





'''simple docstring'''






import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
					import transformers






def      __A						(							a_						:  int	):
		random.seed(a_	)
		np.random.seed(a_	)
		torch.manual_seed(a_	)
		torch.cuda.manual_seed_all(a_	)
		# ^^ safe to call this function even if cuda is not available

class 		lowerCamelCase  :



	def __init__(   self		,      a_		,      a_ = 0.9999		,      a_ = 0.0		,      a_ = 0		,      a_ = False		,      a_ = 1.0		,      a_ = 2 / 3		,      a_ = None		,      a_ = None		,      **a_		,      ):

			if isinstance(a_		,      torch.nn.Module	):
					lowerCAmelCase     :    List[str]						=   (
					    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
					    "Please pass the parameters of the module instead."
					)
					deprecate(
					    "passing a `torch.nn.Module` to `ExponentialMovingAverage`"		,      "1.0.0"		,      a_		,      standard_warn=a_		,      )
					lowerCAmelCase     :    Optional[Any]						=   parameters.parameters()

					# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
					lowerCAmelCase     :    Optional[int]						=   True

			if kwargs.get("max_value"		,      a_	) is not None:
					lowerCAmelCase     :    Union[str, Any]						=   "The `max_value` argument is deprecated. Please use `decay` instead."
					deprecate("max_value"		,      "1.0.0"		,      a_		,      standard_warn=a_	)
					lowerCAmelCase     :    Any						=   kwargs["max_value"]

			if kwargs.get("min_value"		,      a_	) is not None:
					lowerCAmelCase     :    Optional[Any]						=   "The `min_value` argument is deprecated. Please use `min_decay` instead."
					deprecate("min_value"		,      "1.0.0"		,      a_		,      standard_warn=a_	)
					lowerCAmelCase     :    Any						=   kwargs["min_value"]

			lowerCAmelCase     :    Dict						=   list(a_	)
			lowerCAmelCase     :    List[str]						=   [p.clone().detach() for p in parameters]

			if kwargs.get("device"		,      a_	) is not None:
					lowerCAmelCase     :    List[str]						=   "The `device` argument is deprecated. Please use `to` instead."
					deprecate("device"		,      "1.0.0"		,      a_		,      standard_warn=a_	)
					self.to(device=kwargs["device"]	)

			lowerCAmelCase     :    Dict						=   None

			lowerCAmelCase     :    int						=   decay
			lowerCAmelCase     :    Optional[Any]						=   min_decay
			lowerCAmelCase     :    Dict						=   update_after_step
			lowerCAmelCase     :    Optional[Any]						=   use_ema_warmup
			lowerCAmelCase     :    Any						=   inv_gamma
			lowerCAmelCase     :    Optional[int]						=   power
			lowerCAmelCase     :    int						=   0
			lowerCAmelCase     :    List[Any]						=   None  # set in `step()`

			lowerCAmelCase     :    Dict						=   model_cls
			lowerCAmelCase     :    str						=   model_config



	@classmethod
	def   _lowerCamelCase    (   cls		,      a_		,      a_	):
			lowerCAmelCase	,		lowerCAmelCase     :    Dict						=   model_cls.load_config(a_		,      return_unused_kwargs=a_	)
			lowerCAmelCase     :    Optional[Any]						=   model_cls.from_pretrained(a_	)

			lowerCAmelCase     :    Dict						=   cls(model.parameters()		,      model_cls=a_		,      model_config=model.config	)

			ema_model.load_state_dict(a_	)
			return ema_model



	def   _lowerCamelCase    (   self		,      a_	):
			if self.model_cls is None:
					raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."	)

			if self.model_config is None:
					raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."	)

			lowerCAmelCase     :    Union[str, Any]						=   self.model_cls.from_config(self.model_config	)
			lowerCAmelCase     :    int						=   self.state_dict()
			state_dict.pop("shadow_params"		,      a_	)

			model.register_to_config(**a_	)
			self.copy_to(model.parameters()	)
			model.save_pretrained(a_	)



	def   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    List[Any]						=   max(0		,      optimization_step - self.update_after_step - 1	)

			if step <= 0:
					return 0.0

			if self.use_ema_warmup:
					lowerCAmelCase     :    Any						=   1 - (1 + step / self.inv_gamma) ** -self.power
			else:
					lowerCAmelCase     :    List[Any]						=   (1 + step) / (10 + step)

			lowerCAmelCase     :    str						=   min(a_		,      self.decay	)
			# make sure decay is not smaller than min_decay
			lowerCAmelCase     :    List[Any]						=   max(a_		,      self.min_decay	)
			return cur_decay_value



	@torch.no_grad()
	def   _lowerCamelCase    (   self		,      a_	):
			if isinstance(a_		,      torch.nn.Module	):
					lowerCAmelCase     :    List[str]						=   (
					    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
					    "Please pass the parameters of the module instead."
					)
					deprecate(
					    "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"		,      "1.0.0"		,      a_		,      standard_warn=a_		,      )
					lowerCAmelCase     :    str						=   parameters.parameters()

			lowerCAmelCase     :    Dict						=   list(a_	)

			self.optimization_step += 1

			# Compute the decay factor for the exponential moving average.
			lowerCAmelCase     :    str						=   self.get_decay(self.optimization_step	)
			lowerCAmelCase     :    int						=   decay
			lowerCAmelCase     :    Any						=   1 - decay

			lowerCAmelCase     :    Optional[int]						=   contextlib.nullcontext
			if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
					import deepspeed

			for s_param, param in zip(self.shadow_params		,      a_	):
					if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
							lowerCAmelCase     :    Dict						=   deepspeed.zero.GatheredParameters(a_		,      modifier_rank=a_	)

					with context_manager():
							if param.requires_grad:
									s_param.sub_(one_minus_decay * (s_param - param)	)
							else:
									s_param.copy_(a_	)



	def   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    Tuple						=   list(a_	)
			for s_param, param in zip(self.shadow_params		,      a_	):
					param.data.copy_(s_param.to(param.device	).data	)



	def   _lowerCamelCase    (   self		,      a_=None		,      a_=None	):
			lowerCAmelCase     :    str						=   [
			    p.to(device=a_		,      dtype=a_	) if p.is_floating_point() else p.to(device=a_	)
			    for p in self.shadow_params
			]



	def   _lowerCamelCase    (   self	):
			return {
			    "decay": self.decay,
			    "min_decay": self.min_decay,
			    "optimization_step": self.optimization_step,
			    "update_after_step": self.update_after_step,
			    "use_ema_warmup": self.use_ema_warmup,
			    "inv_gamma": self.inv_gamma,
			    "power": self.power,
			    "shadow_params": self.shadow_params,
			}



	def   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    Tuple						=   [param.detach().cpu().clone() for param in parameters]



	def   _lowerCamelCase    (   self		,      a_	):
			if self.temp_stored_params is None:
					raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"	)
			for c_param, param in zip(self.temp_stored_params		,      a_	):
					param.data.copy_(c_param.data	)

			# Better memory-wise.
			lowerCAmelCase     :    Dict						=   None





	def   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    Any						=   copy.deepcopy(a_	)

			lowerCAmelCase     :    str						=   state_dict.get("decay"		,      self.decay	)
			if self.decay < 0.0 or self.decay > 1.0:
					raise ValueError("Decay must be between 0 and 1"	)

			lowerCAmelCase     :    Optional[Any]						=   state_dict.get("min_decay"		,      self.min_decay	)
			if not isinstance(self.min_decay		,      a_	):
					raise ValueError("Invalid min_decay"	)

			lowerCAmelCase     :    str						=   state_dict.get("optimization_step"		,      self.optimization_step	)
			if not isinstance(self.optimization_step		,      a_	):
					raise ValueError("Invalid optimization_step"	)

			lowerCAmelCase     :    Dict						=   state_dict.get("update_after_step"		,      self.update_after_step	)
			if not isinstance(self.update_after_step		,      a_	):
					raise ValueError("Invalid update_after_step"	)

			lowerCAmelCase     :    int						=   state_dict.get("use_ema_warmup"		,      self.use_ema_warmup	)
			if not isinstance(self.use_ema_warmup		,      a_	):
					raise ValueError("Invalid use_ema_warmup"	)

			lowerCAmelCase     :    Optional[int]						=   state_dict.get("inv_gamma"		,      self.inv_gamma	)
			if not isinstance(self.inv_gamma		,      (float, int)	):
					raise ValueError("Invalid inv_gamma"	)

			lowerCAmelCase     :    Any						=   state_dict.get("power"		,      self.power	)
			if not isinstance(self.power		,      (float, int)	):
					raise ValueError("Invalid power"	)

			lowerCAmelCase     :    Dict						=   state_dict.get("shadow_params"		,      a_	)
			if shadow_params is not None:
					lowerCAmelCase     :    Any						=   shadow_params
					if not isinstance(self.shadow_params		,      a_	):
							raise ValueError("shadow_params must be a list"	)
					if not all(isinstance(a_		,      torch.Tensor	) for p in self.shadow_params	):
							raise ValueError("shadow_params must all be Tensors"	)










'''simple docstring'''






# 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 		lowerCamelCase  (      _A       ):
	snake_case_      =   "microsoft/speecht5_tts"
	snake_case_      =   (
	    "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."
	)
	snake_case_      =   "text_reader"
	snake_case_      =   SpeechTaProcessor
	snake_case_      =   SpeechTaForTextToSpeech
	snake_case_      =   SpeechTaHifiGan

	snake_case_      =   ["text"]
	snake_case_      =   ["audio"]



	def   _lowerCamelCase    (   self	):
			if self.post_processor is None:
					lowerCAmelCase     :    str						=   "microsoft/speecht5_hifigan"
			super().setup()



	def   _lowerCamelCase    (   self		,      a_		,      a_=None	):
			lowerCAmelCase     :    str						=   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."	)

					lowerCAmelCase     :    Tuple						=   load_dataset("Matthijs/cmu-arctic-xvectors"		,      split="validation"	)
					lowerCAmelCase     :    Union[str, Any]						=   torch.tensor(embeddings_dataset[7_305]["xvector"]	).unsqueeze(0	)

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



	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.model.generate_speech(**a_	)





	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.post_processor(a_	).cpu().detach()



