
'''simple docstring'''






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_							: 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 			UpperCAmelCase__    (							nn.Module			):



    def __init__(						self   :							List[Any],__A   :							List[str]  ):
          super().__init__()
          _lowerCamelCase					: Optional[Any]		    = torchvision.models.resnetaaa(pretrained=__A  )
          _lowerCamelCase					: Optional[Any]		    = list(model.children()  )[:-2]
          _lowerCamelCase					: Optional[Any]		    = nn.Sequential(*__A  )
          _lowerCamelCase					: Optional[int]		    = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )



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


class 			UpperCAmelCase__    (							A			):



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

          _lowerCamelCase					: Tuple		    = transforms



    def __len__(						self   :							Union[str, Any]  ):
          return len(self.data  )



    def __getitem__(						self   :							List[str],__A   :							Any  ):
          _lowerCamelCase					: List[Any]		    = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"],add_special_tokens=__A  )  )
          _lowerCamelCase    ,		_lowerCamelCase    ,		_lowerCamelCase					: List[Any]		    = sentence[0], sentence[1:-1], sentence[-1]
          _lowerCamelCase					: Tuple		    = sentence[: self.max_seq_length]

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

          _lowerCamelCase					: str		    = Image.open(os.path.join(self.data_dir,self.data[index]["img"]  )  ).convert("RGB"  )
          _lowerCamelCase					: Optional[int]		    = self.transforms(__A  )

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



    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          _lowerCamelCase					: str		    = Counter()
          for row in self.data:
                label_freqs.update(row["label"]  )
          return label_freqs







def 		A_							(						_lowerCAmelCase :     Any				):



      """simple docstring"""




      _lowerCamelCase					: Optional[int]		    = [len(row["sentence"]				) for row in batch]
      _lowerCamelCase    ,		_lowerCamelCase					: List[Any]		    = len(_lowerCAmelCase				), max(_lowerCAmelCase				)

      _lowerCamelCase					: Tuple		    = torch.zeros(_lowerCAmelCase					,						_lowerCAmelCase					,						dtype=torch.long				)
      _lowerCamelCase					: Tuple		    = torch.zeros(_lowerCAmelCase					,						_lowerCAmelCase					,						dtype=torch.long				)

      for i_batch, (input_row, length) in enumerate(zip(_lowerCAmelCase					,						_lowerCAmelCase				)				):
            _lowerCamelCase					: Optional[int]		    = input_row["sentence"]
            _lowerCamelCase					: Tuple		    = 1

      _lowerCamelCase					: Any		    = torch.stack([row["image"] for row in batch]				)
      _lowerCamelCase					: Union[str, Any]		    = torch.stack([row["label"] for row in batch]				)
      _lowerCamelCase					: int		    = torch.stack([row["image_start_token"] for row in batch]				)
      _lowerCamelCase					: Optional[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 		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 gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

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,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class     lowerCAmelCase_     (       __lowercase,  __lowercase,  __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     StableUnCLIPPipeline
							UpperCAmelCase				   =     TEXT_TO_IMAGE_PARAMS
							UpperCAmelCase				   =     TEXT_TO_IMAGE_BATCH_PARAMS
							UpperCAmelCase				   =     TEXT_TO_IMAGE_IMAGE_PARAMS
							UpperCAmelCase				   =     TEXT_TO_IMAGE_IMAGE_PARAMS

							# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
							UpperCAmelCase				   =     False







							def  UpperCamelCase_						(   self      :     Optional[int]		):
												_UpperCamelCase			     =     32
												_UpperCamelCase			     =     embedder_hidden_size

												# prior components

												torch.manual_seed(0		)
												_UpperCamelCase			     =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

												torch.manual_seed(0		)
												_UpperCamelCase			     =     CLIPTextModelWithProjection(
												    CLIPTextConfig(
												        bos_token_id=0 ,			eos_token_id=2 ,			hidden_size=_A ,			projection_dim=_A ,			intermediate_size=37 ,			layer_norm_eps=1e-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			pad_token_id=1 ,			vocab_size=1000 ,			)		)

												torch.manual_seed(0		)
												_UpperCamelCase			     =     PriorTransformer(
												    num_attention_heads=2 ,			attention_head_dim=12 ,			embedding_dim=_A ,			num_layers=1 ,			)

												torch.manual_seed(0		)
												_UpperCamelCase			     =     DDPMScheduler(
												    variance_type='''fixed_small_log''' ,			prediction_type='''sample''' ,			num_train_timesteps=1000 ,			clip_sample=_A ,			clip_sample_range=5.0 ,			beta_schedule='''squaredcos_cap_v2''' ,			)

												# regular denoising components

												torch.manual_seed(0		)
												_UpperCamelCase			     =     StableUnCLIPImageNormalizer(embedding_dim=_A		)
												_UpperCamelCase			     =     DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''		)

												torch.manual_seed(0		)
												_UpperCamelCase			     =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

												torch.manual_seed(0		)
												_UpperCamelCase			     =     CLIPTextModel(
												    CLIPTextConfig(
												        bos_token_id=0 ,			eos_token_id=2 ,			hidden_size=_A ,			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=1000 ,			)		)

												torch.manual_seed(0		)
												_UpperCamelCase			     =     UNetaDConditionModel(
												    sample_size=32 ,			in_channels=4 ,			out_channels=4 ,			down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') ,			up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') ,			block_out_channels=(32, 64) ,			attention_head_dim=(2, 4) ,			class_embed_type='''projection''' ,			projection_class_embeddings_input_dim=embedder_projection_dim * 2 ,			cross_attention_dim=_A ,			layers_per_block=1 ,			upcast_attention=_A ,			use_linear_projection=_A ,			)

												torch.manual_seed(0		)
												_UpperCamelCase			     =     DDIMScheduler(
												    beta_schedule='''scaled_linear''' ,			beta_start=0.0_0085 ,			beta_end=0.012 ,			prediction_type='''v_prediction''' ,			set_alpha_to_one=_A ,			steps_offset=1 ,			)

												torch.manual_seed(0		)
												_UpperCamelCase			     =     AutoencoderKL()

												_UpperCamelCase			     =     {
												    # prior components
												    '''prior_tokenizer''': prior_tokenizer,
												    '''prior_text_encoder''': prior_text_encoder,
												    '''prior''': prior,
												    '''prior_scheduler''': prior_scheduler,
												    # image noising components
												    '''image_normalizer''': image_normalizer,
												    '''image_noising_scheduler''': image_noising_scheduler,
												    # regular denoising components
												    '''tokenizer''': tokenizer,
												    '''text_encoder''': text_encoder,
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''vae''': vae,
												}

												return components







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     Tuple ,			_A      :     Dict=0		):
												if str(_A		).startswith('''mps'''		):
																	_UpperCamelCase			     =     torch.manual_seed(_A		)
												else:
																	_UpperCamelCase			     =     torch.Generator(device=_A		).manual_seed(_A		)
												_UpperCamelCase			     =     {
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''prior_num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}
												return inputs







							def  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     torch_device == '''cpu'''

												self._test_attention_slicing_forward_pass(test_max_difference=_A		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     torch_device in ['''cpu''', '''mps''']

												self._test_inference_batch_single_identical(test_max_difference=_A		)




@slow
@require_torch_gpu
class     lowerCAmelCase_     (       unittest.TestCase ):







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''		)

												_UpperCamelCase			     =     StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' ,			torch_dtype=torch.floataa		)
												pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)
												# stable unclip will oom when integration tests are run on a V100,
												# so turn on memory savings
												pipe.enable_attention_slicing()
												pipe.enable_sequential_cpu_offload()

												_UpperCamelCase			     =     torch.Generator(device='''cpu'''		).manual_seed(0		)
												_UpperCamelCase			     =     pipe('''anime turle''' ,			generator=_A ,			output_type='''np'''		)

												_UpperCamelCase			     =     output.images[0]

												assert image.shape == (768, 768, 3)

												assert_mean_pixel_difference(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												_UpperCamelCase			     =     StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' ,			torch_dtype=torch.floataa		)
												_UpperCamelCase			     =     pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)
												pipe.enable_attention_slicing()
												pipe.enable_sequential_cpu_offload()

												_UpperCamelCase			     =     pipe(
												    '''anime turtle''' ,			prior_num_inference_steps=2 ,			num_inference_steps=2 ,			output_type='''np''' ,			)

												_UpperCamelCase			     =     torch.cuda.max_memory_allocated()
												# make sure that less than 7 GB is allocated
												assert mem_bytes < 7 * 10**9
