





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


_lowerCAmelCase 				=     {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      ,       a__       ):
         super().__init__()
         A_     :				List[Any]			      =	torchvision.models.resnetaaa(pretrained=a__       )
         A_     :				Dict			      =	list(model.children()       )[:-2]
         A_     :				Optional[Any]			      =	nn.Sequential(*a__       )
         A_     :				Any			      =	nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]       )






      def 		_lowerCamelCase      (		self      ,       a__       ):
         # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
         A_     :				List[Any]			      =	self.pool(self.model(a__       )       )
         A_     :				Optional[Any]			      =	torch.flatten(a__      ,       start_dim=2       )
         A_     :				int			      =	out.transpose(1      ,       2       ).contiguous()
         return out  # BxNx2048






class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __init__(		self      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__       ):
         A_     :				Optional[int]			      =	[json.loads(a__       ) for l in open(a__       )]
         A_     :				List[str]			      =	os.path.dirname(a__       )
         A_     :				str			      =	tokenizer
         A_     :				Dict			      =	labels
         A_     :				Dict			      =	len(a__       )
         A_     :				List[str]			      =	max_seq_length

         A_     :				Any			      =	transforms
      def __len__(		self       ):
         return len(self.data       )
      def __getitem__(		self      ,       a__       ):
         A_     :				List[Any]			      =	torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]      ,       add_special_tokens=a__       )       )
         A_      ,				A_      ,				A_     :				Optional[Any]			      =	sentence[0], sentence[1:-1], sentence[-1]
         A_     :				int			      =	sentence[: self.max_seq_length]

         A_     :				Dict			      =	torch.zeros(self.n_classes       )
         A_     :				Union[str, Any]			      =	1

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

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






      def 		_lowerCamelCase      (		self       ):
         A_     :				Dict			      =	Counter()
         for row in self.data:
            label_freqs.update(row["""label"""]       )
         return label_freqs






def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Tuple			      =	[len(row["""sentence"""]     ) for row in batch]
   A_      ,				A_     :				Dict			      =	len(_lowerCAmelCase     ), max(_lowerCAmelCase     )

   A_     :				Any			      =	torch.zeros(_lowerCAmelCase  ,_lowerCAmelCase  ,dtype=torch.long     )
   A_     :				Optional[Any]			      =	torch.zeros(_lowerCAmelCase  ,_lowerCAmelCase  ,dtype=torch.long     )

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

   A_     :				str			      =	torch.stack([row["""image"""] for row in batch]     )
   A_     :				List[str]			      =	torch.stack([row["""label"""] for row in batch]     )
   A_     :				Optional[Any]			      =	torch.stack([row["""image_start_token"""] for row in batch]     )
   A_     :				Union[str, 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		(  ):
   '''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     _lowerCAmelCase		(  ):
   '''simple docstring'''


   return transforms.Compose(
       [
           transforms.Resize(2_5_6     ),
           transforms.CenterCrop(2_2_4     ),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]  ,std=[0.1222_1994, 0.1214_5835, 0.1438_0469]  ,),
       ]     )













import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class    _UpperCAmelCase							(		_lowerCamelCase			,       _lowerCamelCase			,       _lowerCamelCase			,       unittest.TestCase     ):
      a						       =							AltDiffusionPipeline
      a						       =							TEXT_TO_IMAGE_PARAMS
      a						       =							TEXT_TO_IMAGE_BATCH_PARAMS
      a						       =							TEXT_TO_IMAGE_IMAGE_PARAMS
      a						       =							TEXT_TO_IMAGE_IMAGE_PARAMS
      def 		_lowerCamelCase      (		self       ):
         torch.manual_seed(0       )
         A_     :				Optional[int]			      =	UNetaDConditionModel(
             block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=4      ,       out_channels=4      ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,       cross_attention_dim=32      ,       )
         A_     :				List[Any]			      =	DDIMScheduler(
             beta_start=0.0_0085      ,       beta_end=0.012      ,       beta_schedule="""scaled_linear"""      ,       clip_sample=a__      ,       set_alpha_to_one=a__      ,       )
         torch.manual_seed(0       )
         A_     :				int			      =	AutoencoderKL(
             block_out_channels=[32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,       latent_channels=4      ,       )

         # TODO: address the non-deterministic text encoder (fails for save-load tests)
         # torch.manual_seed(0)
         # text_encoder_config = RobertaSeriesConfig(
         #     hidden_size=32,
         #     project_dim=32,
         #     intermediate_size=37,
         #     layer_norm_eps=1e-05,
         #     num_attention_heads=4,
         #     num_hidden_layers=5,
         #     vocab_size=5002,
         # )
         # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

         torch.manual_seed(0       )
         A_     :				Optional[int]			      =	CLIPTextConfig(
             bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=32      ,       projection_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=5002      ,       )
         A_     :				Optional[Any]			      =	CLIPTextModel(a__       )

         A_     :				Union[str, Any]			      =	XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""       )
         A_     :				str			      =	77

         A_     :				Optional[Any]			      =	{
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         return components
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__=0       ):
         if str(a__       ).startswith("""mps"""       ):
            A_     :				Union[str, Any]			      =	torch.manual_seed(a__       )
         else:
            A_     :				Dict			      =	torch.Generator(device=a__       ).manual_seed(a__       )
         A_     :				Dict			      =	{
             """prompt""": """A painting of a squirrel eating a burger""",
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """output_type""": """numpy""",
         }
         return inputs
      def 		_lowerCamelCase      (		self       ):
         super().test_attention_slicing_forward_pass(expected_max_diff=3E-3       )
      def 		_lowerCamelCase      (		self       ):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

         A_     :				Union[str, Any]			      =	self.get_dummy_components()
         torch.manual_seed(0       )
         A_     :				List[str]			      =	RobertaSeriesConfig(
             hidden_size=32      ,       project_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       vocab_size=5002      ,       )
         # TODO: remove after fixing the non-deterministic text encoder
         A_     :				Dict			      =	RobertaSeriesModelWithTransformation(a__       )
         A_     :				Any			      =	text_encoder

         A_     :				Tuple			      =	AltDiffusionPipeline(**a__       )
         A_     :				int			      =	alt_pipe.to(a__       )
         alt_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Union[str, Any]			      =	self.get_dummy_inputs(a__       )
         A_     :				Tuple			      =	"""A photo of an astronaut"""
         A_     :				Dict			      =	alt_pipe(**a__       )
         A_     :				List[str]			      =	output.images
         A_     :				List[Any]			      =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         A_     :				Tuple			      =	np.array(
             [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2






      def 		_lowerCamelCase      (		self       ):
         A_     :				Tuple			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

         A_     :				Union[str, Any]			      =	self.get_dummy_components()
         A_     :				str			      =	PNDMScheduler(skip_prk_steps=a__       )
         torch.manual_seed(0       )
         A_     :				Any			      =	RobertaSeriesConfig(
             hidden_size=32      ,       project_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       vocab_size=5002      ,       )
         # TODO: remove after fixing the non-deterministic text encoder
         A_     :				int			      =	RobertaSeriesModelWithTransformation(a__       )
         A_     :				Any			      =	text_encoder
         A_     :				Any			      =	AltDiffusionPipeline(**a__       )
         A_     :				int			      =	alt_pipe.to(a__       )
         alt_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Union[str, Any]			      =	self.get_dummy_inputs(a__       )
         A_     :				Union[str, Any]			      =	alt_pipe(**a__       )
         A_     :				List[str]			      =	output.images
         A_     :				List[Any]			      =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         A_     :				List[Any]			      =	np.array(
             [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2






@slow
@require_torch_gpu
class    _UpperCAmelCase							(		unittest.TestCase     ):
      def 		_lowerCamelCase      (		self       ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
      def 		_lowerCamelCase      (		self       ):
         # make sure here that pndm scheduler skips prk
         A_     :				Any			      =	AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""      ,       safety_checker=a__       )
         A_     :				Optional[int]			      =	alt_pipe.to(a__       )
         alt_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Tuple			      =	"""A painting of a squirrel eating a burger"""
         A_     :				Tuple			      =	torch.manual_seed(0       )
         A_     :				int			      =	alt_pipe([prompt]      ,       generator=a__      ,       guidance_scale=6.0      ,       num_inference_steps=20      ,       output_type="""np"""       )

         A_     :				Optional[Any]			      =	output.images

         A_     :				int			      =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         A_     :				str			      =	np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2






      def 		_lowerCamelCase      (		self       ):
         A_     :				Dict			      =	DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""      ,       subfolder="""scheduler"""       )

         A_     :				int			      =	AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""      ,       scheduler=a__      ,       safety_checker=a__       )
         A_     :				Dict			      =	alt_pipe.to(a__       )
         alt_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Optional[int]			      =	"""A painting of a squirrel eating a burger"""
         A_     :				Any			      =	torch.manual_seed(0       )

         A_     :				List[str]			      =	alt_pipe([prompt]      ,       generator=a__      ,       num_inference_steps=2      ,       output_type="""numpy"""       )
         A_     :				List[Any]			      =	output.images

         A_     :				Tuple			      =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         A_     :				Any			      =	np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2






