




'''simple docstring'''





from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin




@dataclass
class  _snake_case		(	lowercase_							):
			lowerCAmelCase_     :      torch.FloatTensor




class  _snake_case		(	lowercase_						,						lowercase_							):




			@register_to_config
			def __init__(					self				,							a__ = 32				,							a__ = 64				,							a__ = 20				,							a__ = 768				,							a__=77				,							a__=4				,							a__ = 0.0				,							a__ = "silu"				,							a__ = None				,							a__ = None				,							a__ = "linear"				,							a__ = "prd"				,							a__ = None				,							a__ = None				,							a__ = None				,							)				->				Tuple:

										'''simple docstring'''




										super().__init__()
										snake_case_						=      num_attention_heads
										snake_case_						=      attention_head_dim
										snake_case_						=      num_attention_heads * attention_head_dim
										snake_case_						=      additional_embeddings

										snake_case_						=      time_embed_dim or inner_dim
										snake_case_						=      embedding_proj_dim or embedding_dim
										snake_case_						=      clip_embed_dim or embedding_dim

										snake_case_						=      Timesteps(a__				,							a__				,							0							)
										snake_case_						=      TimestepEmbedding(a__				,							a__				,							out_dim=a__				,							act_fn=a__							)

										snake_case_						=      nn.Linear(a__				,							a__							)

										if embedding_proj_norm_type is None:
																	snake_case_						=      None
										elif embedding_proj_norm_type == "layer":
																	snake_case_						=      nn.LayerNorm(a__							)
										else:
																	raise ValueError(F'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'							)

										snake_case_						=      nn.Linear(a__				,							a__							)

										if encoder_hid_proj_type is None:
																	snake_case_						=      None
										elif encoder_hid_proj_type == "linear":
																	snake_case_						=      nn.Linear(a__				,							a__							)
										else:
																	raise ValueError(F'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'							)

										snake_case_						=      nn.Parameter(torch.zeros(1				,							num_embeddings + additional_embeddings				,							a__							)							)

										if added_emb_type == "prd":
																	snake_case_						=      nn.Parameter(torch.zeros(1				,							1				,							a__							)							)
										elif added_emb_type is None:
																	snake_case_						=      None
										else:
																	raise ValueError(
																	    F'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'							)

										snake_case_						=      nn.ModuleList(
										    [
										        BasicTransformerBlock(
										            a__				,							a__				,							a__				,							dropout=a__				,							activation_fn="gelu"				,							attention_bias=a__				,							)
										        for d in range(a__							)
										    ]							)

										if norm_in_type == "layer":
																	snake_case_						=      nn.LayerNorm(a__							)
										elif norm_in_type is None:
																	snake_case_						=      None
										else:
																	raise ValueError(F'Unsupported norm_in_type: {norm_in_type}.'							)

										snake_case_						=      nn.LayerNorm(a__							)

										snake_case_						=      nn.Linear(a__				,							a__							)

										snake_case_						=      torch.full(
										    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]				,							-1_0_0_0_0.0							)
										causal_attention_mask.triu_(1							)
										snake_case_						=      causal_attention_mask[None, ...]
										self.register_buffer("causal_attention_mask"				,							a__				,							persistent=a__							)

										snake_case_						=      nn.Parameter(torch.zeros(1				,							a__							)							)
										snake_case_						=      nn.Parameter(torch.zeros(1				,							a__							)							)




			@property
			# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
			def    lowerCAmelCase__				(					self							)				->				Dict[str, AttentionProcessor]:

										'''simple docstring'''




										snake_case_						=      {}

										def fn_recursive_add_processors(a__				,							a__				,							a__							):
																	if hasattr(a__				,							"set_processor"							):
																								snake_case_						=      module.processor

																	for sub_name, child in module.named_children():
																								fn_recursive_add_processors(F'{name}.{sub_name}'				,							a__				,							a__							)

																	return processors

										for name, module in self.named_children():
																	fn_recursive_add_processors(a__				,							a__				,							a__							)

										return processors




			def    lowerCAmelCase__				(					self				,							a__							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      len(self.attn_processors.keys()							)

										if isinstance(a__				,							a__							) and len(a__							) != count:
																	raise ValueError(
																	    F'A dict of processors was passed, but the number of processors {len(a__							)} does not match the'
																	    F' number of attention layers: {count}. Please make sure to pass {count} processor classes.'							)

										def fn_recursive_attn_processor(a__				,							a__				,							a__							):
																	if hasattr(a__				,							"set_processor"							):
																								if not isinstance(a__				,							a__							):
																															module.set_processor(a__							)
																								else:
																															module.set_processor(processor.pop(F'{name}.processor'							)							)

																	for sub_name, child in module.named_children():
																								fn_recursive_attn_processor(F'{name}.{sub_name}'				,							a__				,							a__							)

										for name, module in self.named_children():
																	fn_recursive_attn_processor(a__				,							a__				,							a__							)




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										self.set_attn_processor(AttnProcessor()							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__				,							a__ = None				,							a__ = None				,							a__ = True				,							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      hidden_states.shape[0]

										snake_case_						=      timestep
										if not torch.is_tensor(a__							):
																	snake_case_						=      torch.tensor([timesteps]				,							dtype=torch.long				,							device=hidden_states.device							)
										elif torch.is_tensor(a__							) and len(timesteps.shape							) == 0:
																	snake_case_						=      timesteps[None].to(hidden_states.device							)

										# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
										snake_case_						=      timesteps * torch.ones(a__				,							dtype=timesteps.dtype				,							device=timesteps.device							)

										snake_case_						=      self.time_proj(a__							)

										# timesteps does not contain any weights and will always return f32 tensors
										# but time_embedding might be fp16, so we need to cast here.
										snake_case_						=      timesteps_projected.to(dtype=self.dtype							)
										snake_case_						=      self.time_embedding(a__							)

										if self.embedding_proj_norm is not None:
																	snake_case_						=      self.embedding_proj_norm(a__							)

										snake_case_						=      self.embedding_proj(a__							)
										if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
																	snake_case_						=      self.encoder_hidden_states_proj(a__							)
										elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
																	raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"							)

										snake_case_						=      self.proj_in(a__							)

										snake_case_						=      self.positional_embedding.to(hidden_states.dtype							)

										snake_case_						=      []
										snake_case_						=      0

										if encoder_hidden_states is not None:
																	additional_embeds.append(a__							)
																	additional_embeddings_len += encoder_hidden_states.shape[1]

										if len(proj_embeddings.shape							) == 2:
																	snake_case_						=      proj_embeddings[:, None, :]

										if len(hidden_states.shape							) == 2:
																	snake_case_						=      hidden_states[:, None, :]

										snake_case_						=      additional_embeds + [
										    proj_embeddings,
										    time_embeddings[:, None, :],
										    hidden_states,
										]

										if self.prd_embedding is not None:
																	snake_case_						=      self.prd_embedding.to(hidden_states.dtype							).expand(a__				,							-1				,							-1							)
																	additional_embeds.append(a__							)

										snake_case_						=      torch.cat(
										    a__				,							dim=1				,							)

										# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
										snake_case_						=      additional_embeddings_len + proj_embeddings.shape[1] + 1
										if positional_embeddings.shape[1] < hidden_states.shape[1]:
																	snake_case_						=      F.pad(
																	    a__				,							(
																	        0,
																	        0,
																	        additional_embeddings_len,
																	        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
																	    )				,							value=0.0				,							)

										snake_case_						=      hidden_states + positional_embeddings

										if attention_mask is not None:
																	snake_case_						=      (1 - attention_mask.to(hidden_states.dtype							)) * -1_0_0_0_0.0
																	snake_case_						=      F.pad(a__				,							(0, self.additional_embeddings)				,							value=0.0							)
																	snake_case_						=      (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype							)
																	snake_case_						=      attention_mask.repeat_interleave(self.config.num_attention_heads				,							dim=0							)

										if self.norm_in is not None:
																	snake_case_						=      self.norm_in(a__							)

										for block in self.transformer_blocks:
																	snake_case_						=      block(a__				,							attention_mask=a__							)

										snake_case_						=      self.norm_out(a__							)

										if self.prd_embedding is not None:
																	snake_case_						=      hidden_states[:, -1]
										else:
																	snake_case_						=      hidden_states[:, additional_embeddings_len:]

										snake_case_						=      self.proj_to_clip_embeddings(a__							)

										if not return_dict:
																	return (predicted_image_embedding,)

										return PriorTransformerOutput(predicted_image_embedding=a__							)




			def    lowerCAmelCase__				(					self				,							a__							)				->				str:

										'''simple docstring'''




										snake_case_						=      (prior_latents * self.clip_std) + self.clip_mean
										return prior_latents












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     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):





	def __init__(		self				:					List[str] ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					Any=13 ,  __lowerCamelCase				:					Dict=3 ,  __lowerCamelCase				:					int=224 ,  __lowerCamelCase				:					Any=30 ,  __lowerCamelCase				:					Tuple=400 ,  __lowerCamelCase				:					int=True ,  __lowerCamelCase				:					List[str]=None ,  __lowerCamelCase				:					Any=True ,  __lowerCamelCase				:					Dict=[0.5, 0.5, 0.5] ,  __lowerCamelCase				:					List[Any]=[0.5, 0.5, 0.5] ,  ):
		UpperCamelCase     :List[Any]          =	size if size is not None else {"""height""": 18, """width""": 18}
		UpperCamelCase     :str          =	parent
		UpperCamelCase     :Optional[int]          =	batch_size
		UpperCamelCase     :Dict          =	num_channels
		UpperCamelCase     :str          =	image_size
		UpperCamelCase     :Dict          =	min_resolution
		UpperCamelCase     :str          =	max_resolution
		UpperCamelCase     :Union[str, Any]          =	do_resize
		UpperCamelCase     :Optional[Any]          =	size
		UpperCamelCase     :Any          =	do_normalize
		UpperCamelCase     :Optional[Any]          =	image_mean
		UpperCamelCase     :Tuple          =	image_std





	def 						_A (		self				:					int	):
		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     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase      ):
	snake_case__							:     List[Any]     	=  ViTImageProcessor if is_vision_available() else None





	def 						_A (		self				:					str	):
		UpperCamelCase     :Tuple          =	EfficientFormerImageProcessorTester(self	)





	@property
	def 						_A (		self				:					List[str]	):
		return self.image_proc_tester.prepare_image_processor_dict()





	def 						_A (		self				:					int	):
		UpperCamelCase     :List[Any]          =	self.image_processing_class(**self.image_processor_dict	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """image_mean"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """image_std"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_normalize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """do_resize"""	)	)
		self.assertTrue(hasattr(__lowerCamelCase ,  """size"""	)	)





	def 						_A (		self				:					Optional[int]	):
		pass





	def 						_A (		self				:					str	):
		# Initialize image_processor
		UpperCamelCase     :Union[str, Any]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PIL images
		UpperCamelCase     :Union[str, Any]          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  Image.Image	)

		# Test not batched input
		UpperCamelCase     :List[str]          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :List[Any]          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )





	def 						_A (		self				:					Union[str, Any]	):
		# Initialize image_processor
		UpperCamelCase     :Optional[int]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random numpy tensors
		UpperCamelCase     :List[Any]          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase ,  numpify=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  np.ndarray	)

		# Test not batched input
		UpperCamelCase     :Dict          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :Tuple          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )





	def 						_A (		self				:					List[Any]	):
		# Initialize image_processor
		UpperCamelCase     :List[str]          =	self.image_processing_class(**self.image_processor_dict	)
		# create random PyTorch tensors
		UpperCamelCase     :Any          =	prepare_image_inputs(self.image_proc_tester ,  equal_resolution=__lowerCamelCase ,  torchify=__lowerCamelCase	)
		for image in image_inputs:
			self.assertIsInstance(__lowerCamelCase ,  torch.Tensor	)

		# Test not batched input
		UpperCamelCase     :List[Any]          =	image_processor(image_inputs[0] ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        1,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )

		# Test batched
		UpperCamelCase     :str          =	image_processor(__lowerCamelCase ,  return_tensors="""pt"""	).pixel_values
		self.assertEqual(
		    encoded_images.shape ,  (
		        self.image_proc_tester.batch_size,
		        self.image_proc_tester.num_channels,
		        self.image_proc_tester.size["""height"""],
		        self.image_proc_tester.size["""width"""],
		    ) ,  )
