






'''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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Union[str, Any]:
							lowerCamelCase__      :			Dict 						= SwinConfig()
							lowerCamelCase__      :			str 						= swin_name.split("""_"""   )

							lowerCamelCase__      :			str 						= name_split[1]
							lowerCamelCase__      :			Optional[int] 						= int(name_split[4]   )
							lowerCamelCase__      :			Dict 						= int(name_split[3][-1]   )

							if model_size == "tiny":
														lowerCamelCase__      :			List[Any] 						= 96
														lowerCamelCase__      :			Optional[int] 						= (2, 2, 6, 2)
														lowerCamelCase__      :			str 						= (3, 6, 12, 24)
							elif model_size == "small":
														lowerCamelCase__      :			Optional[Any] 						= 96
														lowerCamelCase__      :			Union[str, Any] 						= (2, 2, 18, 2)
														lowerCamelCase__      :			Optional[Any] 						= (3, 6, 12, 24)
							elif model_size == "base":
														lowerCamelCase__      :			Any 						= 128
														lowerCamelCase__      :			Union[str, Any] 						= (2, 2, 18, 2)
														lowerCamelCase__      :			Dict 						= (4, 8, 16, 32)
							else:
														lowerCamelCase__      :			Optional[int] 						= 192
														lowerCamelCase__      :			Any 						= (2, 2, 18, 2)
														lowerCamelCase__      :			Optional[Any] 						= (6, 12, 24, 48)

							if "in22k" in swin_name:
														lowerCamelCase__      :			Any 						= 21841
							else:
														lowerCamelCase__      :			Optional[Any] 						= 1000
														lowerCamelCase__      :			Optional[int] 						= """huggingface/label-files"""
														lowerCamelCase__      :			Union[str, Any] 						= """imagenet-1k-id2label.json"""
														lowerCamelCase__      :			Optional[Any] 						= json.load(open(hf_hub_download(UpperCamelCase       ,    UpperCamelCase       ,    repo_type="""dataset"""   )       ,    """r"""   )   )
														lowerCamelCase__      :			int 						= {int(UpperCamelCase   ): v for k, v in idalabel.items()}
														lowerCamelCase__      :			Optional[Any] 						= idalabel
														lowerCamelCase__      :			Dict 						= {v: k for k, v in idalabel.items()}

							lowerCamelCase__      :			List[Any] 						= img_size
							lowerCamelCase__      :			List[Any] 						= num_classes
							lowerCamelCase__      :			Optional[Any] 						= embed_dim
							lowerCamelCase__      :			Optional[int] 						= depths
							lowerCamelCase__      :			Any 						= num_heads
							lowerCamelCase__      :			List[str] 						= window_size

							return config





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

							if name == "norm.weight":
														lowerCamelCase__      :			int 						= """layernorm.weight"""
							if name == "norm.bias":
														lowerCamelCase__      :			Any 						= """layernorm.bias"""

							if "head" in name:
														lowerCamelCase__      :			List[Any] 						= name.replace("""head"""       ,    """classifier"""   )
							else:
														lowerCamelCase__      :			List[Any] 						= """swin.""" + name

							return name





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> List[str]:
							for key in orig_state_dict.copy().keys():
														lowerCamelCase__      :			Optional[int] 						= orig_state_dict.pop(UpperCamelCase   )

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

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

							return orig_state_dict





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> int:
							lowerCamelCase__      :			Optional[int] 						= timm.create_model(UpperCamelCase       ,    pretrained=UpperCamelCase   )
							timm_model.eval()

							lowerCamelCase__      :			Optional[Any] 						= get_swin_config(UpperCamelCase   )
							lowerCamelCase__      :			Dict 						= SwinForImageClassification(UpperCamelCase   )
							model.eval()

							lowerCamelCase__      :			Tuple 						= convert_state_dict(timm_model.state_dict()       ,    UpperCamelCase   )
							model.load_state_dict(UpperCamelCase   )

							lowerCamelCase__      :			List[str] 						= """http://images.cocodataset.org/val2017/000000039769.jpg"""

							lowerCamelCase__      :			Union[str, Any] 						= AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""       ,    """-"""   )   )   )
							lowerCamelCase__      :			List[str] 						= Image.open(requests.get(UpperCamelCase       ,    stream=UpperCamelCase   ).raw   )
							lowerCamelCase__      :			List[str] 						= image_processor(images=UpperCamelCase       ,    return_tensors="""pt"""   )

							lowerCamelCase__      :			Optional[int] 						= timm_model(inputs["""pixel_values"""]   )
							lowerCamelCase__      :			Any 						= model(**UpperCamelCase   ).logits

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

							print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''   )
							model.save_pretrained(UpperCamelCase   )

							print(f'''Saving image processor to {pytorch_dump_folder_path}'''   )
							image_processor.save_pretrained(UpperCamelCase   )


if __name__ == "__main__":
			_A			:    str					  =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.'''
			)

			_A			:    List[str]					  =parser.parse_args()
			convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)









'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:    Optional[Any]					  =logging.get_logger(__name__)

_A			:    Optional[int]					  ={
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}

class _lowercase			(       _lowercase	):
							a          = """rwkv"""
							a          = {"""max_position_embeddings""": """context_length"""}




							def __init__(							self:       Tuple    ,							UpperCamelCase__:       Optional[Any]=50_277    ,							UpperCamelCase__:       Union[str, Any]=1_024    ,							UpperCamelCase__:       Tuple=4_096    ,							UpperCamelCase__:       List[Any]=32    ,							UpperCamelCase__:       Dict=None    ,							UpperCamelCase__:       Dict=None    ,							UpperCamelCase__:       int=1e-5    ,							UpperCamelCase__:       Any=0    ,							UpperCamelCase__:       str=0    ,							UpperCamelCase__:       Union[str, Any]=6    ,							UpperCamelCase__:       Optional[int]=False    ,							UpperCamelCase__:       Dict=True    ,							**UpperCamelCase__:       Dict    ,							):
														lowerCamelCase__      :			Dict 						= vocab_size
														lowerCamelCase__      :			Optional[Any] 						= context_length
														lowerCamelCase__      :			Optional[Any] 						= hidden_size
														lowerCamelCase__      :			Any 						= num_hidden_layers
														lowerCamelCase__      :			int 						= attention_hidden_size if attention_hidden_size is not None else hidden_size
														lowerCamelCase__      :			Union[str, Any] 						= intermediate_size if intermediate_size is not None else 4 * hidden_size
														lowerCamelCase__      :			List[str] 						= layer_norm_epsilon
														lowerCamelCase__      :			int 						= rescale_every
														lowerCamelCase__      :			Optional[int] 						= use_cache

														lowerCamelCase__      :			Dict 						= bos_token_id
														lowerCamelCase__      :			Any 						= eos_token_id

														super().__init__(
														    tie_word_embeddings=UpperCamelCase__    ,							bos_token_id=UpperCamelCase__    ,							eos_token_id=UpperCamelCase__    ,							**UpperCamelCase__  )

