


# 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       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''naver-clova-ix/donut-base-finetuned-docvqa'''
					A__     =					(
					    '''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.'''
					)
					A__     =					'''document_qa'''
					A__     =					AutoProcessor
					A__     =					VisionEncoderDecoderModel

					A__     =					['''image''', '''text''']
					A__     =					['''text''']

					def __init__(self       :		List[Any] ,   *_UpperCAmelCase       :		int ,   **_UpperCAmelCase       :		Optional[Any]						)		->  Optional[Any]:




							"""simple docstring"""


							if not is_vision_available():
									raise ValueError("""Pillow must be installed to use the DocumentQuestionAnsweringTool."""						)

							super().__init__(*_UpperCAmelCase ,   **_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		"Image" ,   _UpperCAmelCase       :		str						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						"""<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
							lowercase__	     =						task_prompt.replace("""{user_input}""" ,   _UpperCAmelCase						)
							lowercase__	     =						self.pre_processor.tokenizer(
							    _UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase ,   return_tensors="""pt"""						).input_ids
							lowercase__	     =						self.pre_processor(_UpperCAmelCase ,   return_tensors="""pt"""						).pixel_values

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

					def     lowerCamelCase__	(self       :		str ,   _UpperCAmelCase       :		Dict						)		->  str:




							"""simple docstring"""


							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     lowerCamelCase__	(self       :		str ,   _UpperCAmelCase       :		List[str]						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						self.pre_processor.batch_decode(_UpperCAmelCase						)[0]
							lowercase__	     =						sequence.replace(self.pre_processor.tokenizer.eos_token ,   """"""						)
							lowercase__	     =						sequence.replace(self.pre_processor.tokenizer.pad_token ,   """"""						)
							lowercase__	     =						re.sub(r"""<.*?>""" ,   """""" ,   _UpperCAmelCase ,   count=1						).strip()  # remove first task start token
							lowercase__	     =						self.pre_processor.tokenajson(_UpperCAmelCase						)

							return sequence["answer"]




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


_lowerCamelCase      :					int					    =      {
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ASTForAudioClassification''',
       '''ASTModel''',
       '''ASTPreTrainedModel''',
   ]

try:
   if not is_speech_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Optional[Any]					    =      ['''ASTFeatureExtractor''']

if TYPE_CHECKING:
   from .configuration_audio_spectrogram_transformer import (
       AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       ASTConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_audio_spectrogram_transformer import (
          AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ASTForAudioClassification,
          ASTModel,
          ASTPreTrainedModel,
      )

   try:
      if not is_speech_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
   import sys

   _lowerCamelCase      :					Optional[int]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


