'''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_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


_A			:      Optional[int]					      =				logging.get_logger(__name__)







class 				_lowercase     (				__lowercase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Optional[Any]      	=					["pixel_values"]
 def __init__(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	Dict[str, int] = None      ,       SCREAMING_SNAKE_CASE__   :	float = None      ,       SCREAMING_SNAKE_CASE__   :	PILImageResampling = PILImageResampling.BILINEAR      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	Union[int, float] = 1 / 2_55      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[float, List[float]]] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[float, List[float]]] = None      ,       **SCREAMING_SNAKE_CASE__   :	Tuple      ,       )  ->    List[str]:
  super().__init__(**snake_case_							)
  __lowerCAmelCase											=						size if size is not None else {'''shortest_edge''': 3_84}
  __lowerCAmelCase											=						get_size_dict(snake_case_      ,       default_to_square=snake_case_							)

  __lowerCAmelCase											=						do_resize
  __lowerCAmelCase											=						size
  # Default value set here for backwards compatibility where the value in config is None
  __lowerCAmelCase											=						crop_pct if crop_pct is not None else 2_24 / 2_56
  __lowerCAmelCase											=						resample
  __lowerCAmelCase											=						do_rescale
  __lowerCAmelCase											=						rescale_factor
  __lowerCAmelCase											=						do_normalize
  __lowerCAmelCase											=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  __lowerCAmelCase											=						image_std if image_std is not None else IMAGENET_STANDARD_STD
 def a					(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	np.ndarray      ,       SCREAMING_SNAKE_CASE__   :	Dict[str, int]      ,       SCREAMING_SNAKE_CASE__   :	float      ,       SCREAMING_SNAKE_CASE__   :	PILImageResampling = PILImageResampling.BICUBIC      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[str, ChannelDimension]] = None      ,       **SCREAMING_SNAKE_CASE__   :	str      ,       )  ->    Union[str, Any]:
  __lowerCAmelCase											=						get_size_dict(snake_case_      ,       default_to_square=snake_case_							)
  if "shortest_edge" not in size:
   raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""							)
  __lowerCAmelCase											=						size['''shortest_edge''']

  if shortest_edge < 3_84:
   # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
   __lowerCAmelCase											=						int(shortest_edge / crop_pct							)
   __lowerCAmelCase											=						get_resize_output_image_size(snake_case_      ,       size=snake_case_      ,       default_to_square=snake_case_							)
   __lowerCAmelCase											=						resize(image=snake_case_      ,       size=snake_case_      ,       resample=snake_case_      ,       data_format=snake_case_      ,       **snake_case_							)
   # then crop to (shortest_edge, shortest_edge)
   return center_crop(image=snake_case_      ,       size=(shortest_edge, shortest_edge)      ,       data_format=snake_case_      ,       **snake_case_							)
  else:
   # warping (no cropping) when evaluated at 384 or larger
   return resize(
       snake_case_      ,       size=(shortest_edge, shortest_edge)      ,       resample=snake_case_      ,       data_format=snake_case_      ,       **snake_case_							)
 def a					(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	np.ndarray      ,       SCREAMING_SNAKE_CASE__   :	Union[int, float]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[str, ChannelDimension]] = None      ,       **SCREAMING_SNAKE_CASE__   :	Union[str, Any]      ,       )  ->    Optional[int]:
  return rescale(snake_case_      ,       scale=snake_case_      ,       data_format=snake_case_      ,       **snake_case_							)
 def a					(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	np.ndarray      ,       SCREAMING_SNAKE_CASE__   :	Union[float, List[float]]      ,       SCREAMING_SNAKE_CASE__   :	Union[float, List[float]]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[str, ChannelDimension]] = None      ,       **SCREAMING_SNAKE_CASE__   :	Union[str, Any]      ,       )  ->    Optional[Any]:
  return normalize(snake_case_      ,       mean=snake_case_      ,       std=snake_case_      ,       data_format=snake_case_      ,       **snake_case_							)





 def a					(      self   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	ImageInput      ,       SCREAMING_SNAKE_CASE__   :	bool = None      ,       SCREAMING_SNAKE_CASE__   :	Dict[str, int] = None      ,       SCREAMING_SNAKE_CASE__   :	float = None      ,       SCREAMING_SNAKE_CASE__   :	PILImageResampling = None      ,       SCREAMING_SNAKE_CASE__   :	bool = None      ,       SCREAMING_SNAKE_CASE__   :	float = None      ,       SCREAMING_SNAKE_CASE__   :	bool = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[float, List[float]]] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[float, List[float]]] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[str, TensorType]] = None      ,       SCREAMING_SNAKE_CASE__   :	ChannelDimension = ChannelDimension.FIRST      ,       **SCREAMING_SNAKE_CASE__   :	Dict      ,       )  ->    Tuple:
  __lowerCAmelCase											=						do_resize if do_resize is not None else self.do_resize
  __lowerCAmelCase											=						crop_pct if crop_pct is not None else self.crop_pct
  __lowerCAmelCase											=						resample if resample is not None else self.resample
  __lowerCAmelCase											=						do_rescale if do_rescale is not None else self.do_rescale
  __lowerCAmelCase											=						rescale_factor if rescale_factor is not None else self.rescale_factor
  __lowerCAmelCase											=						do_normalize if do_normalize is not None else self.do_normalize
  __lowerCAmelCase											=						image_mean if image_mean is not None else self.image_mean
  __lowerCAmelCase											=						image_std if image_std is not None else self.image_std

  __lowerCAmelCase											=						size if size is not None else self.size
  __lowerCAmelCase											=						get_size_dict(snake_case_      ,       default_to_square=snake_case_							)

  __lowerCAmelCase											=						make_list_of_images(snake_case_							)

  if not valid_images(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_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
   raise ValueError("""crop_pct must be specified if size < 384."""							)

  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.
  __lowerCAmelCase											=						[to_numpy_array(snake_case_							) for image in images]

  if do_resize:
   __lowerCAmelCase											=						[self.resize(image=snake_case_      ,       size=snake_case_      ,       crop_pct=snake_case_      ,       resample=snake_case_							) for image in images]

  if do_rescale:
   __lowerCAmelCase											=						[self.rescale(image=snake_case_      ,       scale=snake_case_							) for image in images]

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

  __lowerCAmelCase											=						[to_channel_dimension_format(snake_case_      ,       snake_case_							) for image in images]

  __lowerCAmelCase											=						{'''pixel_values''': images}
  return BatchFeature(data=snake_case_      ,       tensor_type=snake_case_							)








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__       =['text', 'image', 'audio']




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ):
 __a :  Optional[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((5_1_2, 5_1_2)  )  )
  elif input_type == "audio":
   inputs.append(torch.ones(3_0_0_0  )  )
  elif isinstance(lowerCAmelCase__  ,   lowerCAmelCase__  ):
   inputs.append(create_inputs(lowerCAmelCase__  )  )
  else:
   raise ValueError(f"Invalid type requested: {input_type}"  )

 return inputs




def    __UpperCamelCase     (				lowerCAmelCase__							:				List  ):
 __a :  List[str]						=  []

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

 return output_types



@is_tool_test
class 		UpperCamelCase__  :
   def       lowerCAmelCase			(self :					Any   ):
    self.assertTrue(hasattr(self.tool					,				'''inputs'''   )   )
    self.assertTrue(hasattr(self.tool					,				'''outputs'''   )   )

    __a :  Any						=  self.tool.inputs
    for _input in inputs:
     if isinstance(_input					,				snake_case_   ):
      for __input in _input:
       self.assertTrue(__input in authorized_types   )
     else:
      self.assertTrue(_input in authorized_types   )

    __a :  Optional[int]						=  self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  Union[str, Any]						=  create_inputs(self.tool.inputs   )
    __a :  List[Any]						=  self.tool(*snake_case_   )

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

    self.assertListEqual(output_types(snake_case_   )					,				self.tool.outputs   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    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       lowerCAmelCase			(self :					Any   ):
    __a :  Any						=  create_inputs(self.tool.inputs   )
    __a :  Union[str, Any]						=  self.tool(*snake_case_   )

    if not isinstance(snake_case_					,				snake_case_   ):
     __a :  Tuple						=  [outputs]

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

    for output, output_type in zip(snake_case_					,				self.tool.outputs   ):
     __a :  List[Any]						=  AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(snake_case_					,				snake_case_   )   )







   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  Any						=  create_inputs(self.tool.inputs   )

    __a :  Dict						=  []

    for _input, input_type in zip(snake_case_					,				self.tool.inputs   ):
     if isinstance(snake_case_					,				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
    __a :  Optional[Any]						=  self.tool(*snake_case_   )

    if not isinstance(snake_case_					,				snake_case_   ):
     __a :  Dict						=  [outputs]

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