




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       Optional[Any]      	=		(UnCLIPScheduler,)






	def 					lowerCamelCase__     (					self		:						List[Any]					,     **lowerCAmelCase		:						List[Any]	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      {
						    """num_train_timesteps""": 1000,
						    """variance_type""": """fixed_small_log""",
						    """clip_sample""": True,
						    """clip_sample_range""": 1.0,
						    """prediction_type""": """epsilon""",
						}

						config.update(**lowerCAmelCase	)
						return config






	def 					lowerCamelCase__     (					self		:						int	)	->       Any:







						"""simple docstring"""







						for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Tuple	)	->       int:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						Any	)	->       str:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						str	)	->       Tuple:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						int	)	->       Any:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       List[Any]:







						"""simple docstring"""







						for time_step in [0, 500, 999]:
											for prev_timestep in [None, 5, 100, 250, 500, 750]:
																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		:						Optional[Any]	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      self.scheduler_classes[0]
						__UpperCamelCase					:	Optional[int]             =      self.get_scheduler_config(variance_type="""fixed_small_log"""	)
						__UpperCamelCase					:	int             =      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(487	) - 0.0_54_96_25	)	) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999	) - 0.9_99_49_87	)	) < 1E-5






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      self.scheduler_classes[0]
						__UpperCamelCase					:	Dict             =      self.get_scheduler_config(variance_type="""learned_range"""	)
						__UpperCamelCase					:	Any             =      scheduler_class(**lowerCAmelCase	)

						__UpperCamelCase					:	List[str]             =      0.5

						assert scheduler._get_variance(1					,     predicted_variance=lowerCAmelCase	) - -10.1_71_27_90 < 1E-5
						assert scheduler._get_variance(487					,     predicted_variance=lowerCAmelCase	) - -5.7_99_80_52 < 1E-5
						assert scheduler._get_variance(999					,     predicted_variance=lowerCAmelCase	) - -0.0_01_00_11 < 1E-5






	def 					lowerCamelCase__     (					self		:						List[str]	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Union[str, Any]             =      self.scheduler_classes[0]
						__UpperCamelCase					:	Tuple             =      self.get_scheduler_config()
						__UpperCamelCase					:	Dict             =      scheduler_class(**lowerCAmelCase	)

						__UpperCamelCase					:	Union[str, Any]             =      scheduler.timesteps

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

						for i, t in enumerate(lowerCAmelCase	):
											# 1. predict noise residual
											__UpperCamelCase					:	List[Any]             =      model(lowerCAmelCase					,     lowerCAmelCase	)

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

											__UpperCamelCase					:	Tuple             =      pred_prev_sample

						__UpperCamelCase					:	Union[str, Any]             =      torch.sum(torch.abs(lowerCAmelCase	)	)
						__UpperCamelCase					:	Dict             =      torch.mean(torch.abs(lowerCAmelCase	)	)

						assert abs(result_sum.item() - 2_52.2_68_24_95	) < 1E-2
						assert abs(result_mean.item() - 0.3_28_47_43	) < 1E-3






	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       Union[str, Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      self.scheduler_classes[0]
						__UpperCamelCase					:	Dict             =      self.get_scheduler_config()
						__UpperCamelCase					:	str             =      scheduler_class(**lowerCAmelCase	)

						scheduler.set_timesteps(25	)

						__UpperCamelCase					:	Optional[int]             =      scheduler.timesteps

						__UpperCamelCase					:	Optional[Any]             =      self.dummy_model()
						__UpperCamelCase					:	int             =      self.dummy_sample_deter
						__UpperCamelCase					:	Union[str, Any]             =      torch.manual_seed(0	)

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

											if i + 1 == timesteps.shape[0]:
																__UpperCamelCase					:	int             =      None
											else:
																__UpperCamelCase					:	Optional[Any]             =      timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											__UpperCamelCase					:	str             =      scheduler.step(
											    lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase					,     prev_timestep=lowerCAmelCase					,     generator=lowerCAmelCase	).prev_sample

											__UpperCamelCase					:	Dict             =      pred_prev_sample

						__UpperCamelCase					:	Optional[Any]             =      torch.sum(torch.abs(lowerCAmelCase	)	)
						__UpperCamelCase					:	Any             =      torch.mean(torch.abs(lowerCAmelCase	)	)

						assert abs(result_sum.item() - 2_58.2_04_49_83	) < 1E-2
						assert abs(result_mean.item() - 0.3_36_20_38	) < 1E-3






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       int:







						"""simple docstring"""







						pass






	def 					lowerCamelCase__     (					self		:						Any	)	->       Optional[int]:







						"""simple docstring"""







						pass












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


a__		     =     {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    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						List[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	Tuple             =      torchvision.models.resnetaaa(pretrained=lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      list(model.children()	)[:-2]
						__UpperCamelCase					:	List[str]             =      nn.Sequential(*lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						Optional[Any]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.pool(self.model(lowerCAmelCase	)	)
						__UpperCamelCase					:	Dict             =      torch.flatten(lowerCAmelCase					,     start_dim=2	)
						__UpperCamelCase					:	Optional[int]             =      out.transpose(1					,     2	).contiguous()
						return out  # BxNx2048






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def __init__(					self		:						Optional[Any]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Optional[int]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      [json.loads(lowerCAmelCase	) for l in open(lowerCAmelCase	)]
						__UpperCamelCase					:	Optional[Any]             =      os.path.dirname(lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      tokenizer
						__UpperCamelCase					:	Dict             =      labels
						__UpperCamelCase					:	int             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      max_seq_length

						__UpperCamelCase					:	int             =      transforms






	def __len__(					self		:						Union[str, Any]	)	->       List[Any]:







						"""simple docstring"""







						return len(self.data	)






	def __getitem__(					self		:						Optional[Any]					,     lowerCAmelCase		:						List[Any]	)	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]					,     add_special_tokens=lowerCAmelCase	)	)
						__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      sentence[0], sentence[1:-1], sentence[-1]
						__UpperCamelCase					:	Optional[int]             =      sentence[: self.max_seq_length]

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

						__UpperCamelCase					:	List[Any]             =      Image.open(os.path.join(self.data_dir					,     self.data[index]["""img"""]	)	).convert("""RGB"""	)
						__UpperCamelCase					:	Dict             =      self.transforms(lowerCAmelCase	)

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






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      Counter()
						for row in self.data:
											label_freqs.update(row["""label"""]	)
						return label_freqs






def 				A__ (snake_case   :		int				)     ->						Optional[int]:
					__UpperCamelCase					:	str             =      [len(row["""sentence"""]				) for row in batch]
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      len(snake_case				), max(snake_case				)

					__UpperCamelCase					:	List[str]             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)
					__UpperCamelCase					:	Tuple             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)

					for i_batch, (input_row, length) in enumerate(zip(snake_case		,	snake_case				)				):
										__UpperCamelCase					:	Optional[Any]             =      input_row["""sentence"""]
										__UpperCamelCase					:	Any             =      1

					__UpperCamelCase					:	List[Any]             =      torch.stack([row["""image"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""label"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""image_start_token"""] for row in batch]				)
					__UpperCamelCase					:	List[str]             =      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 				A__ ()     ->						Optional[Any]:
					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 				A__ ()     ->						Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(2_56				),
					        transforms.CenterCrop(2_24				),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46777044, 0.44531429, 0.40661017]		,	std=[0.12221994, 0.12145835, 0.14380469]		,	),
					    ]				)






