



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      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					['''vqvae''']




     def __init__(					self			,    a__			,    a__			,    a__			,    a__			,    ):
         super().__init__()
         self.register_modules(unet=a__			,    scheduler=a__			,    mel=a__			,    vqvae=a__)




     def 		snake_case_		(					self):
         return 5_0 if isinstance(self.scheduler			,    a__) else 1_0_0_0




     @torch.no_grad()
     def __call__(					self			,    a__ = 1			,    a__ = None			,    a__ = None			,    a__ = 0			,    a__ = 0			,    a__ = None			,    a__ = None			,    a__ = 0			,    a__ = 0			,    a__ = None			,    a__ = 0			,    a__ = None			,    a__ = None			,    a__=True			,    ):
         A__					=       steps or self.get_default_steps()
         self.scheduler.set_timesteps(a__)
         A__					=       step_generator or generator
         # For backwards compatibility
         if type(self.unet.config.sample_size) == int:
             A__					=       (self.unet.config.sample_size, self.unet.config.sample_size)
         if noise is None:
             A__					=       randn_tensor(
                 (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size[0],
                     self.unet.config.sample_size[1],
                 )			,    generator=a__			,    device=self.device			,    )
         A__					=       noise
         A__					=       None

         if audio_file is not None or raw_audio is not None:
             self.mel.load_audio(a__			,    a__)
             A__					=       self.mel.audio_slice_to_image(a__)
             A__					=       np.frombuffer(input_image.tobytes()			,    dtype='''uint8''').reshape(
                 (input_image.height, input_image.width))
             A__					=       (input_image / 2_5_5) * 2 - 1
             A__					=       torch.tensor(input_image[np.newaxis, :, :]			,    dtype=torch.float).to(self.device)

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

             if start_step > 0:
                 A__					=       self.scheduler.add_noise(a__			,    a__			,    self.scheduler.timesteps[start_step - 1])

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

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

             if isinstance(self.scheduler			,    a__):
                 A__					=       self.scheduler.step(
                     model_output=a__			,    timestep=a__			,    sample=a__			,    eta=a__			,    generator=a__			,    )['''prev_sample''']
             else:
                 A__					=       self.scheduler.step(
                     model_output=a__			,    timestep=a__			,    sample=a__			,    generator=a__			,    )['''prev_sample''']

             if mask is not None:
                 if mask_start > 0:
                     A__					=       mask[:, step, :, :mask_start]
                 if mask_end > 0:
                     A__					=       mask[:, step, :, -mask_end:]

         if self.vqvae is not None:
             # 0.18215 was scaling factor used in training to ensure unit variance
             A__					=       1 / self.vqvae.config.scaling_factor * images
             A__					=       self.vqvae.decode(a__)['''sample''']

         A__					=       (images / 2 + 0.5).clamp(0			,    1)
         A__					=       images.cpu().permute(0			,    2			,    3			,    1).numpy()
         A__					=       (images * 2_5_5).round().astype('''uint8''')
         A__					=       list(
             (Image.fromarray(_[:, :, 0]) for _ in images)
             if images.shape[3] == 1
             else (Image.fromarray(a__			,    mode='''RGB''').convert('''L''') for _ in images))

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

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




     @torch.no_grad()
     def 		snake_case_		(					self			,    a__			,    a__ = 5_0):
         assert isinstance(self.scheduler			,    a__)
         self.scheduler.set_timesteps(a__)
         A__					=       np.array(
             [np.frombuffer(image.tobytes()			,    dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
         A__					=       (sample / 2_5_5) * 2 - 1
         A__					=       torch.Tensor(a__).to(self.device)

         for t in self.progress_bar(torch.flip(self.scheduler.timesteps			,    (0,))):
             A__					=       t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
             A__					=       self.scheduler.alphas_cumprod[t]
             A__					=       (
                 self.scheduler.alphas_cumprod[prev_timestep]
                 if prev_timestep >= 0
                 else self.scheduler.final_alpha_cumprod
             )
             A__					=       1 - alpha_prod_t
             A__					=       self.unet(a__			,    a__)['''sample''']
             A__					=       (1 - alpha_prod_t_prev) ** 0.5 * model_output
             A__					=       (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
             A__					=       sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

         return sample




     @staticmethod
     def 		snake_case_		(					a__			,    a__			,    a__):
         A__					=       acos(torch.dot(torch.flatten(a__)			,    torch.flatten(a__)) / torch.norm(a__) / torch.norm(a__))
         return sin((1 - alpha) * theta) * xa / sin(a__) + sin(alpha * theta) * xa / sin(a__)





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str						)->	int:
    A__					=       '''huggingface/label-files'''
    A__					=       '''imagenet-1k-id2label.json'''
    A__					=       json.load(open(hf_hub_download(UpperCamelCase_			,							UpperCamelCase_			,							repo_type='''dataset'''						)			,							'''r'''						)						)
    A__					=       {int(UpperCamelCase_						): v for k, v in idalabel.items()}
    A__					=       {v: k for k, v in idalabel.items()}

    A__					=       '''std_conv''' if '''bit''' in model_name else False

    # note that when using BiT as backbone for ViT-hybrid checkpoints,
    # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
    # config.conv_layer = "std_conv_same"
    A__					=       BitConfig(
        conv_layer=UpperCamelCase_			,							num_labels=1_0_0_0			,							idalabel=UpperCamelCase_			,							labelaid=UpperCamelCase_			,							)

    return config



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[Any]						)->	Optional[int]:
    if "stem.conv" in name:
        A__					=       name.replace('''stem.conv'''			,							'''bit.embedder.convolution'''						)
    if "blocks" in name:
        A__					=       name.replace('''blocks'''			,							'''layers'''						)
    if "head.fc" in name:
        A__					=       name.replace('''head.fc'''			,							'''classifier.1'''						)
    if name.startswith('''norm'''						):
        A__					=       '''bit.''' + name
    if "bit" not in name and "classifier" not in name:
        A__					=       '''bit.encoder.''' + name

    return name



def 		lowerCAmelCase__     (       )->	str:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							Tuple			,							UpperCamelCase_					:							List[Any]=False						)->	Optional[int]:
    A__					=       get_config(UpperCamelCase_						)

    # load original model from timm
    A__					=       create_model(UpperCamelCase_			,							pretrained=UpperCamelCase_						)
    timm_model.eval()

    # load state_dict of original model
    A__					=       timm_model.state_dict()
    for key in state_dict.copy().keys():
        A__					=       state_dict.pop(UpperCamelCase_						)
        A__					=       val.squeeze() if '''head''' in key else val

    # load HuggingFace model
    A__					=       BitForImageClassification(UpperCamelCase_						)
    model.eval()
    model.load_state_dict(UpperCamelCase_						)

    # create image processor
    A__					=       create_transform(**resolve_data_config({}			,							model=UpperCamelCase_						)						)
    A__					=       transform.transforms

    A__					=       {
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    A__					=       BitImageProcessor(
        do_resize=UpperCamelCase_			,							size={'''shortest_edge''': timm_transforms[0].size}			,							resample=pillow_resamplings[timm_transforms[0].interpolation.value]			,							do_center_crop=UpperCamelCase_			,							crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}			,							do_normalize=UpperCamelCase_			,							image_mean=timm_transforms[-1].mean.tolist()			,							image_std=timm_transforms[-1].std.tolist()			,							)

    A__					=       prepare_img()
    A__					=       transform(UpperCamelCase_						).unsqueeze(0						)
    A__					=       processor(UpperCamelCase_			,							return_tensors='''pt'''						).pixel_values

    # verify pixel values
    assert torch.allclose(UpperCamelCase_			,							UpperCamelCase_						)

    # verify logits
    with torch.no_grad():
        A__					=       model(UpperCamelCase_						)
        A__					=       outputs.logits

    print('''Logits:'''			,							logits[0, :3]						)
    print('''Predicted class:'''			,							model.config.idalabel[logits.argmax(-1						).item()]						)
    A__					=       timm_model(UpperCamelCase_						)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(UpperCamelCase_			,							outputs.logits			,							atol=1E-3						)
    print('''Looks ok!'''						)

    if pytorch_dump_folder_path is not None:
        Path(UpperCamelCase_						).mkdir(exist_ok=UpperCamelCase_						)
        print(f"Saving model {model_name} and processor to {pytorch_dump_folder_path}"						)
        model.save_pretrained(UpperCamelCase_						)
        processor.save_pretrained(UpperCamelCase_						)

    if push_to_hub:
        print(f"Pushing model {model_name} and processor to the hub"						)
        model.push_to_hub(f"ybelkada/{model_name}"						)
        processor.push_to_hub(f"ybelkada/{model_name}"						)


if __name__ == "__main__":
   _lowercase    	=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="resnetv2_50x1_bitm",
       type=str,
       help="Name of the BiT timm model you'd like to convert.",
   )
   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 to push the model to the hub.",
   )

   _lowercase    	=		parser.parse_args()
   convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
