"""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    a__    (   _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 	snake_case__	(     self  ):
        '''simple docstring'''



        if self.post_processor is None:
             lowercase__				 =							"microsoft/speecht5_hifigan"
        super().setup()


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase=None  ):
        '''simple docstring'''



        lowercase__				 =							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."  )

             lowercase__				 =							load_dataset("Matthijs/cmu-arctic-xvectors",				split="validation"  )
             lowercase__				 =							torch.tensor(embeddings_dataset[7305]["xvector"]  ).unsqueeze(0  )

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


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        with torch.no_grad():
             return self.model.generate_speech(**_UpperCAmelCase  )


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        with torch.no_grad():
             return self.post_processor(_UpperCAmelCase  ).cpu().detach()





"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_:					List[Any]     =					logging.get_logger(__name__)

lowerCAmelCase_:					int     =					{
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}






class    a__    (   _a	):
   snake_case_							=      "markuplm"


   def __init__(     self,				_UpperCAmelCase=3_0522,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=512,				_UpperCAmelCase=2,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=0,				_UpperCAmelCase=0,				_UpperCAmelCase=2,				_UpperCAmelCase=256,				_UpperCAmelCase=1024,				_UpperCAmelCase=216,				_UpperCAmelCase=1001,				_UpperCAmelCase=32,				_UpperCAmelCase=50,				_UpperCAmelCase="absolute",				_UpperCAmelCase=True,				_UpperCAmelCase=None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            pad_token_id=_UpperCAmelCase,				bos_token_id=_UpperCAmelCase,				eos_token_id=_UpperCAmelCase,				**_UpperCAmelCase,				)
        lowercase__				 =							vocab_size
        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							hidden_act
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							max_position_embeddings
        lowercase__				 =							type_vocab_size
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps
        lowercase__				 =							position_embedding_type
        lowercase__				 =							use_cache
        lowercase__				 =							classifier_dropout
        # additional properties
        lowercase__				 =							max_depth
        lowercase__				 =							max_xpath_tag_unit_embeddings
        lowercase__				 =							max_xpath_subs_unit_embeddings
        lowercase__				 =							tag_pad_id
        lowercase__				 =							subs_pad_id
        lowercase__				 =							xpath_unit_hidden_size




