


"""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  ):







    """simple docstring"""




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

    UpperCAmelCase       :							Optional[Any]	=       ["""text"""]
    UpperCAmelCase       :							List[Any]	=       ["""audio"""]






    def 	__snake_case							(					self				:    Tuple):
      if self.post_processor is None:
        a						:		Tuple      							=			"microsoft/speecht5_hifigan"
      super().setup()






    def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    List[Any]				, __UpperCAmelCase				:    List[Any]=None):
      a						:		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[str]      							=			load_dataset("Matthijs/cmu-arctic-xvectors"				, split="validation")
        a						:		Optional[int]      							=			torch.tensor(embeddings_dataset[7305]["xvector"]).unsqueeze(0)

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






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    Optional[int]):
      with torch.no_grad():
        return self.model.generate_speech(**__UpperCAmelCase)






    def 	__snake_case							(					self				:    Tuple				, __UpperCAmelCase				:    str):
      with torch.no_grad():
        return self.post_processor(__UpperCAmelCase).cpu().detach()










"""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 re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
 from PIL import Image






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       """naver-clova-ix/donut-base-finetuned-docvqa"""
    UpperCAmelCase       :							Tuple	=       (
        """This is a tool that answers a question about an document (pdf). It takes an input named `document` which """
        """should be the document containing the information, as well as a `question` that is the question about the """
        """document. It returns a text that contains the answer to the question."""
    )
    UpperCAmelCase       :							List[str]	=       """document_qa"""
    UpperCAmelCase       :							str	=       AutoProcessor
    UpperCAmelCase       :							Optional[int]	=       VisionEncoderDecoderModel

    UpperCAmelCase       :							int	=       ["""image""", """text"""]
    UpperCAmelCase       :							int	=       ["""text"""]






    def __init__(					self				:    Tuple				, *__UpperCAmelCase				:    Union[str, Any]				, **__UpperCAmelCase				:    Any):
      if not is_vision_available():
        raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool.")

      super().__init__(*__UpperCAmelCase				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Tuple				, __UpperCAmelCase				:    "Image"				, __UpperCAmelCase				:    str):
      a						:		Any      							=			"<s_docvqa><s_question>{user_input}</s_question><s_answer>"
      a						:		Union[str, Any]      							=			task_prompt.replace("{user_input}"				, __UpperCAmelCase)
      a						:		Optional[Any]      							=			self.pre_processor.tokenizer(
          __UpperCAmelCase				, add_special_tokens=__UpperCAmelCase				, return_tensors="pt").input_ids
      a						:		Any      							=			self.pre_processor(__UpperCAmelCase				, return_tensors="pt").pixel_values

      return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    int):
      return self.model.generate(
          inputs["pixel_values"].to(self.device)				, decoder_input_ids=inputs["decoder_input_ids"].to(self.device)				, max_length=self.model.decoder.config.max_position_embeddings				, early_stopping=__UpperCAmelCase				, pad_token_id=self.pre_processor.tokenizer.pad_token_id				, eos_token_id=self.pre_processor.tokenizer.eos_token_id				, use_cache=__UpperCAmelCase				, num_beams=1				, bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]				, return_dict_in_generate=__UpperCAmelCase				, ).sequences






    def 	__snake_case							(					self				:    str				, __UpperCAmelCase				:    List[Any]):
      a						:		Union[str, Any]      							=			self.pre_processor.batch_decode(__UpperCAmelCase)[0]
      a						:		Optional[Any]      							=			sequence.replace(self.pre_processor.tokenizer.eos_token				, "")
      a						:		Any      							=			sequence.replace(self.pre_processor.tokenizer.pad_token				, "")
      a						:		Optional[Any]      							=			re.sub(r"<.*?>"				, ""				, __UpperCAmelCase				, count=1).strip()  # remove first task start token
      a						:		List[str]      							=			self.pre_processor.tokenajson(__UpperCAmelCase)

      return sequence["answer"]






