






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase       :       Dict        =				logging.get_logger(__name__)


if is_vision_available():
	import PIL

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	Optional[Any]     =					['''pixel_values''']



					def __init__(     self :		Union[str, Any]						,   __a :		bool = True						,   __a :		Dict[str, int] = None						,   __a :		PILImageResampling = PILImageResampling.BICUBIC						,   __a :		bool = True						,   __a :		Dict[str, int] = None						,   __a :		bool = True						,   __a :		Union[int, float] = 1 / 255						,   __a :		bool = True						,   __a :		Optional[Union[float, List[float]]] = None						,   __a :		Optional[Union[float, List[float]]] = None						,   __a :		bool = True						,   **__a :		List[str]						,   )	->     None:


												"""simple docstring"""

												super().__init__(**__a							)
												__lowercase					:							Any			=   size if size is not None else {"""shortest_edge""": 224}
												__lowercase					:							Optional[int]			=   get_size_dict(__a						,   default_to_square=__a							)
												__lowercase					:							List[str]			=   crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
												__lowercase					:							str			=   get_size_dict(__a						,   default_to_square=__a						,   param_name="""crop_size"""							)

												__lowercase					:							int			=   do_resize
												__lowercase					:							Any			=   size
												__lowercase					:							Optional[Any]			=   resample
												__lowercase					:							Optional[int]			=   do_center_crop
												__lowercase					:							Optional[int]			=   crop_size
												__lowercase					:							List[Any]			=   do_rescale
												__lowercase					:							List[str]			=   rescale_factor
												__lowercase					:							Any			=   do_normalize
												__lowercase					:							Optional[int]			=   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
												__lowercase					:							List[Any]			=   image_std if image_std is not None else OPENAI_CLIP_STD
												__lowercase					:							int			=   do_convert_rgb



					def 					lowerCAmelCase (     self :		Optional[Any]						,   __a :		np.ndarray						,   __a :		Dict[str, int]						,   __a :		PILImageResampling = PILImageResampling.BICUBIC						,   __a :		Optional[Union[str, ChannelDimension]] = None						,   **__a :		Union[str, Any]						,   )	->     np.ndarray:


												"""simple docstring"""

												__lowercase					:							Dict			=   get_size_dict(__a						,   default_to_square=__a							)
												if "shortest_edge" not in size:
																			raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"							)
												__lowercase					:							Union[str, Any]			=   get_resize_output_image_size(__a						,   size=size["""shortest_edge"""]						,   default_to_square=__a							)
												return resize(__a						,   size=__a						,   resample=__a						,   data_format=__a						,   **__a							)



					def 					lowerCAmelCase (     self :		Tuple						,   __a :		np.ndarray						,   __a :		Dict[str, int]						,   __a :		Optional[Union[str, ChannelDimension]] = None						,   **__a :		int						,   )	->     np.ndarray:


												"""simple docstring"""

												__lowercase					:							List[Any]			=   get_size_dict(__a							)
												if "height" not in size or "width" not in size:
																			raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"							)
												return center_crop(__a						,   size=(size["""height"""], size["""width"""])						,   data_format=__a						,   **__a							)



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		np.ndarray						,   __a :		Union[int, float]						,   __a :		Optional[Union[str, ChannelDimension]] = None						,   **__a :		Union[str, Any]						,   )	->     List[str]:


												"""simple docstring"""

												return rescale(__a						,   scale=__a						,   data_format=__a						,   **__a							)



					def 					lowerCAmelCase (     self :		Optional[Any]						,   __a :		np.ndarray						,   __a :		Union[float, List[float]]						,   __a :		Union[float, List[float]]						,   __a :		Optional[Union[str, ChannelDimension]] = None						,   **__a :		Any						,   )	->     np.ndarray:


												"""simple docstring"""

												return normalize(__a						,   mean=__a						,   std=__a						,   data_format=__a						,   **__a							)



					def 					lowerCAmelCase (     self :		Union[str, Any]						,   __a :		ImageInput						,   __a :		bool = None						,   __a :		Dict[str, int] = None						,   __a :		PILImageResampling = None						,   __a :		bool = None						,   __a :		int = None						,   __a :		bool = None						,   __a :		float = None						,   __a :		bool = None						,   __a :		Optional[Union[float, List[float]]] = None						,   __a :		Optional[Union[float, List[float]]] = None						,   __a :		bool = None						,   __a :		Optional[Union[str, TensorType]] = None						,   __a :		Optional[ChannelDimension] = ChannelDimension.FIRST						,   **__a :		Optional[int]						,   )	->     PIL.Image.Image:


												"""simple docstring"""

												__lowercase					:							List[str]			=   do_resize if do_resize is not None else self.do_resize
												__lowercase					:							Optional[int]			=   size if size is not None else self.size
												__lowercase					:							Tuple			=   get_size_dict(__a						,   param_name="""size"""						,   default_to_square=__a							)
												__lowercase					:							Optional[int]			=   resample if resample is not None else self.resample
												__lowercase					:							Optional[Any]			=   do_center_crop if do_center_crop is not None else self.do_center_crop
												__lowercase					:							int			=   crop_size if crop_size is not None else self.crop_size
												__lowercase					:							Optional[int]			=   get_size_dict(__a						,   param_name="""crop_size"""						,   default_to_square=__a							)
												__lowercase					:							List[str]			=   do_rescale if do_rescale is not None else self.do_rescale
												__lowercase					:							Optional[int]			=   rescale_factor if rescale_factor is not None else self.rescale_factor
												__lowercase					:							int			=   do_normalize if do_normalize is not None else self.do_normalize
												__lowercase					:							List[Any]			=   image_mean if image_mean is not None else self.image_mean
												__lowercase					:							List[str]			=   image_std if image_std is not None else self.image_std
												__lowercase					:							Dict			=   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

												__lowercase					:							Dict			=   make_list_of_images(__a							)

												if not valid_images(__a							):
																			raise ValueError(
																			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																			    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

												if do_resize and size is None:
																			raise ValueError("""Size must be specified if do_resize is True."""							)

												if do_center_crop and crop_size is None:
																			raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

												if do_rescale and rescale_factor is None:
																			raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

												if do_normalize and (image_mean is None or image_std is None):
																			raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

												# PIL RGBA images are converted to RGB
												if do_convert_rgb:
																			__lowercase					:							Union[str, Any]			=   [convert_to_rgb(__a							) for image in images]

												# All transformations expect numpy arrays.
												__lowercase					:							int			=   [to_numpy_array(__a							) for image in images]

												if do_resize:
																			__lowercase					:							Tuple			=   [self.resize(image=__a						,   size=__a						,   resample=__a							) for image in images]

												if do_center_crop:
																			__lowercase					:							Dict			=   [self.center_crop(image=__a						,   size=__a							) for image in images]

												if do_rescale:
																			__lowercase					:							Dict			=   [self.rescale(image=__a						,   scale=__a							) for image in images]

												if do_normalize:
																			__lowercase					:							Tuple			=   [self.normalize(image=__a						,   mean=__a						,   std=__a							) for image in images]

												__lowercase					:							str			=   [to_channel_dimension_format(__a						,   __a							) for image in images]

												__lowercase					:							List[str]			=   {"""pixel_values""": images}
												return BatchFeature(data=__a						,   tensor_type=__a							)







# 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   (       __a	):

					'''simple docstring'''

					_A  :	List[str]     =					'''naver-clova-ix/donut-base-finetuned-docvqa'''
					_A  :	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  :	Dict     =					AutoProcessor
					_A  :	Tuple     =					VisionEncoderDecoderModel

					_A  :	Optional[int]     =					['''image''', '''text''']
					_A  :	Optional[int]     =					['''text''']



					def __init__(     self :		Any						,   *__a :		List[str]						,   **__a :		Any							)	->     Optional[Any]:


												"""simple docstring"""

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

												super().__init__(*__a						,   **__a							)



					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		"Image"						,   __a :		str							)	->     List[str]:


												"""simple docstring"""

												__lowercase					:							int			=   """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
												__lowercase					:							str			=   task_prompt.replace("""{user_input}"""						,   __a							)
												__lowercase					:							Union[str, Any]			=   self.pre_processor.tokenizer(
												    __a						,   add_special_tokens=__a						,   return_tensors="""pt"""							).input_ids
												__lowercase					:							int			=   self.pre_processor(__a						,   return_tensors="""pt"""							).pixel_values

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



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		int							)	->     int:


												"""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=__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 :		Union[str, Any]						,   __a :		Union[str, Any]							)	->     Union[str, Any]:


												"""simple docstring"""

												__lowercase					:							Tuple			=   self.pre_processor.batch_decode(__a							)[0]
												__lowercase					:							int			=   sequence.replace(self.pre_processor.tokenizer.eos_token						,   """"""							)
												__lowercase					:							Union[str, Any]			=   sequence.replace(self.pre_processor.tokenizer.pad_token						,   """"""							)
												__lowercase					:							Optional[Any]			=   re.sub(r"""<.*?>"""						,   """"""						,   __a						,   count=1							).strip()  # remove first task start token
												__lowercase					:							Dict			=   self.pre_processor.tokenajson(__a							)

												return sequence["answer"]