

"""simple docstring"""





import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
   import torch

if is_vision_available():
   import PIL


_a       =							logging.get_logger(__name__)




def   _A  (  UpperCamelCase_     :       List[str],			UpperCamelCase_     :       Optional[Any],			UpperCamelCase_     :       Dict,			UpperCamelCase_     :       int)	->						Tuple:
   '''simple docstring'''


   def constraint_to_multiple_of(UpperCamelCase_     :       Optional[int],			UpperCamelCase_     :       Optional[int],			UpperCamelCase_     :       Union[str, Any]=0,			UpperCamelCase_     :       Tuple=None):
      __lowercase								=						round(val / multiple) * multiple

      if max_val is not None and x > max_val:
         __lowercase								=						math.floor(val / multiple) * multiple

      if x < min_val:
         __lowercase								=						math.ceil(val / multiple) * multiple

      return x

   __lowercase								=						(output_size, output_size) if isinstance(lowercase__,			lowercase__) else output_size

   __lowercase								=						get_image_size(lowercase__)
   __lowercase								=						output_size

   # determine new height and width
   __lowercase								=						output_height / input_height
   __lowercase								=						output_width / input_width

   if keep_aspect_ratio:
      # scale as little as possible
      if abs(1 - scale_width) < abs(1 - scale_height):
         # fit width
         __lowercase								=						scale_width
      else:
         # fit height
         __lowercase								=						scale_height

   __lowercase								=						constraint_to_multiple_of(scale_height * input_height,			multiple=lowercase__)
   __lowercase								=						constraint_to_multiple_of(scale_width * input_width,			multiple=lowercase__)

   return (new_height, new_width)




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					int   		=	["pixel_values"]


 def __init__(			self							:			List[str],					UpperCAmelCase__							:			List[Any] = True,					UpperCAmelCase__							:			int = None,					UpperCAmelCase__							:			str = PILImageResampling.BILINEAR,					UpperCAmelCase__							:			Tuple = False,					UpperCAmelCase__							:			Optional[Any] = 1,					UpperCAmelCase__							:			List[Any] = True,					UpperCAmelCase__							:			str = 1 / 2_5_5,					UpperCAmelCase__							:			Optional[Any] = True,					UpperCAmelCase__							:			Tuple = None,					UpperCAmelCase__							:			int = None,					**UpperCAmelCase__							:			Tuple,					):
    super().__init__(**UpperCAmelCase__	)
    __lowercase								=						size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
    __lowercase								=						get_size_dict(UpperCAmelCase__	)
    __lowercase								=						do_resize
    __lowercase								=						size
    __lowercase								=						keep_aspect_ratio
    __lowercase								=						ensure_multiple_of
    __lowercase								=						resample
    __lowercase								=						do_rescale
    __lowercase								=						rescale_factor
    __lowercase								=						do_normalize
    __lowercase								=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __lowercase								=						image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			Optional[Any] = False,					UpperCAmelCase__							:			List[Any] = 1,					UpperCAmelCase__							:			int = PILImageResampling.BICUBIC,					UpperCAmelCase__							:			List[Any] = None,					**UpperCAmelCase__							:			Optional[Any],					):
    __lowercase								=						get_size_dict(UpperCAmelCase__	)
    if "height" not in size or "width" not in size:
       raise ValueError(F"""The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}"""	)
    __lowercase								=						get_resize_output_image_size(
        UpperCAmelCase__,					output_size=(size["height"], size["width"]),					keep_aspect_ratio=UpperCAmelCase__,					multiple=UpperCAmelCase__,					)
    return resize(UpperCAmelCase__,					size=UpperCAmelCase__,					resample=UpperCAmelCase__,					data_format=UpperCAmelCase__,					**UpperCAmelCase__	)


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			str = None,					**UpperCAmelCase__							:			int,					):
    return rescale(UpperCAmelCase__,					scale=UpperCAmelCase__,					data_format=UpperCAmelCase__,					**UpperCAmelCase__	)


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			str = None,					**UpperCAmelCase__							:			Optional[Any],					):
    return normalize(UpperCAmelCase__,					mean=UpperCAmelCase__,					std=UpperCAmelCase__,					data_format=UpperCAmelCase__,					**UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Tuple = None,					UpperCAmelCase__							:			Optional[Any] = None,					UpperCAmelCase__							:			Union[str, Any] = None,					UpperCAmelCase__							:			Union[str, Any] = None,					UpperCAmelCase__							:			Any = None,					UpperCAmelCase__							:			List[str] = None,					UpperCAmelCase__							:			Optional[Any] = None,					UpperCAmelCase__							:			Optional[int] = None,					UpperCAmelCase__							:			Optional[int] = None,					UpperCAmelCase__							:			List[str] = None,					UpperCAmelCase__							:			Tuple = None,					UpperCAmelCase__							:			Any = ChannelDimension.FIRST,					**UpperCAmelCase__							:			Tuple,					):
    __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(UpperCAmelCase__	)
    __lowercase								=						keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
    __lowercase								=						ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
    __lowercase								=						resample if resample is not None else self.resample
    __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								=						make_list_of_images(UpperCAmelCase__	)

    if not valid_images(UpperCAmelCase__	):
       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_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.
    __lowercase								=						[to_numpy_array(UpperCAmelCase__	) for image in images]

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

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

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

    __lowercase								=						[to_channel_dimension_format(UpperCAmelCase__,					UpperCAmelCase__	) for image in images]

    __lowercase								=						{'pixel_values': images}
    return BatchFeature(data=UpperCAmelCase__,					tensor_type=UpperCAmelCase__	)



 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Dict = None	):
    __lowercase								=						outputs.logits

    # Resize logits and compute semantic segmentation maps
    if target_sizes is not None:
       if len(UpperCAmelCase__	) != len(UpperCAmelCase__	):
          raise ValueError(
              "Make sure that you pass in as many target sizes as the batch dimension of the logits"	)

       if is_torch_tensor(UpperCAmelCase__	):
          __lowercase								=						target_sizes.numpy()

       __lowercase								=						[]

       for idx in range(len(UpperCAmelCase__	)	):
          __lowercase								=						torch.nn.functional.interpolate(
              logits[idx].unsqueeze(dim=0	),					size=target_sizes[idx],					mode="bilinear",					align_corners=UpperCAmelCase__	)
          __lowercase								=						resized_logits[0].argmax(dim=0	)
          semantic_segmentation.append(UpperCAmelCase__	)
    else:
       __lowercase								=						logits.argmax(dim=1	)
       __lowercase								=						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

    return semantic_segmentation










"""simple docstring"""






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

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


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






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=768  ):
							super().__init__(lowercase  )
							_lowerCamelCase     :			Any    =    proj_size

							_lowerCamelCase     :			Dict    =    CLIPVisionModel(lowercase  )
							_lowerCamelCase     :			List[str]    =    PaintByExampleMapper(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    nn.LayerNorm(config.hidden_size  )
							_lowerCamelCase     :			int    =    nn.Linear(config.hidden_size  ,      self.proj_size  )

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






			def 					A_    (  self  ,      lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.model(pixel_values=lowercase  )
							_lowerCamelCase     :			int    =    clip_output.pooler_output
							_lowerCamelCase     :			str    =    self.mapper(latent_states[:, None]  )
							_lowerCamelCase     :			List[Any]    =    self.final_layer_norm(lowercase  )
							_lowerCamelCase     :			Dict    =    self.proj_out(lowercase  )
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states






class lowerCAmelCase__	(     nn.Module						):






			'''simple docstring'''



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






			def 					A_    (  self  ,      lowercase  ):
							for block in self.blocks:
											_lowerCamelCase     :			Tuple    =    block(lowercase  )

							return hidden_states