


import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   torch.Tensor     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   bool = False     ,	SCREAMING_SNAKE_CASE__     :   float = 1     ,	SCREAMING_SNAKE_CASE__     :   float = 1     ,	SCREAMING_SNAKE_CASE__     :   int = 1_00_00     ,	):
     assert len(timesteps.shape							) == 1, "Timesteps should be a 1d-array"

     __UpperCamelCase       				=embedding_dim // 2
     __UpperCamelCase       				=-math.log(SCREAMING_SNAKE_CASE__							) * torch.arange(
         start=0     ,	end=SCREAMING_SNAKE_CASE__     ,	dtype=torch.floataa     ,	device=timesteps.device							)
     __UpperCamelCase       				=exponent / (half_dim - downscale_freq_shift)

     __UpperCamelCase       				=torch.exp(SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=timesteps[:, None].float() * emb[None, :]

     # scale embeddings
     __UpperCamelCase       				=scale * emb

     # concat sine and cosine embeddings
     __UpperCamelCase       				=torch.cat([torch.sin(SCREAMING_SNAKE_CASE__							), torch.cos(SCREAMING_SNAKE_CASE__							)]     ,	dim=-1							)

     # flip sine and cosine embeddings
     if flip_sin_to_cos:
          __UpperCamelCase       				=torch.cat([emb[:, half_dim:], emb[:, :half_dim]]     ,	dim=-1							)

     # zero pad
     if embedding_dim % 2 == 1:
          __UpperCamelCase       				=torch.nn.functional.pad(SCREAMING_SNAKE_CASE__     ,	(0, 1, 0, 0)							)
     return emb

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Tuple     ,	SCREAMING_SNAKE_CASE__     :   Tuple     ,	SCREAMING_SNAKE_CASE__     :   Any=False     ,	SCREAMING_SNAKE_CASE__     :   List[Any]=0							):
     __UpperCamelCase       				=np.arange(SCREAMING_SNAKE_CASE__     ,	dtype=np.floataa							)
     __UpperCamelCase       				=np.arange(SCREAMING_SNAKE_CASE__     ,	dtype=np.floataa							)
     __UpperCamelCase       				=np.meshgrid(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)  # here w goes first
     __UpperCamelCase       				=np.stack(SCREAMING_SNAKE_CASE__     ,	axis=0							)

     __UpperCamelCase       				=grid.reshape([2, 1, grid_size, grid_size]							)
     __UpperCamelCase       				=get_ad_sincos_pos_embed_from_grid(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)
     if cls_token and extra_tokens > 0:
          __UpperCamelCase       				=np.concatenate([np.zeros([extra_tokens, embed_dim]							), pos_embed]     ,	axis=0							)
     return pos_embed

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     if embed_dim % 2 != 0:
          raise ValueError('embed_dim must be divisible by 2'							)

     # use half of dimensions to encode grid_h
     __UpperCamelCase       				=get_ad_sincos_pos_embed_from_grid(embed_dim // 2     ,	grid[0]							)  # (H*W, D/2)
     __UpperCamelCase       				=get_ad_sincos_pos_embed_from_grid(embed_dim // 2     ,	grid[1]							)  # (H*W, D/2)

     __UpperCamelCase       				=np.concatenate([emb_h, emb_w]     ,	axis=1							)  # (H*W, D)
     return emb

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   List[str]							):
     if embed_dim % 2 != 0:
          raise ValueError('embed_dim must be divisible by 2'							)

     __UpperCamelCase       				=np.arange(embed_dim // 2     ,	dtype=np.floataa							)
     omega /= embed_dim / 2.0
     __UpperCamelCase       				=1.0 / 1_00_00**omega  # (D/2,)

     __UpperCamelCase       				=pos.reshape(-1							)  # (M,)
     __UpperCamelCase       				=np.einsum('m,d->md'     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)  # (M, D/2), outer product

     __UpperCamelCase       				=np.sin(SCREAMING_SNAKE_CASE__							)  # (M, D/2)
     __UpperCamelCase       				=np.cos(SCREAMING_SNAKE_CASE__							)  # (M, D/2)

     __UpperCamelCase       				=np.concatenate([emb_sin, emb_cos]     ,	axis=1							)  # (M, D)
     return emb






class        UpperCAmelCase__			(   nn.Module						):



    """simple docstring"""






    def __init__(     self   ,  A_=224   ,  A_=224   ,  A_=16   ,  A_=3   ,  A_=768   ,  A_=False   ,  A_=True   ,  A_=True   ,  )    ->			int:
         super().__init__()

         __UpperCamelCase       				=(height // patch_size) * (width // patch_size)
         __UpperCamelCase       				=flatten
         __UpperCamelCase       				=layer_norm

         __UpperCamelCase       				=nn.Convad(
             A_   ,  A_   ,  kernel_size=(patch_size, patch_size)   ,  stride=A_   ,  bias=A_       )
         if layer_norm:
              __UpperCamelCase       				=nn.LayerNorm(A_   ,  elementwise_affine=A_   ,  eps=1E-6       )
         else:
              __UpperCamelCase       				=None

         __UpperCamelCase       				=get_ad_sincos_pos_embed(A_   ,  int(num_patches**0.5       )       )
         self.register_buffer('pos_embed'   ,  torch.from_numpy(A_       ).float().unsqueeze(0       )   ,  persistent=A_       )






    def _a   (     self   ,  A_       )    ->			List[Any]:
         __UpperCamelCase       				=self.proj(A_       )
         if self.flatten:
              __UpperCamelCase       				=latent.flatten(2       ).transpose(1   ,  2       )  # BCHW -> BNC
         if self.layer_norm:
              __UpperCamelCase       				=self.norm(A_       )
         return latent + self.pos_embed






class        UpperCAmelCase__			(   nn.Module						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_   ,  A_ = "silu"   ,  A_ = None   ,  A_ = None   ,  A_=None   ,  )    ->			Any:
         super().__init__()

         __UpperCamelCase       				=nn.Linear(A_   ,  A_       )

         if cond_proj_dim is not None:
              __UpperCamelCase       				=nn.Linear(A_   ,  A_   ,  bias=A_       )
         else:
              __UpperCamelCase       				=None

         __UpperCamelCase       				=get_activation(A_       )

         if out_dim is not None:
              __UpperCamelCase       				=out_dim
         else:
              __UpperCamelCase       				=time_embed_dim
         __UpperCamelCase       				=nn.Linear(A_   ,  A_       )

         if post_act_fn is None:
              __UpperCamelCase       				=None
         else:
              __UpperCamelCase       				=get_activation(A_       )






    def _a   (     self   ,  A_   ,  A_=None       )    ->			Optional[Any]:
         if condition is not None:
              __UpperCamelCase       				=sample + self.cond_proj(A_       )
         __UpperCamelCase       				=self.linear_a(A_       )

         if self.act is not None:
              __UpperCamelCase       				=self.act(A_       )

         __UpperCamelCase       				=self.linear_a(A_       )

         if self.post_act is not None:
              __UpperCamelCase       				=self.post_act(A_       )
         return sample






class        UpperCAmelCase__			(   nn.Module						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_   ,  A_       )    ->			Tuple:
         super().__init__()
         __UpperCamelCase       				=num_channels
         __UpperCamelCase       				=flip_sin_to_cos
         __UpperCamelCase       				=downscale_freq_shift






    def _a   (     self   ,  A_       )    ->			Optional[int]:
         __UpperCamelCase       				=get_timestep_embedding(
             A_   ,  self.num_channels   ,  flip_sin_to_cos=self.flip_sin_to_cos   ,  downscale_freq_shift=self.downscale_freq_shift   ,  )
         return t_emb






class        UpperCAmelCase__			(   nn.Module						):



    """simple docstring"""






    def __init__(     self   ,  A_ = 256   ,  A_ = 1.0   ,  A_=True   ,  A_=True   ,  A_=False       )    ->			Optional[Any]:
         super().__init__()
         __UpperCamelCase       				=nn.Parameter(torch.randn(A_       ) * scale   ,  requires_grad=A_       )
         __UpperCamelCase       				=log
         __UpperCamelCase       				=flip_sin_to_cos

         if set_W_to_weight:
              # to delete later
              __UpperCamelCase       				=nn.Parameter(torch.randn(A_       ) * scale   ,  requires_grad=A_       )

              __UpperCamelCase       				=self.W






    def _a   (     self   ,  A_       )    ->			Any:
         if self.log:
              __UpperCamelCase       				=torch.log(A_       )

         __UpperCamelCase       				=x[:, None] * self.weight[None, :] * 2 * np.pi

         if self.flip_sin_to_cos:
              __UpperCamelCase       				=torch.cat([torch.cos(A_       ), torch.sin(A_       )]   ,  dim=-1       )
         else:
              __UpperCamelCase       				=torch.cat([torch.sin(A_       ), torch.cos(A_       )]   ,  dim=-1       )
         return out






class        UpperCAmelCase__			(   nn.Module						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			List[Any]:
         super().__init__()

         __UpperCamelCase       				=height
         __UpperCamelCase       				=width
         __UpperCamelCase       				=num_embed
         __UpperCamelCase       				=embed_dim

         __UpperCamelCase       				=nn.Embedding(self.num_embed   ,  A_       )
         __UpperCamelCase       				=nn.Embedding(self.height   ,  A_       )
         __UpperCamelCase       				=nn.Embedding(self.width   ,  A_       )






    def _a   (     self   ,  A_       )    ->			Optional[int]:
         __UpperCamelCase       				=self.emb(A_       )

         __UpperCamelCase       				=self.height_emb(torch.arange(self.height   ,  device=index.device       ).view(1   ,  self.height       )       )

         # 1 x H x D -> 1 x H x 1 x D
         __UpperCamelCase       				=height_emb.unsqueeze(2       )

         __UpperCamelCase       				=self.width_emb(torch.arange(self.width   ,  device=index.device       ).view(1   ,  self.width       )       )

         # 1 x W x D -> 1 x 1 x W x D
         __UpperCamelCase       				=width_emb.unsqueeze(1       )

         __UpperCamelCase       				=height_emb + width_emb

         # 1 x H x W x D -> 1 x L xD
         __UpperCamelCase       				=pos_emb.view(1   ,  self.height * self.width   ,  -1       )

         __UpperCamelCase       				=emb + pos_emb[:, : emb.shape[1], :]

         return emb






class        UpperCAmelCase__			(   nn.Module						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_   ,  A_       )    ->			int:
         super().__init__()
         __UpperCamelCase       				=dropout_prob > 0
         __UpperCamelCase       				=nn.Embedding(num_classes + use_cfg_embedding   ,  A_       )
         __UpperCamelCase       				=num_classes
         __UpperCamelCase       				=dropout_prob






    def _a   (     self   ,  A_   ,  A_=None       )    ->			Dict:
         if force_drop_ids is None:
              __UpperCamelCase       				=torch.rand(labels.shape[0]   ,  device=labels.device       ) < self.dropout_prob
         else:
              __UpperCamelCase       				=torch.tensor(force_drop_ids == 1       )
         __UpperCamelCase       				=torch.where(A_   ,  self.num_classes   ,  A_       )
         return labels






    def _a   (     self   ,  A_   ,  A_=None       )    ->			Optional[Any]:
         __UpperCamelCase       				=self.dropout_prob > 0
         if (self.training and use_dropout) or (force_drop_ids is not None):
              __UpperCamelCase       				=self.token_drop(A_   ,  A_       )
         __UpperCamelCase       				=self.embedding_table(A_       )
         return embeddings






class        UpperCAmelCase__			(   nn.Module						):



    """simple docstring"""






    def __init__(     self   ,  A_ = 1024   ,  A_ = 768   ,  A_ = 768   ,  A_ = 10   ,  )    ->			int:
         super().__init__()

         __UpperCamelCase       				=num_image_text_embeds
         __UpperCamelCase       				=nn.Linear(A_   ,  self.num_image_text_embeds * cross_attention_dim       )
         __UpperCamelCase       				=nn.Linear(A_   ,  A_       )






    def _a   (     self   ,  A_   ,  A_       )    ->			Dict:
         __UpperCamelCase       				=text_embeds.shape[0]

         # image
         __UpperCamelCase       				=self.image_embeds(A_       )
         __UpperCamelCase       				=image_text_embeds.reshape(A_   ,  self.num_image_text_embeds   ,  -1       )

         # text
         __UpperCamelCase       				=self.text_proj(A_       )

         return torch.cat([image_text_embeds, text_embeds]   ,  dim=1       )






class        UpperCAmelCase__			(   nn.Module						):



    """simple docstring"""






    def __init__(     self   ,  A_ = 768   ,  A_ = 768   ,  A_ = 32   ,  )    ->			Dict:
         super().__init__()

         __UpperCamelCase       				=num_image_text_embeds
         __UpperCamelCase       				=nn.Linear(A_   ,  self.num_image_text_embeds * cross_attention_dim       )
         __UpperCamelCase       				=nn.LayerNorm(A_       )






    def _a   (     self   ,  A_       )    ->			Dict:
         __UpperCamelCase       				=image_embeds.shape[0]

         # image
         __UpperCamelCase       				=self.image_embeds(A_       )
         __UpperCamelCase       				=image_embeds.reshape(A_   ,  self.num_image_text_embeds   ,  -1       )
         __UpperCamelCase       				=self.norm(A_       )
         return image_embeds






class        UpperCAmelCase__			(   nn.Module						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_   ,  A_=0.1       )    ->			Dict:
         super().__init__()

         __UpperCamelCase       				=Timesteps(num_channels=256   ,  flip_sin_to_cos=A_   ,  downscale_freq_shift=1       )
         __UpperCamelCase       				=TimestepEmbedding(in_channels=256   ,  time_embed_dim=A_       )
         __UpperCamelCase       				=LabelEmbedding(A_   ,  A_   ,  A_       )






    def _a   (     self   ,  A_   ,  A_   ,  A_=None       )    ->			str:
         __UpperCamelCase       				=self.time_proj(A_       )
         __UpperCamelCase       				=self.timestep_embedder(timesteps_proj.to(dtype=A_       )       )  # (N, D)

         __UpperCamelCase       				=self.class_embedder(A_       )  # (N, D)

         __UpperCamelCase       				=timesteps_emb + class_labels  # (N, D)

         return conditioning






class        UpperCAmelCase__			(   nn.Module						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_   ,  A_ = 64       )    ->			Tuple:
         super().__init__()
         __UpperCamelCase       				=nn.LayerNorm(A_       )
         __UpperCamelCase       				=AttentionPooling(A_   ,  A_       )
         __UpperCamelCase       				=nn.Linear(A_   ,  A_       )
         __UpperCamelCase       				=nn.LayerNorm(A_       )






    def _a   (     self   ,  A_       )    ->			Tuple:
         __UpperCamelCase       				=self.norma(A_       )
         __UpperCamelCase       				=self.pool(A_       )
         __UpperCamelCase       				=self.proj(A_       )
         __UpperCamelCase       				=self.norma(A_       )
         return hidden_states






class        UpperCAmelCase__			(   nn.Module						):



    """simple docstring"""






    def __init__(     self   ,  A_ = 768   ,  A_ = 768   ,  A_ = 1536       )    ->			List[str]:
         super().__init__()
         __UpperCamelCase       				=nn.Linear(A_   ,  A_       )
         __UpperCamelCase       				=nn.LayerNorm(A_       )
         __UpperCamelCase       				=nn.Linear(A_   ,  A_       )






    def _a   (     self   ,  A_   ,  A_       )    ->			Tuple:
         # text
         __UpperCamelCase       				=self.text_proj(A_       )
         __UpperCamelCase       				=self.text_norm(A_       )

         # image
         __UpperCamelCase       				=self.image_proj(A_       )

         return time_image_embeds + time_text_embeds






class        UpperCAmelCase__			(   nn.Module						):



    """simple docstring"""






    def __init__(     self   ,  A_ = 768   ,  A_ = 1536       )    ->			int:
         super().__init__()
         __UpperCamelCase       				=nn.Linear(A_   ,  A_       )
         __UpperCamelCase       				=nn.LayerNorm(A_       )






    def _a   (     self   ,  A_       )    ->			Union[str, Any]:
         # image
         __UpperCamelCase       				=self.image_proj(A_       )
         __UpperCamelCase       				=self.image_norm(A_       )
         return time_image_embeds






class        UpperCAmelCase__			(   nn.Module						):



    """simple docstring"""






    def __init__(     self   ,  A_ = 768   ,  A_ = 1536       )    ->			int:
         super().__init__()
         __UpperCamelCase       				=nn.Linear(A_   ,  A_       )
         __UpperCamelCase       				=nn.LayerNorm(A_       )
         __UpperCamelCase       				=nn.Sequential(
             nn.Convad(3   ,  16   ,  3   ,  padding=1       )   ,  nn.SiLU()   ,  nn.Convad(16   ,  16   ,  3   ,  padding=1       )   ,  nn.SiLU()   ,  nn.Convad(16   ,  32   ,  3   ,  padding=1   ,  stride=2       )   ,  nn.SiLU()   ,  nn.Convad(32   ,  32   ,  3   ,  padding=1       )   ,  nn.SiLU()   ,  nn.Convad(32   ,  96   ,  3   ,  padding=1   ,  stride=2       )   ,  nn.SiLU()   ,  nn.Convad(96   ,  96   ,  3   ,  padding=1       )   ,  nn.SiLU()   ,  nn.Convad(96   ,  256   ,  3   ,  padding=1   ,  stride=2       )   ,  nn.SiLU()   ,  nn.Convad(256   ,  4   ,  3   ,  padding=1       )   ,  )






    def _a   (     self   ,  A_   ,  A_       )    ->			List[Any]:
         # image
         __UpperCamelCase       				=self.image_proj(A_       )
         __UpperCamelCase       				=self.image_norm(A_       )
         __UpperCamelCase       				=self.input_hint_block(A_       )
         return time_image_embeds, hint






class        UpperCAmelCase__			(   nn.Module						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_   ,  A_=None       )    ->			List[str]:
         super().__init__()
         __UpperCamelCase       				=dtype
         __UpperCamelCase       				=nn.Parameter(torch.randn(1   ,  A_       ) / embed_dim**0.5       )
         __UpperCamelCase       				=nn.Linear(A_   ,  A_   ,  dtype=self.dtype       )
         __UpperCamelCase       				=nn.Linear(A_   ,  A_   ,  dtype=self.dtype       )
         __UpperCamelCase       				=nn.Linear(A_   ,  A_   ,  dtype=self.dtype       )
         __UpperCamelCase       				=num_heads
         __UpperCamelCase       				=embed_dim // self.num_heads






    def _a   (     self   ,  A_       )    ->			Union[str, Any]:
         __UpperCamelCase				, __UpperCamelCase				, __UpperCamelCase       				=x.size()

         def shape(A_       ):
              # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
              __UpperCamelCase       				=x.view(A_   ,  -1   ,  self.num_heads   ,  self.dim_per_head       )
              # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
              __UpperCamelCase       				=x.transpose(1   ,  2       )
              # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
              __UpperCamelCase       				=x.reshape(bs * self.num_heads   ,  -1   ,  self.dim_per_head       )
              # (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
              __UpperCamelCase       				=x.transpose(1   ,  2       )
              return x

         __UpperCamelCase       				=x.mean(dim=1   ,  keepdim=A_       ) + self.positional_embedding.to(x.dtype       )
         __UpperCamelCase       				=torch.cat([class_token, x]   ,  dim=1       )  # (bs, length+1, width)

         # (bs*n_heads, class_token_length, dim_per_head)
         __UpperCamelCase       				=shape(self.q_proj(A_       )       )
         # (bs*n_heads, length+class_token_length, dim_per_head)
         __UpperCamelCase       				=shape(self.k_proj(A_       )       )
         __UpperCamelCase       				=shape(self.v_proj(A_       )       )

         # (bs*n_heads, class_token_length, length+class_token_length):
         __UpperCamelCase       				=1 / math.sqrt(math.sqrt(self.dim_per_head       )       )
         __UpperCamelCase       				=torch.einsum('bct,bcs->bts'   ,  q * scale   ,  k * scale       )  # More stable with f16 than dividing afterwards
         __UpperCamelCase       				=torch.softmax(weight.float()   ,  dim=-1       ).type(weight.dtype       )

         # (bs*n_heads, dim_per_head, class_token_length)
         __UpperCamelCase       				=torch.einsum('bts,bcs->bct'   ,  A_   ,  A_       )

         # (bs, length+1, width)
         __UpperCamelCase       				=a.reshape(A_   ,  -1   ,  1       ).transpose(1   ,  2       )

         return a[:, 0, :]  # cls_token









import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import EfficientNetImageProcessor






class        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_=13   ,  A_=3   ,  A_=18   ,  A_=30   ,  A_=400   ,  A_=True   ,  A_=None   ,  A_=True   ,  A_=[0.5, 0.5, 0.5]   ,  A_=[0.5, 0.5, 0.5]   ,  )    ->			int:
         __UpperCamelCase       				=size if size is not None else {'height': 18, 'width': 18}
         __UpperCamelCase       				=parent
         __UpperCamelCase       				=batch_size
         __UpperCamelCase       				=num_channels
         __UpperCamelCase       				=image_size
         __UpperCamelCase       				=min_resolution
         __UpperCamelCase       				=max_resolution
         __UpperCamelCase       				=do_resize
         __UpperCamelCase       				=size
         __UpperCamelCase       				=do_normalize
         __UpperCamelCase       				=image_mean
         __UpperCamelCase       				=image_std






    def _a   (     self       )    ->			Optional[Any]:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }






@require_torch
@require_vision
class        UpperCAmelCase__			(   A_  ,    unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						List[Any]    =    EfficientNetImageProcessor if is_vision_available() else None






    def _a   (     self       )    ->			List[str]:
         __UpperCamelCase       				=EfficientNetImageProcessorTester(self       )






    @property
    def _a   (     self       )    ->			Optional[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()






    def _a   (     self       )    ->			str:
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(A_   ,  'image_mean'       )       )
         self.assertTrue(hasattr(A_   ,  'image_std'       )       )
         self.assertTrue(hasattr(A_   ,  'do_normalize'       )       )
         self.assertTrue(hasattr(A_   ,  'do_resize'       )       )
         self.assertTrue(hasattr(A_   ,  'size'       )       )






    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size   ,  {'height': 18, 'width': 18}       )

         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict   ,  size=42       )
         self.assertEqual(image_processor.size   ,  {'height': 42, 'width': 42}       )






    def _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  Image.Image       )

         # Test not batched input
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )






    def _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_   ,  numpify=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  np.ndarray       )

         # Test not batched input
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )






    def _a   (     self       )    ->			Dict:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_   ,  torchify=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  torch.Tensor       )

         # Test not batched input
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )





