
"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


_lowercase							      = logging.get_logger(__name__)







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			List[str]					=			["pixel_values"]


						def __init__(       self   :					str     ,A_   :					bool = True     ,A_   :					Dict[str, int] = None     ,A_   :					int = 0.9     ,A_   :					PILImageResampling = PILImageResampling.BICUBIC     ,A_   :					bool = True     ,A_   :					Dict[str, int] = None     ,A_   :					Union[int, float] = 1 / 255     ,A_   :					bool = True     ,A_   :					bool = True     ,A_   :					Optional[Union[float, List[float]]] = None     ,A_   :					Optional[Union[float, List[float]]] = None     ,**A_   :					List[str]     ,)   ->      Dict:
							super().__init__(**A_					)
							A        =      size if size is not None else {'shortest_edge': 224}
							A        =      get_size_dict(A_     ,default_to_square=A_					)
							A        =      crop_size if crop_size is not None else {'height': 224, 'width': 224}
							A        =      get_size_dict(A_     ,param_name='crop_size'					)

							A        =      do_resize
							A        =      size
							A        =      crop_pct
							A        =      resample
							A        =      do_center_crop
							A        =      crop_size
							A        =      do_rescale
							A        =      rescale_factor
							A        =      do_normalize
							A        =      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							A        =      image_std if image_std is not None else IMAGENET_DEFAULT_STD


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,A_   :					np.ndarray     ,A_   :					Dict[str, int]     ,A_   :					Optional[float] = None     ,A_   :					PILImageResampling = PILImageResampling.BICUBIC     ,A_   :					Optional[Union[str, ChannelDimension]] = None     ,**A_   :					Union[str, Any]     ,)   ->      Union[str, Any]:
							A        =      get_size_dict(A_     ,default_to_square=A_					)
							if "shortest_edge" not in size and ("height" not in size or "width" not in size):
								raise ValueError(F'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'					)
							if crop_pct is not None:
								if "shortest_edge" in size:
									A        =      int(size['shortest_edge'] / crop_pct					)
								elif "height" in size and "width" in size:
									if size["height"] == size["width"]:
										A        =      int(size['height'] / crop_pct					)
									else:
										A        =      (int(size['height'] / crop_pct					), int(size['width'] / crop_pct					))
								else:
									raise ValueError('Invalid size for resize: {}'.format(A_					)					)

								A        =      get_resize_output_image_size(A_     ,size=A_     ,default_to_square=A_					)
							else:
								if "shortest_edge" in size:
									A        =      get_resize_output_image_size(A_     ,size=size['shortest_edge']     ,default_to_square=A_					)
								elif "height" in size and "width" in size:
									A        =      (size['height'], size['width'])
								else:
									raise ValueError('Invalid size for resize: {}'.format(A_					)					)

							return resize(A_     ,size=A_     ,resample=A_     ,data_format=A_     ,**A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					int     ,A_   :					np.ndarray     ,A_   :					Dict[str, int]     ,A_   :					Optional[Union[str, ChannelDimension]] = None     ,**A_   :					Union[str, Any]     ,)   ->      int:
							A        =      get_size_dict(A_					)
							if "height" not in size or "width" not in size:
								raise ValueError(F'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'					)
							return center_crop(A_     ,size=(size['height'], size['width'])     ,data_format=A_     ,**A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					str     ,A_   :					np.ndarray     ,A_   :					Union[int, float]     ,A_   :					Optional[Union[str, ChannelDimension]] = None     ,**A_   :					Optional[int]     ,)   ->      Optional[int]:
							return rescale(A_     ,scale=A_     ,data_format=A_     ,**A_					)


						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_   :					Union[str, Any]     ,)   ->      str:
							return normalize(A_     ,mean=A_     ,std=A_     ,data_format=A_     ,**A_					)




						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]     ,A_   :					ImageInput     ,A_   :					bool = None     ,A_   :					Dict[str, int] = None     ,A_   :					int = None     ,A_   :					PILImageResampling = None     ,A_   :					bool = None     ,A_   :					Dict[str, int] = None     ,A_   :					bool = None     ,A_   :					float = None     ,A_   :					bool = None     ,A_   :					Optional[Union[float, List[float]]] = None     ,A_   :					Optional[Union[float, List[float]]] = None     ,A_   :					Optional[Union[str, TensorType]] = None     ,A_   :					ChannelDimension = ChannelDimension.FIRST     ,**A_   :					Optional[int]     ,)   ->      Union[str, Any]:
							A        =      do_resize if do_resize is not None else self.do_resize
							A        =      crop_pct if crop_pct is not None else self.crop_pct
							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        =      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        =      size if size is not None else self.size
							A        =      get_size_dict(A_     ,default_to_square=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_pct is None:
								raise ValueError('Crop_pct 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_     ,crop_pct=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_					) for image in images]

							if do_normalize:
								A        =      [self.normalize(image=A_     ,mean=A_     ,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_					)

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class    SCREAMING_SNAKE_CASE__     (	nn.Module					):





	'''simple docstring'''



	def __init__(  self	:    Optional[int]			,  lowercase	:    int = 16			,  lowercase	:    int = 88			,  lowercase	:    Optional[int] = None			,  lowercase	:    int = 1			,  lowercase	:    float = 0.0			,  lowercase	:    int = 32			,  lowercase	:    Optional[int] = None			,  lowercase	:    bool = False			,  lowercase	:    Optional[int] = None			,  lowercase	:    Optional[int] = None			,  lowercase	:    str = "geglu"			,  lowercase	:    Optional[int] = None			,  ):


				'''simple docstring'''


				super().__init__()
				_snake_case		=			nn.ModuleList(
				    [
				        TransformeraDModel(
				            num_attention_heads=lowercase			,  attention_head_dim=lowercase			,  in_channels=lowercase			,  num_layers=lowercase			,  dropout=lowercase			,  norm_num_groups=lowercase			,  cross_attention_dim=lowercase			,  attention_bias=lowercase			,  sample_size=lowercase			,  num_vector_embeds=lowercase			,  activation_fn=lowercase			,  num_embeds_ada_norm=lowercase			,  )
				        for _ in range(2	)
				    ]	)

				# Variables that can be set by a pipeline:

				# The ratio of transformer1 to transformer2's output states to be combined during inference
				_snake_case		=			0.5

				# The shape of `encoder_hidden_states` is expected to be
				# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
				_snake_case		=			[77, 257]

				# Which transformer to use to encode which condition.
				# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
				_snake_case		=			[1, 0]





	def  A							(  self	:    Optional[int]			,  lowercase	:    Optional[int]			,  lowercase	:    List[Any]			,  lowercase	:    List[str]=None			,  lowercase	:    Tuple=None			,  lowercase	:    Dict=None			,  lowercase	:    bool = True			,  ):


				'''simple docstring'''


				_snake_case		=			hidden_states

				_snake_case		=			[]
				_snake_case		=			0
				# attention_mask is not used yet
				for i in range(2	):
							# for each of the two transformers, pass the corresponding condition tokens
							_snake_case		=			encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
							_snake_case		=			self.transformer_index_for_condition[i]
							_snake_case		=			self.transformers[transformer_index](
							    lowercase			,  encoder_hidden_states=lowercase			,  timestep=lowercase			,  cross_attention_kwargs=lowercase			,  return_dict=lowercase			,  )[0]
							encoded_states.append(encoded_state - input_states	)
							tokens_start += self.condition_lengths[i]

				_snake_case		=			encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
				_snake_case		=			output_states + input_states

				if not return_dict:
							return (output_states,)

				return TransformeraDModelOutput(sample=lowercase	)