



"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_UpperCAmelCase     =			logging.get_logger(__name__)


if is_vision_available():
   import PIL
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				List[Any]     						=					['pixel_values']



  def __init__(						self :					int       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					PILImageResampling = PILImageResampling.BICUBIC       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Union[int, float] = 1 / 255       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Optional[Union[float, List[float]]] = None       ,		lowerCAmelCase :					Optional[Union[float, List[float]]] = None       ,		lowerCAmelCase :					bool = True       ,		**lowerCAmelCase :					List[Any]       ,		)     ->					None:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=size if size is not None else {"""shortest_edge""": 224}
     SCREAMING_SNAKE_CASE_: Tuple     			=get_size_dict(lowerCAmelCase       ,		default_to_square=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Dict     			=crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
     SCREAMING_SNAKE_CASE_: List[Any]     			=get_size_dict(lowerCAmelCase       ,		default_to_square=lowerCAmelCase       ,		param_name="""crop_size"""		)

     SCREAMING_SNAKE_CASE_: int     			=do_resize
     SCREAMING_SNAKE_CASE_: int     			=size
     SCREAMING_SNAKE_CASE_: Any     			=resample
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=do_center_crop
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=crop_size
     SCREAMING_SNAKE_CASE_: Dict     			=do_rescale
     SCREAMING_SNAKE_CASE_: int     			=rescale_factor
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=do_normalize
     SCREAMING_SNAKE_CASE_: List[Any]     			=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     SCREAMING_SNAKE_CASE_: str     			=image_std if image_std is not None else OPENAI_CLIP_STD
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=do_convert_rgb



  def 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Dict[str, int]       ,		lowerCAmelCase :					PILImageResampling = PILImageResampling.BICUBIC       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					List[Any]       ,		)     ->					np.ndarray:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=get_size_dict(lowerCAmelCase       ,		default_to_square=lowerCAmelCase		)
     if "shortest_edge" not in size:
        raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
     SCREAMING_SNAKE_CASE_: List[str]     			=get_resize_output_image_size(lowerCAmelCase       ,		size=size["""shortest_edge"""]       ,		default_to_square=lowerCAmelCase		)
     return resize(lowerCAmelCase       ,		size=lowerCAmelCase       ,		resample=lowerCAmelCase       ,		data_format=lowerCAmelCase       ,		**lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Dict[str, int]       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					int       ,		)     ->					np.ndarray:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=get_size_dict(lowerCAmelCase		)
     if "height" not in size or "width" not in size:
        raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''		)
     return center_crop(lowerCAmelCase       ,		size=(size["""height"""], size["""width"""])       ,		data_format=lowerCAmelCase       ,		**lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Union[int, float]       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					Optional[int]       ,		)     ->					Optional[Any]:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Union[float, List[float]]       ,		lowerCAmelCase :					Union[float, List[float]]       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					str       ,		)     ->					np.ndarray:


     '''simple docstring'''




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





  def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					ImageInput       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					PILImageResampling = None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					int = None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					float = 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 :					Optional[ChannelDimension] = ChannelDimension.FIRST       ,		**lowerCAmelCase :					Optional[int]       ,		)     ->					PIL.Image.Image:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE_: Tuple     			=size if size is not None else self.size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=get_size_dict(lowerCAmelCase       ,		param_name="""size"""       ,		default_to_square=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE_: Dict     			=do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE_: Any     			=crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE_: List[Any]     			=get_size_dict(lowerCAmelCase       ,		param_name="""crop_size"""       ,		default_to_square=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE_: str     			=rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE_: Tuple     			=image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=image_std if image_std is not None else self.image_std
     SCREAMING_SNAKE_CASE_: Dict     			=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     SCREAMING_SNAKE_CASE_: Optional[int]     			=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:
        raise ValueError("""Size 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."""		)

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
        SCREAMING_SNAKE_CASE_: List[Any]     			=[convert_to_rgb(lowerCAmelCase		) for image in images]

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[to_numpy_array(lowerCAmelCase		) for image in images]

     if do_resize:
        SCREAMING_SNAKE_CASE_: List[Any]     			=[self.resize(image=lowerCAmelCase       ,		size=lowerCAmelCase       ,		resample=lowerCAmelCase		) for image in images]

     if do_center_crop:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[self.center_crop(image=lowerCAmelCase       ,		size=lowerCAmelCase		) for image in images]

     if do_rescale:
        SCREAMING_SNAKE_CASE_: Tuple     			=[self.rescale(image=lowerCAmelCase       ,		scale=lowerCAmelCase		) for image in images]

     if do_normalize:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=[self.normalize(image=lowerCAmelCase       ,		mean=lowerCAmelCase       ,		std=lowerCAmelCase		) for image in images]

     SCREAMING_SNAKE_CASE_: Tuple     			=[to_channel_dimension_format(lowerCAmelCase       ,		lowerCAmelCase		) for image in images]

     SCREAMING_SNAKE_CASE_: Tuple     			={"""pixel_values""": images}
     return BatchFeature(data=lowerCAmelCase       ,		tensor_type=lowerCAmelCase		)








"""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


_UpperCAmelCase     =			["""text""", """image""", """audio"""]





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=[]

   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(lowercase							,			lowercase ):
         inputs.append(create_inputs(lowercase ) )
      else:
         raise ValueError(f'''Invalid type requested: {input_type}''' )

   return inputs





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: int     			=[]

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

   return output_types
@is_tool_test
class 					a	:



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					List[Any]:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.tool.inputs
     for _input in inputs:
        if isinstance(_input       ,		lowerCAmelCase		):
           for __input in _input:
              self.assertTrue(__input in authorized_types		)
        else:
           self.assertTrue(_input in authorized_types		)

     SCREAMING_SNAKE_CASE_: Any     			=self.tool.outputs
     for _output in outputs:
        self.assertTrue(_output in authorized_types		)



  def 	lowerCamelCase__       (						self :					str		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=create_inputs(self.tool.inputs		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=self.tool(*lowerCAmelCase		)

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

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



  def 	lowerCamelCase__       (						self :					str		)     ->					Optional[Any]:


     '''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 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=create_inputs(self.tool.inputs		)
     SCREAMING_SNAKE_CASE_: Tuple     			=self.tool(*lowerCAmelCase		)

     if not isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: int     			=[outputs]

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

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





  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=create_inputs(self.tool.inputs		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[]

     for _input, input_type in zip(lowerCAmelCase       ,		self.tool.inputs		):
        if isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
           _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
     SCREAMING_SNAKE_CASE_: Dict     			=self.tool(*lowerCAmelCase		)

     if not isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: List[str]     			=[outputs]

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



