






"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 							snake_case (  A__		):
			UpperCAmelCase_	:					Union[str, Any]     	=   SwinConfig(image_size=1_92		)

			if "base" in model_name:
						UpperCAmelCase_	:					Any     	=   6
						UpperCAmelCase_	:					Dict     	=   1_28
						UpperCAmelCase_	:					List[Any]     	=   (2, 2, 18, 2)
						UpperCAmelCase_	:					Dict     	=   (4, 8, 16, 32)
			elif "large" in model_name:
						UpperCAmelCase_	:					List[Any]     	=   12
						UpperCAmelCase_	:					List[str]     	=   1_92
						UpperCAmelCase_	:					Optional[int]     	=   (2, 2, 18, 2)
						UpperCAmelCase_	:					Tuple     	=   (6, 12, 24, 48)
			else:
						raise ValueError("Model not supported, only supports base and large variants"		)

			UpperCAmelCase_	:					List[Any]     	=   window_size
			UpperCAmelCase_	:					Union[str, Any]     	=   embed_dim
			UpperCAmelCase_	:					Any     	=   depths
			UpperCAmelCase_	:					str     	=   num_heads

			return config

def 							snake_case (  A__		):
			if "encoder.mask_token" in name:
						UpperCAmelCase_	:					Union[str, Any]     	=   name.replace("encoder.mask_token"      ,"embeddings.mask_token"		)
			if "encoder.patch_embed.proj" in name:
						UpperCAmelCase_	:					Tuple     	=   name.replace("encoder.patch_embed.proj"      ,"embeddings.patch_embeddings.projection"		)
			if "encoder.patch_embed.norm" in name:
						UpperCAmelCase_	:					str     	=   name.replace("encoder.patch_embed.norm"      ,"embeddings.norm"		)
			if "attn.proj" in name:
						UpperCAmelCase_	:					Optional[Any]     	=   name.replace("attn.proj"      ,"attention.output.dense"		)
			if "attn" in name:
						UpperCAmelCase_	:					Tuple     	=   name.replace("attn"      ,"attention.self"		)
			if "norm1" in name:
						UpperCAmelCase_	:					Optional[int]     	=   name.replace("norm1"      ,"layernorm_before"		)
			if "norm2" in name:
						UpperCAmelCase_	:					Tuple     	=   name.replace("norm2"      ,"layernorm_after"		)
			if "mlp.fc1" in name:
						UpperCAmelCase_	:					Union[str, Any]     	=   name.replace("mlp.fc1"      ,"intermediate.dense"		)
			if "mlp.fc2" in name:
						UpperCAmelCase_	:					Any     	=   name.replace("mlp.fc2"      ,"output.dense"		)

			if name == "encoder.norm.weight":
						UpperCAmelCase_	:					Any     	=   "layernorm.weight"
			if name == "encoder.norm.bias":
						UpperCAmelCase_	:					str     	=   "layernorm.bias"

			if "decoder" in name:
						pass
			else:
						UpperCAmelCase_	:					List[Any]     	=   "swin." + name

			return name

def 							snake_case (  A__      ,A__		):
			for key in orig_state_dict.copy().keys():
						UpperCAmelCase_	:					Tuple     	=   orig_state_dict.pop(A__		)

						if "attn_mask" in key:
									pass
						elif "qkv" in key:
									UpperCAmelCase_	:					str     	=   key.split("."		)
									UpperCAmelCase_	:					Optional[int]     	=   int(key_split[2]		)
									UpperCAmelCase_	:					str     	=   int(key_split[4]		)
									UpperCAmelCase_	:					Optional[int]     	=   model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

			return orig_state_dict

def 							snake_case (  A__      ,A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Tuple     	=   torch.load(A__      ,map_location="cpu"		)["model"]

			UpperCAmelCase_	:					Dict     	=   get_swin_config(A__		)
			UpperCAmelCase_	:					int     	=   SwinForMaskedImageModeling(A__		)
			model.eval()

			UpperCAmelCase_	:					Dict     	=   convert_state_dict(A__      ,A__		)
			model.load_state_dict(A__		)

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

			UpperCAmelCase_	:					Optional[Any]     	=   ViTImageProcessor(size={"height": 1_92, "width": 1_92}		)
			UpperCAmelCase_	:					Tuple     	=   Image.open(requests.get(A__      ,stream=A__		).raw		)
			UpperCAmelCase_	:					Union[str, Any]     	=   image_processor(images=A__      ,return_tensors="pt"		)

			with torch.no_grad():
						UpperCAmelCase_	:					Tuple     	=   model(**A__		).logits

			print(outputs.keys()		)
			print("Looks ok!"		)

			if pytorch_dump_folder_path is not None:
						print(F"""Saving model {model_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 push_to_hub:
						print(F"""Pushing model and image processor for {model_name} to hub"""		)
						model.push_to_hub(F"""microsoft/{model_name}"""		)
						image_processor.push_to_hub(F"""microsoft/{model_name}"""		)


if __name__ == "__main__":
			lowerCamelCase_							      = argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''swin-base-simmim-window6-192''',
			    type=str,
			    choices=['''swin-base-simmim-window6-192''', '''swin-large-simmim-window12-192'''],
			    help='''Name of the Swin SimMIM model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--checkpoint_path''',
			    default='''/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth''',
			    type=str,
			    help='''Path to the original PyTorch checkpoint (.pth file).''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)
			parser.add_argument(
			    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
			)

			lowerCamelCase_							      = parser.parse_args()
			convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











"""simple docstring"""
from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {
    '''huggingface/informer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''informer'''
	__magic_name__       =							{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	    '''num_hidden_layers''': '''encoder_layers''',
	}





	def __init__( self   :					Optional[Any]      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					str = "student_t"      ,      lowerCAmelCase_   :					str = "nll"      ,      lowerCAmelCase_   :					int = 1      ,      lowerCAmelCase_   :					List[int] = None      ,      lowerCAmelCase_   :					Optional[Union[str, bool]] = "mean"      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					Optional[List[int]] = None      ,      lowerCAmelCase_   :					Optional[List[int]] = None      ,      lowerCAmelCase_   :					int = 64      ,      lowerCAmelCase_   :					int = 32      ,      lowerCAmelCase_   :					int = 32      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					str = "gelu"      ,      lowerCAmelCase_   :					float = 0.0_5      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					int = 100      ,      lowerCAmelCase_   :					float = 0.0_2      ,      lowerCAmelCase_   :					List[Any]=True      ,      lowerCAmelCase_   :					str = "prob"      ,      lowerCAmelCase_   :					int = 5      ,      lowerCAmelCase_   :					bool = True      ,      **lowerCAmelCase_   :					Tuple      ,      )		->     Tuple:
				# time series specific configuration
				UpperCAmelCase_	:					str     	=   prediction_length
				UpperCAmelCase_	:					Tuple     	=   context_length or prediction_length
				UpperCAmelCase_	:					Any     	=   distribution_output
				UpperCAmelCase_	:					Union[str, Any]     	=   loss
				UpperCAmelCase_	:					Any     	=   input_size
				UpperCAmelCase_	:					int     	=   num_time_features
				UpperCAmelCase_	:					Union[str, Any]     	=   lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
				UpperCAmelCase_	:					List[Any]     	=   scaling
				UpperCAmelCase_	:					List[str]     	=   num_dynamic_real_features
				UpperCAmelCase_	:					int     	=   num_static_real_features
				UpperCAmelCase_	:					Any     	=   num_static_categorical_features

				# set cardinality
				if cardinality and num_static_categorical_features > 0:
							if len(lowerCAmelCase_		) != num_static_categorical_features:
										raise ValueError(
										    "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
							UpperCAmelCase_	:					Optional[Any]     	=   cardinality
				else:
							UpperCAmelCase_	:					Optional[int]     	=   [0]

				# set embedding_dimension
				if embedding_dimension and num_static_categorical_features > 0:
							if len(lowerCAmelCase_		) != num_static_categorical_features:
										raise ValueError(
										    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
							UpperCAmelCase_	:					Any     	=   embedding_dimension
				else:
							UpperCAmelCase_	:					Optional[Any]     	=   [min(50      ,      (cat + 1) // 2		) for cat in self.cardinality]

				UpperCAmelCase_	:					Dict     	=   num_parallel_samples

				# Transformer architecture configuration
				UpperCAmelCase_	:					Optional[Any]     	=   input_size * len(self.lags_sequence		) + self._number_of_features
				UpperCAmelCase_	:					List[Any]     	=   d_model
				UpperCAmelCase_	:					List[str]     	=   encoder_attention_heads
				UpperCAmelCase_	:					List[str]     	=   decoder_attention_heads
				UpperCAmelCase_	:					Union[str, Any]     	=   encoder_ffn_dim
				UpperCAmelCase_	:					Union[str, Any]     	=   decoder_ffn_dim
				UpperCAmelCase_	:					Tuple     	=   encoder_layers
				UpperCAmelCase_	:					List[Any]     	=   decoder_layers

				UpperCAmelCase_	:					List[str]     	=   dropout
				UpperCAmelCase_	:					Optional[Any]     	=   attention_dropout
				UpperCAmelCase_	:					Any     	=   activation_dropout
				UpperCAmelCase_	:					Union[str, Any]     	=   encoder_layerdrop
				UpperCAmelCase_	:					Optional[int]     	=   decoder_layerdrop

				UpperCAmelCase_	:					Union[str, Any]     	=   activation_function
				UpperCAmelCase_	:					int     	=   init_std

				UpperCAmelCase_	:					Optional[Any]     	=   use_cache

				# Informer
				UpperCAmelCase_	:					int     	=   attention_type
				UpperCAmelCase_	:					List[str]     	=   sampling_factor
				UpperCAmelCase_	:					Union[str, Any]     	=   distil

				super().__init__(is_encoder_decoder=lowerCAmelCase_      ,      **lowerCAmelCase_		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     int:
				return (
				    sum(self.embedding_dimension		)
				    + self.num_dynamic_real_features
				    + self.num_time_features
				    + self.num_static_real_features
				    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
				)



