


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Any													=   SwinConfig(image_size=192      )

				if "base" in model_name:
								SCREAMING_SNAKE_CASE_   :	List[str]													=   6
								SCREAMING_SNAKE_CASE_   :	Tuple													=   128
								SCREAMING_SNAKE_CASE_   :	int													=   (2, 2, 18, 2)
								SCREAMING_SNAKE_CASE_   :	Tuple													=   (4, 8, 16, 32)
				elif "large" in model_name:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   12
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   192
								SCREAMING_SNAKE_CASE_   :	List[str]													=   (2, 2, 18, 2)
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   (6, 12, 24, 48)
				else:
								raise ValueError('Model not supported, only supports base and large variants'      )

				SCREAMING_SNAKE_CASE_   :	str													=   window_size
				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   embed_dim
				SCREAMING_SNAKE_CASE_   :	List[str]													=   depths
				SCREAMING_SNAKE_CASE_   :	str													=   num_heads

				return config



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				if "encoder.mask_token" in name:
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   name.replace('encoder.mask_token'    ,    'embeddings.mask_token'      )
				if "encoder.patch_embed.proj" in name:
								SCREAMING_SNAKE_CASE_   :	int													=   name.replace('encoder.patch_embed.proj'    ,    'embeddings.patch_embeddings.projection'      )
				if "encoder.patch_embed.norm" in name:
								SCREAMING_SNAKE_CASE_   :	str													=   name.replace('encoder.patch_embed.norm'    ,    'embeddings.norm'      )
				if "attn.proj" in name:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   name.replace('attn.proj'    ,    'attention.output.dense'      )
				if "attn" in name:
								SCREAMING_SNAKE_CASE_   :	int													=   name.replace('attn'    ,    'attention.self'      )
				if "norm1" in name:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   name.replace('norm1'    ,    'layernorm_before'      )
				if "norm2" in name:
								SCREAMING_SNAKE_CASE_   :	Dict													=   name.replace('norm2'    ,    'layernorm_after'      )
				if "mlp.fc1" in name:
								SCREAMING_SNAKE_CASE_   :	str													=   name.replace('mlp.fc1'    ,    'intermediate.dense'      )
				if "mlp.fc2" in name:
								SCREAMING_SNAKE_CASE_   :	Dict													=   name.replace('mlp.fc2'    ,    'output.dense'      )

				if name == "encoder.norm.weight":
								SCREAMING_SNAKE_CASE_   :	Dict													=   'layernorm.weight'
				if name == "encoder.norm.bias":
								SCREAMING_SNAKE_CASE_   :	Any													=   'layernorm.bias'

				if "decoder" in name:
								pass
				else:
								SCREAMING_SNAKE_CASE_   :	Dict													=   'swin.' + name

				return name



def 					_lowerCamelCase(			lowerCAmelCase__				:		Optional[Any]    ,    lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







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

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

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

				return orig_state_dict



def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		Any    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.load(lowerCAmelCase__    ,    map_location='cpu'      )['model']

				SCREAMING_SNAKE_CASE_   :	Dict													=   get_swin_config(lowerCAmelCase__      )
				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   SwinForMaskedImageModeling(lowerCAmelCase__      )
				model.eval()

				SCREAMING_SNAKE_CASE_   :	str													=   convert_state_dict(lowerCAmelCase__    ,    lowerCAmelCase__      )
				model.load_state_dict(lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   'http://images.cocodataset.org/val2017/000000039769.jpg'

				SCREAMING_SNAKE_CASE_   :	str													=   ViTImageProcessor(size={'height': 192, 'width': 192}      )
				SCREAMING_SNAKE_CASE_   :	Dict													=   Image.open(requests.get(lowerCAmelCase__    ,    stream=lowerCAmelCase__      ).raw      )
				SCREAMING_SNAKE_CASE_   :	Tuple													=   image_processor(images=lowerCAmelCase__    ,    return_tensors='pt'      )

				with torch.no_grad():
								SCREAMING_SNAKE_CASE_   :	Tuple													=   model(**lowerCAmelCase__      ).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(lowerCAmelCase__      )

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

				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__":
				A        =			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.'
				)

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







import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm
				import torch_xla.debug.metrics as met


class 							__a							(  __A						):





		'''simple docstring'''



		def __init__(						self ,    *UpperCamelCase__ ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    **UpperCamelCase__       ):
						super().__init__(*UpperCamelCase__ ,    **UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   eval_examples
						SCREAMING_SNAKE_CASE_   :	int													=   post_process_function


		def   __snake_case			(						self ,    UpperCamelCase__ = None ,    UpperCamelCase__=None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = "eval" ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	int													=   gen_kwargs.copy()
						SCREAMING_SNAKE_CASE_   :	Tuple													=   (
						    gen_kwargs['max_length'] if gen_kwargs.get('max_length'       ) is not None else self.args.generation_max_length
						)
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   (
						    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'       ) is not None else self.args.generation_num_beams
						)
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   gen_kwargs

						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.eval_dataset if eval_dataset is None else eval_dataset
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_eval_dataloader(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   self.eval_examples if eval_examples is None else eval_examples

						# Temporarily disable metric computation, we will do it in the loop here.
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.compute_metrics
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   None
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   time.time()
						SCREAMING_SNAKE_CASE_   :	int													=   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
						try:
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   eval_loop(
										    UpperCamelCase__ ,    description='Evaluation' ,    prediction_loss_only=True if compute_metrics is None else None ,    ignore_keys=UpperCamelCase__ ,    metric_key_prefix=UpperCamelCase__ ,    )
						finally:
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   compute_metrics
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.args.eval_batch_size * self.args.world_size
						if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
										start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
						output.metrics.update(
						    speed_metrics(
						        UpperCamelCase__ ,    UpperCamelCase__ ,    num_samples=output.num_samples ,    num_steps=math.ceil(output.num_samples / total_batch_size       ) ,    )       )

						if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
										# Only the main node write the results by default
										SCREAMING_SNAKE_CASE_   :	List[str]													=   self.post_process_function(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.compute_metrics(UpperCamelCase__       )

										# Prefix all keys with metric_key_prefix + '_'
										for key in list(metrics.keys()       ):
														if not key.startswith(F'''{metric_key_prefix}_'''       ):
																		SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   metrics.pop(UpperCamelCase__       )

										metrics.update(output.metrics       )
						else:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   output.metrics

						if self.args.should_log:
										# Only the main node log the results by default
										self.log(UpperCamelCase__       )

						if self.args.tpu_metrics_debug or self.args.debug:
										# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
										xm.master_print(met.metrics_report()       )

						SCREAMING_SNAKE_CASE_   :	Dict													=   self.callback_handler.on_evaluate(self.args ,    self.state ,    self.control ,    UpperCamelCase__       )
						return metrics






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__=None ,    UpperCamelCase__ = "test" ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   gen_kwargs.copy()

						SCREAMING_SNAKE_CASE_   :	Any													=   self.get_test_dataloader(UpperCamelCase__       )

						# Temporarily disable metric computation, we will do it in the loop here.
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.compute_metrics
						SCREAMING_SNAKE_CASE_   :	str													=   None
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   time.time()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
						try:
										SCREAMING_SNAKE_CASE_   :	List[str]													=   eval_loop(
										    UpperCamelCase__ ,    description='Prediction' ,    prediction_loss_only=True if compute_metrics is None else None ,    ignore_keys=UpperCamelCase__ ,    metric_key_prefix=UpperCamelCase__ ,    )
						finally:
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   compute_metrics

						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.args.eval_batch_size * self.args.world_size
						if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
										start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
						output.metrics.update(
						    speed_metrics(
						        UpperCamelCase__ ,    UpperCamelCase__ ,    num_samples=output.num_samples ,    num_steps=math.ceil(output.num_samples / total_batch_size       ) ,    )       )
						if self.post_process_function is None or self.compute_metrics is None:
										return output

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.post_process_function(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    'predict'       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.compute_metrics(UpperCamelCase__       )

						# Prefix all keys with metric_key_prefix + '_'
						for key in list(metrics.keys()       ):
										if not key.startswith(F'''{metric_key_prefix}_'''       ):
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   metrics.pop(UpperCamelCase__       )
						metrics.update(output.metrics       )
						return PredictionOutput(predictions=predictions.predictions ,    label_ids=predictions.label_ids ,    metrics=UpperCamelCase__       )