


"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def        lowercase     (SCREAMING_SNAKE_CASE_						:							Union[str, Any]    )  ->		Any:
						SCREAMING_SNAKE_CASE						=   SwinConfig(image_size=1_92    )

						if "base" in model_name:
												SCREAMING_SNAKE_CASE						=   6
												SCREAMING_SNAKE_CASE						=   1_28
												SCREAMING_SNAKE_CASE						=   (2, 2, 18, 2)
												SCREAMING_SNAKE_CASE						=   (4, 8, 16, 32)
						elif "large" in model_name:
												SCREAMING_SNAKE_CASE						=   12
												SCREAMING_SNAKE_CASE						=   1_92
												SCREAMING_SNAKE_CASE						=   (2, 2, 18, 2)
												SCREAMING_SNAKE_CASE						=   (6, 12, 24, 48)
						else:
												raise ValueError('Model not supported, only supports base and large variants'    )

						SCREAMING_SNAKE_CASE						=   window_size
						SCREAMING_SNAKE_CASE						=   embed_dim
						SCREAMING_SNAKE_CASE						=   depths
						SCREAMING_SNAKE_CASE						=   num_heads

						return config


def        lowercase     (SCREAMING_SNAKE_CASE_						:							Dict    )  ->		Optional[int]:


						if "encoder.mask_token" in name:
												SCREAMING_SNAKE_CASE						=   name.replace('encoder.mask_token'					,     'embeddings.mask_token'    )
						if "encoder.patch_embed.proj" in name:
												SCREAMING_SNAKE_CASE						=   name.replace('encoder.patch_embed.proj'					,     'embeddings.patch_embeddings.projection'    )
						if "encoder.patch_embed.norm" in name:
												SCREAMING_SNAKE_CASE						=   name.replace('encoder.patch_embed.norm'					,     'embeddings.norm'    )
						if "attn.proj" in name:
												SCREAMING_SNAKE_CASE						=   name.replace('attn.proj'					,     'attention.output.dense'    )
						if "attn" in name:
												SCREAMING_SNAKE_CASE						=   name.replace('attn'					,     'attention.self'    )
						if "norm1" in name:
												SCREAMING_SNAKE_CASE						=   name.replace('norm1'					,     'layernorm_before'    )
						if "norm2" in name:
												SCREAMING_SNAKE_CASE						=   name.replace('norm2'					,     'layernorm_after'    )
						if "mlp.fc1" in name:
												SCREAMING_SNAKE_CASE						=   name.replace('mlp.fc1'					,     'intermediate.dense'    )
						if "mlp.fc2" in name:
												SCREAMING_SNAKE_CASE						=   name.replace('mlp.fc2'					,     'output.dense'    )

						if name == "encoder.norm.weight":
												SCREAMING_SNAKE_CASE						=   'layernorm.weight'
						if name == "encoder.norm.bias":
												SCREAMING_SNAKE_CASE						=   'layernorm.bias'

						if "decoder" in name:
												pass
						else:
												SCREAMING_SNAKE_CASE						=   'swin.' + name

						return name


def        lowercase     (SCREAMING_SNAKE_CASE_						:							Optional[Any]					,     SCREAMING_SNAKE_CASE_						:							List[Any]    )  ->		str:


						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE						=   orig_state_dict.pop(__lowerCAmelCase    )

												if "attn_mask" in key:
																		pass
												elif "qkv" in key:
																		SCREAMING_SNAKE_CASE						=   key.split('.'    )
																		SCREAMING_SNAKE_CASE						=   int(key_split[2]    )
																		SCREAMING_SNAKE_CASE						=   int(key_split[4]    )
																		SCREAMING_SNAKE_CASE						=   model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																		if "weight" in key:
																								SCREAMING_SNAKE_CASE						=   val[:dim, :]
																								SCREAMING_SNAKE_CASE						=   val[
																								    dim : dim * 2, :
																								]
																								SCREAMING_SNAKE_CASE						=   val[-dim:, :]
																		else:
																								SCREAMING_SNAKE_CASE						=   val[
																								    :dim
																								]
																								SCREAMING_SNAKE_CASE						=   val[
																								    dim : dim * 2
																								]
																								SCREAMING_SNAKE_CASE						=   val[
																								    -dim:
																								]
												else:
																		SCREAMING_SNAKE_CASE						=   val

						return orig_state_dict


def        lowercase     (SCREAMING_SNAKE_CASE_						:							Optional[Any]					,     SCREAMING_SNAKE_CASE_						:							List[Any]					,     SCREAMING_SNAKE_CASE_						:							str					,     SCREAMING_SNAKE_CASE_						:							Optional[Any]    )  ->		Optional[Any]:
						SCREAMING_SNAKE_CASE						=   torch.load(__lowerCAmelCase					,     map_location='cpu'    )['model']

						SCREAMING_SNAKE_CASE						=   get_swin_config(__lowerCAmelCase    )
						SCREAMING_SNAKE_CASE						=   SwinForMaskedImageModeling(__lowerCAmelCase    )
						model.eval()

						SCREAMING_SNAKE_CASE						=   convert_state_dict(__lowerCAmelCase					,     __lowerCAmelCase    )
						model.load_state_dict(__lowerCAmelCase    )

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

						SCREAMING_SNAKE_CASE						=   ViTImageProcessor(size={'height': 1_92, 'width': 1_92}    )
						SCREAMING_SNAKE_CASE						=   Image.open(requests.get(__lowerCAmelCase					,     stream=__lowerCAmelCase    ).raw    )
						SCREAMING_SNAKE_CASE						=   image_processor(images=__lowerCAmelCase					,     return_tensors='pt'    )

						with torch.no_grad():
												SCREAMING_SNAKE_CASE						=   model(**__lowerCAmelCase    ).logits

						print(outputs.keys()    )
						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(__lowerCAmelCase    )

												print(F'Saving image processor to {pytorch_dump_folder_path}'    )
												image_processor.save_pretrained(__lowerCAmelCase    )

						if push_to_hub:
												print(F'Pushing model and image processor for {model_name} to hub'    )
												model.push_to_hub(F'microsoft/{model_name}'    )
												image_processor.push_to_hub(F'microsoft/{model_name}'    )


if __name__ == "__main__":
	__UpperCamelCase		= argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--model_name''',
	    default='''swin-base-simmim-window6-192''',
	    type=str,
	    choices=['''swin-base-simmim-window6-192''', '''swin-large-simmim-window12-192'''],
	    help='''Name of the Swin SimMIM model you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--checkpoint_path''',
	    default='''/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth''',
	    type=str,
	    help='''Path to the original PyTorch checkpoint (.pth file).''',
	)
	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.'''
	)

	__UpperCamelCase		= parser.parse_args()
	convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


_A  =	datasets.utils.logging.get_logger(__name__)





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilderConfig			):
				_lowercase     						=None
				_lowercase     						=None





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilder			):
				_lowercase     						=datasets.Audio()
				_lowercase     						='''audio'''
				_lowercase     						=AudioFolderConfig
				_lowercase     						=42  # definition at the bottom of the script
				_lowercase     						=AudioClassification(audio_column='''audio'''   , label_column='''label'''			)


_A  =	[
    ".aiff",
    ".au",
    ".avr",
    ".caf",
    ".flac",
    ".htk",
    ".svx",
    ".mat4",
    ".mat5",
    ".mpc2k",
    ".ogg",
    ".paf",
    ".pvf",
    ".raw",
    ".rf64",
    ".sd2",
    ".sds",
    ".ircam",
    ".voc",
    ".w64",
    ".wav",
    ".nist",
    ".wavex",
    ".wve",
    ".xi",
    ".mp3",
    ".opus",
]
_A  =	AUDIO_EXTENSIONS



