



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_					(  _lowercase    :       Union[str, Any])							->	Optional[int]:







			"""simple docstring"""
			a__       :						List[Any]													=  SwinvaConfig()
			a__       :						Any													=  swinva_name.split("""_""")

			a__       :						Dict													=  name_split[1]
			if "to" in name_split[3]:
						a__       :						Tuple													=  int(name_split[3][-3:])
			else:
						a__       :						Any													=  int(name_split[3])
			if "to" in name_split[2]:
						a__       :						int													=  int(name_split[2][-2:])
			else:
						a__       :						int													=  int(name_split[2][6:])

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

			if "to" in swinva_name:
						a__       :						List[Any]													=  (12, 12, 12, 6)

			if ("22k" in swinva_name) and ("to" not in swinva_name):
						a__       :						List[str]													=  2_1841
						a__       :						List[Any]													=  'huggingface/label-files'
						a__       :						str													=  'imagenet-22k-id2label.json'
						a__       :						int													=  json.load(open(hf_hub_download(_A						,       _A						,       repo_type="""dataset""")						,       """r"""))
						a__       :						Optional[Any]													=  {int(_A): v for k, v in idalabel.items()}
						a__       :						List[str]													=  idalabel
						a__       :						Union[str, Any]													=  {v: k for k, v in idalabel.items()}

			else:
						a__       :						List[Any]													=  1000
						a__       :						Any													=  'huggingface/label-files'
						a__       :						Union[str, Any]													=  'imagenet-1k-id2label.json'
						a__       :						List[Any]													=  json.load(open(hf_hub_download(_A						,       _A						,       repo_type="""dataset""")						,       """r"""))
						a__       :						Optional[Any]													=  {int(_A): v for k, v in idalabel.items()}
						a__       :						Tuple													=  idalabel
						a__       :						Any													=  {v: k for k, v in idalabel.items()}

			a__       :						int													=  img_size
			a__       :						int													=  num_classes
			a__       :						Dict													=  embed_dim
			a__       :						Optional[Any]													=  depths
			a__       :						List[Any]													=  num_heads
			a__       :						List[Any]													=  window_size

			return config

def      lowerCAmelCase_					(  _lowercase    :       str)							->	Union[str, Any]:







			"""simple docstring"""







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

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

			return name

def      lowerCAmelCase_					(  _lowercase    :       List[Any]						,       _lowercase    :       Optional[int])							->	List[Any]:







			"""simple docstring"""







			for key in orig_state_dict.copy().keys():
						a__       :						Optional[Any]													=  orig_state_dict.pop(_A)

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

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

			return orig_state_dict

def      lowerCAmelCase_					(  _lowercase    :       Optional[Any]						,       _lowercase    :       Any)							->	str:







			"""simple docstring"""
			a__       :						Optional[int]													=  timm.create_model(_A						,       pretrained=_A)
			timm_model.eval()

			a__       :						Any													=  get_swinva_config(_A)
			a__       :						Union[str, Any]													=  SwinvaForImageClassification(_A)
			model.eval()

			a__       :						Any													=  convert_state_dict(timm_model.state_dict()						,       _A)
			model.load_state_dict(_A)

			a__       :						Union[str, Any]													=  'http://images.cocodataset.org/val2017/000000039769.jpg'

			a__       :						Tuple													=  AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""						,       """-""")))
			a__       :						List[Any]													=  Image.open(requests.get(_A						,       stream=_A).raw)
			a__       :						int													=  image_processor(images=_A						,       return_tensors="""pt""")

			a__       :						Any													=  timm_model(inputs["""pixel_values"""])
			a__       :						Dict													=  model(**_A).logits

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

			print(F'''Saving model {swinva_name} to {pytorch_dump_folder_path}''')
			model.save_pretrained(_A)

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

			model.push_to_hub(
			    repo_path_or_name=Path(_A						,       _A)						,       organization="""nandwalritik"""						,       commit_message="""Add model"""						,       )


if __name__ == "__main__":
		_lowercase		:				Any      		=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."
		)

		_lowercase		:				str      		=parser.parse_args()
		convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase		:				str      		=logging.getLogger(__name__)

@dataclass
class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[float]      =					field(
						    default=0.0 ,     metadata={"help": "The label smoothing epsilon to apply (if not zero)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "Whether to SortishSamler or not."}		)
						__lowerCAmelCase					:bool      =					field(
						    default=A__ ,     metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "whether to use adafactor"}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Encoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Decoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(default=A__ ,     metadata={"help": "Dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Attention dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[str]      =					field(
						    default="linear" ,     metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}"} ,     )




