



"""simple docstring"""
import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation
def        A_  (      _lowercase, _lowercase, _lowercase = False, _lowercase = 1, _lowercase = 1, _lowercase = 10000, ) ->    List[str]:







						'''simple docstring'''


						assert len(timesteps.shape     ) == 1, "Timesteps should be a 1d-array"

						snake_case_			:Any						=      embedding_dim // 2
						snake_case_			:int						=      -math.log(_lowerCAmelCase     ) * torch.arange(
						    start=0, end=_lowerCAmelCase, dtype=torch.floataa, device=timesteps.device     )
						snake_case_			:str						=      exponent / (half_dim - downscale_freq_shift)

						snake_case_			:int						=      torch.exp(_lowerCAmelCase     )
						snake_case_			:List[str]						=      timesteps[:, None].float() * emb[None, :]

						# scale embeddings
						snake_case_			:Any						=      scale * emb

						# concat sine and cosine embeddings
						snake_case_			:str						=      torch.cat([torch.sin(_lowerCAmelCase     ), torch.cos(_lowerCAmelCase     )], dim=-1     )

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

						# zero pad
						if embedding_dim % 2 == 1:
												snake_case_			:int						=      torch.nn.functional.pad(_lowerCAmelCase, (0, 1, 0, 0)     )
						return emb
def        A_  (      _lowercase, _lowercase, _lowercase=False, _lowercase=0     ) ->    str:







						'''simple docstring'''


						snake_case_			:Optional[Any]						=      np.arange(_lowerCAmelCase, dtype=np.floataa     )
						snake_case_			:Optional[int]						=      np.arange(_lowerCAmelCase, dtype=np.floataa     )
						snake_case_			:Tuple						=      np.meshgrid(_lowerCAmelCase, _lowerCAmelCase     )  # here w goes first
						snake_case_			:List[Any]						=      np.stack(_lowerCAmelCase, axis=0     )

						snake_case_			:Optional[int]						=      grid.reshape([2, 1, grid_size, grid_size]     )
						snake_case_			:Optional[Any]						=      get_ad_sincos_pos_embed_from_grid(_lowerCAmelCase, _lowerCAmelCase     )
						if cls_token and extra_tokens > 0:
												snake_case_			:str						=      np.concatenate([np.zeros([extra_tokens, embed_dim]     ), pos_embed], axis=0     )
						return pos_embed
def        A_  (      _lowercase, _lowercase     ) ->    Any:







						'''simple docstring'''


						if embed_dim % 2 != 0:
												raise ValueError("""embed_dim must be divisible by 2"""     )

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

						snake_case_			:Dict						=      np.concatenate([emb_h, emb_w], axis=1     )  # (H*W, D)
						return emb
def        A_  (      _lowercase, _lowercase     ) ->    List[str]:







						'''simple docstring'''


						if embed_dim % 2 != 0:
												raise ValueError("""embed_dim must be divisible by 2"""     )

						snake_case_			:Optional[int]						=      np.arange(embed_dim // 2, dtype=np.floataa     )
						omega /= embed_dim / 2.0
						snake_case_			:Optional[int]						=      1.0 / 10000**omega  # (D/2,)

						snake_case_			:Tuple						=      pos.reshape(-1     )  # (M,)
						snake_case_			:Optional[Any]						=      np.einsum("""m,d->md""", _lowerCAmelCase, _lowerCAmelCase     )  # (M, D/2), outer product

						snake_case_			:Tuple						=      np.sin(_lowerCAmelCase     )  # (M, D/2)
						snake_case_			:Optional[Any]						=      np.cos(_lowerCAmelCase     )  # (M, D/2)

						snake_case_			:int						=      np.concatenate([emb_sin, emb_cos], axis=1     )  # (M, D)
						return emb
class     lowerCamelCase  (      nn.Module    ):






					'''simple docstring'''





					def __init__(	self:							Optional[Any]						,					snake_case:							Dict=224						,					snake_case:							Union[str, Any]=224						,					snake_case:							Optional[Any]=16						,					snake_case:							Union[str, Any]=3						,					snake_case:							Union[str, Any]=768						,					snake_case:							str=False						,					snake_case:							str=True						,					snake_case:							Optional[Any]=True						,					)       ->     Tuple:
											super().__init__()

											snake_case_			:List[str]						=      (height // patch_size) * (width // patch_size)
											snake_case_			:Any						=      flatten
											snake_case_			:List[Any]						=      layer_norm

											snake_case_			:Dict						=      nn.Convad(
											    __snake_case						,					__snake_case						,					kernel_size=(patch_size, patch_size)						,					stride=__snake_case						,					bias=__snake_case						)
											if layer_norm:
																	snake_case_			:List[str]						=      nn.LayerNorm(__snake_case						,					elementwise_affine=__snake_case						,					eps=1E-6						)
											else:
																	snake_case_			:Optional[Any]						=      None

											snake_case_			:Optional[int]						=      get_ad_sincos_pos_embed(__snake_case						,					int(num_patches**0.5						)						)
											self.register_buffer("""pos_embed"""						,					torch.from_numpy(__snake_case						).float().unsqueeze(0						)						,					persistent=__snake_case						)






					def        lowerCAmelCase_    (	self:							Optional[int]						,					snake_case:							int						)       ->     List[str]:
											snake_case_			:Dict						=      self.proj(__snake_case						)
											if self.flatten:
																	snake_case_			:List[str]						=      latent.flatten(2						).transpose(1						,					2						)  # BCHW -> BNC
											if self.layer_norm:
																	snake_case_			:Any						=      self.norm(__snake_case						)
											return latent + self.pos_embed

class     lowerCamelCase  (      nn.Module    ):






					'''simple docstring'''





					def __init__(	self:							Any						,					snake_case:							int						,					snake_case:							int						,					snake_case:							str = "silu"						,					snake_case:							int = None						,					snake_case:							Optional[str] = None						,					snake_case:							Any=None						,					)       ->     Any:
											super().__init__()

											snake_case_			:Tuple						=      nn.Linear(__snake_case						,					__snake_case						)

											if cond_proj_dim is not None:
																	snake_case_			:int						=      nn.Linear(__snake_case						,					__snake_case						,					bias=__snake_case						)
											else:
																	snake_case_			:Any						=      None

											snake_case_			:int						=      get_activation(__snake_case						)

											if out_dim is not None:
																	snake_case_			:List[Any]						=      out_dim
											else:
																	snake_case_			:Tuple						=      time_embed_dim
											snake_case_			:Any						=      nn.Linear(__snake_case						,					__snake_case						)

											if post_act_fn is None:
																	snake_case_			:str						=      None
											else:
																	snake_case_			:str						=      get_activation(__snake_case						)






					def        lowerCAmelCase_    (	self:							str						,					snake_case:							Optional[int]						,					snake_case:							Tuple=None						)       ->     Union[str, Any]:
											if condition is not None:
																	snake_case_			:int						=      sample + self.cond_proj(__snake_case						)
											snake_case_			:Optional[int]						=      self.linear_a(__snake_case						)

											if self.act is not None:
																	snake_case_			:Any						=      self.act(__snake_case						)

											snake_case_			:Tuple						=      self.linear_a(__snake_case						)

											if self.post_act is not None:
																	snake_case_			:List[str]						=      self.post_act(__snake_case						)
											return sample

class     lowerCamelCase  (      nn.Module    ):






					'''simple docstring'''





					def __init__(	self:							int						,					snake_case:							int						,					snake_case:							bool						,					snake_case:							float						)       ->     Union[str, Any]:
											super().__init__()
											snake_case_			:Union[str, Any]						=      num_channels
											snake_case_			:List[Any]						=      flip_sin_to_cos
											snake_case_			:int						=      downscale_freq_shift






					def        lowerCAmelCase_    (	self:							Union[str, Any]						,					snake_case:							Tuple						)       ->     Optional[Any]:
											snake_case_			:Optional[Any]						=      get_timestep_embedding(
											    __snake_case						,					self.num_channels						,					flip_sin_to_cos=self.flip_sin_to_cos						,					downscale_freq_shift=self.downscale_freq_shift						,					)
											return t_emb

class     lowerCamelCase  (      nn.Module    ):






					'''simple docstring'''





					def __init__(	self:							Union[str, Any]						,					snake_case:							int = 256						,					snake_case:							float = 1.0						,					snake_case:							List[Any]=True						,					snake_case:							Any=True						,					snake_case:							Dict=False						)       ->     Optional[Any]:
											super().__init__()
											snake_case_			:Dict						=      nn.Parameter(torch.randn(__snake_case						) * scale						,					requires_grad=__snake_case						)
											snake_case_			:Dict						=      log
											snake_case_			:Tuple						=      flip_sin_to_cos

											if set_W_to_weight:
																	# to delete later
																	snake_case_			:Dict						=      nn.Parameter(torch.randn(__snake_case						) * scale						,					requires_grad=__snake_case						)

																	snake_case_			:str						=      self.W






					def        lowerCAmelCase_    (	self:							List[str]						,					snake_case:							Optional[int]						)       ->     Tuple:
											if self.log:
																	snake_case_			:Any						=      torch.log(__snake_case						)

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

											if self.flip_sin_to_cos:
																	snake_case_			:Dict						=      torch.cat([torch.cos(__snake_case						), torch.sin(__snake_case						)]						,					dim=-1						)
											else:
																	snake_case_			:Optional[Any]						=      torch.cat([torch.sin(__snake_case						), torch.cos(__snake_case						)]						,					dim=-1						)
											return out

class     lowerCamelCase  (      nn.Module    ):






					'''simple docstring'''





					def __init__(	self:							Any						,					snake_case:							int						,					snake_case:							int						,					snake_case:							int						,					snake_case:							int						,					)       ->     Union[str, Any]:
											super().__init__()

											snake_case_			:Optional[Any]						=      height
											snake_case_			:Any						=      width
											snake_case_			:Optional[Any]						=      num_embed
											snake_case_			:Tuple						=      embed_dim

											snake_case_			:List[Any]						=      nn.Embedding(self.num_embed						,					__snake_case						)
											snake_case_			:List[Any]						=      nn.Embedding(self.height						,					__snake_case						)
											snake_case_			:Dict						=      nn.Embedding(self.width						,					__snake_case						)






					def        lowerCAmelCase_    (	self:							Optional[Any]						,					snake_case:							Union[str, Any]						)       ->     Tuple:
											snake_case_			:Optional[Any]						=      self.emb(__snake_case						)

											snake_case_			:int						=      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
											snake_case_			:str						=      height_emb.unsqueeze(2						)

											snake_case_			:List[str]						=      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
											snake_case_			:Optional[int]						=      width_emb.unsqueeze(1						)

											snake_case_			:Union[str, Any]						=      height_emb + width_emb

											# 1 x H x W x D -> 1 x L xD
											snake_case_			:List[str]						=      pos_emb.view(1						,					self.height * self.width						,					-1						)

											snake_case_			:int						=      emb + pos_emb[:, : emb.shape[1], :]

											return emb

class     lowerCamelCase  (      nn.Module    ):






					'''simple docstring'''





					def __init__(	self:							List[Any]						,					snake_case:							Optional[int]						,					snake_case:							Optional[int]						,					snake_case:							Dict						)       ->     List[Any]:
											super().__init__()
											snake_case_			:List[str]						=      dropout_prob > 0
											snake_case_			:List[Any]						=      nn.Embedding(num_classes + use_cfg_embedding						,					__snake_case						)
											snake_case_			:int						=      num_classes
											snake_case_			:Optional[int]						=      dropout_prob






					def        lowerCAmelCase_    (	self:							List[str]						,					snake_case:							Optional[Any]						,					snake_case:							Dict=None						)       ->     List[Any]:
											if force_drop_ids is None:
																	snake_case_			:str						=      torch.rand(labels.shape[0]						,					device=labels.device						) < self.dropout_prob
											else:
																	snake_case_			:Optional[int]						=      torch.tensor(force_drop_ids == 1						)
											snake_case_			:Tuple						=      torch.where(__snake_case						,					self.num_classes						,					__snake_case						)
											return labels






					def        lowerCAmelCase_    (	self:							Any						,					snake_case:							torch.LongTensor						,					snake_case:							str=None						)       ->     Tuple:
											snake_case_			:str						=      self.dropout_prob > 0
											if (self.training and use_dropout) or (force_drop_ids is not None):
																	snake_case_			:Any						=      self.token_drop(__snake_case						,					__snake_case						)
											snake_case_			:List[str]						=      self.embedding_table(__snake_case						)
											return embeddings

class     lowerCamelCase  (      nn.Module    ):






					'''simple docstring'''





					def __init__(	self:							Dict						,					snake_case:							int = 1_024						,					snake_case:							int = 768						,					snake_case:							int = 768						,					snake_case:							int = 10						,					)       ->     List[str]:
											super().__init__()

											snake_case_			:Any						=      num_image_text_embeds
											snake_case_			:Tuple						=      nn.Linear(__snake_case						,					self.num_image_text_embeds * cross_attention_dim						)
											snake_case_			:Any						=      nn.Linear(__snake_case						,					__snake_case						)






					def        lowerCAmelCase_    (	self:							List[Any]						,					snake_case:							torch.FloatTensor						,					snake_case:							torch.FloatTensor						)       ->     Optional[int]:
											snake_case_			:int						=      text_embeds.shape[0]

											# image
											snake_case_			:List[Any]						=      self.image_embeds(__snake_case						)
											snake_case_			:int						=      image_text_embeds.reshape(__snake_case						,					self.num_image_text_embeds						,					-1						)

											# text
											snake_case_			:List[str]						=      self.text_proj(__snake_case						)

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

class     lowerCamelCase  (      nn.Module    ):






					'''simple docstring'''





					def __init__(	self:							Union[str, Any]						,					snake_case:							int = 768						,					snake_case:							int = 768						,					snake_case:							int = 32						,					)       ->     Dict:
											super().__init__()

											snake_case_			:Any						=      num_image_text_embeds
											snake_case_			:Union[str, Any]						=      nn.Linear(__snake_case						,					self.num_image_text_embeds * cross_attention_dim						)
											snake_case_			:str						=      nn.LayerNorm(__snake_case						)






					def        lowerCAmelCase_    (	self:							int						,					snake_case:							torch.FloatTensor						)       ->     Union[str, Any]:
											snake_case_			:Tuple						=      image_embeds.shape[0]

											# image
											snake_case_			:int						=      self.image_embeds(__snake_case						)
											snake_case_			:Tuple						=      image_embeds.reshape(__snake_case						,					self.num_image_text_embeds						,					-1						)
											snake_case_			:Optional[int]						=      self.norm(__snake_case						)
											return image_embeds

class     lowerCamelCase  (      nn.Module    ):






					'''simple docstring'''





					def __init__(	self:							Dict						,					snake_case:							List[str]						,					snake_case:							Optional[Any]						,					snake_case:							int=0.1						)       ->     Any:
											super().__init__()

											snake_case_			:Union[str, Any]						=      Timesteps(num_channels=256						,					flip_sin_to_cos=__snake_case						,					downscale_freq_shift=1						)
											snake_case_			:Optional[int]						=      TimestepEmbedding(in_channels=256						,					time_embed_dim=__snake_case						)
											snake_case_			:Tuple						=      LabelEmbedding(__snake_case						,					__snake_case						,					__snake_case						)






					def        lowerCAmelCase_    (	self:							Dict						,					snake_case:							Tuple						,					snake_case:							Tuple						,					snake_case:							Any=None						)       ->     Tuple:
											snake_case_			:Optional[Any]						=      self.time_proj(__snake_case						)
											snake_case_			:Any						=      self.timestep_embedder(timesteps_proj.to(dtype=__snake_case						)						)  # (N, D)

											snake_case_			:Tuple						=      self.class_embedder(__snake_case						)  # (N, D)

											snake_case_			:str						=      timesteps_emb + class_labels  # (N, D)

											return conditioning

class     lowerCamelCase  (      nn.Module    ):






					'''simple docstring'''





					def __init__(	self:							Union[str, Any]						,					snake_case:							int						,					snake_case:							int						,					snake_case:							int = 64						)       ->     Tuple:
											super().__init__()
											snake_case_			:Tuple						=      nn.LayerNorm(__snake_case						)
											snake_case_			:Optional[int]						=      AttentionPooling(__snake_case						,					__snake_case						)
											snake_case_			:Tuple						=      nn.Linear(__snake_case						,					__snake_case						)
											snake_case_			:Tuple						=      nn.LayerNorm(__snake_case						)






					def        lowerCAmelCase_    (	self:							Dict						,					snake_case:							int						)       ->     List[Any]:
											snake_case_			:Optional[int]						=      self.norma(__snake_case						)
											snake_case_			:List[Any]						=      self.pool(__snake_case						)
											snake_case_			:Optional[Any]						=      self.proj(__snake_case						)
											snake_case_			:List[str]						=      self.norma(__snake_case						)
											return hidden_states

class     lowerCamelCase  (      nn.Module    ):






					'''simple docstring'''





					def __init__(	self:							int						,					snake_case:							int = 768						,					snake_case:							int = 768						,					snake_case:							int = 1_536						)       ->     Union[str, Any]:
											super().__init__()
											snake_case_			:int						=      nn.Linear(__snake_case						,					__snake_case						)
											snake_case_			:str						=      nn.LayerNorm(__snake_case						)
											snake_case_			:str						=      nn.Linear(__snake_case						,					__snake_case						)






					def        lowerCAmelCase_    (	self:							List[str]						,					snake_case:							torch.FloatTensor						,					snake_case:							torch.FloatTensor						)       ->     Union[str, Any]:
											# text
											snake_case_			:Optional[int]						=      self.text_proj(__snake_case						)
											snake_case_			:Dict						=      self.text_norm(__snake_case						)

											# image
											snake_case_			:List[Any]						=      self.image_proj(__snake_case						)

											return time_image_embeds + time_text_embeds

class     lowerCamelCase  (      nn.Module    ):






					'''simple docstring'''





					def __init__(	self:							int						,					snake_case:							int = 768						,					snake_case:							int = 1_536						)       ->     Optional[int]:
											super().__init__()
											snake_case_			:Any						=      nn.Linear(__snake_case						,					__snake_case						)
											snake_case_			:Union[str, Any]						=      nn.LayerNorm(__snake_case						)






					def        lowerCAmelCase_    (	self:							Optional[Any]						,					snake_case:							torch.FloatTensor						)       ->     Union[str, Any]:
											# image
											snake_case_			:List[str]						=      self.image_proj(__snake_case						)
											snake_case_			:List[Any]						=      self.image_norm(__snake_case						)
											return time_image_embeds

class     lowerCamelCase  (      nn.Module    ):






					'''simple docstring'''





					def __init__(	self:							str						,					snake_case:							int = 768						,					snake_case:							int = 1_536						)       ->     Tuple:
											super().__init__()
											snake_case_			:Dict						=      nn.Linear(__snake_case						,					__snake_case						)
											snake_case_			:Union[str, Any]						=      nn.LayerNorm(__snake_case						)
											snake_case_			:Union[str, Any]						=      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        lowerCAmelCase_    (	self:							Optional[Any]						,					snake_case:							torch.FloatTensor						,					snake_case:							torch.FloatTensor						)       ->     int:
											# image
											snake_case_			:Optional[Any]						=      self.image_proj(__snake_case						)
											snake_case_			:str						=      self.image_norm(__snake_case						)
											snake_case_			:List[Any]						=      self.input_hint_block(__snake_case						)
											return time_image_embeds, hint

class     lowerCamelCase  (      nn.Module    ):






					'''simple docstring'''





					def __init__(	self:							str						,					snake_case:							Any						,					snake_case:							Optional[int]						,					snake_case:							List[Any]=None						)       ->     Any:
											super().__init__()
											snake_case_			:Tuple						=      dtype
											snake_case_			:Tuple						=      nn.Parameter(torch.randn(1						,					__snake_case						) / embed_dim**0.5						)
											snake_case_			:List[str]						=      nn.Linear(__snake_case						,					__snake_case						,					dtype=self.dtype						)
											snake_case_			:int						=      nn.Linear(__snake_case						,					__snake_case						,					dtype=self.dtype						)
											snake_case_			:str						=      nn.Linear(__snake_case						,					__snake_case						,					dtype=self.dtype						)
											snake_case_			:str						=      num_heads
											snake_case_			:List[Any]						=      embed_dim // self.num_heads






					def        lowerCAmelCase_    (	self:							Union[str, Any]						,					snake_case:							List[Any]						)       ->     Optional[int]:
											snake_case_			:List[str]						=      x.size()

											def shape(snake_case:							Optional[Any]						):
																	# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
																	snake_case_			:str						=      x.view(__snake_case						,					-1						,					self.num_heads						,					self.dim_per_head						)
																	# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
																	snake_case_			:Union[str, Any]						=      x.transpose(1						,					2						)
																	# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
																	snake_case_			:int						=      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)
																	snake_case_			:int						=      x.transpose(1						,					2						)
																	return x

											snake_case_			:List[str]						=      x.mean(dim=1						,					keepdim=__snake_case						) + self.positional_embedding.to(x.dtype						)
											snake_case_			:List[str]						=      torch.cat([class_token, x]						,					dim=1						)  # (bs, length+1, width)

											# (bs*n_heads, class_token_length, dim_per_head)
											snake_case_			:str						=      shape(self.q_proj(__snake_case						)						)
											# (bs*n_heads, length+class_token_length, dim_per_head)
											snake_case_			:Optional[Any]						=      shape(self.k_proj(__snake_case						)						)
											snake_case_			:Union[str, Any]						=      shape(self.v_proj(__snake_case						)						)

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

											# (bs*n_heads, dim_per_head, class_token_length)
											snake_case_			:int						=      torch.einsum("""bts,bcs->bct"""						,					__snake_case						,					__snake_case						)

											# (bs, length+1, width)
											snake_case_			:List[str]						=      a.reshape(__snake_case						,					-1						,					1						).transpose(1						,					2						)

											return a[:, 0, :]  # cls_token





"""simple docstring"""
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 ViTImageProcessor
class     lowerCamelCase  (      unittest.TestCase    ):






					'''simple docstring'''





					def __init__(	self:							int						,					snake_case:							List[str]						,					snake_case:							List[str]=7						,					snake_case:							Union[str, Any]=3						,					snake_case:							List[str]=18						,					snake_case:							Any=30						,					snake_case:							Any=400						,					snake_case:							Any=True						,					snake_case:							List[str]=None						,					snake_case:							Union[str, Any]=True						,					snake_case:							int=[0.5, 0.5, 0.5]						,					snake_case:							Tuple=[0.5, 0.5, 0.5]						,					)       ->     int:
											snake_case_			:Dict						=      size if size is not None else {"""height""": 18, """width""": 18}
											snake_case_			:Optional[Any]						=      parent
											snake_case_			:Tuple						=      batch_size
											snake_case_			:Tuple						=      num_channels
											snake_case_			:Optional[int]						=      image_size
											snake_case_			:Tuple						=      min_resolution
											snake_case_			:str						=      max_resolution
											snake_case_			:Any						=      do_resize
											snake_case_			:Union[str, Any]						=      size
											snake_case_			:Tuple						=      do_normalize
											snake_case_			:int						=      image_mean
											snake_case_			:List[Any]						=      image_std






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     List[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     lowerCamelCase  (      _lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





					_A		:      Tuple						=					ViTImageProcessor if is_vision_available() else None






					def        lowerCAmelCase_    (	self:							str						)       ->     Optional[Any]:
											snake_case_			:Dict						=      ViTImageProcessingTester(self						)






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






					def        lowerCAmelCase_    (	self:							str						)       ->     List[str]:
											snake_case_			:List[str]						=      self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(snake_case						,					"""image_mean"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""image_std"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""do_normalize"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""do_resize"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""size"""						)						)






					def        lowerCAmelCase_    (	self:							Dict						)       ->     Union[str, Any]:
											snake_case_			:int						=      self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size						,					{"""height""": 18, """width""": 18}						)

											snake_case_			:int						=      self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						)
											self.assertEqual(image_processor.size						,					{"""height""": 42, """width""": 42}						)






					def        lowerCAmelCase_    (	self:							Tuple						)       ->     int:
											pass






					def        lowerCAmelCase_    (	self:							Dict						)       ->     Union[str, Any]:
											# Initialize image_processing
											snake_case_			:Any						=      self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											snake_case_			:str						=      prepare_image_inputs(self.image_processor_tester						,					equal_resolution=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,					Image.Image						)

											# Test not batched input
											snake_case_			:List[str]						=      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
											snake_case_			:Dict						=      image_processing(snake_case						,					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        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Tuple:
											# Initialize image_processing
											snake_case_			:int						=      self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											snake_case_			:str						=      prepare_image_inputs(self.image_processor_tester						,					equal_resolution=snake_case						,					numpify=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,					np.ndarray						)

											# Test not batched input
											snake_case_			:Any						=      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
											snake_case_			:Any						=      image_processing(snake_case						,					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        lowerCAmelCase_    (	self:							List[Any]						)       ->     Optional[int]:
											# Initialize image_processing
											snake_case_			:Union[str, Any]						=      self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											snake_case_			:List[Any]						=      prepare_image_inputs(self.image_processor_tester						,					equal_resolution=snake_case						,					torchify=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,					torch.Tensor						)

											# Test not batched input
											snake_case_			:Optional[Any]						=      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
											snake_case_			:List[Any]						=      image_processing(snake_case						,					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"""],
											    )						,					)
