



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_    :  List[Any]						  =		{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  lowerCamelCase__	(    nn.Module):
     """simple docstring"""



     def __init__(self					,      __a     ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase             =		torchvision.models.resnetaaa(pretrained=__a     )
           lowerCamelCase             =		list(model.children()     )[:-2]
           lowerCamelCase             =		nn.Sequential(*__a     )
           lowerCamelCase             =		nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]     )







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.pool(self.model(__a     )     )
           lowerCamelCase             =		torch.flatten(__a					,      start_dim=2     )
           lowerCamelCase             =		out.transpose(1					,      2     ).contiguous()
           return out  # BxNx2048





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a					,      __a					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		[json.loads(__a     ) for l in open(__a     )]
           lowerCamelCase             =		os.path.dirname(__a     )
           lowerCamelCase             =		tokenizer
           lowerCamelCase             =		labels
           lowerCamelCase             =		len(__a     )
           lowerCamelCase             =		max_seq_length

           lowerCamelCase             =		transforms







     def __len__(self     ):






           '''simple docstring'''



           return len(self.data     )







     def __getitem__(self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]					,      add_special_tokens=__a     )     )
           lowerCamelCase      ,				lowerCamelCase      ,				lowerCamelCase             =		sentence[0], sentence[1:-1], sentence[-1]
           lowerCamelCase             =		sentence[: self.max_seq_length]

           lowerCamelCase             =		torch.zeros(self.n_classes     )
           lowerCamelCase             =		1

           lowerCamelCase             =		Image.open(os.path.join(self.data_dir					,      self.data[index]["img"]     )     ).convert("RGB"     )
           lowerCamelCase             =		self.transforms(__a     )

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







     def     _a   (self     ):






           '''simple docstring'''



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



def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		[len(row["sentence"]			) for row in batch]
      lowerCamelCase      ,				lowerCamelCase             =		len(UpperCAmelCase__			), max(UpperCAmelCase__			)

      lowerCamelCase             =		torch.zeros(UpperCAmelCase__ , UpperCAmelCase__ , dtype=torch.long			)
      lowerCamelCase             =		torch.zeros(UpperCAmelCase__ , UpperCAmelCase__ , dtype=torch.long			)

      for i_batch, (input_row, length) in enumerate(zip(UpperCAmelCase__ , UpperCAmelCase__			)			):
            lowerCamelCase             =		input_row["sentence"]
            lowerCamelCase             =		1

      lowerCamelCase             =		torch.stack([row["image"] for row in batch]			)
      lowerCamelCase             =		torch.stack([row["label"] for row in batch]			)
      lowerCamelCase             =		torch.stack([row["image_start_token"] for row in batch]			)
      lowerCamelCase             =		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      __lowercase(						):


      """simple docstring"""



      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      __lowercase(						):


      """simple docstring"""



      return transforms.Compose(
          [
              transforms.Resize(256			),
              transforms.CenterCrop(224			),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7] , std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9] , ),
          ]			)




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      (PNDMScheduler,)
     _A      =      (('num_inference_steps', 50),)







     def     _a   (self					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		{
               "num_train_timesteps": 10_00,
               "beta_start": 0.0001,
               "beta_end": 0.02,
               "beta_schedule": "linear",
           }

           config.update(**__a     )
           return config







     def     _a   (self					,      __a=0					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		dict(self.forward_default_kwargs     )
           lowerCamelCase             =		kwargs.pop("num_inference_steps"					,      __a     )
           lowerCamelCase             =		self.dummy_sample
           lowerCamelCase             =		0.1 * sample
           lowerCamelCase             =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.get_scheduler_config(**__a     )
                 lowerCamelCase             =		scheduler_class(**__a     )
                 scheduler.set_timesteps(__a     )
                 # copy over dummy past residuals
                 lowerCamelCase             =		dummy_past_residuals[:]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__a     )
                       lowerCamelCase             =		scheduler_class.from_pretrained(__a     )
                       new_scheduler.set_timesteps(__a     )
                       # copy over dummy past residuals
                       lowerCamelCase             =		dummy_past_residuals[:]

                 lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample

                 assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"

                 lowerCamelCase             =		scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample

                 assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"







     def     _a   (self     ):






           '''simple docstring'''



           pass







     def     _a   (self					,      __a=0					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		dict(self.forward_default_kwargs     )
           lowerCamelCase             =		kwargs.pop("num_inference_steps"					,      __a     )
           lowerCamelCase             =		self.dummy_sample
           lowerCamelCase             =		0.1 * sample
           lowerCamelCase             =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )
                 scheduler.set_timesteps(__a     )

                 # copy over dummy past residuals (must be after setting timesteps)
                 lowerCamelCase             =		dummy_past_residuals[:]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__a     )
                       lowerCamelCase             =		scheduler_class.from_pretrained(__a     )
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(__a     )

                       # copy over dummy past residual (must be after setting timesteps)
                       lowerCamelCase             =		dummy_past_residuals[:]

                 lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_prk(__a					,      __a					,      __a					,      **__a     ).prev_sample

                 assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"

                 lowerCamelCase             =		scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		new_scheduler.step_plms(__a					,      __a					,      __a					,      **__a     ).prev_sample

                 assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"







     def     _a   (self					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.scheduler_classes[0]
           lowerCamelCase             =		self.get_scheduler_config(**__a     )
           lowerCamelCase             =		scheduler_class(**__a     )

           lowerCamelCase             =		10
           lowerCamelCase             =		self.dummy_model()
           lowerCamelCase             =		self.dummy_sample_deter
           scheduler.set_timesteps(__a     )

           for i, t in enumerate(scheduler.prk_timesteps     ):
                 lowerCamelCase             =		model(__a					,      __a     )
                 lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a     ).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps     ):
                 lowerCamelCase             =		model(__a					,      __a     )
                 lowerCamelCase             =		scheduler.step_plms(__a					,      __a					,      __a     ).prev_sample

           return sample







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		dict(self.forward_default_kwargs     )

           lowerCamelCase             =		kwargs.pop("num_inference_steps"					,      __a     )

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )

                 lowerCamelCase             =		self.dummy_sample
                 lowerCamelCase             =		0.1 * sample

                 if num_inference_steps is not None and hasattr(__a					,      "set_timesteps"     ):
                       scheduler.set_timesteps(__a     )
                 elif num_inference_steps is not None and not hasattr(__a					,      "set_timesteps"     ):
                       lowerCamelCase             =		num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 lowerCamelCase             =		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                 lowerCamelCase             =		dummy_past_residuals[:]

                 lowerCamelCase             =		scheduler.step_prk(__a					,      0					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		scheduler.step_prk(__a					,      1					,      __a					,      **__a     ).prev_sample

                 self.assertEqual(output_a.shape					,      sample.shape     )
                 self.assertEqual(output_a.shape					,      output_a.shape     )

                 lowerCamelCase             =		scheduler.step_plms(__a					,      0					,      __a					,      **__a     ).prev_sample
                 lowerCamelCase             =		scheduler.step_plms(__a					,      1					,      __a					,      **__a     ).prev_sample

                 self.assertEqual(output_a.shape					,      sample.shape     )
                 self.assertEqual(output_a.shape					,      output_a.shape     )







     def     _a   (self     ):






           '''simple docstring'''



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







     def     _a   (self     ):






           '''simple docstring'''



           for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__a     )

           lowerCamelCase             =		self.scheduler_classes[0]
           lowerCamelCase             =		self.get_scheduler_config(steps_offset=1     )
           lowerCamelCase             =		scheduler_class(**__a     )
           scheduler.set_timesteps(10     )
           assert torch.equal(
               scheduler.timesteps					,      torch.LongTensor(
                   [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]     )					,      )







     def     _a   (self     ):






           '''simple docstring'''



           for beta_start, beta_end in zip([0.0001, 0.001]					,      [0.002, 0.02]     ):
                 self.check_over_configs(beta_start=__a					,      beta_end=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for t in [1, 5, 10]:
                 self.check_over_forward(time_step=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           for t, num_inference_steps in zip([1, 5, 10]					,      [10, 50, 1_00]     ):
                 self.check_over_forward(num_inference_steps=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		27

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase             =		self.dummy_sample
                 lowerCamelCase             =		0.1 * sample

                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )

                 scheduler.set_timesteps(__a     )

                 # before power of 3 fix, would error on first step, so we only need to do two
                 for i, t in enumerate(scheduler.prk_timesteps[:2]     ):
                       lowerCamelCase             =		scheduler.step_prk(__a					,      __a					,      __a     ).prev_sample







     def     _a   (self     ):






           '''simple docstring'''



           with self.assertRaises(__a     ):
                 lowerCamelCase             =		self.scheduler_classes[0]
                 lowerCamelCase             =		self.get_scheduler_config()
                 lowerCamelCase             =		scheduler_class(**__a     )

                 scheduler.step_plms(self.dummy_sample					,      1					,      self.dummy_sample     ).prev_sample







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop()
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 198.1318     ) < 1E-2
           assert abs(result_mean.item() - 0.2580     ) < 1E-3







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop(prediction_type="v_prediction"     )
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 67.3986     ) < 1E-2
           assert abs(result_mean.item() - 0.0878     ) < 1E-3







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop(set_alpha_to_one=__a					,      beta_start=0.01     )
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 230.0399     ) < 1E-2
           assert abs(result_mean.item() - 0.2995     ) < 1E-3







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.full_loop(set_alpha_to_one=__a					,      beta_start=0.01     )
           lowerCamelCase             =		torch.sum(torch.abs(__a     )     )
           lowerCamelCase             =		torch.mean(torch.abs(__a     )     )

           assert abs(result_sum.item() - 186.9482     ) < 1E-2
           assert abs(result_mean.item() - 0.2434     ) < 1E-3