



'''simple docstring'''






from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class 							a__(					__lowerCamelCase	):
    lowercase__ =    "EncodecFeatureExtractor"
    lowercase__ =    ("T5Tokenizer", "T5TokenizerFast")







    def __init__(      self :  Optional[int]						,			__snake_case :  Optional[Any]						,			__snake_case :  List[Any]	):
      super().__init__(__A						,			__A	)
      a						:					Optional[int]        =     self.feature_extractor
      a						:					Any        =     False







    def 			lowercase_       (      self :  Dict						,			__snake_case :  Dict=None						,			__snake_case :  Dict=None						,			__snake_case :  Union[str, Any]=True	):
      return self.tokenizer.get_decoder_prompt_ids(task=__A						,			language=__A						,			no_timestamps=__A	)







    def __call__(      self :  Union[str, Any]						,			*__snake_case :  List[Any]						,			**__snake_case :  List[Any]	):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*__A						,			**__A	)

      a						:					str        =     kwargs.pop('audio'						,			__A	)
      a						:					Union[str, Any]        =     kwargs.pop('sampling_rate'						,			__A	)
      a						:					Union[str, Any]        =     kwargs.pop('text'						,			__A	)
      if len(__A	) > 0:
        a						:					Dict        =     args[0]
        a						:					Dict        =     args[1:]

      if audio is None and text is None:
        raise ValueError('You need to specify either an `audio` or `text` input to process.'	)

      if text is not None:
        a						:					Optional[int]        =     self.tokenizer(__A						,			**__A	)

      if audio is not None:
        a						:					Optional[Any]        =     self.feature_extractor(__A						,			*__A						,			sampling_rate=__A						,			**__A	)

      if audio is None:
        return inputs

      elif text is None:
        return audio_inputs

      else:
        a						:					Tuple        =     audio_inputs['input_values']
        if "padding_mask" in audio_inputs:
          a						:					Tuple        =     audio_inputs['padding_mask']
        return inputs







    def 			lowercase_       (      self :  Optional[Any]						,			*__snake_case :  str						,			**__snake_case :  Dict	):
      a						:					List[str]        =     kwargs.pop('audio'						,			__A	)
      a						:					int        =     kwargs.pop('padding_mask'						,			__A	)

      if len(__A	) > 0:
        a						:					str        =     args[0]
        a						:					int        =     args[1:]

      if audio_values is not None:
        return self._decode_audio(__A						,			padding_mask=__A	)
      else:
        return self.tokenizer.batch_decode(*__A						,			**__A	)







    def 			lowercase_       (      self :  str						,			*__snake_case :  List[str]						,			**__snake_case :  List[Any]	):
      return self.tokenizer.decode(*__A						,			**__A	)







    def 			lowercase_       (      self :  Dict						,			__snake_case :  List[Any]						,			__snake_case :  Optional = None	):
      a						:					str        =     to_numpy(__A	)
      a   ,	a   ,	a						:					int        =     audio_values.shape

      if padding_mask is None:
        return list(__A	)

      a						:					int        =     to_numpy(__A	)

      # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
      # token (so that the generated audio values are **not** treated as padded tokens)
      a						:					str        =     seq_len - padding_mask.shape[-1]
      a						:					List[str]        =     1 - self.feature_extractor.padding_value
      a						:					Optional[int]        =     np.pad(__A						,			((0, 0), (0, difference))						,			'constant'						,			constant_values=__A	)

      a						:					List[Any]        =     audio_values.tolist()
      for i in range(__A	):
        a						:					Optional[int]        =     np.asarray(audio_values[i]	)[
            padding_mask[i][None, :] != self.feature_extractor.padding_value
        ]
        a						:					Optional[Any]        =     sliced_audio.reshape(__A						,			-1	)

      return audio_values






'''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      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					="naver-clova-ix/donut-base-finetuned-docvqa"
 SCREAMING_SNAKE_CASE_	:					Dict					=(
     "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."
 )
 SCREAMING_SNAKE_CASE_	:					List[str]					="document_qa"
 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=AutoProcessor
 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=VisionEncoderDecoderModel

 SCREAMING_SNAKE_CASE_	:					List[Any]					=["image", "text"]
 SCREAMING_SNAKE_CASE_	:					Any					=["text"]






 def __init__(       self       :							Optional[int]							,	*__A       :							List[str]							,	**__A       :							List[Any]	):
        if not is_vision_available():
               raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.'	)

        super().__init__(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							"Image"							,	__A       :							str	):
        __UpperCamelCase       =  '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
        __UpperCamelCase       =  task_prompt.replace('{user_input}'							,	__A	)
        __UpperCamelCase       =  self.pre_processor.tokenizer(
            __A							,	add_special_tokens=__A							,	return_tensors='pt'	).input_ids
        __UpperCamelCase       =  self.pre_processor(__A							,	return_tensors='pt'	).pixel_values

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






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							Optional[Any]	):
        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=__A							,	pad_token_id=self.pre_processor.tokenizer.pad_token_id							,	eos_token_id=self.pre_processor.tokenizer.eos_token_id							,	use_cache=__A							,	num_beams=1							,	bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]							,	return_dict_in_generate=__A							,	).sequences






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							List[Any]	):
        __UpperCamelCase       =  self.pre_processor.batch_decode(__A	)[0]
        __UpperCamelCase       =  sequence.replace(self.pre_processor.tokenizer.eos_token							,	''	)
        __UpperCamelCase       =  sequence.replace(self.pre_processor.tokenizer.pad_token							,	''	)
        __UpperCamelCase       =  re.sub(R'<.*?>'							,	''							,	__A							,	count=1	).strip()  # remove first task start token
        __UpperCamelCase       =  self.pre_processor.tokenajson(__A	)

        return sequence["answer"]
