


'''simple docstring'''



import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str       ):
					__a      :	Tuple    =  SwinConfig(image_size=192       )

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

					__a      :	Tuple    =  window_size
					__a      :	Optional[int]    =  embed_dim
					__a      :	str    =  depths
					__a      :	Dict    =  num_heads

					return config






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

					if name == "encoder.norm.weight":
										__a      :	List[str]    =  'layernorm.weight'
					if name == "encoder.norm.bias":
										__a      :	Optional[Any]    =  'layernorm.bias'

					if "decoder" in name:
										pass
					else:
										__a      :	Optional[Any]    =  'swin.' + name

					return name






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       List[Any]       ):
					for key in orig_state_dict.copy().keys():
										__a      :	int    =  orig_state_dict.pop(_SCREAMING_SNAKE_CASE       )

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

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

					return orig_state_dict






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Any						,				_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       Optional[int]       ):
					__a      :	int    =  torch.load(_SCREAMING_SNAKE_CASE						,				map_location='cpu'       )['model']

					__a      :	Any    =  get_swin_config(_SCREAMING_SNAKE_CASE       )
					__a      :	List[str]    =  SwinForMaskedImageModeling(_SCREAMING_SNAKE_CASE       )
					model.eval()

					__a      :	List[Any]    =  convert_state_dict(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
					model.load_state_dict(_SCREAMING_SNAKE_CASE       )

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

					__a      :	Any    =  ViTImageProcessor(size={'height': 192, 'width': 192}       )
					__a      :	Optional[int]    =  Image.open(requests.get(_SCREAMING_SNAKE_CASE						,				stream=_SCREAMING_SNAKE_CASE       ).raw       )
					__a      :	List[str]    =  image_processor(images=_SCREAMING_SNAKE_CASE						,				return_tensors='pt'       )

					with torch.no_grad():
										__a      :	Optional[Any]    =  model(**_SCREAMING_SNAKE_CASE       ).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(_SCREAMING_SNAKE_CASE       )

										print(F"""Saving image processor to {pytorch_dump_folder_path}"""       )
										image_processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

					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__":
	__lowercase     :						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.'
	)

	__lowercase     :						Tuple	=     parser.parse_args()
	convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''



import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list





@require_torchaudio
@require_sentencepiece
class 							__UpperCamelCase    (		unittest.TestCase ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  'laion/clap-htsat-unfused'
						__a      :	Optional[Any]    =  tempfile.mkdtemp()




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return RobertaTokenizer.from_pretrained(self.checkpoint						,    **__a							)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						return ClapFeatureExtractor.from_pretrained(self.checkpoint						,    **__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  self.get_tokenizer()
						__a      :	List[str]    =  self.get_feature_extractor()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						processor.save_pretrained(self.tmpdirname							)
						__a      :	Tuple    =  ClapProcessor.from_pretrained(self.tmpdirname							)

						self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer.get_vocab()							)
						self.assertIsInstance(processor.tokenizer						,    __a							)

						self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  ClapProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						__a      :	int    =  self.get_tokenizer(bos_token='(BOS)'						,    eos_token='(EOS)'							)
						__a      :	List[str]    =  self.get_feature_extractor(do_normalize=__a						,    padding_value=1.0							)

						__a      :	Tuple    =  ClapProcessor.from_pretrained(
						    self.tmpdirname						,    bos_token='(BOS)'						,    eos_token='(EOS)'						,    do_normalize=__a						,    padding_value=1.0							)

						self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer_add_kwargs.get_vocab()							)
						self.assertIsInstance(processor.tokenizer						,    __a							)

						self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor_add_kwargs.to_json_string()							)
						self.assertIsInstance(processor.feature_extractor						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	int    =  self.get_tokenizer()

						__a      :	str    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	int    =  floats_list((3, 1000)							)

						__a      :	str    =  feature_extractor(__a						,    return_tensors='np'							)
						__a      :	int    =  processor(audios=__a						,    return_tensors='np'							)

						for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()						,    input_processor[key].sum()						,    delta=1E-2							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.get_feature_extractor()
						__a      :	Any    =  self.get_tokenizer()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	Union[str, Any]    =  'This is a test string'

						__a      :	Union[str, Any]    =  processor(text=__a							)

						__a      :	Tuple    =  tokenizer(__a							)

						for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]						,    encoded_processor[key]							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	str    =  self.get_tokenizer()

						__a      :	List[str]    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	Dict    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__a      :	Optional[int]    =  processor.batch_decode(__a							)
						__a      :	Optional[Any]    =  tokenizer.batch_decode(__a							)

						self.assertListEqual(__a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.get_feature_extractor()
						__a      :	Optional[int]    =  self.get_tokenizer()

						__a      :	int    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						self.assertListEqual(
						    processor.model_input_names[2:]						,    feature_extractor.model_input_names						,    msg='`processor` and `feature_extractor` model input names do not match'						,    )



