


'''simple docstring'''

# 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}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''
class 						__lowerCAmelCase		(lowercase_  ):





      '''simple docstring'''






      lowerCAmelCase__      :  Optional[int]						      =							"""google/flan-t5-base"""
      lowerCAmelCase__      :  Union[str, Any]						      =							(
          """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."""
      )
      lowerCAmelCase__      :  Union[str, Any]						      =							"""text_qa"""
      lowerCAmelCase__      :  int						      =							AutoTokenizer
      lowerCAmelCase__      :  List[str]						      =							AutoModelForSeqaSeqLM

      lowerCAmelCase__      :  Union[str, Any]						      =							["""text""", """text"""]
      lowerCAmelCase__      :  str						      =							["""text"""]



      def    UpperCamelCase__	(self					:					Any     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					str				):

             '''simple docstring'''







             lowercase__       =				QA_PROMPT.format(text=UpperCamelCase     ,		question=UpperCamelCase				)
             return self.pre_processor(UpperCamelCase     ,		return_tensors='''pt'''				)



      def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					Dict				):

             '''simple docstring'''







             lowercase__       =				self.model.generate(**UpperCamelCase				)

             lowercase__    ,lowercase__       =				inputs['''input_ids'''].shape
             lowercase__       =				output_ids.shape[0]

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






      def    UpperCamelCase__	(self					:					int     ,		UpperCamelCase					:					Optional[int]				):

             '''simple docstring'''







             return self.pre_processor.decode(UpperCamelCase     ,		skip_special_tokens=UpperCamelCase     ,		clean_up_tokenization_spaces=UpperCamelCase				)








'''simple docstring'''

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							:     Optional[int]								=			logging.get_logger(__name__)
class 						__lowerCAmelCase		(lowercase_  ):





      '''simple docstring'''






      lowerCAmelCase__      :  List[str]						      =							["""pixel_values"""]



      def __init__(self					:					Optional[int]     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					PILImageResampling = PIL.Image.NEAREST     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					Union[int, float] = 1 / 255     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					bool = True     ,		**UpperCamelCase					:					Optional[int]     ,		):

             '''simple docstring'''







             super().__init__(**UpperCamelCase				)
             lowercase__       =				size if size is not None else {'''height''': 346, '''width''': 346}
             lowercase__       =				get_size_dict(UpperCamelCase				)
             lowercase__       =				crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
             lowercase__       =				get_size_dict(UpperCamelCase     ,		param_name='''crop_size'''				)

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



      def    UpperCamelCase__	(self					:					Dict     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Dict[str, int]     ,		UpperCamelCase					:					PILImageResampling = PIL.Image.NEAREST     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					Dict     ,		):

             '''simple docstring'''







             lowercase__       =				get_size_dict(UpperCamelCase				)
             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(
                 UpperCamelCase     ,		size=(size['''height'''], size['''width'''])     ,		resample=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)



      def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Dict[str, int]     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					Union[str, Any]     ,		):

             '''simple docstring'''







             lowercase__       =				get_size_dict(UpperCamelCase				)
             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(UpperCamelCase     ,		size=(size['''height'''], size['''width'''])     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)



      def    UpperCamelCase__	(self					:					Tuple     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Union[int, float]     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					List[str]     ,		):

             '''simple docstring'''







             if offset:
                    lowercase__       =				(image - 1_27.5) * scale
                    if data_format is not None:
                           lowercase__       =				to_channel_dimension_format(UpperCamelCase     ,		UpperCamelCase				)
                    lowercase__       =				rescaled_image.astype(np.floataa				)
             else:
                    lowercase__       =				rescale(UpperCamelCase     ,		scale=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)
             return rescaled_image



      def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Union[float, List[float]]     ,		UpperCamelCase					:					Union[float, List[float]]     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					List[Any]     ,		):

             '''simple docstring'''







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






      def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					ImageInput     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					Union[str, Any]=None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					float = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[Union[str, TensorType]] = None     ,		UpperCamelCase					:					ChannelDimension = ChannelDimension.FIRST     ,		**UpperCamelCase					:					List[Any]     ,		):

             '''simple docstring'''







             lowercase__       =				do_resize if do_resize is not None else self.do_resize
             lowercase__       =				resample if resample is not None else self.resample
             lowercase__       =				do_center_crop if do_center_crop is not None else self.do_center_crop
             lowercase__       =				do_rescale if do_rescale is not None else self.do_rescale
             lowercase__       =				rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__       =				rescale_offset if rescale_offset is not None else self.rescale_offset
             lowercase__       =				do_normalize if do_normalize is not None else self.do_normalize
             lowercase__       =				image_mean if image_mean is not None else self.image_mean
             lowercase__       =				image_std if image_std is not None else self.image_std
             lowercase__       =				include_top if include_top is not None else self.include_top

             lowercase__       =				size if size is not None else self.size
             lowercase__       =				get_size_dict(UpperCamelCase				)
             lowercase__       =				crop_size if crop_size is not None else self.crop_size
             lowercase__       =				get_size_dict(UpperCamelCase     ,		param_name='''crop_size'''				)

             lowercase__       =				make_list_of_images(UpperCamelCase				)

             if not valid_images(UpperCamelCase				):
                    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.
             lowercase__       =				[to_numpy_array(UpperCamelCase				) for image in images]

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

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

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

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

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

             lowercase__       =				[to_channel_dimension_format(UpperCamelCase     ,		UpperCamelCase				) for image in images]

             lowercase__       =				{'''pixel_values''': images}
             return BatchFeature(data=UpperCamelCase     ,		tensor_type=UpperCamelCase				)




