from typing import Dict, List, Optional, Tuple, 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_torch_available, is_torch_tensor, logging


if is_torch_available():
     import torch


__magic_name__								=logging.get_logger(__name__)



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Any						=["pixel_values"]






  def __init__(self   ,				SCREAMING_SNAKE_CASE_ = True   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR   ,				SCREAMING_SNAKE_CASE_ = True   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = True   ,				SCREAMING_SNAKE_CASE_ = 1 / 255   ,				SCREAMING_SNAKE_CASE_ = True   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      None:





       '''simple docstring'''





       super().__init__(**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      size if size is not None else {'''shortest_edge''': 256}
       UpperCamelCase__										=      get_size_dict(SCREAMING_SNAKE_CASE_   ,				default_to_square=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
       UpperCamelCase__										=      get_size_dict(SCREAMING_SNAKE_CASE_   ,				param_name='''crop_size'''	)
       UpperCamelCase__										=      do_resize
       UpperCamelCase__										=      size
       UpperCamelCase__										=      resample
       UpperCamelCase__										=      do_center_crop
       UpperCamelCase__										=      crop_size
       UpperCamelCase__										=      do_rescale
       UpperCamelCase__										=      rescale_factor
       UpperCamelCase__										=      do_normalize
       UpperCamelCase__										=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCamelCase__										=      image_std if image_std is not None else IMAGENET_STANDARD_STD






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      np.ndarray:





       '''simple docstring'''





       UpperCamelCase__										=      get_size_dict(SCREAMING_SNAKE_CASE_   ,				default_to_square=SCREAMING_SNAKE_CASE_	)
       if "shortest_edge" not in size:
            raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"	)
       UpperCamelCase__										=      get_resize_output_image_size(SCREAMING_SNAKE_CASE_   ,				size=size['''shortest_edge''']   ,				default_to_square=SCREAMING_SNAKE_CASE_	)
       return resize(SCREAMING_SNAKE_CASE_   ,				size=SCREAMING_SNAKE_CASE_   ,				resample=SCREAMING_SNAKE_CASE_   ,				data_format=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      np.ndarray:





       '''simple docstring'''





       UpperCamelCase__										=      get_size_dict(SCREAMING_SNAKE_CASE_	)
       if "height" not in size or "width" not in size:
            raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"	)
       return center_crop(SCREAMING_SNAKE_CASE_   ,				size=(size['''height'''], size['''width'''])   ,				data_format=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_	)		->      np.ndarray:





       '''simple docstring'''





       return rescale(SCREAMING_SNAKE_CASE_   ,				scale=SCREAMING_SNAKE_CASE_   ,				data_format=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      np.ndarray:





       '''simple docstring'''





       return normalize(SCREAMING_SNAKE_CASE_   ,				mean=SCREAMING_SNAKE_CASE_   ,				std=SCREAMING_SNAKE_CASE_   ,				data_format=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__										=      size if size is not None else self.size
       UpperCamelCase__										=      get_size_dict(SCREAMING_SNAKE_CASE_   ,				default_to_square=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      resample if resample is not None else self.resample
       UpperCamelCase__										=      do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCamelCase__										=      crop_size if crop_size is not None else self.crop_size
       UpperCamelCase__										=      get_size_dict(SCREAMING_SNAKE_CASE_   ,				param_name='''crop_size'''	)
       UpperCamelCase__										=      do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__										=      rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase__										=      do_normalize if do_normalize is not None else self.do_normalize
       UpperCamelCase__										=      image_mean if image_mean is not None else self.image_mean
       UpperCamelCase__										=      image_std if image_std is not None else self.image_std

       UpperCamelCase__										=      make_list_of_images(SCREAMING_SNAKE_CASE_	)

       if not valid_images(SCREAMING_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.'''	)

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

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

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

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

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

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

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      outputs.logits

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

            if is_torch_tensor(SCREAMING_SNAKE_CASE_	):
                 UpperCamelCase__										=      target_sizes.numpy()

            UpperCamelCase__										=      []

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

       return semantic_segmentation




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

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


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



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=768	)		->      Dict:





       '''simple docstring'''





       super().__init__(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      proj_size

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

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model(pixel_values=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      clip_output.pooler_output
       UpperCamelCase__										=      self.mapper(latent_states[:, None]	)
       UpperCamelCase__										=      self.final_layer_norm(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      self.proj_out(SCREAMING_SNAKE_CASE_	)
       if return_uncond_vector:
            return latent_states, self.uncond_vector

       return latent_states



class     _A   (    nn.Module							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       super().__init__()
       UpperCamelCase__										=      (config.num_hidden_layers + 1) // 5
       UpperCamelCase__										=      config.hidden_size
       UpperCamelCase__										=      1
       UpperCamelCase__										=      nn.ModuleList(
           [
               BasicTransformerBlock(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				activation_fn='''gelu'''   ,				attention_bias=SCREAMING_SNAKE_CASE_	)
               for _ in range(SCREAMING_SNAKE_CASE_	)
           ]	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       for block in self.blocks:
            UpperCamelCase__										=      block(SCREAMING_SNAKE_CASE_	)

       return hidden_states



