
import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


snake_case__					=				{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}







class 				UpperCAmelCase						( nn.Module       ):

					def __init__(      self   :       Dict			,   lowerCAmelCase   :       Union[str, Any]       ):
											super().__init__()
											lowercase							:     Union[str, Any]									=	torchvision.models.resnetaaa(pretrained=lowerCamelCase_       )
											lowercase							:     Any									=	list(model.children()       )[:-2]
											lowercase							:     Any									=	nn.Sequential(*lowerCamelCase_       )
											lowercase							:     Union[str, Any]									=	nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]       )




					def 							_lowerCAmelCase						(      self   :       List[str]			,   lowerCAmelCase   :       List[Any]       ):
											lowercase							:     List[Any]									=	self.pool(self.model(lowerCamelCase_       )       )
											lowercase							:     int									=	torch.flatten(lowerCamelCase_			,   start_dim=2       )
											lowercase							:     List[Any]									=	out.transpose(1			,   2       ).contiguous()
											return out  # BxNx2048







class 				UpperCAmelCase						( lowercase_       ):

					def __init__(      self   :       str			,   lowerCAmelCase   :       List[Any]			,   lowerCAmelCase   :       List[str]			,   lowerCAmelCase   :       List[Any]			,   lowerCAmelCase   :       List[Any]			,   lowerCAmelCase   :       Tuple       ):
											lowercase							:     Tuple									=	[json.loads(lowerCamelCase_       ) for l in open(lowerCamelCase_       )]
											lowercase							:     Optional[Any]									=	os.path.dirname(lowerCamelCase_       )
											lowercase							:     Tuple									=	tokenizer
											lowercase							:     Union[str, Any]									=	labels
											lowercase							:     str									=	len(lowerCamelCase_       )
											lowercase							:     int									=	max_seq_length

											lowercase							:     List[Any]									=	transforms

					def __len__(      self   :       Any       ):
											return len(self.data       )

					def __getitem__(      self   :       Optional[int]			,   lowerCAmelCase   :       List[str]       ):
											lowercase							:     List[Any]									=	torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']			,   add_special_tokens=lowerCamelCase_       )       )
											lowercase							:     Tuple									=	sentence[0], sentence[1:-1], sentence[-1]
											lowercase							:     int									=	sentence[: self.max_seq_length]

											lowercase							:     List[Any]									=	torch.zeros(self.n_classes       )
											lowercase							:     List[str]									=	1

											lowercase							:     int									=	Image.open(os.path.join(self.data_dir			,   self.data[index]['''img''']       )       ).convert('''RGB'''       )
											lowercase							:     str									=	self.transforms(lowerCamelCase_       )

											return {
											    "image_start_token": start_token,
											    "image_end_token": end_token,
											    "sentence": sentence,
											    "image": image,
											    "label": label,
											}




					def 							_lowerCAmelCase						(      self   :       Optional[Any]       ):
											lowercase							:     Any									=	Counter()
											for row in self.data:
																	label_freqs.update(row['''label''']       )
											return label_freqs





def 							lowerCamelCase_   (      UpperCAmelCase_				:					Optional[Any]							):
						lowercase							:     Tuple									=	[len(row['''sentence''']							) for row in batch]
						lowercase							:     Any									=	len(lowerCamelCase_							), max(lowerCamelCase_							)

						lowercase							:     int									=	torch.zeros(lowerCamelCase_				,      lowerCamelCase_				,      dtype=torch.long							)
						lowercase							:     Any									=	torch.zeros(lowerCamelCase_				,      lowerCamelCase_				,      dtype=torch.long							)

						for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase_				,      lowerCamelCase_							)							):
												lowercase							:     Union[str, Any]									=	input_row["""sentence"""]
												lowercase							:     Union[str, Any]									=	1

						lowercase							:     List[str]									=	torch.stack([row['''image'''] for row in batch]							)
						lowercase							:     int									=	torch.stack([row['''label'''] for row in batch]							)
						lowercase							:     str									=	torch.stack([row['''image_start_token'''] for row in batch]							)
						lowercase							:     Any									=	torch.stack([row['''image_end_token'''] for row in batch]							)

						return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor

def 							lowerCamelCase_   (      ):
						return [
						    "Crime",
						    "Drama",
						    "Thriller",
						    "Action",
						    "Comedy",
						    "Romance",
						    "Documentary",
						    "Short",
						    "Mystery",
						    "History",
						    "Family",
						    "Adventure",
						    "Fantasy",
						    "Sci-Fi",
						    "Western",
						    "Horror",
						    "Sport",
						    "War",
						    "Music",
						    "Musical",
						    "Animation",
						    "Biography",
						    "Film-Noir",
						]




def 							lowerCamelCase_   (      ):
						return transforms.Compose(
						    [
						        transforms.Resize(2_56							),
						        transforms.CenterCrop(2_24							),
						        transforms.ToTensor(),
						        transforms.Normalize(
						            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]				,      std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]				,      ),
						    ]							)









'''simple docstring'''




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	(UnCLIPScheduler,)


					def lowerCamelCase_			(						self				:			List[str]      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			{
							    """num_train_timesteps""": 10_00,
							    """variance_type""": """fixed_small_log""",
							    """clip_sample""": True,
							    """clip_sample_range""": 1.0,
							    """prediction_type""": """epsilon""",
							}

							config.update(**lowerCamelCase_						)
							return config


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for timesteps in [1, 5, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							for variance in ["fixed_small_log", "learned_range"]:
									self.check_over_configs(variance_type=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							for clip_sample_range in [1, 5, 10, 20]:
									self.check_over_configs(clip_sample_range=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(prediction_type=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for time_step in [0, 5_00, 9_99]:
									for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
											if prev_timestep is not None and prev_timestep >= time_step:
													continue

											self.check_over_forward(time_step=lowerCamelCase_      ,						prev_timestep=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_scheduler_config(variance_type="""fixed_small_log"""						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler_class(**lowerCamelCase_						)

							assert torch.sum(torch.abs(scheduler._get_variance(0						) - 1.0000e-10						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87						) - 0.0_549_625						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.9_994_987						)						) < 1e-5


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.get_scheduler_config(variance_type="""learned_range"""						)
							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				List[str]		=			0.5

							assert scheduler._get_variance(1      ,						predicted_variance=lowerCamelCase_						) - -10.1_712_790 < 1e-5
							assert scheduler._get_variance(4_87      ,						predicted_variance=lowerCamelCase_						) - -5.7_998_052 < 1e-5
							assert scheduler._get_variance(9_99      ,						predicted_variance=lowerCamelCase_						) - -0.0_010_011 < 1e-5


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config()
							SCREAMING_SNAKE_CASE :				List[Any]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler.timesteps

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_sample_deter
							SCREAMING_SNAKE_CASE :				str		=			torch.manual_seed(0						)

							for i, t in enumerate(lowerCamelCase_						):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE :				List[str]		=			model(lowerCamelCase_      ,						lowerCamelCase_						)

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE :				Optional[int]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						generator=lowerCamelCase_						).prev_sample

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			pred_prev_sample

							SCREAMING_SNAKE_CASE :				Optional[int]		=			torch.sum(torch.abs(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				int		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_sum.item() - 252.2_682_495						) < 1e-2
							assert abs(result_mean.item() - 0.3_284_743						) < 1e-3


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_scheduler_config()
							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							scheduler.set_timesteps(25						)

							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler.timesteps

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Tuple		=			self.dummy_sample_deter
							SCREAMING_SNAKE_CASE :				Any		=			torch.manual_seed(0						)

							for i, t in enumerate(lowerCamelCase_						):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE :				int		=			model(lowerCamelCase_      ,						lowerCamelCase_						)

									if i + 1 == timesteps.shape[0]:
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			None
									else:
											SCREAMING_SNAKE_CASE :				int		=			timesteps[i + 1]

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE :				int		=			scheduler.step(
									    lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						prev_timestep=lowerCamelCase_      ,						generator=lowerCamelCase_						).prev_sample

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			pred_prev_sample

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.sum(torch.abs(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_sum.item() - 258.2_044_983						) < 1e-2
							assert abs(result_mean.item() - 0.3_362_038						) < 1e-3


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							pass



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							pass



