






"""simple docstring"""







import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwiftFormerForImageClassification, SwiftFormerModel
	from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     str				,		__A:     Tuple				,		__A:     Optional[Any]=13				,		__A:     Optional[int]=3				,		__A:     Dict=True				,		__A:     Union[str, Any]=True				,		__A:     Union[str, Any]=0.1				,		__A:     Any=0.1				,		__A:     Union[str, Any]=224				,		__A:     Dict=1000				,		__A:     Optional[int]=[3, 3, 6, 4]				,		__A:     int=[48, 56, 112, 220]				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  batch_size
												a__          =  num_channels
												a__          =  is_training
												a__          =  use_labels
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  num_labels
												a__          =  image_size
												a__          =  layer_depths
												a__          =  embed_dims




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

												a__          =  None
												if self.use_labels:
																	a__          =  ids_tensor([self.batch_size]				,		self.num_labels    )

												a__          =  self.get_config()

												return config, pixel_values, labels




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												return SwiftFormerConfig(
												    depths=self.layer_depths				,		embed_dims=self.embed_dims				,		mlp_ratio=4				,		downsamples=[True, True, True, True]				,		hidden_act='''gelu'''				,		num_labels=self.num_labels				,		down_patch_size=3				,		down_stride=2				,		down_pad=1				,		drop_rate=0.0				,		drop_path_rate=0.0				,		use_layer_scale=__A				,		layer_scale_init_value=1e-5				,		)




							def  lowercase		(   self:     Tuple				,		__A:     List[str]				,		__A:     List[str]				,		__A:     List[Any]    ):

												'''simple docstring'''

												a__          =  SwiftFormerModel(config=__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A    )
												self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.embed_dims[-1], 7, 7)    )




							def  lowercase		(   self:     List[str]				,		__A:     Dict				,		__A:     Optional[int]				,		__A:     str    ):

												'''simple docstring'''

												a__          =  self.num_labels
												a__          =  SwiftFormerForImageClassification(__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A				,		labels=__A    )
												self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)    )

												a__          =  SwiftFormerForImageClassification(__A    )
												model.to(__A    )
												model.eval()

												a__          =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
												a__          =  model(__A    )
												self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)    )





							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												((a__)       ,(a__)       ,(a__))          =  self.prepare_config_and_inputs()
												a__          =  {'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_torch
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE    =(
							    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  SwiftFormerModelTester(self    )
												a__          =  ConfigTester(
												    self				,		config_class=__A				,		has_text_modality=__A				,		hidden_size=37				,		num_attention_heads=12				,		num_hidden_layers=12				,		)




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												self.config_tester.run_common_tests()




							@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''    )
							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												pass




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	a__          =  model_class(__A    )
																	a__          =  model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(__A				,		nn.Linear    )    )




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	a__          =  model_class(__A    )
																	a__          =  inspect.signature(model.forward    )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	a__          =  [*signature.parameters.keys()]

																	a__          =  ['''pixel_values''']
																	self.assertListEqual(arg_names[:1]				,		__A    )




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__A    )




							@slow
							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	a__          =  SwiftFormerModel.from_pretrained(__A    )
																	self.assertIsNotNone(__A    )




							@unittest.skip(reason='''SwiftFormer does not output attentions'''    )
							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												pass




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												def check_hidden_states_output(__A:     List[Any]				,		__A:     int				,		__A:     List[str]    ):
																	a__          =  model_class(__A    )
																	model.to(__A    )
																	model.eval()

																	with torch.no_grad():
																						a__          =  model(**self._prepare_for_class(__A				,		__A    )    )

																	a__          =  outputs.hidden_states

																	a__          =  8
																	self.assertEqual(len(__A    )				,		__A    )  # TODO

																	# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
																	# with the width and height being successively divided by 2, after every 2 blocks
																	for i in range(len(__A    )    ):
																						self.assertEqual(
																						    hidden_states[i].shape				,		torch.Size(
																						        [
																						            self.model_tester.batch_size,
																						            self.model_tester.embed_dims[i // 2],
																						            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																						            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																						        ]    )				,		)

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	a__          =  True
																	check_hidden_states_output(__A				,		__A				,		__A    )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	a__          =  True

																	check_hidden_states_output(__A				,		__A				,		__A    )




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												def _config_zero_init(__A:     List[str]    ):
																	a__          =  copy.deepcopy(__A    )
																	for key in configs_no_init.__dict__.keys():
																						if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																											setattr(__A				,		__A				,		1e-10    )
																						if isinstance(getattr(__A				,		__A				,		__A    )				,		__A    ):
																											a__          =  _config_zero_init(getattr(__A				,		__A    )    )
																											setattr(__A				,		__A				,		__A    )
																	return configs_no_init

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()

												a__          =  _config_zero_init(__A    )
												for model_class in self.all_model_classes:
																	a__          =  model_class(config=__A    )
																	for name, param in model.named_parameters():
																						if param.requires_grad:
																											self.assertIn(
																											    ((param.data.mean() * 1e9) / 1e9).round().item()				,		[0.0, 1.0]				,		msg=F'Parameter {name} of model {model_class} seems not properly initialized'				,		)





							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												pass





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
					return image







@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@cached_property
							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''    ) if is_vision_available() else None





							@slow
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''    ).to(__A    )

												a__          =  self.default_image_processor
												a__          =  prepare_img()
												a__          =  image_processor(images=__A				,		return_tensors='''pt'''    ).to(__A    )

												# forward pass
												with torch.no_grad():
																	a__          =  model(**__A    )

												# verify the logits
												a__          =  torch.Size((1, 1000)    )
												self.assertEqual(outputs.logits.shape				,		__A    )

												a__          =  torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]    ).to(__A    )
												self.assertTrue(torch.allclose(outputs.logits[0, :3]				,		__A				,		atol=1e-4    )    )










"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__a       :	int             = logging.get_logger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False ,     lowerCamelCase_=False):
					a__          =  '''backbone.''' if is_semantic else ''''''

					a__          =  []
					for i in range(config.num_hidden_layers):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((f'{prefix}blocks.{i}.norm1.weight', f'beit.encoder.layer.{i}.layernorm_before.weight'))
										rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'beit.encoder.layer.{i}.layernorm_before.bias'))
										rename_keys.append(
										    (f'{prefix}blocks.{i}.attn.proj.weight', f'beit.encoder.layer.{i}.attention.output.dense.weight'))
										rename_keys.append(
										    (f'{prefix}blocks.{i}.attn.proj.bias', f'beit.encoder.layer.{i}.attention.output.dense.bias'))
										rename_keys.append((f'{prefix}blocks.{i}.norm2.weight', f'beit.encoder.layer.{i}.layernorm_after.weight'))
										rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'beit.encoder.layer.{i}.layernorm_after.bias'))
										rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.weight', f'beit.encoder.layer.{i}.intermediate.dense.weight'))
										rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.bias', f'beit.encoder.layer.{i}.intermediate.dense.bias'))
										rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'beit.encoder.layer.{i}.output.dense.weight'))
										rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'beit.encoder.layer.{i}.output.dense.bias'))

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        (f'{prefix}cls_token', '''beit.embeddings.cls_token'''),
					        (f'{prefix}patch_embed.proj.weight', '''beit.embeddings.patch_embeddings.projection.weight'''),
					        (f'{prefix}patch_embed.proj.bias', '''beit.embeddings.patch_embeddings.projection.bias'''),
					        (f'{prefix}pos_embed', '''beit.embeddings.position_embeddings'''),
					    ])

					if has_lm_head:
										# mask token + layernorm
										rename_keys.extend(
										    [
										        ('''mask_token''', '''beit.embeddings.mask_token'''),
										        ('''norm.weight''', '''layernorm.weight'''),
										        ('''norm.bias''', '''layernorm.bias'''),
										    ])
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
										        ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
										        ('''head.weight''', '''classifier.weight'''),
										        ('''head.bias''', '''classifier.bias'''),
										    ])

					return rename_keys





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False ,     lowerCamelCase_=False):
					for i in range(config.num_hidden_layers):
										a__          =  '''backbone.''' if is_semantic else ''''''
										# queries, keys and values
										a__          =  state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight')
										a__          =  state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias')
										a__          =  state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias')

										a__          =  in_proj_weight[
										    : config.hidden_size, :
										]
										a__          =  q_bias
										a__          =  in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										a__          =  in_proj_weight[
										    -config.hidden_size :, :
										]
										a__          =  v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										a__          =  state_dict.pop(f'{prefix}blocks.{i}.gamma_1')
										a__          =  state_dict.pop(f'{prefix}blocks.{i}.gamma_2')

										a__          =  gamma_a
										a__          =  gamma_a





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  dct.pop(lowerCamelCase_)
					a__          =  val





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					a__          =  Image.open(requests.get(lowerCamelCase_ ,     stream=lowerCamelCase_).raw)
					return im





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					a__          =  False if '''rvlcdip''' in checkpoint_url else True
					a__          =  BeitConfig(use_absolute_position_embeddings=lowerCamelCase_ ,     use_mask_token=lowerCamelCase_)

					# size of the architecture
					if "large" in checkpoint_url or "dit-l" in checkpoint_url:
										a__          =  1024
										a__          =  4096
										a__          =  24
										a__          =  16

					# labels
					if "rvlcdip" in checkpoint_url:
										a__          =  16
										a__          =  '''huggingface/label-files'''
										a__          =  '''rvlcdip-id2label.json'''
										a__          =  json.load(open(hf_hub_download(lowerCamelCase_ ,     lowerCamelCase_ ,     repo_type='''dataset''') ,     '''r'''))
										a__          =  {int(lowerCamelCase_): v for k, v in idalabel.items()}
										a__          =  idalabel
										a__          =  {v: k for k, v in idalabel.items()}

					# load state_dict of original model, remove and rename some keys
					a__          =  torch.hub.load_state_dict_from_url(lowerCamelCase_ ,     map_location='''cpu''')['''model''']

					a__          =  create_rename_keys(lowerCamelCase_ ,     has_lm_head=lowerCamelCase_)
					for src, dest in rename_keys:
										rename_key(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					read_in_q_k_v(lowerCamelCase_ ,     lowerCamelCase_ ,     has_lm_head=lowerCamelCase_)

					# load HuggingFace model
					a__          =  BeitForMaskedImageModeling(lowerCamelCase_) if has_lm_head else BeitForImageClassification(lowerCamelCase_)
					model.eval()
					model.load_state_dict(lowerCamelCase_)

					# Check outputs on an image
					a__          =  BeitImageProcessor(
					    size=config.image_size ,     resample=PILImageResampling.BILINEAR ,     do_center_crop=lowerCamelCase_)
					a__          =  prepare_img()

					a__          =  image_processor(images=lowerCamelCase_ ,     return_tensors='''pt''')
					a__          =  encoding['''pixel_values''']

					a__          =  model(lowerCamelCase_)
					a__          =  outputs.logits

					# verify logits
					a__          =  [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
					assert logits.shape == torch.Size(lowerCamelCase_), "Shape of logits not as expected"

					Path(lowerCamelCase_).mkdir(exist_ok=lowerCamelCase_)
					print(f'Saving model 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:
										if has_lm_head:
															a__          =  '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
										else:
															a__          =  '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
										image_processor.push_to_hub(
										    repo_path_or_name=Path(lowerCamelCase_ ,     lowerCamelCase_) ,     organization='''nielsr''' ,     commit_message='''Add image processor''' ,     use_temp_dir=lowerCamelCase_ ,     )
										model.push_to_hub(
										    repo_path_or_name=Path(lowerCamelCase_ ,     lowerCamelCase_) ,     organization='''nielsr''' ,     commit_message='''Add model''' ,     use_temp_dir=lowerCamelCase_ ,     )


if __name__ == "__main__":
	__a       :	Dict             = argparse.ArgumentParser()

	parser.add_argument(
	    '--checkpoint_url',
	    default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
	    type=str,
	    help='URL to the original PyTorch checkpoint (.pth file).',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
	)
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	)
	__a       :	List[Any]             = parser.parse_args()
	convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


