






'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	str:
							lowerCamelCase__ :     str	  =	SwinConfig(image_size=192				)

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

							lowerCamelCase__ :     Dict	  =	window_size
							lowerCamelCase__ :     str	  =	embed_dim
							lowerCamelCase__ :     Optional[Any]	  =	depths
							lowerCamelCase__ :     List[Any]	  =	num_heads

							return config





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

							if name == "encoder.norm.weight":
														lowerCamelCase__ :     int	  =	'''layernorm.weight'''
							if name == "encoder.norm.bias":
														lowerCamelCase__ :     Optional[int]	  =	'''layernorm.bias'''

							if "decoder" in name:
														pass
							else:
														lowerCamelCase__ :     Any	  =	'''swin.''' + name

							return name





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	Dict:
							for key in orig_state_dict.copy().keys():
														lowerCamelCase__ :     Optional[int]	  =	orig_state_dict.pop(UpperCamelCase__				)

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

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

							return orig_state_dict





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	List[Any]:
							lowerCamelCase__ :     Any	  =	torch.load(UpperCamelCase__    ,      map_location="""cpu"""				)['''model''']

							lowerCamelCase__ :     Union[str, Any]	  =	get_swin_config(UpperCamelCase__				)
							lowerCamelCase__ :     Tuple	  =	SwinForMaskedImageModeling(UpperCamelCase__				)
							model.eval()

							lowerCamelCase__ :     Tuple	  =	convert_state_dict(UpperCamelCase__    ,      UpperCamelCase__				)
							model.load_state_dict(UpperCamelCase__				)

							lowerCamelCase__ :     str	  =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''

							lowerCamelCase__ :     Optional[int]	  =	ViTImageProcessor(size={"""height""": 192, """width""": 192}				)
							lowerCamelCase__ :     Tuple	  =	Image.open(requests.get(UpperCamelCase__    ,      stream=UpperCamelCase__				).raw				)
							lowerCamelCase__ :     Optional[int]	  =	image_processor(images=UpperCamelCase__    ,      return_tensors="""pt"""				)

							with torch.no_grad():
														lowerCamelCase__ :     str	  =	model(**UpperCamelCase__				).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(UpperCamelCase__				)

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

							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     :      Optional[Any]   					=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     :      Optional[Any]   					=parser.parse_args()
			convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''







import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase=None    ,      UpperCamelCase=None				)				->	Any:
							return field(default_factory=lambda: default    ,      metadata=UpperCamelCase				)

@dataclass
class _lowercase     :
							a    				=	field(
							    metadata={"""help""": """The csv file to plot."""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Disable logarithmic scale when plotting"""}    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={
							        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
							    }    ,				)
							a    				=	field(
							    default=_lowercase    ,				metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}    ,				)
							a    				=	list_field(
							    default=_lowercase    ,				metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}		)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							try:
														int(UpperCamelCase				)
														return True
							except ValueError:
														return False





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	int:
							try:
														float(UpperCamelCase				)
														return True
							except ValueError:
														return False

class _lowercase     :




							def __init__(							self:    Tuple      ,							UpperCamelCase__:    str					):
														lowerCamelCase__ :     int	  =	args
														lowerCamelCase__ :     Optional[int]	  =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

														with open(self.args.csv_file      ,							newline=""""""					) as csv_file:
																					lowerCamelCase__ :     str	  =	csv.DictReader(UpperCamelCase__					)
																					for row in reader:
																												lowerCamelCase__ :     Optional[int]	  =	row["""model"""]
																												self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]					)					)
																												self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]					)					)
																												if can_convert_to_int(row["""result"""]					):
																																			# value is not None
																																			lowerCamelCase__ :     Tuple	  =	int(row["""result"""]					)
																												elif can_convert_to_float(row["""result"""]					):
																																			# value is not None
																																			lowerCamelCase__ :     Any	  =	float(row["""result"""]					)




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__  ,		lowerCamelCase__ :     Tuple	  =	plt.subplots()
														lowerCamelCase__ :     Any	  =	"""Time usage""" if self.args.is_time else """Memory usage"""
														lowerCamelCase__ :     List[str]	  =	title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

														if not self.args.no_log_scale:
																					# set logarithm scales
																					ax.set_xscale("""log"""					)
																					ax.set_yscale("""log"""					)

														for axis in [ax.xaxis, ax.yaxis]:
																					axis.set_major_formatter(ScalarFormatter()					)

														for model_name_idx, model_name in enumerate(self.result_dict.keys()					):
																					lowerCamelCase__ :     Any	  =	sorted(set(self.result_dict[model_name]["""bsz"""]					)					)
																					lowerCamelCase__ :     int	  =	sorted(set(self.result_dict[model_name]["""seq_len"""]					)					)
																					lowerCamelCase__ :     Any	  =	self.result_dict[model_name]["""result"""]

																					((lowerCamelCase__)  ,		(lowerCamelCase__)) :     Dict	  =	(
																					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																					)

																					lowerCamelCase__ :     Any	  =	(
																					    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
																					)

																					for inner_loop_value in inner_loop_array:
																												if self.args.plot_along_batch:
																																			lowerCamelCase__ :     int	  =	np.asarray(
																																			    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,							dtype=UpperCamelCase__      ,							)
																												else:
																																			lowerCamelCase__ :     List[Any]	  =	np.asarray(
																																			    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,							dtype=np.floataa      ,							)

																												((lowerCamelCase__)  ,		(lowerCamelCase__)) :     List[str]	  =	(
																												    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																												)

																												lowerCamelCase__ :     int	  =	np.asarray(UpperCamelCase__      ,							UpperCamelCase__					)[: len(UpperCamelCase__					)]
																												plt.scatter(
																												    UpperCamelCase__      ,							UpperCamelCase__      ,							label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''					)
																												plt.plot(UpperCamelCase__      ,							UpperCamelCase__      ,							"""--"""					)

																					title_str += F''' {label_model_name} vs.'''

														lowerCamelCase__ :     Any	  =	title_str[:-4]
														lowerCamelCase__ :     Optional[int]	  =	"""Time in s""" if self.args.is_time else """Memory in MB"""

														# plot
														plt.title(UpperCamelCase__					)
														plt.xlabel(UpperCamelCase__					)
														plt.ylabel(UpperCamelCase__					)
														plt.legend()

														if self.args.figure_png_file is not None:
																					plt.savefig(self.args.figure_png_file					)
														else:
																					plt.show()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     str	  =	HfArgumentParser(UpperCamelCase				)
							lowerCamelCase__ :     str	  =	parser.parse_args_into_dataclasses()[0]
							lowerCamelCase__ :     Any	  =	Plot(args=UpperCamelCase				)
							plot.plot()


if __name__ == "__main__":
			main()

