "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."
	)
	_lowerCAmelCase							       =				"text_reader"
	_lowerCAmelCase							       =				SpeechTaProcessor
	_lowerCAmelCase							       =				SpeechTaForTextToSpeech
	_lowerCAmelCase							       =				SpeechTaHifiGan

	_lowerCAmelCase							       =				["text"]
	_lowerCAmelCase							       =				["audio"]





	def   __snake_case				(			self		):
					if self.post_processor is None:
									A__  :	int         =							'''microsoft/speecht5_hifigan'''
					super().setup()





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__=None		):
					A__  :	List[Any]         =							self.pre_processor(text=UpperCamelCase__				,       return_tensors='''pt'''				,       truncation=UpperCamelCase__		)

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

									A__  :	List[Any]         =							load_dataset('''Matthijs/cmu-arctic-xvectors'''				,       split='''validation'''		)
									A__  :	Dict         =							torch.tensor(embeddings_dataset[7305]['''xvector''']		).unsqueeze(0		)

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





	def   __snake_case				(			self				,       UpperCamelCase__		):
					with torch.no_grad():
									return self.model.generate_speech(**UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					with torch.no_grad():
									return self.post_processor(UpperCamelCase__		).cpu().detach()