

'''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    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    List[str]:
			a__     = SwinConfig()
			a__     = swin_name.split("_"     )

			a__     = name_split[1]
			a__     = int(name_split[4]     )
			a__     = int(name_split[3][-1]     )

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

			if "in22k" in swin_name:
						a__     = 2_1841
			else:
						a__     = 1000
						a__     = "huggingface/label-files"
						a__     = "imagenet-1k-id2label.json"
						a__     = json.load(open(hf_hub_download(snake_case__					,  snake_case__					,  repo_type="dataset"     )					,  "r"     )     )
						a__     = {int(snake_case__     ): v for k, v in idalabel.items()}
						a__     = idalabel
						a__     = {v: k for k, v in idalabel.items()}

			a__     = img_size
			a__     = num_classes
			a__     = embed_dim
			a__     = depths
			a__     = num_heads
			a__     = window_size

			return config







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

			if name == "norm.weight":
						a__     = "layernorm.weight"
			if name == "norm.bias":
						a__     = "layernorm.bias"

			if "head" in name:
						a__     = name.replace("head"					,  "classifier"     )
			else:
						a__     = "swin." + name

			return name







def    _lowerCamelCase  (__lowerCamelCase  :     Union[str, Any]					,  __lowerCamelCase  :     Optional[int]     )   ->    List[Any]:
			for key in orig_state_dict.copy().keys():
						a__     = orig_state_dict.pop(snake_case__     )

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

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

			return orig_state_dict







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     str     )   ->    Tuple:
			a__     = timm.create_model(snake_case__					,  pretrained=snake_case__     )
			timm_model.eval()

			a__     = get_swin_config(snake_case__     )
			a__     = SwinForImageClassification(snake_case__     )
			model.eval()

			a__     = convert_state_dict(timm_model.state_dict()					,  snake_case__     )
			model.load_state_dict(snake_case__     )

			a__     = "http://images.cocodataset.org/val2017/000000039769.jpg"

			a__     = AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"					,  "-"     )     )     )
			a__     = Image.open(requests.get(snake_case__					,  stream=snake_case__     ).raw     )
			a__     = image_processor(images=snake_case__					,  return_tensors="pt"     )

			a__     = timm_model(inputs["pixel_values"]     )
			a__     = model(**snake_case__     ).logits

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

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

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


if __name__ == "__main__":
		lowerCAmelCase_   :   Optional[Any]       =					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."
		)

		lowerCAmelCase_   :   Tuple       =					parser.parse_args()
		convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)










'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    Optional[int]:
			a__     = [
			    "encoder.version",
			    "decoder.version",
			    "model.encoder.version",
			    "model.decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "encoder.embed_positions._float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    List[Any]:
			a__   ,				a__     = emb.weight.shape
			a__     = nn.Linear(__lowerCamelCase					,  __lowerCamelCase					,  bias=__lowerCamelCase     )
			a__     = emb.weight.data
			return lin_layer







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    Any:
			a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )
			a__     = mam_aaa["args"] or mam_aaa["cfg"]["model"]
			a__     = mam_aaa["model"]
			remove_ignore_keys_(__lowerCamelCase     )
			a__     = state_dict["encoder.embed_tokens.weight"].shape[0]

			a__     = MaMaaaConfig(
			    vocab_size=__lowerCamelCase					,  max_position_embeddings=1024					,  encoder_layers=args.encoder_layers					,  decoder_layers=args.decoder_layers					,  encoder_attention_heads=args.encoder_attention_heads					,  decoder_attention_heads=args.decoder_attention_heads					,  encoder_ffn_dim=args.encoder_ffn_embed_dim					,  decoder_ffn_dim=args.decoder_ffn_embed_dim					,  d_model=args.encoder_embed_dim					,  encoder_layerdrop=args.encoder_layerdrop					,  decoder_layerdrop=args.decoder_layerdrop					,  dropout=args.dropout					,  attention_dropout=args.attention_dropout					,  activation_dropout=args.activation_dropout					,  activation_function="relu"					,  )

			a__     = state_dict["decoder.embed_tokens.weight"]
			a__     = MaMaaaForConditionalGeneration(__lowerCamelCase     )
			model.model.load_state_dict(__lowerCamelCase					,  strict=__lowerCamelCase     )
			a__     = make_linear_from_emb(model.model.shared     )

			return model


if __name__ == "__main__":
		lowerCAmelCase_   :   Tuple       =					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		lowerCAmelCase_   :   Tuple       =					parser.parse_args()
		lowerCAmelCase_   :   int       =					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)



