






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 				__UpperCAmelCase		(_a   ):
			__snake_case    :				Union[str, Any]									=							["""image_processor""", """tokenizer"""]
			__snake_case    :				Union[str, Any]									=							"""LayoutLMv3ImageProcessor"""
			__snake_case    :				Any									=							("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")







			def __init__(     self:  int				,     UpperCAmelCase_:  Tuple=None				,     UpperCAmelCase_:  Optional[Any]=None				,     **UpperCAmelCase_:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""				,     __lowerCAmelCase				,     )
															_SCREAMING_SNAKE_CASE     				=  kwargs.pop("""feature_extractor"""					)

									_SCREAMING_SNAKE_CASE     				=  image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("""You need to specify an `image_processor`."""					)
									if tokenizer is None:
															raise ValueError("""You need to specify a `tokenizer`."""					)

									super().__init__(__lowerCAmelCase				,     __lowerCAmelCase					)







			def __call__(     self:  Optional[Any]				,     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  Any = None				,     UpperCAmelCase_:  Any = None				,     UpperCAmelCase_:  Optional[Any] = None				,     UpperCAmelCase_:  Tuple = None				,     UpperCAmelCase_:  Optional[Any] = True				,     UpperCAmelCase_:  Optional[Any] = False				,     UpperCAmelCase_:  str = None				,     UpperCAmelCase_:  List[str] = None				,     UpperCAmelCase_:  str = 0				,     UpperCAmelCase_:  str = None				,     UpperCAmelCase_:  Dict = None				,     UpperCAmelCase_:  int = None				,     UpperCAmelCase_:  Optional[Any] = False				,     UpperCAmelCase_:  List[str] = False				,     UpperCAmelCase_:  Tuple = False				,     UpperCAmelCase_:  Tuple = False				,     UpperCAmelCase_:  Dict = True				,     UpperCAmelCase_:  Dict = None				,     **UpperCAmelCase_:  str				,     ):







									'''simple docstring'''




									if self.image_processor.apply_ocr and (boxes is not None):
															raise ValueError(
															    """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""					)

									if self.image_processor.apply_ocr and (word_labels is not None):
															raise ValueError(
															    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""					)

									# first, apply the image processor
									_SCREAMING_SNAKE_CASE     				=  self.image_processor(images=__lowerCAmelCase				,     return_tensors=__lowerCAmelCase					)

									# second, apply the tokenizer
									if text is not None and self.image_processor.apply_ocr and text_pair is None:
															if isinstance(__lowerCAmelCase				,     __lowerCAmelCase					):
																					_SCREAMING_SNAKE_CASE     				=  [text]  # add batch dimension (as the image processor always adds a batch dimension)
															_SCREAMING_SNAKE_CASE     				=  features["""words"""]

									_SCREAMING_SNAKE_CASE     				=  self.tokenizer(
									    text=text if text is not None else features["""words"""]				,     text_pair=text_pair if text_pair is not None else None				,     boxes=boxes if boxes is not None else features["""boxes"""]				,     word_labels=__lowerCAmelCase				,     add_special_tokens=__lowerCAmelCase				,     padding=__lowerCAmelCase				,     truncation=__lowerCAmelCase				,     max_length=__lowerCAmelCase				,     stride=__lowerCAmelCase				,     pad_to_multiple_of=__lowerCAmelCase				,     return_token_type_ids=__lowerCAmelCase				,     return_attention_mask=__lowerCAmelCase				,     return_overflowing_tokens=__lowerCAmelCase				,     return_special_tokens_mask=__lowerCAmelCase				,     return_offsets_mapping=__lowerCAmelCase				,     return_length=__lowerCAmelCase				,     verbose=__lowerCAmelCase				,     return_tensors=__lowerCAmelCase				,     **__lowerCAmelCase				,     )

									# add pixel values
									_SCREAMING_SNAKE_CASE     				=  features.pop("""pixel_values"""					)
									if return_overflowing_tokens is True:
															_SCREAMING_SNAKE_CASE     				=  self.get_overflowing_images(__lowerCAmelCase				,     encoded_inputs["""overflow_to_sample_mapping"""]					)
									_SCREAMING_SNAKE_CASE     				=  images

									return encoded_inputs







			def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  Optional[Any]				,     UpperCAmelCase_:  Optional[Any]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  []
									for sample_idx in overflow_to_sample_mapping:
															images_with_overflow.append(images[sample_idx]					)

									if len(__lowerCAmelCase					) != len(__lowerCAmelCase					):
															raise ValueError(
															    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
															    F' {len(__lowerCAmelCase					)} and {len(__lowerCAmelCase					)}'					)

									return images_with_overflow







			def 							UpperCamelCase				(     self:  Optional[Any]				,     *UpperCAmelCase_:  List[Any]				,     **UpperCAmelCase_:  int					):







									'''simple docstring'''




									return self.tokenizer.batch_decode(*__lowerCAmelCase				,     **__lowerCAmelCase					)







			def 							UpperCamelCase				(     self:  Optional[Any]				,     *UpperCAmelCase_:  str				,     **UpperCAmelCase_:  List[Any]					):







									'''simple docstring'''




									return self.tokenizer.decode(*__lowerCAmelCase				,     **__lowerCAmelCase					)







			@property
			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									return ["input_ids", "bbox", "attention_mask", "pixel_values"]







			@property
			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,     __lowerCAmelCase				,     )
									return self.image_processor_class







			@property
			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,     __lowerCAmelCase				,     )
									return self.image_processor









# 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 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Dict									=							"naver-clova-ix/donut-base-finetuned-docvqa"
			__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."
			)
			__snake_case    :				Dict									=							"document_qa"
			__snake_case    :				Any									=							AutoProcessor
			__snake_case    :				int									=							VisionEncoderDecoderModel

			__snake_case    :				Union[str, Any]									=							["image", "text"]
			__snake_case    :				Optional[int]									=							["text"]







			def __init__(     self:  List[Any]				,     *UpperCAmelCase_:  Optional[int]				,     **UpperCAmelCase_:  List[str]					):







									'''simple docstring'''




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

									super().__init__(*UpperCAmelCase_				,     **UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  "Image"				,     UpperCAmelCase_:  str					):







									'''simple docstring'''




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

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







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  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 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  Tuple					):







									'''simple docstring'''




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

									return sequence["answer"]

