






import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)

def      _lowerCamelCase	(						__A    :							Any )      ->					Tuple:
    _UpperCAmelCase				:      Tuple			  =				VideoMAEConfig()

    set_architecture_configs(__A		,		__A )

    if "finetuned" not in model_name:
        _UpperCAmelCase				:      Union[str, Any]			  =				False

    if "finetuned" in model_name:
        _UpperCAmelCase				:      Union[str, Any]			  =				'''huggingface/label-files'''
        if "kinetics" in model_name:
            _UpperCAmelCase				:      Dict			  =				400
            _UpperCAmelCase				:      List[str]			  =				'''kinetics400-id2label.json'''
        elif "ssv2" in model_name:
            _UpperCAmelCase				:      Optional[int]			  =				174
            _UpperCAmelCase				:      List[Any]			  =				'''something-something-v2-id2label.json'''
        else:
            raise ValueError('''Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.''' )
        _UpperCAmelCase				:      Union[str, Any]			  =				json.load(open(hf_hub_download(__A		,		__A		,		repo_type='''dataset''' )		,		'''r''' ) )
        _UpperCAmelCase				:      Tuple			  =				{int(__A ): v for k, v in idalabel.items()}
        _UpperCAmelCase				:      Dict			  =				idalabel
        _UpperCAmelCase				:      str			  =				{v: k for k, v in idalabel.items()}

    return config

def      _lowerCamelCase	(						__A    :							Any		,		__A    :							List[Any] )      ->					int:
    if "small" in model_name:
        _UpperCAmelCase				:      Dict			  =				384
        _UpperCAmelCase				:      Union[str, Any]			  =				1_536
        _UpperCAmelCase				:      str			  =				12
        _UpperCAmelCase				:      List[Any]			  =				16
        _UpperCAmelCase				:      List[Any]			  =				12
        _UpperCAmelCase				:      Dict			  =				3
        _UpperCAmelCase				:      Dict			  =				192
        _UpperCAmelCase				:      List[str]			  =				768
    elif "large" in model_name:
        _UpperCAmelCase				:      Tuple			  =				1_024
        _UpperCAmelCase				:      int			  =				4_096
        _UpperCAmelCase				:      Tuple			  =				24
        _UpperCAmelCase				:      str			  =				16
        _UpperCAmelCase				:      Tuple			  =				12
        _UpperCAmelCase				:      List[Any]			  =				8
        _UpperCAmelCase				:      List[str]			  =				512
        _UpperCAmelCase				:      List[Any]			  =				2_048
    elif "huge" in model_name:
        _UpperCAmelCase				:      List[Any]			  =				1_280
        _UpperCAmelCase				:      Optional[int]			  =				5_120
        _UpperCAmelCase				:      str			  =				32
        _UpperCAmelCase				:      int			  =				16
        _UpperCAmelCase				:      int			  =				12
        _UpperCAmelCase				:      Any			  =				8
        _UpperCAmelCase				:      Optional[Any]			  =				640
        _UpperCAmelCase				:      Optional[int]			  =				2_560
    elif "base" not in model_name:
        raise ValueError('''Model name should include either "small", "base", "large", or "huge"''' )

def      _lowerCamelCase	(						__A    :							Optional[Any] )      ->					Dict:
    if "encoder." in name:
        _UpperCAmelCase				:      Any			  =				name.replace('''encoder.'''		,		'''''' )
    if "cls_token" in name:
        _UpperCAmelCase				:      Optional[int]			  =				name.replace('''cls_token'''		,		'''videomae.embeddings.cls_token''' )
    if "decoder_pos_embed" in name:
        _UpperCAmelCase				:      Optional[Any]			  =				name.replace('''decoder_pos_embed'''		,		'''decoder.decoder_pos_embed''' )
    if "pos_embed" in name and "decoder" not in name:
        _UpperCAmelCase				:      str			  =				name.replace('''pos_embed'''		,		'''videomae.embeddings.position_embeddings''' )
    if "patch_embed.proj" in name:
        _UpperCAmelCase				:      Union[str, Any]			  =				name.replace('''patch_embed.proj'''		,		'''videomae.embeddings.patch_embeddings.projection''' )
    if "patch_embed.norm" in name:
        _UpperCAmelCase				:      Optional[Any]			  =				name.replace('''patch_embed.norm'''		,		'''videomae.embeddings.norm''' )
    if "decoder.blocks" in name:
        _UpperCAmelCase				:      Optional[Any]			  =				name.replace('''decoder.blocks'''		,		'''decoder.decoder_layers''' )
    if "blocks" in name:
        _UpperCAmelCase				:      Union[str, Any]			  =				name.replace('''blocks'''		,		'''videomae.encoder.layer''' )
    if "attn.proj" in name:
        _UpperCAmelCase				:      Optional[Any]			  =				name.replace('''attn.proj'''		,		'''attention.output.dense''' )
    if "attn" in name and "bias" not in name:
        _UpperCAmelCase				:      Any			  =				name.replace('''attn'''		,		'''attention.self''' )
    if "attn" in name:
        _UpperCAmelCase				:      int			  =				name.replace('''attn'''		,		'''attention.attention''' )
    if "norm1" in name:
        _UpperCAmelCase				:      str			  =				name.replace('''norm1'''		,		'''layernorm_before''' )
    if "norm2" in name:
        _UpperCAmelCase				:      Tuple			  =				name.replace('''norm2'''		,		'''layernorm_after''' )
    if "mlp.fc1" in name:
        _UpperCAmelCase				:      Optional[int]			  =				name.replace('''mlp.fc1'''		,		'''intermediate.dense''' )
    if "mlp.fc2" in name:
        _UpperCAmelCase				:      List[Any]			  =				name.replace('''mlp.fc2'''		,		'''output.dense''' )
    if "decoder_embed" in name:
        _UpperCAmelCase				:      Optional[int]			  =				name.replace('''decoder_embed'''		,		'''decoder.decoder_embed''' )
    if "decoder_norm" in name:
        _UpperCAmelCase				:      List[Any]			  =				name.replace('''decoder_norm'''		,		'''decoder.decoder_norm''' )
    if "decoder_pred" in name:
        _UpperCAmelCase				:      Optional[Any]			  =				name.replace('''decoder_pred'''		,		'''decoder.decoder_pred''' )
    if "norm.weight" in name and "decoder" not in name and "fc" not in name:
        _UpperCAmelCase				:      Optional[Any]			  =				name.replace('''norm.weight'''		,		'''videomae.layernorm.weight''' )
    if "norm.bias" in name and "decoder" not in name and "fc" not in name:
        _UpperCAmelCase				:      Optional[int]			  =				name.replace('''norm.bias'''		,		'''videomae.layernorm.bias''' )
    if "head" in name and "decoder" not in name:
        _UpperCAmelCase				:      int			  =				name.replace('''head'''		,		'''classifier''' )

    return name

def      _lowerCamelCase	(						__A    :							str		,		__A    :							Optional[Any] )      ->					str:
    for key in orig_state_dict.copy().keys():
        _UpperCAmelCase				:      Dict			  =				orig_state_dict.pop(__A )

        if key.startswith('''encoder.''' ):
            _UpperCAmelCase				:      int			  =				key.replace('''encoder.'''		,		'''''' )

        if "qkv" in key:
            _UpperCAmelCase				:      Any			  =				key.split('''.''' )
            if key.startswith('''decoder.blocks''' ):
                _UpperCAmelCase				:      List[str]			  =				config.decoder_hidden_size
                _UpperCAmelCase				:      List[str]			  =				int(key_split[2] )
                _UpperCAmelCase				:      str			  =				'''decoder.decoder_layers.'''
                if "weight" in key:
                    _UpperCAmelCase				:      Tuple			  =				val[:dim, :]
                    _UpperCAmelCase				:      List[Any]			  =				val[dim : dim * 2, :]
                    _UpperCAmelCase				:      Tuple			  =				val[-dim:, :]
            else:
                _UpperCAmelCase				:      Union[str, Any]			  =				config.hidden_size
                _UpperCAmelCase				:      Union[str, Any]			  =				int(key_split[1] )
                _UpperCAmelCase				:      List[str]			  =				'''videomae.encoder.layer.'''
                if "weight" in key:
                    _UpperCAmelCase				:      Dict			  =				val[:dim, :]
                    _UpperCAmelCase				:      Optional[int]			  =				val[dim : dim * 2, :]
                    _UpperCAmelCase				:      Tuple			  =				val[-dim:, :]
        else:
            _UpperCAmelCase				:      Dict			  =				val

    return orig_state_dict

def      _lowerCamelCase	(						)      ->					Dict:
    _UpperCAmelCase				:      Tuple			  =				hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video'''		,		filename='''eating_spaghetti.npy'''		,		repo_type='''dataset''' )
    _UpperCAmelCase				:      Union[str, Any]			  =				np.load(__A )
    return list(__A )

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							Optional[int]		,		__A    :							Any		,		__A    :							Optional[int] )      ->					Optional[int]:
    _UpperCAmelCase				:      Optional[int]			  =				get_videomae_config(__A )

    if "finetuned" in model_name:
        _UpperCAmelCase				:      Dict			  =				VideoMAEForVideoClassification(__A )
    else:
        _UpperCAmelCase				:      Union[str, Any]			  =				VideoMAEForPreTraining(__A )

    # download original checkpoint, hosted on Google Drive
    _UpperCAmelCase				:      List[str]			  =				'''pytorch_model.bin'''
    gdown.cached_download(__A		,		__A		,		quiet=__A )
    _UpperCAmelCase				:      Any			  =				torch.load(__A		,		map_location='''cpu''' )
    if "model" in files:
        _UpperCAmelCase				:      Any			  =				files['''model''']
    else:
        _UpperCAmelCase				:      Tuple			  =				files['''module''']
    _UpperCAmelCase				:      Any			  =				convert_state_dict(__A		,		__A )

    model.load_state_dict(__A )
    model.eval()

    # verify model on basic input
    _UpperCAmelCase				:      Union[str, Any]			  =				VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]		,		image_std=[0.5, 0.5, 0.5] )
    _UpperCAmelCase				:      List[str]			  =				prepare_video()
    _UpperCAmelCase				:      Tuple			  =				image_processor(__A		,		return_tensors='''pt''' )

    if "finetuned" not in model_name:
        _UpperCAmelCase				:      Tuple			  =				hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''		,		filename='''bool_masked_pos.pt''' )
        _UpperCAmelCase				:      List[Any]			  =				torch.load(__A )

    _UpperCAmelCase				:      int			  =				model(**__A )
    _UpperCAmelCase				:      List[Any]			  =				outputs.logits

    _UpperCAmelCase				:      Optional[int]			  =				[
        '''videomae-small-finetuned-kinetics''',
        '''videomae-small-finetuned-ssv2''',
        # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
        '''videomae-base-short''',
        '''videomae-base-short-finetuned-kinetics''',
        '''videomae-base''',
        '''videomae-base-finetuned-kinetics''',
        '''videomae-large''',
        '''videomae-large-finetuned-kinetics''',
        '''videomae-huge-finetuned-kinetics''',
        # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
        '''videomae-base-short-ssv2''',
        '''videomae-base-short-finetuned-ssv2''',
        '''videomae-base-ssv2''',
        '''videomae-base-finetuned-ssv2''',
    ]

    # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
    if model_name == "videomae-small-finetuned-kinetics":
        _UpperCAmelCase				:      List[Any]			  =				torch.Size([1, 400] )
        _UpperCAmelCase				:      List[Any]			  =				torch.tensor([-0.9291, -0.4061, -0.9307] )
    elif model_name == "videomae-small-finetuned-ssv2":
        _UpperCAmelCase				:      int			  =				torch.Size([1, 174] )
        _UpperCAmelCase				:      List[str]			  =				torch.tensor([0.2671, -0.4689, -0.8235] )
    elif model_name == "videomae-base":
        _UpperCAmelCase				:      Union[str, Any]			  =				torch.Size([1, 1_408, 1_536] )
        _UpperCAmelCase				:      Tuple			  =				torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]] )
    elif model_name == "videomae-base-short":
        _UpperCAmelCase				:      str			  =				torch.Size([1, 1_408, 1_536] )
        _UpperCAmelCase				:      Union[str, Any]			  =				torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]] )
        # we verified the loss both for normalized and unnormalized targets for this one
        _UpperCAmelCase				:      Optional[int]			  =				torch.tensor([0.5142] ) if config.norm_pix_loss else torch.tensor([0.6469] )
    elif model_name == "videomae-large":
        _UpperCAmelCase				:      Dict			  =				torch.Size([1, 1_408, 1_536] )
        _UpperCAmelCase				:      List[str]			  =				torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]] )
    elif model_name == "videomae-large-finetuned-kinetics":
        _UpperCAmelCase				:      Tuple			  =				torch.Size([1, 400] )
        _UpperCAmelCase				:      str			  =				torch.tensor([0.0771, 0.0011, -0.3625] )
    elif model_name == "videomae-huge-finetuned-kinetics":
        _UpperCAmelCase				:      Optional[Any]			  =				torch.Size([1, 400] )
        _UpperCAmelCase				:      List[str]			  =				torch.tensor([0.2433, 0.1632, -0.4894] )
    elif model_name == "videomae-base-short-finetuned-kinetics":
        _UpperCAmelCase				:      List[Any]			  =				torch.Size([1, 400] )
        _UpperCAmelCase				:      int			  =				torch.tensor([0.6588, 0.0990, -0.2493] )
    elif model_name == "videomae-base-finetuned-kinetics":
        _UpperCAmelCase				:      List[str]			  =				torch.Size([1, 400] )
        _UpperCAmelCase				:      Union[str, Any]			  =				torch.tensor([0.3669, -0.0688, -0.2421] )
    elif model_name == "videomae-base-short-ssv2":
        _UpperCAmelCase				:      Optional[Any]			  =				torch.Size([1, 1_408, 1_536] )
        _UpperCAmelCase				:      str			  =				torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]] )
    elif model_name == "videomae-base-short-finetuned-ssv2":
        _UpperCAmelCase				:      List[Any]			  =				torch.Size([1, 174] )
        _UpperCAmelCase				:      Any			  =				torch.tensor([-0.0537, -0.1539, -0.3266] )
    elif model_name == "videomae-base-ssv2":
        _UpperCAmelCase				:      Optional[int]			  =				torch.Size([1, 1_408, 1_536] )
        _UpperCAmelCase				:      Optional[Any]			  =				torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]] )
    elif model_name == "videomae-base-finetuned-ssv2":
        _UpperCAmelCase				:      Tuple			  =				torch.Size([1, 174] )
        _UpperCAmelCase				:      str			  =				torch.tensor([0.1961, -0.8337, -0.6389] )
    else:
        raise ValueError(f'''Model name not supported. Should be one of {model_names}''' )

    # verify logits
    assert logits.shape == expected_shape
    if "finetuned" in model_name:
        assert torch.allclose(logits[0, :3]		,		__A		,		atol=1e-4 )
    else:
        print('''Logits:'''		,		logits[0, :3, :3] )
        assert torch.allclose(logits[0, :3, :3]		,		__A		,		atol=1e-4 )
    print('''Logits ok!''' )

    # verify loss, if applicable
    if model_name == "videomae-base-short":
        _UpperCAmelCase				:      List[str]			  =				outputs.loss
        assert torch.allclose(__A		,		__A		,		atol=1e-4 )
        print('''Loss ok!''' )

    if pytorch_dump_folder_path is not None:
        print(f'''Saving model and image processor to {pytorch_dump_folder_path}''' )
        image_processor.save_pretrained(__A )
        model.save_pretrained(__A )

    if push_to_hub:
        print('''Pushing to the hub...''' )
        model.push_to_hub(__A		,		organization='''nielsr''' )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--checkpoint_url',
      default='https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4',
      type=str,
      help=(
          'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
          ' download link.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default='/Users/nielsrogge/Documents/VideoMAE/Test',
      type=str,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument('--model_name', default='videomae-base', type=str, help='Name of the model.')
  parser.add_argument(
      '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
  )

  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)












import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 							A_    (						__lowercase ,   __lowercase ,   unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Dict =					StableDiffusionXLImgaImgPipeline
 _SCREAMING_SNAKE_CASE							:			Any =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
 _SCREAMING_SNAKE_CASE							:			List[Any] =					PipelineTesterMixin.required_optional_params - {"latents"}
 _SCREAMING_SNAKE_CASE							:			Dict =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 _SCREAMING_SNAKE_CASE							:			List[Any] =					IMAGE_TO_IMAGE_IMAGE_PARAMS
 _SCREAMING_SNAKE_CASE							:			Dict =					IMAGE_TO_IMAGE_IMAGE_PARAMS


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     torch.manual_seed(0)
     _UpperCAmelCase				:      Tuple			  =				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''')							,  attention_head_dim=(2, 4)							,  use_linear_projection=_A							,  addition_embed_type='''text_time'''							,  addition_time_embed_dim=8							,  transformer_layers_per_block=(1, 2)							,  projection_class_embeddings_input_dim=80							,  cross_attention_dim=64							,  )
     _UpperCAmelCase				:      int			  =				EulerDiscreteScheduler(
         beta_start=0.00085							,  beta_end=0.012							,  steps_offset=1							,  beta_schedule='''scaled_linear'''							,  timestep_spacing='''leading'''							,  )
     torch.manual_seed(0)
     _UpperCAmelCase				:      Optional[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							,  sample_size=128							,  )
     torch.manual_seed(0)
     _UpperCAmelCase				:      Tuple			  =				CLIPTextConfig(
         bos_token_id=0							,  eos_token_id=2							,  hidden_size=32							,  intermediate_size=37							,  layer_norm_eps=1e-05							,  num_attention_heads=4							,  num_hidden_layers=5							,  pad_token_id=1							,  vocab_size=1000							,  hidden_act='''gelu'''							,  projection_dim=32							,  )
     _UpperCAmelCase				:      int			  =				CLIPTextModel(_A)
     _UpperCAmelCase				:      Tuple			  =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,  local_files_only=_A)

     _UpperCAmelCase				:      int			  =				CLIPTextModelWithProjection(_A)
     _UpperCAmelCase				:      Optional[int]			  =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,  local_files_only=_A)

     _UpperCAmelCase				:      Any			  =				{
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''vae''': vae,
         '''text_encoder''': text_encoder,
         '''tokenizer''': tokenizer,
         '''text_encoder_2''': text_encoder_a,
         '''tokenizer_2''': tokenizer_a,
         # "safety_checker": None,
         # "feature_extractor": None,
     }
     return components


 def  snake_case__				(	self							,  _A							,  _A=0) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				floats_tensor((1, 3, 32, 32)							,  rng=random.Random(_A)).to(_A)
     _UpperCAmelCase				:      List[Any]			  =				image / 2 + 0.5
     if str(_A).startswith('''mps'''):
         _UpperCAmelCase				:      Union[str, Any]			  =				torch.manual_seed(_A)
     else:
         _UpperCAmelCase				:      List[str]			  =				torch.Generator(device=_A).manual_seed(_A)
     _UpperCAmelCase				:      Tuple			  =				{
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''guidance_scale''': 5.0,
         '''output_type''': '''numpy''',
         '''strength''': 0.75,
     }
     return inputs


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

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
     _UpperCAmelCase				:      Optional[Any]			  =				self.get_dummy_components()
     _UpperCAmelCase				:      Any			  =				StableDiffusionXLImgaImgPipeline(**_A)
     _UpperCAmelCase				:      List[str]			  =				sd_pipe.to(_A)
     sd_pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      Tuple			  =				self.get_dummy_inputs(_A)
     _UpperCAmelCase				:      Any			  =				sd_pipe(**_A).images
     _UpperCAmelCase				:      Any			  =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)

     _UpperCAmelCase				:      Optional[Any]			  =				np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165])

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


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

     """simple docstring"""



     super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)


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

     """simple docstring"""



     super().test_inference_batch_single_identical(expected_max_diff=3e-3)


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

     """simple docstring"""



     pass


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

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				self.get_dummy_components()
     _UpperCAmelCase				:      str			  =				StableDiffusionXLImgaImgPipeline(**_A)
     _UpperCAmelCase				:      Any			  =				sd_pipe.to(_A)
     _UpperCAmelCase				:      Tuple			  =				sd_pipe.to(_A)
     sd_pipe.set_progress_bar_config(disable=_A)

     # forward without prompt embeds
     _UpperCAmelCase				:      str			  =				self.get_dummy_inputs(_A)
     _UpperCAmelCase				:      Optional[Any]			  =				3 * ['''this is a negative prompt''']
     _UpperCAmelCase				:      Optional[int]			  =				negative_prompt
     _UpperCAmelCase				:      Optional[int]			  =				3 * [inputs['''prompt''']]

     _UpperCAmelCase				:      Optional[Any]			  =				sd_pipe(**_A)
     _UpperCAmelCase				:      Any			  =				output.images[0, -3:, -3:, -1]

     # forward with prompt embeds
     _UpperCAmelCase				:      Dict			  =				self.get_dummy_inputs(_A)
     _UpperCAmelCase				:      Optional[Any]			  =				3 * ['''this is a negative prompt''']
     _UpperCAmelCase				:      Dict			  =				3 * [inputs.pop('''prompt''')]

     (
         (
         _UpperCAmelCase
     )  , (
         _UpperCAmelCase
     )  , (
         _UpperCAmelCase
     )  , (
         _UpperCAmelCase
     )  , 
     )				:      str			  =				sd_pipe.encode_prompt(_A							,  negative_prompt=_A)

     _UpperCAmelCase				:      str			  =				sd_pipe(
         **_A							,  prompt_embeds=_A							,  negative_prompt_embeds=_A							,  pooled_prompt_embeds=_A							,  negative_pooled_prompt_embeds=_A							,  )
     _UpperCAmelCase				:      List[str]			  =				output.images[0, -3:, -3:, -1]

     # make sure that it's equal
     assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()).max() < 1e-4



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






 '''simple docstring'''




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

     """simple docstring"""



     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()


 def  snake_case__				(	self							,  _A							,  _A="cpu"							,  _A=torch.floataa							,  _A=0) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				torch.Generator(device=_A).manual_seed(_A)
     _UpperCAmelCase				:      Any			  =				np.random.RandomState(_A).standard_normal((1, 4, 64, 64))
     _UpperCAmelCase				:      Dict			  =				torch.from_numpy(_A).to(device=_A							,  dtype=_A)
     _UpperCAmelCase				:      str			  =				{
         '''prompt''': '''a photograph of an astronaut riding a horse''',
         '''latents''': latents,
         '''generator''': generator,
         '''num_inference_steps''': 3,
         '''guidance_scale''': 7.5,
         '''output_type''': '''numpy''',
     }
     return inputs


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

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base''')
     pipe.to(_A)
     pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      str			  =				self.get_inputs(_A)
     _UpperCAmelCase				:      Union[str, Any]			  =				pipe(**_A).images
     _UpperCAmelCase				:      Optional[int]			  =				image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 512, 512, 3)
     _UpperCAmelCase				:      int			  =				np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506])
     assert np.abs(image_slice - expected_slice).max() < 7e-3




