
'''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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


_lowercase :  str    							=       logging.get_logger(__name__)






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     ['''pixel_values''']






					def __init__(   self				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = 0.9				,							__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = 1 / 2_55				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[str]													=       size if size is not None else {'''shortest_edge''': 2_24}
											lowercase_	:     Tuple													=       get_size_dict(__SCREAMING_SNAKE_CASE				,							default_to_square=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Any													=       crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
											lowercase_	:     Optional[Any]													=       get_size_dict(__SCREAMING_SNAKE_CASE				,							param_name='''crop_size'''		)

											lowercase_	:     Dict													=       do_resize
											lowercase_	:     Any													=       size
											lowercase_	:     List[str]													=       crop_pct
											lowercase_	:     Union[str, Any]													=       resample
											lowercase_	:     List[Any]													=       do_center_crop
											lowercase_	:     Union[str, Any]													=       crop_size
											lowercase_	:     Dict													=       do_rescale
											lowercase_	:     Dict													=       rescale_factor
											lowercase_	:     str													=       do_normalize
											lowercase_	:     Union[str, Any]													=       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											lowercase_	:     Dict													=       image_std if image_std is not None else IMAGENET_DEFAULT_STD






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,							__SCREAMING_SNAKE_CASE = None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       get_size_dict(__SCREAMING_SNAKE_CASE				,							default_to_square=__SCREAMING_SNAKE_CASE		)
											if "shortest_edge" not in size and ("height" not in size or "width" not in size):
																	raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''		)
											if crop_pct is not None:
																	if "shortest_edge" in size:
																							lowercase_	:     List[str]													=       int(size['''shortest_edge'''] / crop_pct		)
																	elif "height" in size and "width" in size:
																							if size["height"] == size["width"]:
																													lowercase_	:     Dict													=       int(size['''height'''] / crop_pct		)
																							else:
																													lowercase_	:     int													=       (int(size['''height'''] / crop_pct		), int(size['''width'''] / crop_pct		))
																	else:
																							raise ValueError('''Invalid size for resize: {}'''.format(__SCREAMING_SNAKE_CASE		)		)

																	lowercase_	:     int													=       get_resize_output_image_size(__SCREAMING_SNAKE_CASE				,							size=__SCREAMING_SNAKE_CASE				,							default_to_square=__SCREAMING_SNAKE_CASE		)
											else:
																	if "shortest_edge" in size:
																							lowercase_	:     Optional[Any]													=       get_resize_output_image_size(__SCREAMING_SNAKE_CASE				,							size=size['''shortest_edge''']				,							default_to_square=__SCREAMING_SNAKE_CASE		)
																	elif "height" in size and "width" in size:
																							lowercase_	:     Dict													=       (size['''height'''], size['''width'''])
																	else:
																							raise ValueError('''Invalid size for resize: {}'''.format(__SCREAMING_SNAKE_CASE		)		)

											return resize(__SCREAMING_SNAKE_CASE				,							size=__SCREAMING_SNAKE_CASE				,							resample=__SCREAMING_SNAKE_CASE				,							data_format=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Any													=       get_size_dict(__SCREAMING_SNAKE_CASE		)
											if "height" not in size or "width" not in size:
																	raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''		)
											return center_crop(__SCREAMING_SNAKE_CASE				,							size=(size['''height'''], size['''width'''])				,							data_format=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       do_resize if do_resize is not None else self.do_resize
											lowercase_	:     Optional[Any]													=       crop_pct if crop_pct is not None else self.crop_pct
											lowercase_	:     Optional[int]													=       resample if resample is not None else self.resample
											lowercase_	:     List[str]													=       do_center_crop if do_center_crop is not None else self.do_center_crop
											lowercase_	:     Optional[int]													=       do_rescale if do_rescale is not None else self.do_rescale
											lowercase_	:     Any													=       rescale_factor if rescale_factor is not None else self.rescale_factor
											lowercase_	:     List[Any]													=       do_normalize if do_normalize is not None else self.do_normalize
											lowercase_	:     Any													=       image_mean if image_mean is not None else self.image_mean
											lowercase_	:     str													=       image_std if image_std is not None else self.image_std

											lowercase_	:     Optional[int]													=       size if size is not None else self.size
											lowercase_	:     Union[str, Any]													=       get_size_dict(__SCREAMING_SNAKE_CASE				,							default_to_square=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       crop_size if crop_size is not None else self.crop_size
											lowercase_	:     Optional[int]													=       get_size_dict(__SCREAMING_SNAKE_CASE				,							param_name='''crop_size'''		)

											lowercase_	:     Union[str, Any]													=       make_list_of_images(__SCREAMING_SNAKE_CASE		)

											if not valid_images(__SCREAMING_SNAKE_CASE		):
																	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_pct is None:
																	raise ValueError('''Crop_pct 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_	:     Union[str, Any]													=       [to_numpy_array(__SCREAMING_SNAKE_CASE		) for image in images]

											if do_resize:
																	lowercase_	:     Union[str, Any]													=       [self.resize(image=__SCREAMING_SNAKE_CASE				,							size=__SCREAMING_SNAKE_CASE				,							crop_pct=__SCREAMING_SNAKE_CASE				,							resample=__SCREAMING_SNAKE_CASE		) for image in images]

											if do_center_crop:
																	lowercase_	:     Optional[Any]													=       [self.center_crop(image=__SCREAMING_SNAKE_CASE				,							size=__SCREAMING_SNAKE_CASE		) for image in images]

											if do_rescale:
																	lowercase_	:     List[str]													=       [self.rescale(image=__SCREAMING_SNAKE_CASE				,							scale=__SCREAMING_SNAKE_CASE		) for image in images]

											if do_normalize:
																	lowercase_	:     Tuple													=       [self.normalize(image=__SCREAMING_SNAKE_CASE				,							mean=__SCREAMING_SNAKE_CASE				,							std=__SCREAMING_SNAKE_CASE		) for image in images]

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

											lowercase_	:     Union[str, Any]													=       {'''pixel_values''': images}
											return BatchFeature(data=__SCREAMING_SNAKE_CASE				,							tensor_type=__SCREAMING_SNAKE_CASE		)





'''simple docstring'''







from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image


_lowercase :  List[str]    							=       ["text", "image", "audio"]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]							):



						"""simple docstring"""





						lowercase_	:     int													=       []

						for input_type in input_types:
												if input_type == "text":
																		inputs.append('''Text input'''							)
												elif input_type == "image":
																		inputs.append(
																		    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''							)							) / '''000000039769.png'''							).resize((512, 512)							)							)
												elif input_type == "audio":
																		inputs.append(torch.ones(3000							)							)
												elif isinstance(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							):
																		inputs.append(create_inputs(__SCREAMING_SNAKE_CASE							)							)
												else:
																		raise ValueError(F'''Invalid type requested: {input_type}'''							)

						return inputs
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List							):



						"""simple docstring"""





						lowercase_	:     Optional[Any]													=       []

						for output in outputs:
												if isinstance(__SCREAMING_SNAKE_CASE      ,      (str, AgentText)							):
																		output_types.append('''text'''							)
												elif isinstance(__SCREAMING_SNAKE_CASE      ,      (Image.Image, AgentImage)							):
																		output_types.append('''image'''							)
												elif isinstance(__SCREAMING_SNAKE_CASE      ,      (torch.Tensor, AgentAudio)							):
																		output_types.append('''audio'''							)
												else:
																		raise ValueError(F'''Invalid output: {output}'''							)

						return output_types






@is_tool_test
class  lowerCAmelCase__   :






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertTrue(hasattr(self.tool				,							'''inputs'''		)		)
											self.assertTrue(hasattr(self.tool				,							'''outputs'''		)		)

											lowercase_	:     Optional[Any]													=       self.tool.inputs
											for _input in inputs:
																	if isinstance(_input				,							__SCREAMING_SNAKE_CASE		):
																							for __input in _input:
																													self.assertTrue(__input in authorized_types		)
																	else:
																							self.assertTrue(_input in authorized_types		)

											lowercase_	:     int													=       self.tool.outputs
											for _output in outputs:
																	self.assertTrue(_output in authorized_types		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       create_inputs(self.tool.inputs		)
											lowercase_	:     Tuple													=       self.tool(*__SCREAMING_SNAKE_CASE		)

											# There is a single output
											if len(self.tool.outputs		) == 1:
																	lowercase_	:     Any													=       [outputs]

											self.assertListEqual(output_types(__SCREAMING_SNAKE_CASE		)				,							self.tool.outputs		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertTrue(hasattr(self.tool				,							'''description'''		)		)
											self.assertTrue(hasattr(self.tool				,							'''default_checkpoint'''		)		)
											self.assertTrue(self.tool.description.startswith('''This is a tool that'''		)		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       create_inputs(self.tool.inputs		)
											lowercase_	:     int													=       self.tool(*__SCREAMING_SNAKE_CASE		)

											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Optional[Any]													=       [outputs]

											self.assertEqual(len(__SCREAMING_SNAKE_CASE		)				,							len(self.tool.outputs		)		)

											for output, output_type in zip(__SCREAMING_SNAKE_CASE				,							self.tool.outputs		):
																	lowercase_	:     Optional[int]													=       AGENT_TYPE_MAPPING[output_type]
																	self.assertTrue(isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       create_inputs(self.tool.inputs		)

											lowercase_	:     int													=       []

											for _input, input_type in zip(__SCREAMING_SNAKE_CASE				,							self.tool.inputs		):
																	if isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																							_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input		) for _input_type in input_type]		)
																	else:
																							_inputs.append(AGENT_TYPE_MAPPING[input_type](_input		)		)

        # Should not raise an error
											lowercase_	:     Optional[Any]													=       self.tool(*__SCREAMING_SNAKE_CASE		)

											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Dict													=       [outputs]

											self.assertEqual(len(__SCREAMING_SNAKE_CASE		)				,							len(self.tool.outputs		)		)



