



# 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      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		List[Any]			       =    "naver-clova-ix/donut-base-finetuned-docvqa"
					A__				:		Optional[Any]			       =    (
					    "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__				:		Tuple			       =    "document_qa"
					A__				:		str			       =    AutoProcessor
					A__				:		Union[str, Any]			       =    VisionEncoderDecoderModel

					A__				:		int			       =    ["image", "text"]
					A__				:		Any			       =    ["text"]



					def __init__(     self					:       Optional[int]				,					*_snake_case					:       Tuple				,					**_snake_case					:       Tuple					):






								"""simple docstring"""


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

								super().__init__(*_snake_case				,					**_snake_case					)



					def     _a							(     self					:       Union[str, Any]				,					_snake_case					:       "Image"				,					_snake_case					:       str					):






								"""simple docstring"""


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

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



					def     _a							(     self					:       int				,					_snake_case					:       Optional[Any]					):






								"""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=_snake_case				,					pad_token_id=self.pre_processor.tokenizer.pad_token_id				,					eos_token_id=self.pre_processor.tokenizer.eos_token_id				,					use_cache=_snake_case				,					num_beams=1				,					bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]				,					return_dict_in_generate=_snake_case				,					).sequences



					def     _a							(     self					:       List[Any]				,					_snake_case					:       List[str]					):






								"""simple docstring"""


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

								return sequence["answer"]









# Copyright 2023 The HuggingFace 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.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       =      {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MraForMaskedLM''',
							    '''MraForMultipleChoice''',
							    '''MraForQuestionAnswering''',
							    '''MraForSequenceClassification''',
							    '''MraForTokenClassification''',
							    '''MraLayer''',
							    '''MraModel''',
							    '''MraPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							SCREAMING_SNAKE_CASE__       =      _LazyModule(__name__, globals()['''__file__'''], _import_structure)




