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#
# 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 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							str											=      """microsoft/speecht5_tts"""
				SCREAMING_SNAKE_CASE_ :							Tuple											=      (
				    """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."""
				)
				SCREAMING_SNAKE_CASE_ :							Tuple											=      """text_reader"""
				SCREAMING_SNAKE_CASE_ :							Optional[Any]											=      SpeechTaProcessor
				SCREAMING_SNAKE_CASE_ :							Optional[Any]											=      SpeechTaForTextToSpeech
				SCREAMING_SNAKE_CASE_ :							str											=      SpeechTaHifiGan

				SCREAMING_SNAKE_CASE_ :							Optional[Any]											=      ["""text"""]
				SCREAMING_SNAKE_CASE_ :							Dict											=      ["""audio"""]





				def 							__UpperCAmelCase							(   self       :  str							)      ->  List[str]:
							if self.post_processor is None:
										a					=    "microsoft/speecht5_hifigan"
							super().setup()





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  List[Any]=None							)      ->  Optional[Any]:
							a					=    self.pre_processor(text=__lowerCamelCase      ,   return_tensors="pt"      ,   truncation=__lowerCamelCase							)

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

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

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





				def 							__UpperCAmelCase							(   self       :  List[Any]      ,   __lowerCamelCase       :  List[Any]							)      ->  Optional[int]:
							with torch.no_grad():
										return self.model.generate_speech(**__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[Any]      ,   __lowerCamelCase       :  Optional[Any]							)      ->  Optional[int]:
							with torch.no_grad():
										return self.post_processor(__lowerCamelCase							).cpu().detach()






import math

import flax.linen as nn
import jax.numpy as jnp




def     __magic_name__       (				A	:      jnp.ndarray,						A	:      int,						A	:      float = 1,						A	:      float = 1,						A	:      float = 1.0E4,						A	:      bool = False,						A	:      float = 1.0,						):






			'''simple docstring'''







			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
			a					=    float(embedding_dim // 2     )
			a					=    math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
			a					=    min_timescale * jnp.exp(jnp.arange(A,						dtype=jnp.floataa     ) * -log_timescale_increment     )
			a					=    jnp.expand_dims(A,						1     ) * jnp.expand_dims(A,						0     )

			# scale embeddings
			a					=    scale * emb

			if flip_sin_to_cos:
						a					=    jnp.concatenate([jnp.cos(A     ), jnp.sin(A     )],						axis=1     )
			else:
						a					=    jnp.concatenate([jnp.sin(A     ), jnp.cos(A     )],						axis=1     )
			a					=    jnp.reshape(A,						[jnp.shape(A     )[0], embedding_dim]     )
			return signal




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							jnp.dtype											=      jnp.floataa





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  Optional[Any]							)      ->  List[Any]:
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_1"							)(__lowerCamelCase							)
							a					=    nn.silu(__lowerCamelCase							)
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_2"							)(__lowerCamelCase							)
							return temb




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							bool											=      False
				SCREAMING_SNAKE_CASE_ :							float											=      1





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  int							)      ->  Union[str, Any]:
							return get_sinusoidal_embeddings(
							    __lowerCamelCase      ,   embedding_dim=self.dim      ,   flip_sin_to_cos=self.flip_sin_to_cos      ,   freq_shift=self.freq_shift							)






