'''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    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					str           =							SwinConfig()
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							swin_name.split('''_'''		)

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							name_split[1]
						SCREAMING_SNAKE_CASE							:					str           =							int(name_split[4]		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							int(name_split[3][-1]		)

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

						if "in22k" in swin_name:
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							21_841
						else:
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							1_000
												SCREAMING_SNAKE_CASE							:					List[str]           =							'''huggingface/label-files'''
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''imagenet-1k-id2label.json'''
												SCREAMING_SNAKE_CASE							:					Any           =							json.load(open(hf_hub_download(a_	,						a_	,						repo_type='''dataset'''		)	,						'''r'''		)		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{int(a_		): v for k, v in idalabel.items()}
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							idalabel
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{v: k for k, v in idalabel.items()}

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

						return config
def    A			(					_lowercase		):






						if "patch_embed.proj" in name:
												SCREAMING_SNAKE_CASE							:					int           =							name.replace('''patch_embed.proj'''	,						'''embeddings.patch_embeddings.projection'''		)
						if "patch_embed.norm" in name:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							name.replace('''patch_embed.norm'''	,						'''embeddings.norm'''		)
						if "layers" in name:
												SCREAMING_SNAKE_CASE							:					str           =							'''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							:					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							:					Union[str, Any]           =							name.replace('''mlp.fc1'''	,						'''intermediate.dense'''		)
						if "mlp.fc2" in name:
												SCREAMING_SNAKE_CASE							:					List[str]           =							name.replace('''mlp.fc2'''	,						'''output.dense'''		)

						if name == "norm.weight":
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''layernorm.weight'''
						if name == "norm.bias":
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''layernorm.bias'''

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

						return name
def    A			(					_lowercase	,						_lowercase		):






						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:					List[Any]           =							orig_state_dict.pop(a_		)

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

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

						return orig_state_dict
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							timm.create_model(a_	,						pretrained=a_		)
						timm_model.eval()

						SCREAMING_SNAKE_CASE							:					Any           =							get_swin_config(a_		)
						SCREAMING_SNAKE_CASE							:					Any           =							SwinForImageClassification(a_		)
						model.eval()

						SCREAMING_SNAKE_CASE							:					Optional[int]           =							convert_state_dict(timm_model.state_dict()	,						a_		)
						model.load_state_dict(a_		)

						SCREAMING_SNAKE_CASE							:					List[str]           =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''

						SCREAMING_SNAKE_CASE							:					Tuple           =							AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''	,						'''-'''		)		)		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							Image.open(requests.get(a_	,						stream=a_		).raw		)
						SCREAMING_SNAKE_CASE							:					int           =							image_processor(images=a_	,						return_tensors='''pt'''		)

						SCREAMING_SNAKE_CASE							:					str           =							timm_model(inputs['''pixel_values''']		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							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__":
					__UpperCamelCase	: 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.'
					)

					__UpperCamelCase	: List[Any]    		=  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_tf_available, is_torch_available


__UpperCamelCase	: Tuple    		=  {
    'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
    'tokenization_ctrl': ['CTRLTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Dict    		=  [
					    'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CTRLForSequenceClassification',
					    'CTRLLMHeadModel',
					    'CTRLModel',
					    'CTRLPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Any    		=  [
					    'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCTRLForSequenceClassification',
					    'TFCTRLLMHeadModel',
					    'TFCTRLModel',
					    'TFCTRLPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
					from .tokenization_ctrl import CTRLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ctrl import (
										    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CTRLForSequenceClassification,
										    CTRLLMHeadModel,
										    CTRLModel,
										    CTRLPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ctrl import (
										    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCTRLForSequenceClassification,
										    TFCTRLLMHeadModel,
										    TFCTRLModel,
										    TFCTRLPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase	: Optional[Any]    		=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





