




import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 		__UpperCAmelCase(		lowercase__       ,	lowercase__       ,	unittest.TestCase ):





     """simple docstring"""



     __UpperCAmelCase	: Any						=    IFImgaImgPipeline
     __UpperCAmelCase	: List[Any]						=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
     __UpperCAmelCase	: Optional[int]						=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     __UpperCAmelCase	: str						=    PipelineTesterMixin.required_optional_params - {"""latents"""}

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Any:






         """simple docstring"""
         return self._get_dummy_components()

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]						,				A :       List[Any]						,				A :       List[Any]=0    )			->			Union[str, Any]:






         """simple docstring"""
         if str(A    ).startswith("""mps"""    ):
             a__      =       torch.manual_seed(A    )
         else:
             a__      =       torch.Generator(device=A    ).manual_seed(A    )

         a__      =       floats_tensor((1, 3, 32, 32)						,				rng=random.Random(A    )    ).to(A    )

         a__      =       {
             """prompt""": """A painting of a squirrel eating a burger""",
             """image""": image,
             """generator""": generator,
             """num_inference_steps""": 2,
             """output_type""": """numpy""",
         }

         return inputs

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			Optional[int]:






         """simple docstring"""
         self._test_save_load_optional_components()

     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()						,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,				)
     def 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			Dict:






         """simple docstring"""
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3    )

     @unittest.skipIf(torch_device != """cuda"""						,				reason="""float16 requires CUDA"""    )
     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			Any:






         """simple docstring"""
         super().test_save_load_floataa(expected_max_diff=1E-1    )

     @unittest.skipIf(torch_device != """cuda"""						,				reason="""float16 requires CUDA"""    )
     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			List[Any]:






         """simple docstring"""
         super().test_floataa_inference(expected_max_diff=1E-1    )

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






         """simple docstring"""
         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2    )

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






         """simple docstring"""
         self._test_save_load_local()






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






         """simple docstring"""
         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2						,				)












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, 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


SCREAMING_SNAKE_CASE_						:					Any			   =       logging.get_logger(__name__)







class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     __UpperCAmelCase	: Optional[Any]						=    ["""pixel_values"""]

     def __init__(      self :       int						,				A :       bool = True						,				A :       Dict[str, int] = None						,				A :       PILImageResampling = PIL.Image.NEAREST						,				A :       bool = False						,				A :       Dict[str, int] = None						,				A :       Union[int, float] = 1 / 2_55						,				A :       bool = False						,				A :       bool = True						,				A :       bool = True						,				A :       Optional[Union[float, List[float]]] = None						,				A :       Optional[Union[float, List[float]]] = None						,				A :       bool = True						,				**A :       Optional[int]						,				)			->			None:






         """simple docstring"""
         super().__init__(**A    )
         a__      =       size if size is not None else {"""height""": 3_46, """width""": 3_46}
         a__      =       get_size_dict(A    )
         a__      =       crop_size if crop_size is not None else {"""height""": 2_89, """width""": 2_89}
         a__      =       get_size_dict(A						,				param_name="""crop_size"""    )

         a__      =       do_resize
         a__      =       size
         a__      =       resample
         a__      =       do_center_crop
         a__      =       crop_size
         a__      =       do_rescale
         a__      =       rescale_factor
         a__      =       rescale_offset
         a__      =       do_normalize
         a__      =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         a__      =       image_std if image_std is not None else IMAGENET_STANDARD_STD
         a__      =       include_top

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]						,				A :       np.ndarray						,				A :       Dict[str, int]						,				A :       PILImageResampling = PIL.Image.NEAREST						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       Dict						,				)			->			np.ndarray:






         """simple docstring"""
         a__      =       get_size_dict(A    )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
         return resize(
             A						,				size=(size["""height"""], size["""width"""])						,				resample=A						,				data_format=A						,				**A    )

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






         """simple docstring"""
         a__      =       get_size_dict(A    )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
         return center_crop(A						,				size=(size["""height"""], size["""width"""])						,				data_format=A						,				**A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict						,				A :       np.ndarray						,				A :       Union[int, float]						,				A :       bool = True						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       List[str]						,				)			->			Any:






         """simple docstring"""
         if offset:
             a__      =       (image - 1_2_7.5) * scale
             if data_format is not None:
                 a__      =       to_channel_dimension_format(A						,				A    )
             a__      =       rescaled_image.astype(np.floataa    )
         else:
             a__      =       rescale(A						,				scale=A						,				data_format=A						,				**A    )
         return rescaled_image

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       np.ndarray						,				A :       Union[float, List[float]]						,				A :       Union[float, List[float]]						,				A :       Optional[Union[str, ChannelDimension]] = None						,				**A :       str						,				)			->			np.ndarray:






         """simple docstring"""
         return normalize(A						,				mean=A						,				std=A						,				data_format=A						,				**A    )






     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]						,				A :       ImageInput						,				A :       bool = None						,				A :       Dict[str, int] = None						,				A :       Any=None						,				A :       bool = None						,				A :       Dict[str, int] = None						,				A :       bool = None						,				A :       float = None						,				A :       bool = None						,				A :       bool = None						,				A :       Optional[Union[float, List[float]]] = None						,				A :       Optional[Union[float, List[float]]] = None						,				A :       bool = None						,				A :       Optional[Union[str, TensorType]] = None						,				A :       ChannelDimension = ChannelDimension.FIRST						,				**A :       Tuple						,				)			->			PIL.Image.Image:






         """simple docstring"""
         a__      =       do_resize if do_resize is not None else self.do_resize
         a__      =       resample if resample is not None else self.resample
         a__      =       do_center_crop if do_center_crop is not None else self.do_center_crop
         a__      =       do_rescale if do_rescale is not None else self.do_rescale
         a__      =       rescale_factor if rescale_factor is not None else self.rescale_factor
         a__      =       rescale_offset if rescale_offset is not None else self.rescale_offset
         a__      =       do_normalize if do_normalize is not None else self.do_normalize
         a__      =       image_mean if image_mean is not None else self.image_mean
         a__      =       image_std if image_std is not None else self.image_std
         a__      =       include_top if include_top is not None else self.include_top

         a__      =       size if size is not None else self.size
         a__      =       get_size_dict(A    )
         a__      =       crop_size if crop_size is not None else self.crop_size
         a__      =       get_size_dict(A						,				param_name="""crop_size"""    )

         a__      =       make_list_of_images(A    )

         if not valid_images(A    ):
             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_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."""    )

         # All transformations expect numpy arrays.
         a__      =       [to_numpy_array(A    ) for image in images]

         if do_resize:
             a__      =       [self.resize(image=A						,				size=A						,				resample=A    ) for image in images]

         if do_center_crop:
             a__      =       [self.center_crop(image=A						,				size=A    ) for image in images]

         if do_rescale:
             a__      =       [self.rescale(image=A						,				scale=A						,				offset=A    ) for image in images]

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

         if include_top:
             a__      =       [self.normalize(image=A						,				mean=[0, 0, 0]						,				std=A    ) for image in images]

         a__      =       [to_channel_dimension_format(A						,				A    ) for image in images]

         a__      =       {"""pixel_values""": images}
         return BatchFeature(data=A						,				tensor_type=A    )






