





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


UpperCAmelCase     :     Optional[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):







      def __init__(   self		,				lowerCAmelCase__							)       ->						Union[str, Any]:



        '''simple docstring'''




        super().__init__()
        a__     : List[str]  =torchvision.models.resnetaaa(pretrained=lowerCAmelCase__							)
        a__     : List[str]  =list(model.children()							)[:-2]
        a__     : List[str]  =nn.Sequential(*lowerCAmelCase__							)
        a__     : Tuple  =nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]							)







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



        '''simple docstring'''




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







class     __lowerCAmelCase	(   UpperCamelCase__):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						List[str]:



        '''simple docstring'''




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

        a__     : List[Any]  =transforms







      def __len__(   self							)       ->						Optional[Any]:



        '''simple docstring'''




        return len(self.data							)







      def __getitem__(   self		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : str  =torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]		,				add_special_tokens=lowerCAmelCase__							)							)
        a__ ,							a__ ,							a__     : Optional[Any]  =sentence[0], sentence[1:-1], sentence[-1]
        a__     : List[str]  =sentence[: self.max_seq_length]

        a__     : Optional[Any]  =torch.zeros(self.n_classes							)
        a__     : List[Any]  =1

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

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







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Dict  =Counter()
        for row in self.data:
          label_freqs.update(row["label"]							)
        return label_freqs
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]						):





  """simple docstring"""



  a__     : Any  =[len(row["sentence"]						) for row in batch]
  a__ ,							a__     : int  =len(SCREAMING_SNAKE_CASE						), max(SCREAMING_SNAKE_CASE						)

  a__     : Optional[int]  =torch.zeros(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	dtype=torch.long						)
  a__     : int  =torch.zeros(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	dtype=torch.long						)

  for i_batch, (input_row, length) in enumerate(zip(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)						):
    a__     : Optional[Any]  =input_row["sentence"]
    a__     : List[Any]  =1

  a__     : List[str]  =torch.stack([row["image"] for row in batch]						)
  a__     : Union[str, Any]  =torch.stack([row["label"] for row in batch]						)
  a__     : int  =torch.stack([row["image_start_token"] for row in batch]						)
  a__     : 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	(		):





  """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 					_A	(		):





  """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__):
      _lowercase    : Any            =      (PNDMScheduler,)
      _lowercase    : str            =      (("""num_inference_steps""", 50),)







      def        _lowercase  (   self		,				**lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : Dict  ={
            "num_train_timesteps": 1_0_0_0,
            "beta_start": 0.00_01,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**lowerCAmelCase__							)
        return config







      def        _lowercase  (   self		,				lowerCAmelCase__=0		,				**lowerCAmelCase__							)       ->						List[Any]:



        '''simple docstring'''




        a__     : Optional[int]  =dict(self.forward_default_kwargs							)
        a__     : Tuple  =kwargs.pop("num_inference_steps"		,				lowerCAmelCase__							)
        a__     : List[str]  =self.dummy_sample
        a__     : List[str]  =0.1 * sample
        a__     : str  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          a__     : int  =self.get_scheduler_config(**lowerCAmelCase__							)
          a__     : Union[str, Any]  =scheduler_class(**lowerCAmelCase__							)
          scheduler.set_timesteps(lowerCAmelCase__							)
          # copy over dummy past residuals
          a__     : Any  =dummy_past_residuals[:]

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(lowerCAmelCase__							)
            a__     : List[Any]  =scheduler_class.from_pretrained(lowerCAmelCase__							)
            new_scheduler.set_timesteps(lowerCAmelCase__							)
            # copy over dummy past residuals
            a__     : str  =dummy_past_residuals[:]

          a__     : Any  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Dict  =new_scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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

          a__     : Optional[int]  =scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Union[str, Any]  =new_scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        pass







      def        _lowercase  (   self		,				lowerCAmelCase__=0		,				**lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Optional[int]  =dict(self.forward_default_kwargs							)
        a__     : List[str]  =kwargs.pop("num_inference_steps"		,				lowerCAmelCase__							)
        a__     : List[str]  =self.dummy_sample
        a__     : int  =0.1 * sample
        a__     : Tuple  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          a__     : Dict  =self.get_scheduler_config()
          a__     : List[str]  =scheduler_class(**lowerCAmelCase__							)
          scheduler.set_timesteps(lowerCAmelCase__							)

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

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

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

          a__     : Optional[Any]  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : List[Any]  =new_scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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

          a__     : List[str]  =scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Any  =new_scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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







      def        _lowercase  (   self		,				**lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.scheduler_classes[0]
        a__     : Optional[Any]  =self.get_scheduler_config(**lowerCAmelCase__							)
        a__     : Any  =scheduler_class(**lowerCAmelCase__							)

        a__     : int  =1_0
        a__     : Union[str, Any]  =self.dummy_model()
        a__     : Optional[int]  =self.dummy_sample_deter
        scheduler.set_timesteps(lowerCAmelCase__							)

        for i, t in enumerate(scheduler.prk_timesteps							):
          a__     : List[Any]  =model(lowerCAmelCase__		,				lowerCAmelCase__							)
          a__     : Optional[Any]  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps							):
          a__     : int  =model(lowerCAmelCase__		,				lowerCAmelCase__							)
          a__     : int  =scheduler.step_plms(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							).prev_sample

        return sample







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



        '''simple docstring'''




        a__     : str  =dict(self.forward_default_kwargs							)

        a__     : Tuple  =kwargs.pop("num_inference_steps"		,				lowerCAmelCase__							)

        for scheduler_class in self.scheduler_classes:
          a__     : Union[str, Any]  =self.get_scheduler_config()
          a__     : List[str]  =scheduler_class(**lowerCAmelCase__							)

          a__     : List[Any]  =self.dummy_sample
          a__     : Dict  =0.1 * sample

          if num_inference_steps is not None and hasattr(lowerCAmelCase__		,				"set_timesteps"							):
            scheduler.set_timesteps(lowerCAmelCase__							)
          elif num_inference_steps is not None and not hasattr(lowerCAmelCase__		,				"set_timesteps"							):
            a__     : int  =num_inference_steps

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

          a__     : List[Any]  =scheduler.step_prk(lowerCAmelCase__		,				0		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : int  =scheduler.step_prk(lowerCAmelCase__		,				1		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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

          a__     : List[str]  =scheduler.step_plms(lowerCAmelCase__		,				0		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample
          a__     : Dict  =scheduler.step_plms(lowerCAmelCase__		,				1		,				lowerCAmelCase__		,				**lowerCAmelCase__							).prev_sample

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







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        for timesteps in [1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




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

        a__     : Optional[Any]  =self.scheduler_classes[0]
        a__     : Tuple  =self.get_scheduler_config(steps_offset=1							)
        a__     : Optional[Any]  =scheduler_class(**lowerCAmelCase__							)
        scheduler.set_timesteps(1_0							)
        assert torch.equal(
            scheduler.timesteps		,				torch.LongTensor(
                [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]							)		,				)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        for beta_start, beta_end in zip([0.00_01, 0.0_01]		,				[0.0_02, 0.02]							):
          self.check_over_configs(beta_start=lowerCAmelCase__		,				beta_end=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




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







      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




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







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        for t in [1, 5, 1_0]:
          self.check_over_forward(time_step=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        for t, num_inference_steps in zip([1, 5, 1_0]		,				[1_0, 5_0, 1_0_0]							):
          self.check_over_forward(num_inference_steps=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : Dict  =2_7

        for scheduler_class in self.scheduler_classes:
          a__     : Tuple  =self.dummy_sample
          a__     : Dict  =0.1 * sample

          a__     : Dict  =self.get_scheduler_config()
          a__     : int  =scheduler_class(**lowerCAmelCase__							)

          scheduler.set_timesteps(lowerCAmelCase__							)

          # 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]							):
            a__     : Any  =scheduler.step_prk(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							).prev_sample







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        with self.assertRaises(lowerCAmelCase__							):
          a__     : List[Any]  =self.scheduler_classes[0]
          a__     : Dict  =self.get_scheduler_config()
          a__     : Tuple  =scheduler_class(**lowerCAmelCase__							)

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







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : List[Any]  =self.full_loop()
        a__     : str  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Optional[Any]  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 1_98.13_18							) < 1E-2
        assert abs(result_mean.item() - 0.25_80							) < 1E-3







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : str  =self.full_loop(prediction_type="v_prediction"							)
        a__     : int  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Optional[int]  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 67.39_86							) < 1E-2
        assert abs(result_mean.item() - 0.08_78							) < 1E-3







      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Tuple  =self.full_loop(set_alpha_to_one=lowerCAmelCase__		,				beta_start=0.01							)
        a__     : str  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Dict  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 2_30.03_99							) < 1E-2
        assert abs(result_mean.item() - 0.29_95							) < 1E-3







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



        '''simple docstring'''




        a__     : Dict  =self.full_loop(set_alpha_to_one=lowerCAmelCase__		,				beta_start=0.01							)
        a__     : Union[str, Any]  =torch.sum(torch.abs(lowerCAmelCase__							)							)
        a__     : Union[str, Any]  =torch.mean(torch.abs(lowerCAmelCase__							)							)

        assert abs(result_sum.item() - 1_86.94_82							) < 1E-2
        assert abs(result_mean.item() - 0.24_34							) < 1E-3






