






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__	(    _a       :			int    ):
		snake_case_				:							Tuple						 =       SwinConfig()
		snake_case_				:							Optional[int]						 =       swin_name.split("_"    )

		snake_case_				:							List[str]						 =       name_split[1]
		snake_case_				:							Union[str, Any]						 =       int(name_split[4]    )
		snake_case_				:							List[Any]						 =       int(name_split[3][-1]    )

		if model_size == "tiny":
				snake_case_				:							Dict						 =       96
				snake_case_				:							str						 =       (2, 2, 6, 2)
				snake_case_				:							Union[str, Any]						 =       (3, 6, 12, 24)
		elif model_size == "small":
				snake_case_				:							List[str]						 =       96
				snake_case_				:							List[Any]						 =       (2, 2, 18, 2)
				snake_case_				:							Optional[Any]						 =       (3, 6, 12, 24)
		elif model_size == "base":
				snake_case_				:							Tuple						 =       1_28
				snake_case_				:							Optional[Any]						 =       (2, 2, 18, 2)
				snake_case_				:							List[str]						 =       (4, 8, 16, 32)
		else:
				snake_case_				:							Any						 =       1_92
				snake_case_				:							Dict						 =       (2, 2, 18, 2)
				snake_case_				:							List[Any]						 =       (6, 12, 24, 48)

		if "in22k" in swin_name:
				snake_case_				:							Tuple						 =       2_18_41
		else:
				snake_case_				:							str						 =       10_00
				snake_case_				:							List[Any]						 =       "huggingface/label-files"
				snake_case_				:							Tuple						 =       "imagenet-1k-id2label.json"
				snake_case_				:							str						 =       json.load(open(hf_hub_download(_a  ,      _a  ,      repo_type="dataset"    )  ,      "r"    )    )
				snake_case_				:							Any						 =       {int(_a    ): v for k, v in idalabel.items()}
				snake_case_				:							Optional[Any]						 =       idalabel
				snake_case_				:							str						 =       {v: k for k, v in idalabel.items()}

		snake_case_				:							str						 =       img_size
		snake_case_				:							Dict						 =       num_classes
		snake_case_				:							List[Any]						 =       embed_dim
		snake_case_				:							Optional[Any]						 =       depths
		snake_case_				:							Tuple						 =       num_heads
		snake_case_				:							Tuple						 =       window_size

		return config

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

		if name == "norm.weight":
				snake_case_				:							Union[str, Any]						 =       "layernorm.weight"
		if name == "norm.bias":
				snake_case_				:							Dict						 =       "layernorm.bias"

		if "head" in name:
				snake_case_				:							Tuple						 =       name.replace("head"  ,      "classifier"    )
		else:
				snake_case_				:							Union[str, Any]						 =       "swin." + name

		return name

def      lowerCAmelCase__	(    _a       :			List[Any]  ,      _a       :			str    ):
		for key in orig_state_dict.copy().keys():
				snake_case_				:							List[str]						 =       orig_state_dict.pop(_a    )

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

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

		return orig_state_dict

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			Union[str, Any]    ):
		snake_case_				:							Dict						 =       timm.create_model(_a  ,      pretrained=_a    )
		timm_model.eval()

		snake_case_				:							List[str]						 =       get_swin_config(_a    )
		snake_case_				:							int						 =       SwinForImageClassification(_a    )
		model.eval()

		snake_case_				:							str						 =       convert_state_dict(timm_model.state_dict()  ,      _a    )
		model.load_state_dict(_a    )

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

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

		snake_case_				:							List[str]						 =       timm_model(inputs["pixel_values"]    )
		snake_case_				:							List[Any]						 =       model(**_a    ).logits

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

		print(F'''Saving model {swin_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    )


if __name__ == "__main__":
							lowercase  :							Tuple			  =    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.'''
							)

							lowercase  :							Optional[int]			  =    parser.parse_args()
							convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase  :							Optional[int]			  =    {
    '''configuration_nllb_moe''': [
        '''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''NllbMoeConfig''',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[Any]			  =    [
							    '''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''NllbMoeForConditionalGeneration''',
							    '''NllbMoeModel''',
							    '''NllbMoePreTrainedModel''',
							    '''NllbMoeTop2Router''',
							    '''NllbMoeSparseMLP''',
							]


if TYPE_CHECKING:
							from .configuration_nllb_moe import (
							    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    NllbMoeConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nllb_moe import (
														    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NllbMoeForConditionalGeneration,
														    NllbMoeModel,
														    NllbMoePreTrainedModel,
														    NllbMoeSparseMLP,
														    NllbMoeTopaRouter,
														)


else:
							import sys

							lowercase  :							Tuple			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
