"""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[Any]											=     logging.get_logger(__name__)
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			["pixel_values"]







					def __init__(    self       :     Optional[int]   ,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__       :     Union[str, Any]   ,):
									super().__init__(**lowerCamelCase__      )
									UpperCAmelCase__					=							size if size is not None else {'height': 346, 'width': 346}
									UpperCAmelCase__					=							get_size_dict(lowerCamelCase__      )
									UpperCAmelCase__					=							crop_size if crop_size is not None else {'height': 289, 'width': 289}
									UpperCAmelCase__					=							get_size_dict(lowerCamelCase__   ,param_name='crop_size'      )

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







					def 		__lowerCAmelCase						(    self       :     Dict   ,lowerCamelCase__       :     np.ndarray   ,lowerCamelCase__       :     Dict[str, int]   ,lowerCamelCase__       :     PILImageResampling = PIL.Image.NEAREST   ,lowerCamelCase__       :     Optional[Union[str, ChannelDimension]] = None   ,**lowerCamelCase__       :     Dict   ,):
									UpperCAmelCase__					=							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 		__lowerCAmelCase						(    self       :     str   ,lowerCamelCase__       :     np.ndarray   ,lowerCamelCase__       :     Dict[str, int]   ,lowerCamelCase__       :     Optional[Union[str, ChannelDimension]] = None   ,**lowerCamelCase__       :     List[str]   ,):
									UpperCAmelCase__					=							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 		__lowerCAmelCase						(    self       :     Union[str, Any]   ,lowerCamelCase__       :     np.ndarray   ,lowerCamelCase__       :     Union[int, float]   ,lowerCamelCase__       :     bool = True   ,lowerCamelCase__       :     Optional[Union[str, ChannelDimension]] = None   ,**lowerCamelCase__       :     Optional[Any]   ,):
									if offset:
													UpperCAmelCase__					=							(image - 1_2_7.5) * scale
													if data_format is not None:
																	UpperCAmelCase__					=							to_channel_dimension_format(lowerCamelCase__   ,lowerCamelCase__      )
													UpperCAmelCase__					=							rescaled_image.astype(np.floataa      )
									else:
													UpperCAmelCase__					=							rescale(lowerCamelCase__   ,scale=lowerCamelCase__   ,data_format=lowerCamelCase__   ,**lowerCamelCase__      )
									return rescaled_image







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







					def 		__lowerCAmelCase						(    self       :     Optional[Any]   ,lowerCamelCase__       :     ImageInput   ,lowerCamelCase__       :     bool = None   ,lowerCamelCase__       :     Dict[str, int] = None   ,lowerCamelCase__       :     Optional[int]=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__       :     List[str]   ,):
									UpperCAmelCase__					=							do_resize if do_resize is not None else self.do_resize
									UpperCAmelCase__					=							resample if resample is not None else self.resample
									UpperCAmelCase__					=							do_center_crop if do_center_crop is not None else self.do_center_crop
									UpperCAmelCase__					=							do_rescale if do_rescale is not None else self.do_rescale
									UpperCAmelCase__					=							rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCAmelCase__					=							rescale_offset if rescale_offset is not None else self.rescale_offset
									UpperCAmelCase__					=							do_normalize if do_normalize is not None else self.do_normalize
									UpperCAmelCase__					=							image_mean if image_mean is not None else self.image_mean
									UpperCAmelCase__					=							image_std if image_std is not None else self.image_std
									UpperCAmelCase__					=							include_top if include_top is not None else self.include_top

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

									UpperCAmelCase__					=							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.
									UpperCAmelCase__					=							[to_numpy_array(lowerCamelCase__      ) for image in images]

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

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

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

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

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

									UpperCAmelCase__					=							[to_channel_dimension_format(lowerCamelCase__   ,lowerCamelCase__      ) for image in images]

									UpperCAmelCase__					=							{'pixel_values': images}
									return BatchFeature(data=lowerCamelCase__   ,tensor_type=lowerCamelCase__      )




"""simple docstring"""






import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowerCAmelCase__  :  List[Any]											=     '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'
class     snake_case	(			unittest.TestCase				,   __UpperCAmelCase    ):




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							load_tool('summarization'      )
									self.tool.setup()
									UpperCAmelCase__					=							load_tool('summarization'   ,remote=lowerCamelCase__      )







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									UpperCAmelCase__					=							self.tool(lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							self.remote_tool(lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)







					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									UpperCAmelCase__					=							self.tool(text=lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							self.remote_tool(text=lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)



