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

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


_snake_case								=  logging.get_logger(__name__)  # pylint: disable=invalid-name




class     _snake_case  (  __lowerCamelCase ):
					def __init__(     self: Optional[Any]		,     __lowerCamelCase: List[Any]		,     __lowerCamelCase: List[str]=7_68   )			-> Union[str, Any]:
												super().__init__(__lowerCamelCase   )
												__UpperCAmelCase  :	int   			=      proj_size

												__UpperCAmelCase  :	Optional[int]   			=      CLIPVisionModel(__lowerCamelCase   )
												__UpperCAmelCase  :	Tuple   			=      PaintByExampleMapper(__lowerCamelCase   )
												__UpperCAmelCase  :	Dict   			=      nn.LayerNorm(config.hidden_size   )
												__UpperCAmelCase  :	Tuple   			=      nn.Linear(config.hidden_size		,     self.proj_size   )

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





					def       _lowerCamelCase       (     self: str		,     __lowerCamelCase: List[str]		,     __lowerCamelCase: Optional[int]=False   )			-> Optional[Any]:
												__UpperCAmelCase  :	str   			=      self.model(pixel_values=__lowerCamelCase   )
												__UpperCAmelCase  :	List[Any]   			=      clip_output.pooler_output
												__UpperCAmelCase  :	Any   			=      self.mapper(latent_states[:, None]   )
												__UpperCAmelCase  :	Union[str, Any]   			=      self.final_layer_norm(__lowerCamelCase   )
												__UpperCAmelCase  :	List[str]   			=      self.proj_out(__lowerCamelCase   )
												if return_uncond_vector:
																			return latent_states, self.uncond_vector

												return latent_states




class     _snake_case  (  nn.Module ):
					def __init__(     self: Optional[Any]		,     __lowerCamelCase: List[Any]   )			-> Any:
												super().__init__()
												__UpperCAmelCase  :	List[str]   			=      (config.num_hidden_layers + 1) // 5
												__UpperCAmelCase  :	Optional[Any]   			=      config.hidden_size
												__UpperCAmelCase  :	Optional[Any]   			=      1
												__UpperCAmelCase  :	List[Any]   			=      nn.ModuleList(
												    [
												        BasicTransformerBlock(__lowerCamelCase		,     __lowerCamelCase		,     __lowerCamelCase		,     activation_fn="gelu"		,     attention_bias=__lowerCamelCase   )
												        for _ in range(__lowerCamelCase   )
												    ]   )





					def       _lowerCamelCase       (     self: Any		,     __lowerCamelCase: Optional[int]   )			-> Tuple:
												for block in self.blocks:
																			__UpperCAmelCase  :	str   			=      block(__lowerCamelCase   )

												return hidden_states






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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase	      = logging.get_logger(__name__)


if is_vision_available():
	import PIL







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     str							 =       ["""pixel_values"""]





	def __init__(       self		,    snake_case = True		,    snake_case = None		,    snake_case = PILImageResampling.BICUBIC		,    snake_case = True		,    snake_case = None		,    snake_case = True		,    snake_case = 1 / 255		,    snake_case = True		,    snake_case = None		,    snake_case = None		,    snake_case = True		,    **snake_case		,    ):
		super().__init__(**snake_case       )
		lowercase       =    size if size is not None else {'shortest_edge': 224}
		lowercase       =    get_size_dict(snake_case		,    default_to_square=snake_case       )
		lowercase       =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
		lowercase       =    get_size_dict(snake_case		,    default_to_square=snake_case		,    param_name='crop_size'       )

		lowercase       =    do_resize
		lowercase       =    size
		lowercase       =    resample
		lowercase       =    do_center_crop
		lowercase       =    crop_size
		lowercase       =    do_rescale
		lowercase       =    rescale_factor
		lowercase       =    do_normalize
		lowercase       =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
		lowercase       =    image_std if image_std is not None else OPENAI_CLIP_STD
		lowercase       =    do_convert_rgb





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = PILImageResampling.BICUBIC		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    get_size_dict(snake_case		,    default_to_square=snake_case       )
		if "shortest_edge" not in size:
			raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
		lowercase       =    get_resize_output_image_size(snake_case		,    size=size['shortest_edge']		,    default_to_square=snake_case       )
		return resize(snake_case		,    size=snake_case		,    resample=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    get_size_dict(snake_case       )
		if "height" not in size or "width" not in size:
			raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''       )
		return center_crop(snake_case		,    size=(size['height'], size['width'])		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return rescale(snake_case		,    scale=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return normalize(snake_case		,    mean=snake_case		,    std=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = ChannelDimension.FIRST		,    **snake_case		,    ):
		lowercase       =    do_resize if do_resize is not None else self.do_resize
		lowercase       =    size if size is not None else self.size
		lowercase       =    get_size_dict(snake_case		,    param_name='size'		,    default_to_square=snake_case       )
		lowercase       =    resample if resample is not None else self.resample
		lowercase       =    do_center_crop if do_center_crop is not None else self.do_center_crop
		lowercase       =    crop_size if crop_size is not None else self.crop_size
		lowercase       =    get_size_dict(snake_case		,    param_name='crop_size'		,    default_to_square=snake_case       )
		lowercase       =    do_rescale if do_rescale is not None else self.do_rescale
		lowercase       =    rescale_factor if rescale_factor is not None else self.rescale_factor
		lowercase       =    do_normalize if do_normalize is not None else self.do_normalize
		lowercase       =    image_mean if image_mean is not None else self.image_mean
		lowercase       =    image_std if image_std is not None else self.image_std
		lowercase       =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

		lowercase       =    make_list_of_images(snake_case       )

		if not valid_images(snake_case       ):
			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:
			raise ValueError('Size 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.'       )

		# PIL RGBA images are converted to RGB
		if do_convert_rgb:
			lowercase       =    [convert_to_rgb(snake_case       ) for image in images]

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

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

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

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

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

		lowercase       =    [to_channel_dimension_format(snake_case		,    snake_case       ) for image in images]

		lowercase       =    {'pixel_values': images}
		return BatchFeature(data=snake_case		,    tensor_type=snake_case       )

