


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 			lowerCAmelCase__					(     a__:						Union[str, Any]			)    ->						Any:


							'''simple docstring'''





							_UpperCAmelCase											=     SwinConfig(image_size=1_9_2			)

							if "base" in model_name:
														_UpperCAmelCase											=     6
														_UpperCAmelCase											=     1_2_8
														_UpperCAmelCase											=     (2, 2, 1_8, 2)
														_UpperCAmelCase											=     (4, 8, 1_6, 3_2)
							elif "large" in model_name:
														_UpperCAmelCase											=     1_2
														_UpperCAmelCase											=     1_9_2
														_UpperCAmelCase											=     (2, 2, 1_8, 2)
														_UpperCAmelCase											=     (6, 1_2, 2_4, 4_8)
							else:
														raise ValueError('Model not supported, only supports base and large variants'			)

							_UpperCAmelCase											=     window_size
							_UpperCAmelCase											=     embed_dim
							_UpperCAmelCase											=     depths
							_UpperCAmelCase											=     num_heads

							return config

def 			lowerCAmelCase__					(     a__:						Any			)    ->						str:


							'''simple docstring'''





							if "encoder.mask_token" in name:
														_UpperCAmelCase											=     name.replace('encoder.mask_token'     ,    'embeddings.mask_token'			)
							if "encoder.patch_embed.proj" in name:
														_UpperCAmelCase											=     name.replace('encoder.patch_embed.proj'     ,    'embeddings.patch_embeddings.projection'			)
							if "encoder.patch_embed.norm" in name:
														_UpperCAmelCase											=     name.replace('encoder.patch_embed.norm'     ,    'embeddings.norm'			)
							if "attn.proj" in name:
														_UpperCAmelCase											=     name.replace('attn.proj'     ,    'attention.output.dense'			)
							if "attn" in name:
														_UpperCAmelCase											=     name.replace('attn'     ,    'attention.self'			)
							if "norm1" in name:
														_UpperCAmelCase											=     name.replace('norm1'     ,    'layernorm_before'			)
							if "norm2" in name:
														_UpperCAmelCase											=     name.replace('norm2'     ,    'layernorm_after'			)
							if "mlp.fc1" in name:
														_UpperCAmelCase											=     name.replace('mlp.fc1'     ,    'intermediate.dense'			)
							if "mlp.fc2" in name:
														_UpperCAmelCase											=     name.replace('mlp.fc2'     ,    'output.dense'			)

							if name == "encoder.norm.weight":
														_UpperCAmelCase											=     'layernorm.weight'
							if name == "encoder.norm.bias":
														_UpperCAmelCase											=     'layernorm.bias'

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

							return name

def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Optional[Any]			)    ->						Tuple:


							'''simple docstring'''





							for key in orig_state_dict.copy().keys():
														_UpperCAmelCase											=     orig_state_dict.pop(A__			)

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

																					if "weight" in key:
																												_UpperCAmelCase											=     val[:dim, :]
																												_UpperCAmelCase											=     val[
																												    dim : dim * 2, :
																												]
																												_UpperCAmelCase											=     val[-dim:, :]
																					else:
																												_UpperCAmelCase											=     val[
																												    :dim
																												]
																												_UpperCAmelCase											=     val[
																												    dim : dim * 2
																												]
																												_UpperCAmelCase											=     val[
																												    -dim:
																												]
														else:
																					_UpperCAmelCase											=     val

							return orig_state_dict

def 			lowerCAmelCase__					(     a__:						Tuple     ,    a__:						str     ,    a__:						Tuple     ,    a__:						List[str]			)    ->						str:


							'''simple docstring'''





							_UpperCAmelCase											=     torch.load(A__     ,    map_location='cpu'			)['model']

							_UpperCAmelCase											=     get_swin_config(A__			)
							_UpperCAmelCase											=     SwinForMaskedImageModeling(A__			)
							model.eval()

							_UpperCAmelCase											=     convert_state_dict(A__     ,    A__			)
							model.load_state_dict(A__			)

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

							_UpperCAmelCase											=     ViTImageProcessor(size={'height': 1_9_2, 'width': 1_9_2}			)
							_UpperCAmelCase											=     Image.open(requests.get(A__     ,    stream=A__			).raw			)
							_UpperCAmelCase											=     image_processor(images=A__     ,    return_tensors='pt'			)

							with torch.no_grad():
														_UpperCAmelCase											=     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__    :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.'''
		)

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





from sklearn.metrics import matthews_corrcoef

import datasets


a__   :					Any 						=      "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

a__   :					Any 						=      "\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

a__   :					str 						=      "\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,				_KWARGS_DESCRIPTION						)
class    UpperCAmelCase__(				datasets.Metric						):

   '''simple docstring'''




   def       UpperCAmelCase		(    self	:      List[Any])						->					int:




         """simple docstring"""

         return datasets.MetricInfo(
             description=_DESCRIPTION      ,					citation=_CITATION      ,					inputs_description=_KWARGS_DESCRIPTION      ,					features=datasets.Features(
                 {
                     'predictions': datasets.Value('int32'),
                     'references': datasets.Value('int32'),
                 })      ,					reference_urls=[
                 'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'
             ]      ,					)



   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Union[str, Any]=None)						->					Any:




         """simple docstring"""

         return {
             "matthews_correlation": float(matthews_corrcoef(lowerCAmelCase      ,					lowerCAmelCase      ,					sample_weight=lowerCAmelCase)),
         }
