


"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def 						UpperCAmelCase				(			A__:    Tuple     )  ->     Union[str, Any]:
			__lowerCamelCase					:	Dict	       =				SwinConfig(image_size=192     )

			if "base" in model_name:
						__lowerCamelCase					:	Optional[int]	       =				6
						__lowerCamelCase					:	int	       =				128
						__lowerCamelCase					:	Union[str, Any]	       =				(2, 2, 18, 2)
						__lowerCamelCase					:	str	       =				(4, 8, 16, 32)
			elif "large" in model_name:
						__lowerCamelCase					:	Tuple	       =				12
						__lowerCamelCase					:	List[Any]	       =				192
						__lowerCamelCase					:	Tuple	       =				(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					:	Optional[int]	       =				embed_dim
			__lowerCamelCase					:	Optional[Any]	       =				depths
			__lowerCamelCase					:	List[Any]	       =				num_heads

			return config





def 						UpperCAmelCase				(			A__:    Union[str, Any]     )  ->     Optional[Any]:






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

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

			if "decoder" in name:
						pass
			else:
						__lowerCamelCase					:	List[str]	       =				'swin.' + name

			return name





def 						UpperCAmelCase				(			A__:    List[Any]							,				A__:    Optional[Any]     )  ->     Optional[int]:






			for key in orig_state_dict.copy().keys():
						__lowerCamelCase					:	Optional[Any]	       =				orig_state_dict.pop(_snake_case     )

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

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

			return orig_state_dict





def 						UpperCAmelCase				(			A__:    List[Any]							,				A__:    Dict							,				A__:    Union[str, Any]							,				A__:    Any     )  ->     str:
			__lowerCamelCase					:	str	       =				torch.load(_snake_case							,				map_location='cpu'     )['model']

			__lowerCamelCase					:	Tuple	       =				get_swin_config(_snake_case     )
			__lowerCamelCase					:	str	       =				SwinForMaskedImageModeling(_snake_case     )
			model.eval()

			__lowerCamelCase					:	List[Any]	       =				convert_state_dict(_snake_case							,				_snake_case     )
			model.load_state_dict(_snake_case     )

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

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

			with torch.no_grad():
						__lowerCamelCase					:	Dict	       =				model(**_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(_snake_case     )

						print(f'''Saving image processor to {pytorch_dump_folder_path}'''     )
						image_processor.save_pretrained(_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__":
					a_				:    Union[str, 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_				:    int										=     parser.parse_args()
					convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""
import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    DebertaVaForMaskedLM,
					    DebertaVaForMultipleChoice,
					    DebertaVaForQuestionAnswering,
					    DebertaVaForSequenceClassification,
					    DebertaVaForTokenClassification,
					    DebertaVaModel,
					)
					from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST

class __lowercase(      lowercase__				):




					'''simple docstring'''


					def __init__(    self						,	__a						,	__a=13						,	__a=7						,	__a=True						,	__a=True						,	__a=True						,	__a=True						,	__a=99						,	__a=32						,	__a=5						,	__a=4						,	__a=37						,	__a="gelu"						,	__a=0.1						,	__a=0.1						,	__a=512						,	__a=16						,	__a=2						,	__a=0.02						,	__a=False						,	__a=True						,	__a="None"						,	__a=3						,	__a=4						,	__a=None						,	):
								__lowerCamelCase					:	List[str]	       =				parent
								__lowerCamelCase					:	Dict	       =				batch_size
								__lowerCamelCase					:	str	       =				seq_length
								__lowerCamelCase					:	Optional[int]	       =				is_training
								__lowerCamelCase					:	Dict	       =				use_input_mask
								__lowerCamelCase					:	Dict	       =				use_token_type_ids
								__lowerCamelCase					:	Dict	       =				use_labels
								__lowerCamelCase					:	Optional[Any]	       =				vocab_size
								__lowerCamelCase					:	Any	       =				hidden_size
								__lowerCamelCase					:	List[Any]	       =				num_hidden_layers
								__lowerCamelCase					:	Tuple	       =				num_attention_heads
								__lowerCamelCase					:	Any	       =				intermediate_size
								__lowerCamelCase					:	Optional[Any]	       =				hidden_act
								__lowerCamelCase					:	Any	       =				hidden_dropout_prob
								__lowerCamelCase					:	Optional[Any]	       =				attention_probs_dropout_prob
								__lowerCamelCase					:	List[Any]	       =				max_position_embeddings
								__lowerCamelCase					:	Optional[Any]	       =				type_vocab_size
								__lowerCamelCase					:	Dict	       =				type_sequence_label_size
								__lowerCamelCase					:	Any	       =				initializer_range
								__lowerCamelCase					:	Union[str, Any]	       =				num_labels
								__lowerCamelCase					:	Tuple	       =				num_choices
								__lowerCamelCase					:	str	       =				relative_attention
								__lowerCamelCase					:	Optional[int]	       =				position_biased_input
								__lowerCamelCase					:	int	       =				pos_att_type
								__lowerCamelCase					:	str	       =				scope
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Union[str, Any]	       =				ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size     )

								__lowerCamelCase					:	int	       =				None
								if self.use_input_mask:
											__lowerCamelCase					:	int	       =				ids_tensor([self.batch_size, self.seq_length]						,	vocab_size=2     )

								__lowerCamelCase					:	Tuple	       =				None
								if self.use_token_type_ids:
											__lowerCamelCase					:	Any	       =				ids_tensor([self.batch_size, self.seq_length]						,	self.type_vocab_size     )

								__lowerCamelCase					:	Optional[Any]	       =				None
								__lowerCamelCase					:	Optional[Any]	       =				None
								__lowerCamelCase					:	int	       =				None
								if self.use_labels:
											__lowerCamelCase					:	Optional[int]	       =				ids_tensor([self.batch_size]						,	self.type_sequence_label_size     )
											__lowerCamelCase					:	Optional[int]	       =				ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels     )
											__lowerCamelCase					:	List[Any]	       =				ids_tensor([self.batch_size]						,	self.num_choices     )

								__lowerCamelCase					:	Optional[Any]	       =				self.get_config()

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
					def     snake_case_				(    self     ):
								return DebertaVaConfig(
								    vocab_size=self.vocab_size						,	hidden_size=self.hidden_size						,	num_hidden_layers=self.num_hidden_layers						,	num_attention_heads=self.num_attention_heads						,	intermediate_size=self.intermediate_size						,	hidden_act=self.hidden_act						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	type_vocab_size=self.type_vocab_size						,	initializer_range=self.initializer_range						,	relative_attention=self.relative_attention						,	position_biased_input=self.position_biased_input						,	pos_att_type=self.pos_att_type						,	)
					def     snake_case_				(    self						,	__a     ):
								self.parent.assertListEqual(list(result.loss.size()     )						,	[]     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	int	       =				DebertaVaModel(config=__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	str	       =				model(__a						,	attention_mask=__a						,	token_type_ids=__a     )[0]
								__lowerCamelCase					:	str	       =				model(__a						,	token_type_ids=__a     )[0]
								__lowerCamelCase					:	Optional[Any]	       =				model(__a     )[0]

								self.parent.assertListEqual(list(sequence_output.size()     )						,	[self.batch_size, self.seq_length, self.hidden_size]     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	List[str]	       =				DebertaVaForMaskedLM(config=__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	Union[str, Any]	       =				model(__a						,	attention_mask=__a						,	token_type_ids=__a						,	labels=__a     )

								self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	Optional[int]	       =				self.num_labels
								__lowerCamelCase					:	List[Any]	       =				DebertaVaForSequenceClassification(__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	Optional[Any]	       =				model(__a						,	attention_mask=__a						,	token_type_ids=__a						,	labels=__a     )
								self.parent.assertListEqual(list(result.logits.size()     )						,	[self.batch_size, self.num_labels]     )
								self.check_loss_output(__a     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	int	       =				self.num_labels
								__lowerCamelCase					:	Dict	       =				DebertaVaForTokenClassification(config=__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	int	       =				model(__a						,	attention_mask=__a						,	token_type_ids=__a						,	labels=__a     )
								self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	Optional[Any]	       =				DebertaVaForQuestionAnswering(config=__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	Any	       =				model(
								    __a						,	attention_mask=__a						,	token_type_ids=__a						,	start_positions=__a						,	end_positions=__a						,	)
								self.parent.assertEqual(result.start_logits.shape						,	(self.batch_size, self.seq_length)     )
								self.parent.assertEqual(result.end_logits.shape						,	(self.batch_size, self.seq_length)     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a						,	__a     ):
								__lowerCamelCase					:	Any	       =				DebertaVaForMultipleChoice(config=__a     )
								model.to(__a     )
								model.eval()
								__lowerCamelCase					:	Union[str, Any]	       =				input_ids.unsqueeze(1     ).expand(-1						,	self.num_choices						,	-1     ).contiguous()
								__lowerCamelCase					:	Optional[int]	       =				token_type_ids.unsqueeze(1     ).expand(-1						,	self.num_choices						,	-1     ).contiguous()
								__lowerCamelCase					:	int	       =				input_mask.unsqueeze(1     ).expand(-1						,	self.num_choices						,	-1     ).contiguous()
								__lowerCamelCase					:	List[Any]	       =				model(
								    __a						,	attention_mask=__a						,	token_type_ids=__a						,	labels=__a						,	)
								self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)     )





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				self.prepare_config_and_inputs()
								(
								    (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      
								)					:	List[str]	       =				config_and_inputs
								__lowerCamelCase					:	int	       =				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
								return config, inputs_dict



@require_torch
class __lowercase(      lowercase__	,	lowercase__	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						Dict     		= (
					    (
					        DebertaVaModel,
					        DebertaVaForMaskedLM,
					        DebertaVaForSequenceClassification,
					        DebertaVaForTokenClassification,
					        DebertaVaForQuestionAnswering,
					        DebertaVaForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					__a						:						Tuple     		= (
					    {
					        'feature-extraction': DebertaVaModel,
					        'fill-mask': DebertaVaForMaskedLM,
					        'question-answering': DebertaVaForQuestionAnswering,
					        'text-classification': DebertaVaForSequenceClassification,
					        'token-classification': DebertaVaForTokenClassification,
					        'zero-shot': DebertaVaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					__a						:						str     		= True
					__a						:						Dict     		= False
					__a						:						Tuple     		= False
					__a						:						Optional[Any]     		= False
					__a						:						List[Any]     		= False
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				DebertaVaModelTester(self     )
								__lowerCamelCase					:	Tuple	       =				ConfigTester(self						,	config_class=__a						,	hidden_size=37     )
					def     snake_case_				(    self     ):
								self.config_tester.run_common_tests()
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_model(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_sequence_classification(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_masked_lm(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	str	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_question_answering(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_token_classification(*__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_deberta_for_multiple_choice(*__a     )





					@slow
					def     snake_case_				(    self     ):
								for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase					:	Tuple	       =				DebertaVaModel.from_pretrained(__a     )
											self.assertIsNotNone(__a     )



@require_torch
@require_sentencepiece
@require_tokenizers
class __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					@unittest.skip(reason='Model not available yet'     )
					def     snake_case_				(    self     ):
								pass





					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'     )

								__lowerCamelCase					:	Any	       =				torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]     )
								__lowerCamelCase					:	Any	       =				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
								with torch.no_grad():
											__lowerCamelCase					:	Union[str, Any]	       =				model(__a						,	attention_mask=__a     )[0]
								# compare the actual values for a slice.
								__lowerCamelCase					:	str	       =				torch.tensor(
								    [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]     )
								self.assertTrue(torch.allclose(output[:, 1:4, 1:4]						,	__a						,	atol=1E-4     )						,	f'''{output[:, 1:4, 1:4]}'''     )



