


'''simple docstring'''
import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 					a__       (					lowerCAmelCase__						)    ->	List[str]:
    UpperCAmelCase__			:		Optional[Any]													=				SwinConfig()
    UpperCAmelCase__			:		Tuple													=				swin_name.split('''_'''						)

    UpperCAmelCase__			:		Optional[Any]													=				name_split[1]
    UpperCAmelCase__			:		List[Any]													=				int(name_split[4]						)
    UpperCAmelCase__			:		List[str]													=				int(name_split[3][-1]						)

    if model_size == "tiny":
        UpperCAmelCase__			:		List[Any]													=				96
        UpperCAmelCase__			:		Any													=				(2, 2, 6, 2)
        UpperCAmelCase__			:		str													=				(3, 6, 12, 24)
    elif model_size == "small":
        UpperCAmelCase__			:		Tuple													=				96
        UpperCAmelCase__			:		Union[str, Any]													=				(2, 2, 18, 2)
        UpperCAmelCase__			:		Optional[int]													=				(3, 6, 12, 24)
    elif model_size == "base":
        UpperCAmelCase__			:		Tuple													=				1_28
        UpperCAmelCase__			:		Optional[int]													=				(2, 2, 18, 2)
        UpperCAmelCase__			:		Optional[Any]													=				(4, 8, 16, 32)
    else:
        UpperCAmelCase__			:		Optional[int]													=				1_92
        UpperCAmelCase__			:		List[Any]													=				(2, 2, 18, 2)
        UpperCAmelCase__			:		Union[str, Any]													=				(6, 12, 24, 48)

    if "in22k" in swin_name:
        UpperCAmelCase__			:		Optional[int]													=				2_18_41
    else:
        UpperCAmelCase__			:		Tuple													=				10_00
        UpperCAmelCase__			:		Tuple													=				'''huggingface/label-files'''
        UpperCAmelCase__			:		Tuple													=				'''imagenet-1k-id2label.json'''
        UpperCAmelCase__			:		Optional[int]													=				json.load(open(hf_hub_download(lowerCAmelCase__  ,					lowerCAmelCase__  ,					repo_type='''dataset'''						)  ,					'''r'''						)						)
        UpperCAmelCase__			:		Optional[Any]													=				{int(lowerCAmelCase__						): v for k, v in idalabel.items()}
        UpperCAmelCase__			:		str													=				idalabel
        UpperCAmelCase__			:		List[Any]													=				{v: k for k, v in idalabel.items()}

    UpperCAmelCase__			:		List[Any]													=				img_size
    UpperCAmelCase__			:		List[str]													=				num_classes
    UpperCAmelCase__			:		Union[str, Any]													=				embed_dim
    UpperCAmelCase__			:		Optional[int]													=				depths
    UpperCAmelCase__			:		int													=				num_heads
    UpperCAmelCase__			:		List[str]													=				window_size

    return config





def 					a__       (					lowerCAmelCase__						)    ->	Union[str, Any]:
    if "patch_embed.proj" in name:
        UpperCAmelCase__			:		List[str]													=				name.replace('''patch_embed.proj'''  ,					'''embeddings.patch_embeddings.projection'''						)
    if "patch_embed.norm" in name:
        UpperCAmelCase__			:		List[Any]													=				name.replace('''patch_embed.norm'''  ,					'''embeddings.norm'''						)
    if "layers" in name:
        UpperCAmelCase__			:		List[str]													=				'''encoder.''' + name
    if "attn.proj" in name:
        UpperCAmelCase__			:		Optional[int]													=				name.replace('''attn.proj'''  ,					'''attention.output.dense'''						)
    if "attn" in name:
        UpperCAmelCase__			:		Any													=				name.replace('''attn'''  ,					'''attention.self'''						)
    if "norm1" in name:
        UpperCAmelCase__			:		Dict													=				name.replace('''norm1'''  ,					'''layernorm_before'''						)
    if "norm2" in name:
        UpperCAmelCase__			:		List[str]													=				name.replace('''norm2'''  ,					'''layernorm_after'''						)
    if "mlp.fc1" in name:
        UpperCAmelCase__			:		Optional[Any]													=				name.replace('''mlp.fc1'''  ,					'''intermediate.dense'''						)
    if "mlp.fc2" in name:
        UpperCAmelCase__			:		Tuple													=				name.replace('''mlp.fc2'''  ,					'''output.dense'''						)

    if name == "norm.weight":
        UpperCAmelCase__			:		Optional[int]													=				'''layernorm.weight'''
    if name == "norm.bias":
        UpperCAmelCase__			:		Optional[int]													=				'''layernorm.bias'''

    if "head" in name:
        UpperCAmelCase__			:		Optional[Any]													=				name.replace('''head'''  ,					'''classifier'''						)
    else:
        UpperCAmelCase__			:		Dict													=				'''swin.''' + name

    return name





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Any:
    for key in orig_state_dict.copy().keys():
        UpperCAmelCase__			:		Union[str, Any]													=				orig_state_dict.pop(lowerCAmelCase__						)

        if "mask" in key:
            continue
        elif "qkv" in key:
            UpperCAmelCase__			:		int													=				key.split('''.'''						)
            UpperCAmelCase__			:		List[str]													=				int(key_split[1]						)
            UpperCAmelCase__			:		Dict													=				int(key_split[3]						)
            UpperCAmelCase__			:		Any													=				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                UpperCAmelCase__			:		Union[str, Any]													=				val[:dim, :]
                UpperCAmelCase__			:		Dict													=				val[
                    dim : dim * 2, :
                ]
                UpperCAmelCase__			:		Optional[int]													=				val[-dim:, :]
            else:
                UpperCAmelCase__			:		str													=				val[
                    :dim
                ]
                UpperCAmelCase__			:		Optional[Any]													=				val[
                    dim : dim * 2
                ]
                UpperCAmelCase__			:		str													=				val[
                    -dim:
                ]
        else:
            UpperCAmelCase__			:		Any													=				val

    return orig_state_dict





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	List[Any]:
    UpperCAmelCase__			:		str													=				timm.create_model(lowerCAmelCase__  ,					pretrained=lowerCAmelCase__						)
    timm_model.eval()

    UpperCAmelCase__			:		Union[str, Any]													=				get_swin_config(lowerCAmelCase__						)
    UpperCAmelCase__			:		Optional[Any]													=				SwinForImageClassification(lowerCAmelCase__						)
    model.eval()

    UpperCAmelCase__			:		str													=				convert_state_dict(timm_model.state_dict()  ,					lowerCAmelCase__						)
    model.load_state_dict(lowerCAmelCase__						)

    UpperCAmelCase__			:		str													=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''

    UpperCAmelCase__			:		Optional[Any]													=				AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''  ,					'''-'''						)						)						)
    UpperCAmelCase__			:		Dict													=				Image.open(requests.get(lowerCAmelCase__  ,					stream=lowerCAmelCase__						).raw						)
    UpperCAmelCase__			:		str													=				image_processor(images=lowerCAmelCase__  ,					return_tensors='''pt'''						)

    UpperCAmelCase__			:		Tuple													=				timm_model(inputs['''pixel_values''']						)
    UpperCAmelCase__			:		List[Any]													=				model(**lowerCAmelCase__						).logits

    assert torch.allclose(lowerCAmelCase__  ,					lowerCAmelCase__  ,					atol=1E-3						)

    print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""						)
    model.save_pretrained(lowerCAmelCase__						)

    print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
    image_processor.save_pretrained(lowerCAmelCase__						)


if __name__ == "__main__":
  UpperCamelCase__    					=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--swin_name''',
      default='''swin_tiny_patch4_window7_224''',
      type=str,
      help='''Name of the Swin 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.'''
  )

  UpperCamelCase__    					=      parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


UpperCamelCase__    					=      logging.get_logger(__name__)  # pylint: disable=invalid-name




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    Any						,						_A				:    int						,						_A				:    str ):


          '''simple docstring'''


          super().__init__()
          self.register_modules(unet=_A						,						scheduler=_A )






      @torch.no_grad()
      def __call__(					self				:    Union[str, Any]						,						_A				:    int = 1						,						_A				:    int = 100						,						_A				:    Optional[Union[torch.Generator, List[torch.Generator]]] = None						,						_A				:    Optional[float] = None						,						_A				:    bool = True						,						):


          '''simple docstring'''


          if audio_length_in_s is None:
              UpperCAmelCase__			:		List[str]													=				self.unet.config.sample_size / self.unet.config.sample_rate

          UpperCAmelCase__			:		Dict													=				audio_length_in_s * self.unet.config.sample_rate

          UpperCAmelCase__			:		Union[str, Any]													=				2 ** len(self.unet.up_blocks )
          if sample_size < 3 * down_scale_factor:
              raise ValueError(
                  f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
                  f""" {3 * down_scale_factor / self.unet.config.sample_rate}.""" )

          UpperCAmelCase__			:		Optional[Any]													=				int(_A )
          if sample_size % down_scale_factor != 0:
              UpperCAmelCase__			:		List[str]													=				(
                  (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
              ) * down_scale_factor
              logger.info(
                  f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
                  f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
                  ''' process.''' )
          UpperCAmelCase__			:		Union[str, Any]													=				int(_A )

          UpperCAmelCase__			:		Any													=				next(iter(self.unet.parameters() ) ).dtype
          UpperCAmelCase__			:		Union[str, Any]													=				(batch_size, self.unet.config.in_channels, sample_size)
          if isinstance(_A						,						_A ) and len(_A ) != batch_size:
              raise ValueError(
                  f"""You have passed a list of generators of length {len(_A )}, but requested an effective batch"""
                  f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

          UpperCAmelCase__			:		int													=				randn_tensor(_A						,						generator=_A						,						device=self.device						,						dtype=_A )

          # set step values
          self.scheduler.set_timesteps(_A						,						device=audio.device )
          UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler.timesteps.to(_A )

          for t in self.progress_bar(self.scheduler.timesteps ):
              # 1. predict noise model_output
              UpperCAmelCase__			:		Any													=				self.unet(_A						,						_A ).sample

              # 2. compute previous image: x_t -> t_t-1
              UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler.step(_A						,						_A						,						_A ).prev_sample

          UpperCAmelCase__			:		Any													=				audio.clamp(-1						,						1 ).float().cpu().numpy()

          UpperCAmelCase__			:		List[str]													=				audio[:, :, :original_sample_size]

          if not return_dict:
              return (audio,)

          return AudioPipelineOutput(audios=_A )
