
import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	CONFIG_NAME



     @register_to_config
     def __init__(							self    :    int      ,		_A    :    bool = True      ,		_A    :    int = 8      ,		_A    :    str = "lanczos"      ,		_A    :    bool = True      ,		_A    :    bool = False      ,		):







       """simple docstring"""




       super().__init__()



     @staticmethod
     def 				UpperCAmelCase__    (							_A    :    np.ndarray				):







       """simple docstring"""




       if images.ndim == 3:
         __SCREAMING_SNAKE_CASE     :					int			=						images[None, ...]
       __SCREAMING_SNAKE_CASE     :					Any			=						(images * 255).round().astype('''uint8'''				)
       if images.shape[-1] == 1:
         # special case for grayscale (single channel) images
         __SCREAMING_SNAKE_CASE     :					List[str]			=						[Image.fromarray(image.squeeze()      ,		mode='''L'''				) for image in images]
       else:
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						[Image.fromarray(_A				) for image in images]

       return pil_images



     @staticmethod
     def 				UpperCAmelCase__    (							_A    :    Union[List[PIL.Image.Image], PIL.Image.Image]				):







       """simple docstring"""




       if not isinstance(_A      ,		_A				):
         __SCREAMING_SNAKE_CASE     :					Tuple			=						[images]
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						[np.array(_A				).astype(np.floataa				) / 2_55.0 for image in images]
       __SCREAMING_SNAKE_CASE     :					int			=						np.stack(_A      ,		axis=0				)

       return images



     @staticmethod
     def 				UpperCAmelCase__    (							_A    :    np.ndarray				):







       """simple docstring"""




       if images.ndim == 3:
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						images[..., None]

       __SCREAMING_SNAKE_CASE     :					int			=						torch.from_numpy(images.transpose(0      ,		3      ,		1      ,		2				)				)
       return images



     @staticmethod
     def 				UpperCAmelCase__    (							_A    :    torch.FloatTensor				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						images.cpu().permute(0      ,		2      ,		3      ,		1				).float().numpy()
       return images



     @staticmethod
     def 				UpperCAmelCase__    (							_A    :    Optional[Any]				):







       """simple docstring"""




       return 2.0 * images - 1.0



     @staticmethod
     def 				UpperCAmelCase__    (							_A    :    int				):







       """simple docstring"""




       return (images / 2 + 0.5).clamp(0      ,		1				)



     @staticmethod
     def 				UpperCAmelCase__    (							_A    :    PIL.Image.Image				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						image.convert('''RGB'''				)
       return image



     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    PIL.Image.Image      ,		_A    :    Optional[int] = None      ,		_A    :    Optional[int] = None      ,		):







       """simple docstring"""




       if height is None:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						image.height
       if width is None:
         __SCREAMING_SNAKE_CASE     :					List[str]			=						image.width

       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					str			=						(
           x - x % self.config.vae_scale_factor for x in (width, height)
       )  # resize to integer multiple of vae_scale_factor
       __SCREAMING_SNAKE_CASE     :					Dict			=						image.resize((width, height)      ,		resample=PIL_INTERPOLATION[self.config.resample]				)
       return image



     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]      ,		_A    :    Optional[int] = None      ,		_A    :    Optional[int] = None      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						(PIL.Image.Image, np.ndarray, torch.Tensor)
       if isinstance(_A      ,		_A				):
         __SCREAMING_SNAKE_CASE     :					str			=						[image]
       elif not (isinstance(_A      ,		_A				) and all(isinstance(_A      ,		_A				) for i in image				)):
         raise ValueError(
             F'''Input is in incorrect format: {[type(_A				) for i in image]}. Currently, we only support {", ".join(_A				)}'''				)

       if isinstance(image[0]      ,		PIL.Image.Image				):
         if self.config.do_convert_rgb:
           __SCREAMING_SNAKE_CASE     :					Dict			=						[self.convert_to_rgb(_A				) for i in image]
         if self.config.do_resize:
           __SCREAMING_SNAKE_CASE     :					int			=						[self.resize(_A      ,		_A      ,		_A				) for i in image]
         __SCREAMING_SNAKE_CASE     :					str			=						self.pil_to_numpy(_A				)  # to np
         __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.numpy_to_pt(_A				)  # to pt

       elif isinstance(image[0]      ,		np.ndarray				):
         __SCREAMING_SNAKE_CASE     :					Tuple			=						np.concatenate(_A      ,		axis=0				) if image[0].ndim == 4 else np.stack(_A      ,		axis=0				)
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.numpy_to_pt(_A				)
         __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Tuple			=						image.shape
         if self.config.do_resize and (
             height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
         ):
           raise ValueError(
               F'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
               F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''				)

       elif isinstance(image[0]      ,		torch.Tensor				):
         __SCREAMING_SNAKE_CASE     :					str			=						torch.cat(_A      ,		axis=0				) if image[0].ndim == 4 else torch.stack(_A      ,		axis=0				)
         __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Tuple			=						image.shape

         # don't need any preprocess if the image is latents
         if channel == 4:
           return image

         if self.config.do_resize and (
             height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
         ):
           raise ValueError(
               F'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
               F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''				)

        # expected range [0,1], normalize to [-1,1]
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.config.do_normalize
       if image.min() < 0:
         warnings.warn(
             '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
             F'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'''      ,		_A      ,		)
         __SCREAMING_SNAKE_CASE     :					List[str]			=						False

       if do_normalize:
         __SCREAMING_SNAKE_CASE     :					str			=						self.normalize(_A				)

       return image




     def 				UpperCAmelCase__    (							self    :    Dict      ,		_A    :    torch.FloatTensor      ,		_A    :    str = "pil"      ,		_A    :    Optional[List[bool]] = None      ,		):







       """simple docstring"""




       if not isinstance(_A      ,		torch.Tensor				):
         raise ValueError(
             F'''Input for postprocessing is in incorrect format: {type(_A				)}. We only support pytorch tensor'''				)
       if output_type not in ["latent", "pt", "np", "pil"]:
         __SCREAMING_SNAKE_CASE     :					List[str]			=						(
             F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
             '''`pil`, `np`, `pt`, `latent`'''
         )
         deprecate('''Unsupported output_type'''      ,		'''1.0.0'''      ,		_A      ,		standard_warn=_A				)
         __SCREAMING_SNAKE_CASE     :					List[str]			=						'''np'''

       if output_type == "latent":
         return image

       if do_denormalize is None:
         __SCREAMING_SNAKE_CASE     :					Tuple			=						[self.config.do_normalize] * image.shape[0]

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						torch.stack(
           [self.denormalize(image[i]				) if do_denormalize[i] else image[i] for i in range(image.shape[0]				)]				)

       if output_type == "pt":
         return image

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.pt_to_numpy(_A				)

       if output_type == "np":
         return image

       if output_type == "pil":
         return self.numpy_to_pil(_A				)






class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	CONFIG_NAME



     @register_to_config
     def __init__(							self    :    str      ,		_A    :    bool = True      ,		_A    :    int = 8      ,		_A    :    str = "lanczos"      ,		_A    :    bool = True      ,		):







       """simple docstring"""




       super().__init__()



     @staticmethod
     def 				UpperCAmelCase__    (							_A    :    Optional[Any]				):







       """simple docstring"""




       if images.ndim == 3:
         __SCREAMING_SNAKE_CASE     :					str			=						images[None, ...]
       __SCREAMING_SNAKE_CASE     :					int			=						(images * 255).round().astype('''uint8'''				)
       if images.shape[-1] == 1:
         # special case for grayscale (single channel) images
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						[Image.fromarray(image.squeeze()      ,		mode='''L'''				) for image in images]
       else:
         __SCREAMING_SNAKE_CASE     :					List[str]			=						[Image.fromarray(image[:, :, :3]				) for image in images]

       return pil_images



     @staticmethod
     def 				UpperCAmelCase__    (							_A    :    str				):







       """simple docstring"""




       return image[:, :, 1] * 2**8 + image[:, :, 2]



     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    str				):







       """simple docstring"""




       if images.ndim == 3:
         __SCREAMING_SNAKE_CASE     :					int			=						images[None, ...]
       __SCREAMING_SNAKE_CASE     :					Tuple			=						images[:, :, :, 3:]
       if images.shape[-1] == 6:
         __SCREAMING_SNAKE_CASE     :					Optional[int]			=						(images_depth * 255).round().astype('''uint8'''				)
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						[
             Image.fromarray(self.rgblike_to_depthmap(_A				)      ,		mode='''I;16'''				) for image_depth in images_depth
         ]
       elif images.shape[-1] == 4:
         __SCREAMING_SNAKE_CASE     :					Tuple			=						(images_depth * 6_55_35.0).astype(np.uintaa				)
         __SCREAMING_SNAKE_CASE     :					int			=						[Image.fromarray(_A      ,		mode='''I;16'''				) for image_depth in images_depth]
       else:
         raise Exception('''Not supported'''				)

       return pil_images




     def 				UpperCAmelCase__    (							self    :    Optional[int]      ,		_A    :    torch.FloatTensor      ,		_A    :    str = "pil"      ,		_A    :    Optional[List[bool]] = None      ,		):







       """simple docstring"""




       if not isinstance(_A      ,		torch.Tensor				):
         raise ValueError(
             F'''Input for postprocessing is in incorrect format: {type(_A				)}. We only support pytorch tensor'''				)
       if output_type not in ["latent", "pt", "np", "pil"]:
         __SCREAMING_SNAKE_CASE     :					Tuple			=						(
             F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
             '''`pil`, `np`, `pt`, `latent`'''
         )
         deprecate('''Unsupported output_type'''      ,		'''1.0.0'''      ,		_A      ,		standard_warn=_A				)
         __SCREAMING_SNAKE_CASE     :					Any			=						'''np'''

       if do_denormalize is None:
         __SCREAMING_SNAKE_CASE     :					List[Any]			=						[self.config.do_normalize] * image.shape[0]

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						torch.stack(
           [self.denormalize(image[i]				) if do_denormalize[i] else image[i] for i in range(image.shape[0]				)]				)

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.pt_to_numpy(_A				)

       if output_type == "np":
         if image.shape[-1] == 6:
           __SCREAMING_SNAKE_CASE     :					List[Any]			=						np.stack([self.rgblike_to_depthmap(im[:, :, 3:]				) for im in image]      ,		axis=0				)
         else:
           __SCREAMING_SNAKE_CASE     :					Any			=						image[:, :, :, 3:]
         return image[:, :, :, :3], image_depth

       if output_type == "pil":
         return self.numpy_to_pil(_A				), self.numpy_to_depth(_A				)
       else:
         raise Exception(F'''This type {output_type} is not supported'''				)


import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types





class   __UpperCamelCase				(    unittest.TestCase				,		lowerCAmelCase__   ):






     """simple docstring"""







     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					str			=						load_tool('''translation'''				)
       self.tool.setup()
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						load_tool('''translation'''      ,		remote=_A				)



     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						self.tool('''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.remote_tool('''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						self.tool(text='''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.remote_tool(text='''Hey, what\'s up?'''      ,		src_lang='''English'''      ,		tgt_lang='''French'''				)
       self.assertEqual(_A      ,		'''- Hé, comment ça va?'''				)



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.tool(*_A				)

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

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



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.tool(*_A				)

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

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

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




     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						['''Hey, what\'s up?''', '''English''', '''Spanish''']

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						[]

       for _input, input_type in zip(_A      ,		self.tool.inputs				):
         if isinstance(_A      ,		_A				):
           _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     :					Union[str, Any]			=						self.tool(*_A				)

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

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