





"""simple docstring"""



import argparse

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

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  Any						)   ->List[Any]:



    '''simple docstring'''







    __A    :						List[str]						 =     XCLIPTextConfig()

    # derive patch size from model name
    __A    :						List[Any]						 =     model_name.find('''patch'''						)
    __A    :						List[Any]						 =     int(model_name[start_idx + len('''patch'''						) : start_idx + len('''patch'''						) + 2]						)
    __A    :						Dict						 =     XCLIPVisionConfig(patch_size=snake_case_				,num_frames=snake_case_						)

    if "large" in model_name:
        __A    :						Dict						 =     768
        __A    :						Dict						 =     3072
        __A    :						Any						 =     12

        __A    :						str						 =     1024
        __A    :						str						 =     4096
        __A    :						List[str]						 =     16
        __A    :						Union[str, Any]						 =     24
        __A    :						Optional[int]						 =     768
        __A    :						List[str]						 =     3072

    if model_name == "xclip-large-patch14-16-frames":
        __A    :						int						 =     336

    __A    :						str						 =     XCLIPConfig.from_text_vision_configs(snake_case_				,snake_case_						)

    if "large" in model_name:
        __A    :						Union[str, Any]						 =     768

    return config







def 		__lowercase     (   snake_case_  :  List[str]						)   ->str:



    '''simple docstring'''







    if name == "token_embedding.weight":
        __A    :						str						 =     name.replace('''token_embedding.weight'''				,'''text_model.embeddings.token_embedding.weight'''						)
    if name == "positional_embedding":
        __A    :						Optional[Any]						 =     name.replace('''positional_embedding'''				,'''text_model.embeddings.position_embedding.weight'''						)
    if "ln_1" in name:
        __A    :						Tuple						 =     name.replace('''ln_1'''				,'''layer_norm1'''						)
    if "ln_2" in name:
        __A    :						Dict						 =     name.replace('''ln_2'''				,'''layer_norm2'''						)
    if "c_fc" in name:
        __A    :						Any						 =     name.replace('''c_fc'''				,'''fc1'''						)
    if "c_proj" in name:
        __A    :						str						 =     name.replace('''c_proj'''				,'''fc2'''						)
    if name.startswith('''transformer.resblocks'''						):
        __A    :						int						 =     name.replace('''transformer.resblocks'''				,'''text_model.encoder.layers'''						)
    if "attn.out_proj" in name and "message" not in name:
        __A    :						Dict						 =     name.replace('''attn.out_proj'''				,'''self_attn.out_proj'''						)
    if "ln_final" in name:
        __A    :						Union[str, Any]						 =     name.replace('''ln_final'''				,'''text_model.final_layer_norm'''						)
    # visual encoder
    if name == "visual.class_embedding":
        __A    :						List[str]						 =     name.replace('''visual.class_embedding'''				,'''vision_model.embeddings.class_embedding'''						)
    if name == "visual.positional_embedding":
        __A    :						str						 =     name.replace('''visual.positional_embedding'''				,'''vision_model.embeddings.position_embedding.weight'''						)
    if name.startswith('''visual.transformer.resblocks'''						):
        __A    :						str						 =     name.replace('''visual.transformer.resblocks'''				,'''vision_model.encoder.layers'''						)
    if "visual.conv1" in name:
        __A    :						Optional[Any]						 =     name.replace('''visual.conv1'''				,'''vision_model.embeddings.patch_embedding'''						)
    if "visual.ln_pre" in name:
        __A    :						int						 =     name.replace('''visual.ln_pre'''				,'''vision_model.pre_layernorm'''						)
    if "visual.ln_post" in name:
        __A    :						int						 =     name.replace('''visual.ln_post'''				,'''vision_model.post_layernorm'''						)
    if "visual.proj" in name:
        __A    :						int						 =     name.replace('''visual.proj'''				,'''visual_projection.weight'''						)
    if "text_projection" in name:
        __A    :						Union[str, Any]						 =     name.replace('''text_projection'''				,'''text_projection.weight'''						)
    # things on top
    if "prompts_visual_proj" in name:
        __A    :						Optional[int]						 =     name.replace('''prompts_visual_proj'''				,'''prompts_visual_projection'''						)
    if "prompts_visual_ln" in name:
        __A    :						Tuple						 =     name.replace('''prompts_visual_ln'''				,'''prompts_visual_layernorm'''						)
    # mit
    if name == "mit.positional_embedding":
        __A    :						Union[str, Any]						 =     name.replace('''positional'''				,'''position'''						)
    if name.startswith('''mit.resblocks'''						):
        __A    :						Any						 =     name.replace('''mit.resblocks'''				,'''mit.encoder.layers'''						)
    # prompts generator
    if name.startswith('''prompts_generator.norm'''						):
        __A    :						str						 =     name.replace('''prompts_generator.norm'''				,'''prompts_generator.layernorm'''						)

    return name







def 		__lowercase     (   snake_case_  :  List[Any]				,snake_case_  :  Dict						)   ->List[str]:



    '''simple docstring'''







    for key in orig_state_dict.copy().keys():
        __A    :						int						 =     orig_state_dict.pop(snake_case_						)

        if "attn.in_proj" in key:
            __A    :						Tuple						 =     key.split('''.'''						)
            if key.startswith('''visual'''						):
                __A    :						Any						 =     key_split[3]
                __A    :						Any						 =     config.vision_config.hidden_size
                if "message_attn" in key:
                    if "weight" in key:
                        __A    :						Optional[Any]						 =     val[
                            :dim, :
                        ]
                        __A    :						int						 =     val[
                            dim : dim * 2, :
                        ]
                        __A    :						Dict						 =     val[
                            -dim:, :
                        ]
                    else:
                        __A    :						Optional[int]						 =     val[
                            :dim
                        ]
                        __A    :						str						 =     val[
                            dim : dim * 2
                        ]
                        __A    :						List[Any]						 =     val[
                            -dim:
                        ]
                else:
                    if "weight" in key:
                        __A    :						str						 =     val[
                            :dim, :
                        ]
                        __A    :						Tuple						 =     val[
                            dim : dim * 2, :
                        ]
                        __A    :						Dict						 =     val[
                            -dim:, :
                        ]
                    else:
                        __A    :						Optional[Any]						 =     val[:dim]
                        __A    :						Union[str, Any]						 =     val[
                            dim : dim * 2
                        ]
                        __A    :						int						 =     val[-dim:]
            elif key.startswith('''mit'''						):
                __A    :						List[str]						 =     key_split[2]
                __A    :						Optional[int]						 =     config.vision_config.mit_hidden_size
                if "weight" in key:
                    __A    :						List[str]						 =     val[:dim, :]
                    __A    :						List[str]						 =     val[dim : dim * 2, :]
                    __A    :						int						 =     val[-dim:, :]
                else:
                    __A    :						Tuple						 =     val[:dim]
                    __A    :						Optional[Any]						 =     val[dim : dim * 2]
                    __A    :						Tuple						 =     val[-dim:]
            else:
                __A    :						Any						 =     key_split[2]
                __A    :						Optional[int]						 =     config.text_config.hidden_size
                if "weight" in key:
                    __A    :						Optional[Any]						 =     val[:dim, :]
                    __A    :						Tuple						 =     val[
                        dim : dim * 2, :
                    ]
                    __A    :						Optional[int]						 =     val[-dim:, :]
                else:
                    __A    :						List[Any]						 =     val[:dim]
                    __A    :						Dict						 =     val[
                        dim : dim * 2
                    ]
                    __A    :						str						 =     val[-dim:]
        else:
            __A    :						List[Any]						 =     rename_key(snake_case_						)
            if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                __A    :						Dict						 =     val.T
            __A    :						str						 =     val

    return orig_state_dict







def 		__lowercase     (   snake_case_  :  Union[str, Any]						)   ->Optional[int]:



    '''simple docstring'''







    if num_frames == 8:
        __A    :						Optional[Any]						 =     '''eating_spaghetti_8_frames.npy'''
    elif num_frames == 16:
        __A    :						Tuple						 =     '''eating_spaghetti.npy'''
    elif num_frames == 32:
        __A    :						Dict						 =     '''eating_spaghetti_32_frames.npy'''
    __A    :						Optional[Any]						 =     hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video'''				,filename=snake_case_				,repo_type='''dataset'''				,)
    __A    :						Dict						 =     np.load(snake_case_						)
    return list(snake_case_						)







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  Optional[Any]=None				,snake_case_  :  Union[str, Any]=False						)   ->int:



    '''simple docstring'''







    __A    :						Optional[Any]						 =     {
        # fully supervised kinetics-400 checkpoints
        '''xclip-base-patch32''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth''',
        '''xclip-base-patch32-16-frames''': (
            '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'''
        ),
        '''xclip-base-patch16''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth''',
        '''xclip-base-patch16-16-frames''': (
            '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'''
        ),
        '''xclip-large-patch14''': '''https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb''',
        '''xclip-large-patch14-16-frames''': '''https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f''',
        # fully supervised kinetics-600 checkpoints
        '''xclip-base-patch16-kinetics-600''': (
            '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'''
        ),
        '''xclip-base-patch16-kinetics-600-16-frames''': (
            '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'''
        ),
        '''xclip-large-patch14-kinetics-600''': '''https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be''',
        # few shot
        '''xclip-base-patch16-hmdb-2-shot''': (
            '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'''
        ),
        '''xclip-base-patch16-hmdb-4-shot''': (
            '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'''
        ),
        '''xclip-base-patch16-hmdb-8-shot''': (
            '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'''
        ),
        '''xclip-base-patch16-hmdb-16-shot''': (
            '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'''
        ),
        '''xclip-base-patch16-ucf-2-shot''': (
            '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'''
        ),
        '''xclip-base-patch16-ucf-4-shot''': (
            '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'''
        ),
        '''xclip-base-patch16-ucf-8-shot''': (
            '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'''
        ),
        '''xclip-base-patch16-ucf-16-shot''': (
            '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'''
        ),
        # zero shot
        '''xclip-base-patch16-zero-shot''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth''',
    }

    __A    :						Tuple						 =     model_to_url[model_name]
    __A    :						Any						 =     8
    if "16-frames" in model_name:
        __A    :						Tuple						 =     16
    elif "shot" in model_name:
        __A    :						Optional[int]						 =     32

    __A    :						Any						 =     get_xclip_config(snake_case_				,snake_case_						)
    __A    :						List[str]						 =     XCLIPModel(snake_case_						)
    model.eval()

    if "drive" in checkpoint_url:
        __A    :						str						 =     '''pytorch_model.bin'''
        gdown.cached_download(snake_case_				,snake_case_				,quiet=snake_case_						)
        __A    :						Optional[Any]						 =     torch.load(snake_case_				,map_location='''cpu'''						)['''model''']
    else:
        __A    :						List[Any]						 =     torch.hub.load_state_dict_from_url(snake_case_						)['''model''']

    __A    :						List[str]						 =     convert_state_dict(snake_case_				,snake_case_						)

    __A    :						int						 =     XCLIPModel(snake_case_						)
    __A ,       __A    :						Dict						 =     model.load_state_dict(snake_case_				,strict=snake_case_						)
    assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
    model.eval()

    __A    :						Dict						 =     336 if model_name == '''xclip-large-patch14-16-frames''' else 224
    __A    :						Union[str, Any]						 =     VideoMAEImageProcessor(size=snake_case_						)
    __A    :						Dict						 =     CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''						)
    __A    :						Any						 =     CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32'''						)
    __A    :						Optional[Any]						 =     XCLIPProcessor(image_processor=snake_case_				,tokenizer=snake_case_						)

    __A    :						int						 =     prepare_video(snake_case_						)
    __A    :						Optional[Any]						 =     processor(
        text=['''playing sports''', '''eating spaghetti''', '''go shopping''']				,videos=snake_case_				,return_tensors='''pt'''				,padding=snake_case_						)

    print('''Shape of pixel values:'''				,inputs.pixel_values.shape						)

    with torch.no_grad():
        __A    :						Optional[int]						 =     model(**snake_case_						)

    # Verify outputs
    __A    :						Dict						 =     outputs.logits_per_video
    __A    :						Union[str, Any]						 =     logits_per_video.softmax(dim=1						)
    print('''Probs:'''				,snake_case_						)
    # kinetics-400
    if model_name == "xclip-base-patch32":
        __A    :						int						 =     torch.tensor([[0.0_019, 0.9_951, 0.0_030]]						)
    elif model_name == "xclip-base-patch32-16-frames":
        __A    :						Any						 =     torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]						)
    elif model_name == "xclip-base-patch16":
        __A    :						Union[str, Any]						 =     torch.tensor([[0.0_083, 0.9_681, 0.0_236]]						)
    elif model_name == "xclip-base-patch16-16-frames":
        __A    :						Tuple						 =     torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]						)
    elif model_name == "xclip-large-patch14":
        __A    :						str						 =     torch.tensor([[0.0_062, 0.9_864, 0.0_075]]						)
    elif model_name == "xclip-large-patch14-16-frames":
        __A    :						List[str]						 =     torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]						)
    # kinetics-600
    elif model_name == "xclip-base-patch16-kinetics-600":
        __A    :						List[str]						 =     torch.tensor([[0.0_555, 0.8_914, 0.0_531]]						)
    elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
        __A    :						Dict						 =     torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]						)
    elif model_name == "xclip-large-patch14-kinetics-600":
        __A    :						int						 =     torch.tensor([[0.0_036, 0.9_920, 0.0_045]]						)
    # few shot
    elif model_name == "xclip-base-patch16-hmdb-2-shot":
        __A    :						Union[str, Any]						 =     torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]						)
    elif model_name == "xclip-base-patch16-hmdb-4-shot":
        __A    :						Optional[Any]						 =     torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]						)
    elif model_name == "xclip-base-patch16-hmdb-8-shot":
        __A    :						Any						 =     torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]						)
    elif model_name == "xclip-base-patch16-hmdb-16-shot":
        __A    :						Dict						 =     torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]						)
    elif model_name == "xclip-base-patch16-ucf-2-shot":
        __A    :						Union[str, Any]						 =     torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]						)
    elif model_name == "xclip-base-patch16-ucf-4-shot":
        __A    :						Union[str, Any]						 =     torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]						)
    elif model_name == "xclip-base-patch16-ucf-8-shot":
        __A    :						Optional[Any]						 =     torch.tensor([[0.0_027, 0.9_904, 0.0_070]]						)
    elif model_name == "xclip-base-patch16-ucf-16-shot":
        __A    :						Tuple						 =     torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]						)
    # zero shot
    elif model_name == "xclip-base-patch16-zero-shot":
        __A    :						Union[str, Any]						 =     torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]						)
    else:
        raise ValueError(F"""Model name {model_name} not supported"""						)
    assert torch.allclose(snake_case_				,snake_case_				,atol=1e-3						)
    print('''Looks ok!'''						)

    if pytorch_dump_folder_path is not None:
        print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""						)
        model.save_pretrained(snake_case_						)

    if push_to_hub:
        print('''Pushing model, processor and slow tokenizer files to the hub...'''						)
        model.push_to_hub(snake_case_				,organization='''nielsr'''						)
        processor.push_to_hub(snake_case_				,organization='''nielsr'''						)
        slow_tokenizer.push_to_hub(snake_case_				,organization='''nielsr'''						)


if __name__ == "__main__":
       a_ =			argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--model_name""",
           default="""xclip-base-patch32""",
           type=str,
           help="""Name of the model.""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
       )
       parser.add_argument(
           """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
       )

       a_ =			parser.parse_args()
       convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











"""simple docstring"""



from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     ["""vqvae"""]

 def __init__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__()
     self.register_modules(unet=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						mel=__lowerCamelCase					,						vqvae=__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return 50 if isinstance(self.scheduler					,						__lowerCamelCase  ) else 1000

 @torch.no_grad()
 def __call__(    self					,						__lowerCamelCase = 1					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = 0					,						__lowerCamelCase = 0					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = 0					,						__lowerCamelCase = 0					,						__lowerCamelCase = None					,						__lowerCamelCase = 0					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase=True					,						):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     steps or self.get_default_steps()
     self.scheduler.set_timesteps(__lowerCamelCase  )
     __A    :						Any						 =     step_generator or generator
     # For backwards compatibility
     if type(self.unet.config.sample_size  ) == int:
         __A    :						Optional[Any]						 =     (self.unet.config.sample_size, self.unet.config.sample_size)
     if noise is None:
         __A    :						Dict						 =     randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )					,						generator=__lowerCamelCase					,						device=self.device					,						)
     __A    :						Optional[int]						 =     noise
     __A    :						int						 =     None

     if audio_file is not None or raw_audio is not None:
         self.mel.load_audio(__lowerCamelCase					,						__lowerCamelCase  )
         __A    :						int						 =     self.mel.audio_slice_to_image(__lowerCamelCase  )
         __A    :						Optional[Any]						 =     np.frombuffer(input_image.tobytes()					,						dtype='''uint8'''  ).reshape(
             (input_image.height, input_image.width)  )
         __A    :						Union[str, Any]						 =     (input_image / 255) * 2 - 1
         __A    :						Any						 =     torch.tensor(input_image[np.newaxis, :, :]					,						dtype=torch.float  ).to(self.device  )

         if self.vqvae is not None:
             __A    :						Any						 =     self.vqvae.encode(torch.unsqueeze(__lowerCamelCase					,						0  )  ).latent_dist.sample(
                 generator=__lowerCamelCase  )[0]
             __A    :						Optional[Any]						 =     self.vqvae.config.scaling_factor * input_images

         if start_step > 0:
             __A    :						Union[str, Any]						 =     self.scheduler.add_noise(__lowerCamelCase					,						__lowerCamelCase					,						self.scheduler.timesteps[start_step - 1]  )

         __A    :						Optional[int]						 =     (
             self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
         )
         __A    :						int						 =     int(mask_start_secs * pixels_per_second  )
         __A    :						Dict						 =     int(mask_end_secs * pixels_per_second  )
         __A    :						int						 =     self.scheduler.add_noise(__lowerCamelCase					,						__lowerCamelCase					,						torch.tensor(self.scheduler.timesteps[start_step:]  )  )

     for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]  )  ):
         if isinstance(self.unet					,						__lowerCamelCase  ):
             __A    :						List[str]						 =     self.unet(__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  )['''sample''']
         else:
             __A    :						List[str]						 =     self.unet(__lowerCamelCase					,						__lowerCamelCase  )['''sample''']

         if isinstance(self.scheduler					,						__lowerCamelCase  ):
             __A    :						Optional[Any]						 =     self.scheduler.step(
                 model_output=__lowerCamelCase					,						timestep=__lowerCamelCase					,						sample=__lowerCamelCase					,						eta=__lowerCamelCase					,						generator=__lowerCamelCase					,						)['''prev_sample''']
         else:
             __A    :						Optional[Any]						 =     self.scheduler.step(
                 model_output=__lowerCamelCase					,						timestep=__lowerCamelCase					,						sample=__lowerCamelCase					,						generator=__lowerCamelCase					,						)['''prev_sample''']

         if mask is not None:
             if mask_start > 0:
                 __A    :						Optional[Any]						 =     mask[:, step, :, :mask_start]
             if mask_end > 0:
                 __A    :						Tuple						 =     mask[:, step, :, -mask_end:]

     if self.vqvae is not None:
         # 0.18215 was scaling factor used in training to ensure unit variance
         __A    :						Union[str, Any]						 =     1 / self.vqvae.config.scaling_factor * images
         __A    :						List[str]						 =     self.vqvae.decode(__lowerCamelCase  )['''sample''']

     __A    :						Any						 =     (images / 2 + 0.5).clamp(0					,						1  )
     __A    :						Any						 =     images.cpu().permute(0					,						2					,						3					,						1  ).numpy()
     __A    :						Any						 =     (images * 255).round().astype('''uint8'''  )
     __A    :						List[Any]						 =     list(
         (Image.fromarray(_[:, :, 0]  ) for _ in images)
         if images.shape[3] == 1
         else (Image.fromarray(__lowerCamelCase					,						mode='''RGB'''  ).convert('''L'''  ) for _ in images)  )

     __A    :						Dict						 =     [self.mel.image_to_audio(__lowerCamelCase  ) for _ in images]
     if not return_dict:
         return images, (self.mel.get_sample_rate(), audios)

     return BaseOutput(**AudioPipelineOutput(np.array(__lowerCamelCase  )[:, np.newaxis, :]  )					,						**ImagePipelineOutput(__lowerCamelCase  )  )

 @torch.no_grad()
 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase = 50  ):



     '''simple docstring'''




     assert isinstance(self.scheduler					,						__lowerCamelCase  )
     self.scheduler.set_timesteps(__lowerCamelCase  )
     __A    :						str						 =     np.array(
         [np.frombuffer(image.tobytes()					,						dtype='''uint8'''  ).reshape((1, image.height, image.width)  ) for image in images]  )
     __A    :						Any						 =     (sample / 255) * 2 - 1
     __A    :						Dict						 =     torch.Tensor(__lowerCamelCase  ).to(self.device  )

     for t in self.progress_bar(torch.flip(self.scheduler.timesteps					,						(0,)  )  ):
         __A    :						Tuple						 =     t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
         __A    :						List[Any]						 =     self.scheduler.alphas_cumprod[t]
         __A    :						Optional[int]						 =     (
             self.scheduler.alphas_cumprod[prev_timestep]
             if prev_timestep >= 0
             else self.scheduler.final_alpha_cumprod
         )
         __A    :						List[Any]						 =     1 - alpha_prod_t
         __A    :						int						 =     self.unet(__lowerCamelCase					,						__lowerCamelCase  )['''sample''']
         __A    :						Union[str, Any]						 =     (1 - alpha_prod_t_prev) ** 0.5 * model_output
         __A    :						List[Any]						 =     (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
         __A    :						Union[str, Any]						 =     sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

     return sample







 @staticmethod
 def 					UpperCamelCase__(    __lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Tuple						 =     acos(torch.dot(torch.flatten(__lowerCamelCase  )					,						torch.flatten(__lowerCamelCase  )  ) / torch.norm(__lowerCamelCase  ) / torch.norm(__lowerCamelCase  )  )
     return sin((1 - alpha) * theta  ) * xa / sin(__lowerCamelCase  ) + sin(alpha * theta  ) * xa / sin(__lowerCamelCase  )




