
import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification




def 				a						(							snake_case__:					Optional[int]					):


				'''simple docstring'''
				lowercase_       =					SwinvaConfig()
				lowercase_       =					swinva_name.split('''_'''					)

				lowercase_       =					name_split[1]
				if "to" in name_split[3]:
								lowercase_       =					int(name_split[3][-3:]					)
				else:
								lowercase_       =					int(name_split[3]					)
				if "to" in name_split[2]:
								lowercase_       =					int(name_split[2][-2:]					)
				else:
								lowercase_       =					int(name_split[2][6:]					)

				if model_size == "tiny":
								lowercase_       =					96
								lowercase_       =					(2, 2, 6, 2)
								lowercase_       =					(3, 6, 12, 24)
				elif model_size == "small":
								lowercase_       =					96
								lowercase_       =					(2, 2, 18, 2)
								lowercase_       =					(3, 6, 12, 24)
				elif model_size == "base":
								lowercase_       =					128
								lowercase_       =					(2, 2, 18, 2)
								lowercase_       =					(4, 8, 16, 32)
				else:
								lowercase_       =					192
								lowercase_       =					(2, 2, 18, 2)
								lowercase_       =					(6, 12, 24, 48)

				if "to" in swinva_name:
								lowercase_       =					(12, 12, 12, 6)

				if ("22k" in swinva_name) and ("to" not in swinva_name):
								lowercase_       =					21_841
								lowercase_       =					'''huggingface/label-files'''
								lowercase_       =					'''imagenet-22k-id2label.json'''
								lowercase_       =					json.load(open(hf_hub_download(__lowerCAmelCase						,   __lowerCAmelCase						,   repo_type='''dataset'''					)						,   '''r'''					)					)
								lowercase_       =					{int(__lowerCAmelCase					): v for k, v in idalabel.items()}
								lowercase_       =					idalabel
								lowercase_       =					{v: k for k, v in idalabel.items()}

				else:
								lowercase_       =					1_000
								lowercase_       =					'''huggingface/label-files'''
								lowercase_       =					'''imagenet-1k-id2label.json'''
								lowercase_       =					json.load(open(hf_hub_download(__lowerCAmelCase						,   __lowerCAmelCase						,   repo_type='''dataset'''					)						,   '''r'''					)					)
								lowercase_       =					{int(__lowerCAmelCase					): v for k, v in idalabel.items()}
								lowercase_       =					idalabel
								lowercase_       =					{v: k for k, v in idalabel.items()}

				lowercase_       =					img_size
				lowercase_       =					num_classes
				lowercase_       =					embed_dim
				lowercase_       =					depths
				lowercase_       =					num_heads
				lowercase_       =					window_size

				return config




def 				a						(							snake_case__:					Any					):


				'''simple docstring'''
				if "patch_embed.proj" in name:
								lowercase_       =					name.replace('''patch_embed.proj'''						,   '''embeddings.patch_embeddings.projection'''					)
				if "patch_embed.norm" in name:
								lowercase_       =					name.replace('''patch_embed.norm'''						,   '''embeddings.norm'''					)
				if "layers" in name:
								lowercase_       =					'''encoder.''' + name
				if "attn.proj" in name:
								lowercase_       =					name.replace('''attn.proj'''						,   '''attention.output.dense'''					)
				if "attn" in name:
								lowercase_       =					name.replace('''attn'''						,   '''attention.self'''					)
				if "norm1" in name:
								lowercase_       =					name.replace('''norm1'''						,   '''layernorm_before'''					)
				if "norm2" in name:
								lowercase_       =					name.replace('''norm2'''						,   '''layernorm_after'''					)
				if "mlp.fc1" in name:
								lowercase_       =					name.replace('''mlp.fc1'''						,   '''intermediate.dense'''					)
				if "mlp.fc2" in name:
								lowercase_       =					name.replace('''mlp.fc2'''						,   '''output.dense'''					)
				if "q_bias" in name:
								lowercase_       =					name.replace('''q_bias'''						,   '''query.bias'''					)
				if "k_bias" in name:
								lowercase_       =					name.replace('''k_bias'''						,   '''key.bias'''					)
				if "v_bias" in name:
								lowercase_       =					name.replace('''v_bias'''						,   '''value.bias'''					)
				if "cpb_mlp" in name:
								lowercase_       =					name.replace('''cpb_mlp'''						,   '''continuous_position_bias_mlp'''					)
				if name == "norm.weight":
								lowercase_       =					'''layernorm.weight'''
				if name == "norm.bias":
								lowercase_       =					'''layernorm.bias'''

				if "head" in name:
								lowercase_       =					name.replace('''head'''						,   '''classifier'''					)
				else:
								lowercase_       =					'''swinv2.''' + name

				return name




def 				a						(							snake_case__:					Union[str, Any]						,   snake_case__:					Union[str, Any]					):


				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								lowercase_       =					orig_state_dict.pop(__lowerCAmelCase					)

								if "mask" in key:
												continue
								elif "qkv" in key:
												lowercase_       =					key.split('''.'''					)
												lowercase_       =					int(key_split[1]					)
												lowercase_       =					int(key_split[3]					)
												lowercase_       =					model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

				return orig_state_dict




def 				a						(							snake_case__:					List[Any]						,   snake_case__:					Optional[int]					):


				'''simple docstring'''
				lowercase_       =					timm.create_model(__lowerCAmelCase						,   pretrained=__lowerCAmelCase					)
				timm_model.eval()

				lowercase_       =					get_swinva_config(__lowerCAmelCase					)
				lowercase_       =					SwinvaForImageClassification(__lowerCAmelCase					)
				model.eval()

				lowercase_       =					convert_state_dict(timm_model.state_dict()						,   __lowerCAmelCase					)
				model.load_state_dict(__lowerCAmelCase					)

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

				lowercase_       =					AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''						,   '''-'''					)					)					)
				lowercase_       =					Image.open(requests.get(__lowerCAmelCase						,   stream=__lowerCAmelCase					).raw					)
				lowercase_       =					image_processor(images=__lowerCAmelCase						,   return_tensors='''pt'''					)

				lowercase_       =					timm_model(inputs['''pixel_values''']					)
				lowercase_       =					model(**__lowerCAmelCase					).logits

				assert torch.allclose(__lowerCAmelCase						,   __lowerCAmelCase						,   atol=1e-3					)

				print(F'''Saving model {swinva_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					)

				model.push_to_hub(
				    repo_path_or_name=Path(__lowerCAmelCase						,   __lowerCAmelCase					)						,   organization='''nandwalritik'''						,   commit_message='''Add model'''						,   )


if __name__ == "__main__":
						__a  			=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--swinv2_name',
						    default='swinv2_tiny_patch4_window8_256',
						    type=str,
						    help='Name of the Swinv2 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.'
						)

						__a  			=					parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate







@dataclass(frozen=lowerCamelCase_      )
class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    str          =			field(default='''audio-classification'''	,						metadata={'''include_in_asdict_even_if_is_default''': True}      )
					UpperCAmelCase__					:    ClassVar[Features]          =			Features({'''audio''': Audio()}      )
					UpperCAmelCase__					:    ClassVar[Features]          =			Features({'''labels''': ClassLabel}      )
					UpperCAmelCase__					:    str          =			"audio"
					UpperCAmelCase__					:    str          =			"labels"
					def  lowerCamelCase__(      self   :Optional[int]			,__snake_case   :int      )					->    str:
												if self.label_column not in features:
																			raise ValueError(F'Column {self.label_column} is not present in features.'      )
												if not isinstance(features[self.label_column]			,__snake_case      ):
																			raise ValueError(F'Column {self.label_column} is not a ClassLabel.'      )
												a__             =     copy.deepcopy(self      )
												a__             =     self.label_schema.copy()
												a__             =     features[self.label_column]
												a__             =     label_schema
												return task_template



					@property
					def  lowerCamelCase__(      self   :Dict      )					->    Dict[str, str]:
												return {
												    self.audio_column: "audio",
												    self.label_column: "labels",
												}




