




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_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


lowerCamelCase__			:       Optional[Any]         =							logging.get_logger(__name__)





class 	lowerCamelCase_							(				SCREAMING_SNAKE_CASE__ ):


 '''simple docstring'''







 lowercase_       =   ['pixel_values']



 def __init__(							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		bool = True    ,    _lowerCAmelCase  :		Dict[str, int] = None    ,    _lowerCAmelCase  :		float = None    ,    _lowerCAmelCase  :		PILImageResampling = PILImageResampling.BILINEAR    ,    _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  :		List[str]    ,    ):
       super().__init__(**a_						)
       SCREAMING_SNAKE_CASE_     						=      size if size is not None else {'shortest_edge': 384}
       SCREAMING_SNAKE_CASE_     						=      get_size_dict(a_    ,    default_to_square=a_						)

       SCREAMING_SNAKE_CASE_     						=      do_resize
       SCREAMING_SNAKE_CASE_     						=      size
       # Default value set here for backwards compatibility where the value in config is None
       SCREAMING_SNAKE_CASE_     						=      crop_pct if crop_pct is not None else 224 / 256
       SCREAMING_SNAKE_CASE_     						=      resample
       SCREAMING_SNAKE_CASE_     						=      do_rescale
       SCREAMING_SNAKE_CASE_     						=      rescale_factor
       SCREAMING_SNAKE_CASE_     						=      do_normalize
       SCREAMING_SNAKE_CASE_     						=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       SCREAMING_SNAKE_CASE_     						=      image_std if image_std is not None else IMAGENET_STANDARD_STD



 def 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		np.ndarray    ,    _lowerCAmelCase  :		Dict[str, int]    ,    _lowerCAmelCase  :		float    ,    _lowerCAmelCase  :		PILImageResampling = PILImageResampling.BICUBIC    ,    _lowerCAmelCase  :		Optional[Union[str, ChannelDimension]] = None    ,    **_lowerCAmelCase  :		Any    ,    ):
       SCREAMING_SNAKE_CASE_     						=      get_size_dict(a_    ,    default_to_square=a_						)
       if "shortest_edge" not in size:
             raise ValueError(F"Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}"						)
       SCREAMING_SNAKE_CASE_     						=      size['shortest_edge']

       if shortest_edge < 384:
             # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
             SCREAMING_SNAKE_CASE_     						=      int(shortest_edge / crop_pct						)
             SCREAMING_SNAKE_CASE_     						=      get_resize_output_image_size(a_    ,    size=a_    ,    default_to_square=a_						)
             SCREAMING_SNAKE_CASE_     						=      resize(image=a_    ,    size=a_    ,    resample=a_    ,    data_format=a_    ,    **a_						)
             # then crop to (shortest_edge, shortest_edge)
             return center_crop(image=a_    ,    size=(shortest_edge, shortest_edge)    ,    data_format=a_    ,    **a_						)
       else:
             # warping (no cropping) when evaluated at 384 or larger
             return resize(
                 a_    ,    size=(shortest_edge, shortest_edge)    ,    resample=a_    ,    data_format=a_    ,    **a_						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		np.ndarray    ,    _lowerCAmelCase  :		Union[int, float]    ,    _lowerCAmelCase  :		Optional[Union[str, ChannelDimension]] = None    ,    **_lowerCAmelCase  :		Tuple    ,    ):
       return rescale(a_    ,    scale=a_    ,    data_format=a_    ,    **a_						)



 def 				lowerCAmelCase_  (							self  :		str    ,    _lowerCAmelCase  :		np.ndarray    ,    _lowerCAmelCase  :		Union[float, List[float]]    ,    _lowerCAmelCase  :		Union[float, List[float]]    ,    _lowerCAmelCase  :		Optional[Union[str, ChannelDimension]] = None    ,    **_lowerCAmelCase  :		List[str]    ,    ):
       return normalize(a_    ,    mean=a_    ,    std=a_    ,    data_format=a_    ,    **a_						)




 def 				lowerCAmelCase_  (							self  :		str    ,    _lowerCAmelCase  :		ImageInput    ,    _lowerCAmelCase  :		bool = None    ,    _lowerCAmelCase  :		Dict[str, int] = None    ,    _lowerCAmelCase  :		float = None    ,    _lowerCAmelCase  :		PILImageResampling = 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  :		Optional[Union[str, TensorType]] = None    ,    _lowerCAmelCase  :		ChannelDimension = ChannelDimension.FIRST    ,    **_lowerCAmelCase  :		Optional[Any]    ,    ):
       SCREAMING_SNAKE_CASE_     						=      do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE_     						=      crop_pct if crop_pct is not None else self.crop_pct
       SCREAMING_SNAKE_CASE_     						=      resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE_     						=      do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE_     						=      rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE_     						=      do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE_     						=      image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE_     						=      image_std if image_std is not None else self.image_std

       SCREAMING_SNAKE_CASE_     						=      size if size is not None else self.size
       SCREAMING_SNAKE_CASE_     						=      get_size_dict(a_    ,    default_to_square=a_						)

       SCREAMING_SNAKE_CASE_     						=      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_resize and size["shortest_edge"] < 384 and crop_pct is None:
             raise ValueError('crop_pct must be specified if size < 384.'						)

       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.
       SCREAMING_SNAKE_CASE_     						=      [to_numpy_array(a_						) for image in images]

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

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

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

       SCREAMING_SNAKE_CASE_     						=      [to_channel_dimension_format(a_    ,    a_						) for image in images]

       SCREAMING_SNAKE_CASE_     						=      {'pixel_values': images}
       return BatchFeature(data=a_    ,    tensor_type=a_						)





import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowerCamelCase__			:       List[Any]         =							logging.get_logger(__name__)  # pylint: disable=invalid-name





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 def __init__(							self  :		List[Any]    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Tuple=768						):
       super().__init__(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      proj_size

       SCREAMING_SNAKE_CASE_     						=      CLIPVisionModel(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      PaintByExampleMapper(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      nn.LayerNorm(config.hidden_size						)
       SCREAMING_SNAKE_CASE_     						=      nn.Linear(config.hidden_size    ,    self.proj_size						)

       # uncondition for scaling
       SCREAMING_SNAKE_CASE_     						=      nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)




 def 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		int=False						):
       SCREAMING_SNAKE_CASE_     						=      self.model(pixel_values=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      clip_output.pooler_output
       SCREAMING_SNAKE_CASE_     						=      self.mapper(latent_states[:, None]						)
       SCREAMING_SNAKE_CASE_     						=      self.final_layer_norm(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      self.proj_out(_lowerCAmelCase						)
       if return_uncond_vector:
             return latent_states, self.uncond_vector

       return latent_states





class 	lowerCamelCase_							(				nn.Module ):


 '''simple docstring'''







 def __init__(							self  :		int    ,    _lowerCAmelCase  :		Optional[Any]						):
       super().__init__()
       SCREAMING_SNAKE_CASE_     						=      (config.num_hidden_layers + 1) // 5
       SCREAMING_SNAKE_CASE_     						=      config.hidden_size
       SCREAMING_SNAKE_CASE_     						=      1
       SCREAMING_SNAKE_CASE_     						=      nn.ModuleList(
           [
               BasicTransformerBlock(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase    ,    activation_fn='gelu'    ,    attention_bias=_lowerCAmelCase						)
               for _ in range(_lowerCAmelCase						)
           ]						)




 def 				lowerCAmelCase_  (							self  :		Union[str, Any]    ,    _lowerCAmelCase  :		Optional[Any]						):
       for block in self.blocks:
             SCREAMING_SNAKE_CASE_     						=      block(_lowerCAmelCase						)

       return hidden_states