

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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


lowerCAmelCase						:   List[str]										=    logging.get_logger(__name__)
class     SCREAMING_SNAKE_CASE__     (						__a		):





       '''simple docstring'''




       UpperCamelCase__ :     Union[str, Any]				    =       ['''pixel_values''']

       def __init__(						self       :						str    ,		lowerCAmelCase__       :						bool = True    ,		lowerCAmelCase__       :						Dict[str, int] = None    ,		lowerCAmelCase__       :						PILImageResampling = PIL.Image.NEAREST    ,		lowerCAmelCase__       :						bool = False    ,		lowerCAmelCase__       :						Dict[str, int] = None    ,		lowerCAmelCase__       :						Union[int, float] = 1 / 255    ,		lowerCAmelCase__       :						bool = False    ,		lowerCAmelCase__       :						bool = True    ,		lowerCAmelCase__       :						bool = True    ,		lowerCAmelCase__       :						Optional[Union[float, List[float]]] = None    ,		lowerCAmelCase__       :						Optional[Union[float, List[float]]] = None    ,		lowerCAmelCase__       :						bool = True    ,		**lowerCAmelCase__       :						List[Any]    ,		)			->    None:
             super().__init__(**lowerCAmelCase__							)
             snake_case__					=   size if size is not None else {"""height""": 346, """width""": 346}
             snake_case__					=   get_size_dict(lowerCAmelCase__							)
             snake_case__					=   crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
             snake_case__					=   get_size_dict(lowerCAmelCase__    ,		param_name="""crop_size"""							)

             snake_case__					=   do_resize
             snake_case__					=   size
             snake_case__					=   resample
             snake_case__					=   do_center_crop
             snake_case__					=   crop_size
             snake_case__					=   do_rescale
             snake_case__					=   rescale_factor
             snake_case__					=   rescale_offset
             snake_case__					=   do_normalize
             snake_case__					=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             snake_case__					=   image_std if image_std is not None else IMAGENET_STANDARD_STD
             snake_case__					=   include_top

       def UpperCAmelCase_						(						self       :						int    ,		lowerCAmelCase__       :						np.ndarray    ,		lowerCAmelCase__       :						Dict[str, int]    ,		lowerCAmelCase__       :						PILImageResampling = PIL.Image.NEAREST    ,		lowerCAmelCase__       :						Optional[Union[str, ChannelDimension]] = None    ,		**lowerCAmelCase__       :						int    ,		)			->    np.ndarray:
             snake_case__					=   get_size_dict(lowerCAmelCase__							)
             if "height" not in size or "width" not in size:
                   raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''							)
             return resize(
                 lowerCAmelCase__    ,		size=(size["""height"""], size["""width"""])    ,		resample=lowerCAmelCase__    ,		data_format=lowerCAmelCase__    ,		**lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Optional[int]    ,		lowerCAmelCase__       :						np.ndarray    ,		lowerCAmelCase__       :						Dict[str, int]    ,		lowerCAmelCase__       :						Optional[Union[str, ChannelDimension]] = None    ,		**lowerCAmelCase__       :						int    ,		)			->    np.ndarray:
             snake_case__					=   get_size_dict(lowerCAmelCase__							)
             if "height" not in size or "width" not in size:
                   raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''							)
             return center_crop(lowerCAmelCase__    ,		size=(size["""height"""], size["""width"""])    ,		data_format=lowerCAmelCase__    ,		**lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						int    ,		lowerCAmelCase__       :						np.ndarray    ,		lowerCAmelCase__       :						Union[int, float]    ,		lowerCAmelCase__       :						bool = True    ,		lowerCAmelCase__       :						Optional[Union[str, ChannelDimension]] = None    ,		**lowerCAmelCase__       :						List[str]    ,		)			->    Tuple:
             if offset:
                   snake_case__					=   (image - 127.5) * scale
                   if data_format is not None:
                         snake_case__					=   to_channel_dimension_format(lowerCAmelCase__    ,		lowerCAmelCase__							)
                   snake_case__					=   rescaled_image.astype(np.floataa							)
             else:
                   snake_case__					=   rescale(lowerCAmelCase__    ,		scale=lowerCAmelCase__    ,		data_format=lowerCAmelCase__    ,		**lowerCAmelCase__							)
             return rescaled_image

       def UpperCAmelCase_						(						self       :						Any    ,		lowerCAmelCase__       :						np.ndarray    ,		lowerCAmelCase__       :						Union[float, List[float]]    ,		lowerCAmelCase__       :						Union[float, List[float]]    ,		lowerCAmelCase__       :						Optional[Union[str, ChannelDimension]] = None    ,		**lowerCAmelCase__       :						Union[str, Any]    ,		)			->    np.ndarray:
             return normalize(lowerCAmelCase__    ,		mean=lowerCAmelCase__    ,		std=lowerCAmelCase__    ,		data_format=lowerCAmelCase__    ,		**lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Tuple    ,		lowerCAmelCase__       :						ImageInput    ,		lowerCAmelCase__       :						bool = None    ,		lowerCAmelCase__       :						Dict[str, int] = None    ,		lowerCAmelCase__       :						List[str]=None    ,		lowerCAmelCase__       :						bool = None    ,		lowerCAmelCase__       :						Dict[str, int] = None    ,		lowerCAmelCase__       :						bool = None    ,		lowerCAmelCase__       :						float = None    ,		lowerCAmelCase__       :						bool = None    ,		lowerCAmelCase__       :						bool = None    ,		lowerCAmelCase__       :						Optional[Union[float, List[float]]] = None    ,		lowerCAmelCase__       :						Optional[Union[float, List[float]]] = None    ,		lowerCAmelCase__       :						bool = None    ,		lowerCAmelCase__       :						Optional[Union[str, TensorType]] = None    ,		lowerCAmelCase__       :						ChannelDimension = ChannelDimension.FIRST    ,		**lowerCAmelCase__       :						Optional[Any]    ,		)			->    PIL.Image.Image:
             snake_case__					=   do_resize if do_resize is not None else self.do_resize
             snake_case__					=   resample if resample is not None else self.resample
             snake_case__					=   do_center_crop if do_center_crop is not None else self.do_center_crop
             snake_case__					=   do_rescale if do_rescale is not None else self.do_rescale
             snake_case__					=   rescale_factor if rescale_factor is not None else self.rescale_factor
             snake_case__					=   rescale_offset if rescale_offset is not None else self.rescale_offset
             snake_case__					=   do_normalize if do_normalize is not None else self.do_normalize
             snake_case__					=   image_mean if image_mean is not None else self.image_mean
             snake_case__					=   image_std if image_std is not None else self.image_std
             snake_case__					=   include_top if include_top is not None else self.include_top

             snake_case__					=   size if size is not None else self.size
             snake_case__					=   get_size_dict(lowerCAmelCase__							)
             snake_case__					=   crop_size if crop_size is not None else self.crop_size
             snake_case__					=   get_size_dict(lowerCAmelCase__    ,		param_name="""crop_size"""							)

             snake_case__					=   make_list_of_images(lowerCAmelCase__							)

             if not valid_images(lowerCAmelCase__							):
                   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 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."""							)

             # All transformations expect numpy arrays.
             snake_case__					=   [to_numpy_array(lowerCAmelCase__							) for image in images]

             if do_resize:
                   snake_case__					=   [self.resize(image=lowerCAmelCase__    ,		size=lowerCAmelCase__    ,		resample=lowerCAmelCase__							) for image in images]

             if do_center_crop:
                   snake_case__					=   [self.center_crop(image=lowerCAmelCase__    ,		size=lowerCAmelCase__							) for image in images]

             if do_rescale:
                   snake_case__					=   [self.rescale(image=lowerCAmelCase__    ,		scale=lowerCAmelCase__    ,		offset=lowerCAmelCase__							) for image in images]

             if do_normalize:
                   snake_case__					=   [self.normalize(image=lowerCAmelCase__    ,		mean=lowerCAmelCase__    ,		std=lowerCAmelCase__							) for image in images]

             if include_top:
                   snake_case__					=   [self.normalize(image=lowerCAmelCase__    ,		mean=[0, 0, 0]    ,		std=lowerCAmelCase__							) for image in images]

             snake_case__					=   [to_channel_dimension_format(lowerCAmelCase__    ,		lowerCAmelCase__							) for image in images]

             snake_case__					=   {"""pixel_values""": images}
             return BatchFeature(data=lowerCAmelCase__    ,		tensor_type=lowerCAmelCase__							)









# 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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


lowerCAmelCase						:   List[Any]										=    '''Here is a text containing a lot of information: \'\'\'{text}\'\'\'.

Can you answer this question about the text: \'{question}\''''
class     SCREAMING_SNAKE_CASE__     (						__a		):





       '''simple docstring'''




       UpperCamelCase__ :     int				    =       '''google/flan-t5-base'''
       UpperCamelCase__ :     int				    =       (
           '''This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the '''
           '''text where to find the answer, and `question`, which is the question, and returns the answer to the question.'''
       )
       UpperCamelCase__ :     Tuple				    =       '''text_qa'''
       UpperCamelCase__ :     Tuple				    =       AutoTokenizer
       UpperCamelCase__ :     List[Any]				    =       AutoModelForSeqaSeqLM

       UpperCamelCase__ :     Optional[Any]				    =       ['''text''', '''text''']
       UpperCamelCase__ :     Any				    =       ['''text''']

       def UpperCAmelCase_						(						self       :						Tuple    ,		lowerCAmelCase__       :						str    ,		lowerCAmelCase__       :						str							)			->    List[Any]:
             snake_case__					=   QA_PROMPT.format(text=lowerCAmelCase__    ,		question=lowerCAmelCase__							)
             return self.pre_processor(lowerCAmelCase__    ,		return_tensors="""pt"""							)

       def UpperCAmelCase_						(						self       :						Optional[Any]    ,		lowerCAmelCase__       :						Dict							)			->    List[Any]:
             snake_case__					=   self.model.generate(**lowerCAmelCase__							)

             snake_case__	,			snake_case__					=   inputs["""input_ids"""].shape
             snake_case__					=   output_ids.shape[0]

             return output_ids.reshape(lowerCAmelCase__    ,		out_b // in_b    ,		*output_ids.shape[1:]							)[0][0]

       def UpperCAmelCase_						(						self       :						Union[str, Any]    ,		lowerCAmelCase__       :						Dict							)			->    List[Any]:
             return self.pre_processor.decode(lowerCAmelCase__    ,		skip_special_tokens=lowerCAmelCase__    ,		clean_up_tokenization_spaces=lowerCAmelCase__							)






