


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
							import PIL

snake_case_						:			Union[str, Any]													=							logging.get_logger(__name__)







def 						SCREAMING_SNAKE_CASE__(   a__):
				if isinstance(a__	,(list, tuple)) and isinstance(videos[0]	,(list, tuple)) and is_valid_image(videos[0][0]):
								return videos

				elif isinstance(a__	,(list, tuple)) and is_valid_image(videos[0]):
								return [videos]

				elif is_valid_image(a__):
								return [[videos]]

				raise ValueError(f"Could not make batched video from {videos}")


class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						["pixel_values"]




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


										"""simple docstring"""





										super().__init__(**_a  )
										_SCREAMING_SNAKE_CASE			  =size if size is not None else {'''shortest_edge''': 256}
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    default_to_square=_a  )
										_SCREAMING_SNAKE_CASE			  =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    param_name='''crop_size'''  )

										_SCREAMING_SNAKE_CASE			  =do_resize
										_SCREAMING_SNAKE_CASE			  =size
										_SCREAMING_SNAKE_CASE			  =do_center_crop
										_SCREAMING_SNAKE_CASE			  =crop_size
										_SCREAMING_SNAKE_CASE			  =resample
										_SCREAMING_SNAKE_CASE			  =do_rescale
										_SCREAMING_SNAKE_CASE			  =rescale_factor
										_SCREAMING_SNAKE_CASE			  =offset
										_SCREAMING_SNAKE_CASE			  =do_normalize
										_SCREAMING_SNAKE_CASE			  =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_SCREAMING_SNAKE_CASE			  =image_std if image_std is not None else IMAGENET_STANDARD_STD




						def 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      np.ndarray				,    _a					:      Dict[str, int]				,    _a					:      PILImageResampling = PILImageResampling.BILINEAR				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      List[Any]				,    )							->     np.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    default_to_square=_a  )
										if "shortest_edge" in size:
														_SCREAMING_SNAKE_CASE			  =get_resize_output_image_size(_a				,    size['''shortest_edge''']				,    default_to_square=_a  )
										elif "height" in size and "width" in size:
														_SCREAMING_SNAKE_CASE			  =(size['''height'''], size['''width'''])
										else:
														raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"  )
										return resize(_a				,    size=_a				,    resample=_a				,    data_format=_a				,    **_a  )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a  )
										if "height" not in size or "width" not in size:
														raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}"  )
										return center_crop(_a				,    size=(size['''height'''], size['''width'''])				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      np.ndarray				,    _a					:      Union[int, float]				,    _a					:      bool = True				,    _a					:      Optional[Union[str, ChannelDimension]] = None				,    **_a					:      List[str]				,    )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =image.astype(np.floataa  )
										if offset:
														_SCREAMING_SNAKE_CASE			  =image - (scale / 2)
										return rescale(_a				,    scale=_a				,    data_format=_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _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 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      ImageInput				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      PILImageResampling = None				,    _a					:      bool = None				,    _a					:      Dict[str, int] = None				,    _a					:      bool = None				,    _a					:      float = None				,    _a					:      bool = None				,    _a					:      bool = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[Union[float, List[float]]] = None				,    _a					:      Optional[ChannelDimension] = ChannelDimension.FIRST				,    )							->     np.ndarray:


										"""simple docstring"""





										if do_resize and size is None or resample is None:
														raise ValueError('''Size and resample 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.'''  )

										if offset and not do_rescale:
														raise ValueError('''For offset, do_rescale must also be set to True.'''  )

										# All transformations expect numpy arrays.
										_SCREAMING_SNAKE_CASE			  =to_numpy_array(_a  )

										if do_resize:
														_SCREAMING_SNAKE_CASE			  =self.resize(image=_a				,    size=_a				,    resample=_a  )

										if do_center_crop:
														_SCREAMING_SNAKE_CASE			  =self.center_crop(_a				,    size=_a  )

										if do_rescale:
														_SCREAMING_SNAKE_CASE			  =self.rescale(image=_a				,    scale=_a				,    offset=_a  )

										if do_normalize:
														_SCREAMING_SNAKE_CASE			  =self.normalize(image=_a				,    mean=_a				,    std=_a  )

										_SCREAMING_SNAKE_CASE			  =to_channel_dimension_format(_a				,    _a  )
										return image




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =do_resize if do_resize is not None else self.do_resize
										_SCREAMING_SNAKE_CASE			  =resample if resample is not None else self.resample
										_SCREAMING_SNAKE_CASE			  =do_center_crop if do_center_crop is not None else self.do_center_crop
										_SCREAMING_SNAKE_CASE			  =do_rescale if do_rescale is not None else self.do_rescale
										_SCREAMING_SNAKE_CASE			  =rescale_factor if rescale_factor is not None else self.rescale_factor
										_SCREAMING_SNAKE_CASE			  =offset if offset is not None else self.offset
										_SCREAMING_SNAKE_CASE			  =do_normalize if do_normalize is not None else self.do_normalize
										_SCREAMING_SNAKE_CASE			  =image_mean if image_mean is not None else self.image_mean
										_SCREAMING_SNAKE_CASE			  =image_std if image_std is not None else self.image_std

										_SCREAMING_SNAKE_CASE			  =size if size is not None else self.size
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    default_to_square=_a  )
										_SCREAMING_SNAKE_CASE			  =crop_size if crop_size is not None else self.crop_size
										_SCREAMING_SNAKE_CASE			  =get_size_dict(_a				,    param_name='''crop_size'''  )

										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.'''  )

										_SCREAMING_SNAKE_CASE			  =make_batched(_a  )

										_SCREAMING_SNAKE_CASE			  =[
										    [
										        self._preprocess_image(
										            image=_a				,    do_resize=_a				,    size=_a				,    resample=_a				,    do_center_crop=_a				,    crop_size=_a				,    do_rescale=_a				,    rescale_factor=_a				,    offset=_a				,    do_normalize=_a				,    image_mean=_a				,    image_std=_a				,    data_format=_a				,    )
										        for img in video
										    ]
										    for video in videos
										]

										_SCREAMING_SNAKE_CASE			  ={'''pixel_values''': videos}
										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 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"naver-clova-ix/donut-base-finetuned-docvqa"
						UpperCAmelCase             =						(
						    "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."
						)
						UpperCAmelCase             =						"document_qa"
						UpperCAmelCase             =						AutoProcessor
						UpperCAmelCase             =						VisionEncoderDecoderModel

						UpperCAmelCase             =						["image", "text"]
						UpperCAmelCase             =						["text"]




						def __init__(       self					:      Any				,    *_a					:      int				,    **_a					:      Dict  )							->     int:


										"""simple docstring"""





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

										super().__init__(*_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Optional[Any]				,    _a					:      "Image"				,    _a					:      str  )							->     Tuple:


										"""simple docstring"""





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

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




						def 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      Optional[Any]  )							->     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 							__UpperCamelCase				(       self					:      Any				,    _a					:      int  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.pre_processor.batch_decode(_a  )[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'''<.*?>'''				,    ''''''				,    _a				,    count=1  ).strip()  # remove first task start token
										_SCREAMING_SNAKE_CASE			  =self.pre_processor.tokenajson(_a  )

										return sequence["answer"]