





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 		lowerCAmelCase  (	UpperCamelCase__   :   List[Any]					)      ->    Dict:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[str]           =   SwinvaConfig()
						__SCREAMING_SNAKE_CASE:				int           =   swinva_name.split('''_'''					)

						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   name_split[1]
						if "to" in name_split[3]:
												__SCREAMING_SNAKE_CASE:				Dict           =   int(name_split[3][-3:]					)
						else:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   int(name_split[3]					)
						if "to" in name_split[2]:
												__SCREAMING_SNAKE_CASE:				str           =   int(name_split[2][-2:]					)
						else:
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   int(name_split[2][6:]					)

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

						if "to" in swinva_name:
												__SCREAMING_SNAKE_CASE:				List[str]           =   (12, 12, 12, 6)

						if ("22k" in swinva_name) and ("to" not in swinva_name):
												__SCREAMING_SNAKE_CASE:				Any           =   21_841
												__SCREAMING_SNAKE_CASE:				Dict           =   '''huggingface/label-files'''
												__SCREAMING_SNAKE_CASE:				List[Any]           =   '''imagenet-22k-id2label.json'''
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   json.load(open(hf_hub_download(UpperCamelCase__       ,			UpperCamelCase__       ,			repo_type='''dataset'''					)       ,			'''r'''					)					)
												__SCREAMING_SNAKE_CASE:				List[str]           =   {int(UpperCamelCase__					): v for k, v in idalabel.items()}
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   idalabel
												__SCREAMING_SNAKE_CASE:				str           =   {v: k for k, v in idalabel.items()}

						else:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   1_000
												__SCREAMING_SNAKE_CASE:				int           =   '''huggingface/label-files'''
												__SCREAMING_SNAKE_CASE:				str           =   '''imagenet-1k-id2label.json'''
												__SCREAMING_SNAKE_CASE:				Dict           =   json.load(open(hf_hub_download(UpperCamelCase__       ,			UpperCamelCase__       ,			repo_type='''dataset'''					)       ,			'''r'''					)					)
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   {int(UpperCamelCase__					): v for k, v in idalabel.items()}
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   idalabel
												__SCREAMING_SNAKE_CASE:				List[str]           =   {v: k for k, v in idalabel.items()}

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

						return config






def 		lowerCAmelCase  (	UpperCamelCase__   :   Any					)      ->    List[str]:

						"""simple docstring"""





						if "patch_embed.proj" in name:
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   name.replace('''patch_embed.proj'''       ,			'''embeddings.patch_embeddings.projection'''					)
						if "patch_embed.norm" in name:
												__SCREAMING_SNAKE_CASE:				List[Any]           =   name.replace('''patch_embed.norm'''       ,			'''embeddings.norm'''					)
						if "layers" in name:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   '''encoder.''' + name
						if "attn.proj" in name:
												__SCREAMING_SNAKE_CASE:				Tuple           =   name.replace('''attn.proj'''       ,			'''attention.output.dense'''					)
						if "attn" in name:
												__SCREAMING_SNAKE_CASE:				str           =   name.replace('''attn'''       ,			'''attention.self'''					)
						if "norm1" in name:
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   name.replace('''norm1'''       ,			'''layernorm_before'''					)
						if "norm2" in name:
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   name.replace('''norm2'''       ,			'''layernorm_after'''					)
						if "mlp.fc1" in name:
												__SCREAMING_SNAKE_CASE:				Dict           =   name.replace('''mlp.fc1'''       ,			'''intermediate.dense'''					)
						if "mlp.fc2" in name:
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   name.replace('''mlp.fc2'''       ,			'''output.dense'''					)
						if "q_bias" in name:
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   name.replace('''q_bias'''       ,			'''query.bias'''					)
						if "k_bias" in name:
												__SCREAMING_SNAKE_CASE:				Any           =   name.replace('''k_bias'''       ,			'''key.bias'''					)
						if "v_bias" in name:
												__SCREAMING_SNAKE_CASE:				Any           =   name.replace('''v_bias'''       ,			'''value.bias'''					)
						if "cpb_mlp" in name:
												__SCREAMING_SNAKE_CASE:				Tuple           =   name.replace('''cpb_mlp'''       ,			'''continuous_position_bias_mlp'''					)
						if name == "norm.weight":
												__SCREAMING_SNAKE_CASE:				List[str]           =   '''layernorm.weight'''
						if name == "norm.bias":
												__SCREAMING_SNAKE_CASE:				Any           =   '''layernorm.bias'''

						if "head" in name:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   name.replace('''head'''       ,			'''classifier'''					)
						else:
												__SCREAMING_SNAKE_CASE:				Tuple           =   '''swinv2.''' + name

						return name






def 		lowerCAmelCase  (	UpperCamelCase__   :   Tuple       ,			UpperCamelCase__   :   List[str]					)      ->    Optional[int]:

						"""simple docstring"""





						for key in orig_state_dict.copy().keys():
												__SCREAMING_SNAKE_CASE:				Tuple           =   orig_state_dict.pop(UpperCamelCase__					)

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

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

						return orig_state_dict






def 		lowerCAmelCase  (	UpperCamelCase__   :   Any       ,			UpperCamelCase__   :   int					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   timm.create_model(UpperCamelCase__       ,			pretrained=UpperCamelCase__					)
						timm_model.eval()

						__SCREAMING_SNAKE_CASE:				Any           =   get_swinva_config(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Any           =   SwinvaForImageClassification(UpperCamelCase__					)
						model.eval()

						__SCREAMING_SNAKE_CASE:				List[Any]           =   convert_state_dict(timm_model.state_dict()       ,			UpperCamelCase__					)
						model.load_state_dict(UpperCamelCase__					)

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

						__SCREAMING_SNAKE_CASE:				List[str]           =   AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''       ,			'''-'''					)					)					)
						__SCREAMING_SNAKE_CASE:				List[str]           =   Image.open(requests.get(UpperCamelCase__       ,			stream=UpperCamelCase__					).raw					)
						__SCREAMING_SNAKE_CASE:				Dict           =   image_processor(images=UpperCamelCase__       ,			return_tensors='''pt'''					)

						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   timm_model(inputs['''pixel_values''']					)
						__SCREAMING_SNAKE_CASE:				str           =   model(**UpperCamelCase__					).logits

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

						print(F"""Saving model {swinva_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__					)

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


if __name__ == "__main__":
							lowerCAmelCase							:       List[str]					 =	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."""
							)

							lowerCAmelCase							:       Union[str, Any]					 =	parser.parse_args()
							convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:       str					 =	{
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase							:       Optional[Any]					 =	[
							    """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """PegasusXForConditionalGeneration""",
							    """PegasusXModel""",
							    """PegasusXPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pegasus_x import (
														    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PegasusXForConditionalGeneration,
														    PegasusXModel,
														    PegasusXPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase							:       str					 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



