



'''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, 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_				:					Tuple							=      logging.get_logger(__name__)



class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    ["""pixel_values"""]







 def __init__(				self    ,a_ = True    ,a_ = None    ,a_ = PIL.Image.NEAREST    ,a_ = False    ,a_ = None    ,a_ = 1 / 255    ,a_ = False    ,a_ = True    ,a_ = True    ,a_ = None    ,a_ = None    ,a_ = True    ,**a_    ,)						->  None:
        super().__init__(**a_				)
        _UpperCAmelCase    :		List[Any]     	=					size if size is not None else {"""height""": 346, """width""": 346}
        _UpperCAmelCase    :		Optional[Any]     	=					get_size_dict(a_				)
        _UpperCAmelCase    :		Tuple     	=					crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
        _UpperCAmelCase    :		int     	=					get_size_dict(a_    ,param_name="""crop_size"""				)

        _UpperCAmelCase    :		Tuple     	=					do_resize
        _UpperCAmelCase    :		Optional[Any]     	=					size
        _UpperCAmelCase    :		int     	=					resample
        _UpperCAmelCase    :		Optional[int]     	=					do_center_crop
        _UpperCAmelCase    :		List[Any]     	=					crop_size
        _UpperCAmelCase    :		Tuple     	=					do_rescale
        _UpperCAmelCase    :		List[Any]     	=					rescale_factor
        _UpperCAmelCase    :		Optional[int]     	=					rescale_offset
        _UpperCAmelCase    :		Any     	=					do_normalize
        _UpperCAmelCase    :		Union[str, Any]     	=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        _UpperCAmelCase    :		Any     	=					image_std if image_std is not None else IMAGENET_STANDARD_STD
        _UpperCAmelCase    :		Tuple     	=					include_top







 def _snake_case					(				self    ,a_    ,a_    ,a_ = PIL.Image.NEAREST    ,a_ = None    ,**a_    ,)						->  np.ndarray:
        _UpperCAmelCase    :		Union[str, Any]     	=					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 _snake_case					(				self    ,a_    ,a_    ,a_ = None    ,**a_    ,)						->  np.ndarray:
        _UpperCAmelCase    :		str     	=					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 _snake_case					(				self    ,a_    ,a_    ,a_ = True    ,a_ = None    ,**a_    ,)						->  List[str]:
        if offset:
               _UpperCAmelCase    :		int     	=					(image - 127.5) * scale
               if data_format is not None:
                      _UpperCAmelCase    :		Optional[Any]     	=					to_channel_dimension_format(a_    ,a_				)
               _UpperCAmelCase    :		Optional[Any]     	=					rescaled_image.astype(np.floataa				)
        else:
               _UpperCAmelCase    :		int     	=					rescale(a_    ,scale=a_    ,data_format=a_    ,**a_				)
        return rescaled_image







 def _snake_case					(				self    ,a_    ,a_    ,a_    ,a_ = None    ,**a_    ,)						->  np.ndarray:
        return normalize(a_    ,mean=a_    ,std=a_    ,data_format=a_    ,**a_				)







 def _snake_case					(				self    ,a_    ,a_ = None    ,a_ = None    ,a_=None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = ChannelDimension.FIRST    ,**a_    ,)						->  PIL.Image.Image:
        _UpperCAmelCase    :		Dict     	=					do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase    :		Optional[Any]     	=					resample if resample is not None else self.resample
        _UpperCAmelCase    :		Any     	=					do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCAmelCase    :		Optional[int]     	=					do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase    :		List[str]     	=					rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCAmelCase    :		List[str]     	=					rescale_offset if rescale_offset is not None else self.rescale_offset
        _UpperCAmelCase    :		Union[str, Any]     	=					do_normalize if do_normalize is not None else self.do_normalize
        _UpperCAmelCase    :		str     	=					image_mean if image_mean is not None else self.image_mean
        _UpperCAmelCase    :		Union[str, Any]     	=					image_std if image_std is not None else self.image_std
        _UpperCAmelCase    :		Optional[int]     	=					include_top if include_top is not None else self.include_top

        _UpperCAmelCase    :		Optional[int]     	=					size if size is not None else self.size
        _UpperCAmelCase    :		Dict     	=					get_size_dict(a_				)
        _UpperCAmelCase    :		str     	=					crop_size if crop_size is not None else self.crop_size
        _UpperCAmelCase    :		List[Any]     	=					get_size_dict(a_    ,param_name="""crop_size"""				)

        _UpperCAmelCase    :		Dict     	=					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.
        _UpperCAmelCase    :		Union[str, Any]     	=					[to_numpy_array(a_				) for image in images]

        if do_resize:
               _UpperCAmelCase    :		int     	=					[self.resize(image=a_    ,size=a_    ,resample=a_				) for image in images]

        if do_center_crop:
               _UpperCAmelCase    :		Tuple     	=					[self.center_crop(image=a_    ,size=a_				) for image in images]

        if do_rescale:
               _UpperCAmelCase    :		int     	=					[self.rescale(image=a_    ,scale=a_    ,offset=a_				) for image in images]

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

        if include_top:
               _UpperCAmelCase    :		Tuple     	=					[self.normalize(image=a_    ,mean=[0, 0, 0]    ,std=a_				) for image in images]

        _UpperCAmelCase    :		List[str]     	=					[to_channel_dimension_format(a_    ,a_				) for image in images]

        _UpperCAmelCase    :		Union[str, Any]     	=					{"""pixel_values""": images}
        return BatchFeature(data=a_    ,tensor_type=a_				)








import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor





class 		__SCREAMING_SNAKE_CASE      (				unittest.TestCase      ):






				"""simple docstring"""

				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__ = False      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   10

					if only_cross_attention:
						_snake_case					:							Tuple					     =   12
					else:
						# when only cross attention is not set, the cross attention dim must be the same as the query dim
						_snake_case					:							Any					     =   query_dim

					return {
					    "query_dim": query_dim,
					    "cross_attention_dim": cross_attention_dim,
					    "heads": 2,
					    "dim_head": 4,
					    "added_kv_proj_dim": 6,
					    "norm_num_groups": 1,
					    "only_cross_attention": only_cross_attention,
					    "processor": AttnAddedKVProcessor(),
					}







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   2

					_snake_case					:							List[str]					     =   torch.rand(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	3						,	2      )
					_snake_case					:							List[str]					     =   torch.rand(SCREAMING_SNAKE_CASE__						,	4						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   None

					return {
					    "hidden_states": hidden_states,
					    "encoder_hidden_states": encoder_hidden_states,
					    "attention_mask": attention_mask,
					}







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					torch.manual_seed(0      )

					_snake_case					:							Optional[int]					     =   self.get_constructor_arguments(only_cross_attention=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[str]					     =   Attention(**SCREAMING_SNAKE_CASE__      )

					self.assertTrue(attn.to_k is not None      )
					self.assertTrue(attn.to_v is not None      )

					_snake_case					:							Dict					     =   self.get_forward_arguments(
					    query_dim=constructor_args["""query_dim"""]						,	added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]      )

					_snake_case					:							Any					     =   attn(**SCREAMING_SNAKE_CASE__      )

					# only self attention

					torch.manual_seed(0      )

					_snake_case					:							Any					     =   self.get_constructor_arguments(only_cross_attention=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[str]					     =   Attention(**SCREAMING_SNAKE_CASE__      )

					self.assertTrue(attn.to_k is None      )
					self.assertTrue(attn.to_v is None      )

					_snake_case					:							str					     =   self.get_forward_arguments(
					    query_dim=constructor_args["""query_dim"""]						,	added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]      )

					_snake_case					:							Any					     =   attn(**SCREAMING_SNAKE_CASE__      )

					self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()      )





class 		__SCREAMING_SNAKE_CASE      (				unittest.TestCase      ):






				"""simple docstring"""

				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							str					     =   DiffusionPipeline.from_pretrained("""hf-internal-testing/tiny-stable-diffusion-pipe"""						,	safety_checker=SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Tuple					     =   pipe(
					    """foo"""						,	num_inference_steps=2						,	generator=torch.Generator("""cpu"""      ).manual_seed(0      )						,	output_type="""np"""						,	).images

					# the initial conversion succeeds
					_snake_case					:							List[str]					     =   DiffusionPipeline.from_pretrained(
					    """hf-internal-testing/tiny-stable-diffusion-pipe"""						,	device_map="""sequential"""						,	safety_checker=SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Union[str, Any]					     =   pipe(
					    """foo"""						,	num_inference_steps=2						,	generator=torch.Generator("""cpu"""      ).manual_seed(0      )						,	output_type="""np"""						,	).images

					with tempfile.TemporaryDirectory() as tmpdir:
						# save the converted model
						pipe.save_pretrained(SCREAMING_SNAKE_CASE__      )

						# can also load the converted weights
						_snake_case					:							str					     =   DiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__						,	device_map="""sequential"""						,	safety_checker=SCREAMING_SNAKE_CASE__      )

					_snake_case					:							int					     =   pipe(
					    """foo"""						,	num_inference_steps=2						,	generator=torch.Generator("""cpu"""      ).manual_seed(0      )						,	output_type="""np"""						,	).images

					self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	atol=1e-5      )      )
					self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	atol=1e-5      )      )





