

import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowercase     :       Dict        =       "\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 		SCREAMING_SNAKE_CASE__					(		unittest.TestCase						,	lowerCamelCase__   ):







						"""simple docstring"""




						def __lowerCamelCase					(				self    )							->	Union[str, Any]:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      load_tool("summarization"    )
									self.tool.setup()
									__UpperCamelCase    :    str								=      load_tool("summarization" , remote=__UpperCamelCase    )

						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      self.tool(__UpperCamelCase    )
									self.assertEqual(
									    __UpperCamelCase , "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    )							->	int:




									'''simple docstring'''


									__UpperCamelCase    :    Any								=      self.remote_tool(__UpperCamelCase    )
									self.assertEqual(
									    __UpperCamelCase , "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]:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      self.tool(text=__UpperCamelCase    )
									self.assertEqual(
									    __UpperCamelCase , "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    )							->	str:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      self.remote_tool(text=__UpperCamelCase    )
									self.assertEqual(
									    __UpperCamelCase , "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." , )


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


lowercase     :       str        =       logging.get_logger(__name__)


if is_vision_available():
					import PIL
class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				str											=							['pixel_values']

						def __init__(				self , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = True , __UpperCamelCase = False , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_55 , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )							->	None:




									'''simple docstring'''


									super().__init__(**__UpperCamelCase    )

									__UpperCamelCase    :    Optional[Any]								=      size if size is not None else {"height": 25_60, "width": 19_20}
									if isinstance(__UpperCamelCase , (tuple, list)    ):
												# The previous feature extractor size parameter was in (width, height) format
												__UpperCamelCase    :    Dict								=      size[::-1]
									__UpperCamelCase    :    Optional[int]								=      get_size_dict(__UpperCamelCase    )

									__UpperCamelCase    :    List[Any]								=      do_resize
									__UpperCamelCase    :    Tuple								=      size
									__UpperCamelCase    :    Optional[int]								=      resample
									__UpperCamelCase    :    Optional[Any]								=      do_thumbnail
									__UpperCamelCase    :    Any								=      do_align_long_axis
									__UpperCamelCase    :    str								=      do_pad
									__UpperCamelCase    :    str								=      do_rescale
									__UpperCamelCase    :    Union[str, Any]								=      rescale_factor
									__UpperCamelCase    :    Any								=      do_normalize
									__UpperCamelCase    :    List[Any]								=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									__UpperCamelCase    :    Dict								=      image_std if image_std is not None else IMAGENET_STANDARD_STD

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None    )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      get_image_size(__UpperCamelCase    )
									__UpperCamelCase							,				__UpperCamelCase    :    str								=      size["height"], size["width"]

									if (output_width < output_height and input_width > input_height) or (
									    output_width > output_height and input_width < input_height
									):
												__UpperCamelCase    :    str								=      np.rotaa(__UpperCamelCase , 3    )

									if data_format is not None:
												__UpperCamelCase    :    str								=      to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase    )

									return image

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = False , __UpperCamelCase = None , )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Union[str, Any]								=      size["height"], size["width"]
									__UpperCamelCase							,				__UpperCamelCase    :    str								=      get_image_size(__UpperCamelCase    )

									__UpperCamelCase    :    List[str]								=      output_width - input_width
									__UpperCamelCase    :    Union[str, Any]								=      output_height - input_height

									if random_padding:
												__UpperCamelCase    :    Any								=      np.random.randint(low=0 , high=delta_height + 1    )
												__UpperCamelCase    :    Union[str, Any]								=      np.random.randint(low=0 , high=delta_width + 1    )
									else:
												__UpperCamelCase    :    Any								=      delta_height // 2
												__UpperCamelCase    :    Dict								=      delta_width // 2

									__UpperCamelCase    :    Optional[int]								=      delta_height - pad_top
									__UpperCamelCase    :    Dict								=      delta_width - pad_left

									__UpperCamelCase    :    str								=      ((pad_top, pad_bottom), (pad_left, pad_right))
									return pad(__UpperCamelCase , __UpperCamelCase , data_format=__UpperCamelCase    )

						def __lowerCamelCase					(				self , *__UpperCamelCase , **__UpperCamelCase    )							->	Optional[Any]:




									'''simple docstring'''


									logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."    )
									return self.pad_image(*__UpperCamelCase , **__UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BICUBIC , __UpperCamelCase = None , **__UpperCamelCase , )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Optional[int]								=      get_image_size(__UpperCamelCase    )
									__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      size["height"], size["width"]

									# We always resize to the smallest of either the input or output size.
									__UpperCamelCase    :    int								=      min(__UpperCamelCase , __UpperCamelCase    )
									__UpperCamelCase    :    List[str]								=      min(__UpperCamelCase , __UpperCamelCase    )

									if height == input_height and width == input_width:
												return image

									if input_height > input_width:
												__UpperCamelCase    :    List[Any]								=      int(input_width * height / input_height    )
									elif input_width > input_height:
												__UpperCamelCase    :    Tuple								=      int(input_height * width / input_width    )

									return resize(
									    __UpperCamelCase , size=(height, width) , resample=__UpperCamelCase , reducing_gap=2.0 , data_format=__UpperCamelCase , **__UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BICUBIC , __UpperCamelCase = None , **__UpperCamelCase , )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase    :    List[str]								=      get_size_dict(__UpperCamelCase    )
									__UpperCamelCase    :    str								=      min(size["height"] , size["width"]    )
									__UpperCamelCase    :    Tuple								=      get_resize_output_image_size(__UpperCamelCase , size=__UpperCamelCase , default_to_square=__UpperCamelCase    )
									__UpperCamelCase    :    List[Any]								=      resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase    )
									return resized_image

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )							->	Dict:




									'''simple docstring'''


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

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )							->	np.ndarray:




									'''simple docstring'''


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




						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = False , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = ChannelDimension.FIRST , **__UpperCamelCase , )							->	PIL.Image.Image:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      do_resize if do_resize is not None else self.do_resize
									__UpperCamelCase    :    Any								=      size if size is not None else self.size
									if isinstance(__UpperCamelCase , (tuple, list)    ):
												# Previous feature extractor had size in (width, height) format
												__UpperCamelCase    :    List[Any]								=      size[::-1]
									__UpperCamelCase    :    List[Any]								=      get_size_dict(__UpperCamelCase    )
									__UpperCamelCase    :    Union[str, Any]								=      resample if resample is not None else self.resample
									__UpperCamelCase    :    Any								=      do_thumbnail if do_thumbnail is not None else self.do_thumbnail
									__UpperCamelCase    :    List[Any]								=      do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
									__UpperCamelCase    :    Optional[int]								=      do_pad if do_pad is not None else self.do_pad
									__UpperCamelCase    :    Dict								=      do_rescale if do_rescale is not None else self.do_rescale
									__UpperCamelCase    :    List[str]								=      rescale_factor if rescale_factor is not None else self.rescale_factor
									__UpperCamelCase    :    Any								=      do_normalize if do_normalize is not None else self.do_normalize
									__UpperCamelCase    :    Union[str, Any]								=      image_mean if image_mean is not None else self.image_mean
									__UpperCamelCase    :    Dict								=      image_std if image_std is not None else self.image_std

									__UpperCamelCase    :    List[Any]								=      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:
												raise ValueError("Size must be specified if do_resize is True."    )

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

									if do_pad and size is None:
												raise ValueError("Size must be specified if do_pad 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    :    Union[str, Any]								=      [to_numpy_array(__UpperCamelCase    ) for image in images]

									if do_align_long_axis:
												__UpperCamelCase    :    Tuple								=      [self.align_long_axis(__UpperCamelCase , size=__UpperCamelCase    ) for image in images]

									if do_resize:
												__UpperCamelCase    :    Optional[int]								=      [self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase    ) for image in images]

									if do_thumbnail:
												__UpperCamelCase    :    Union[str, Any]								=      [self.thumbnail(image=__UpperCamelCase , size=__UpperCamelCase    ) for image in images]

									if do_pad:
												__UpperCamelCase    :    Optional[Any]								=      [self.pad_image(image=__UpperCamelCase , size=__UpperCamelCase , random_padding=__UpperCamelCase    ) for image in images]

									if do_rescale:
												__UpperCamelCase    :    Optional[int]								=      [self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase    ) for image in images]

									if do_normalize:
												__UpperCamelCase    :    Union[str, Any]								=      [self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase    ) for image in images]

									__UpperCamelCase    :    List[str]								=      [to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase    ) for image in images]

									__UpperCamelCase    :    int								=      {"pixel_values": images}
									return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase    )