


'''simple docstring'''



import inspect
import unittest

from transformers import MobileViTConfig
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 transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
		from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileViTImageProcessor


class 						_snake_case   (       a__		):




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Any       				=       self.config_class(**self.inputs_dict)
								self.parent.assertTrue(hasattr(_lowerCamelCase							,							"""hidden_sizes"""))
								self.parent.assertTrue(hasattr(_lowerCamelCase							,							"""neck_hidden_sizes"""))
								self.parent.assertTrue(hasattr(_lowerCamelCase							,							"""num_attention_heads"""))


class 						_snake_case   :




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=13							,							_lowerCamelCase=32							,							_lowerCamelCase=2							,							_lowerCamelCase=3							,							_lowerCamelCase=640							,							_lowerCamelCase=4							,							_lowerCamelCase="silu"							,							_lowerCamelCase=3							,							_lowerCamelCase=32							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.02							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=10							,							_lowerCamelCase=None							,							):
								UpperCAmelCase__					:   List[str]       				=       parent
								UpperCAmelCase__					:   Tuple       				=       batch_size
								UpperCAmelCase__					:   Union[str, Any]       				=       image_size
								UpperCAmelCase__					:   Union[str, Any]       				=       patch_size
								UpperCAmelCase__					:   int       				=       num_channels
								UpperCAmelCase__					:   Dict       				=       last_hidden_size
								UpperCAmelCase__					:   Any       				=       num_attention_heads
								UpperCAmelCase__					:   Dict       				=       hidden_act
								UpperCAmelCase__					:   Dict       				=       conv_kernel_size
								UpperCAmelCase__					:   List[Any]       				=       output_stride
								UpperCAmelCase__					:   Union[str, Any]       				=       hidden_dropout_prob
								UpperCAmelCase__					:   Any       				=       attention_probs_dropout_prob
								UpperCAmelCase__					:   int       				=       classifier_dropout_prob
								UpperCAmelCase__					:   int       				=       use_labels
								UpperCAmelCase__					:   Any       				=       is_training
								UpperCAmelCase__					:   Tuple       				=       num_labels
								UpperCAmelCase__					:   List[str]       				=       initializer_range
								UpperCAmelCase__					:   List[str]       				=       scope




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								UpperCAmelCase__					:   Dict       				=       None
								UpperCAmelCase__					:   Tuple       				=       None
								if self.use_labels:
											UpperCAmelCase__					:   Any       				=       ids_tensor([self.batch_size]							,							self.num_labels)
											UpperCAmelCase__					:   Dict       				=       ids_tensor([self.batch_size, self.image_size, self.image_size]							,							self.num_labels)

								UpperCAmelCase__					:   Any       				=       self.get_config()

								return config, pixel_values, labels, pixel_labels




					def 			snake_case__  ( self):
								return MobileViTConfig(
								    image_size=self.image_size							,							patch_size=self.patch_size							,							num_channels=self.num_channels							,							num_attention_heads=self.num_attention_heads							,							hidden_act=self.hidden_act							,							conv_kernel_size=self.conv_kernel_size							,							output_stride=self.output_stride							,							hidden_dropout_prob=self.hidden_dropout_prob							,							attention_probs_dropout_prob=self.attention_probs_dropout_prob							,							classifier_dropout_prob=self.classifier_dropout_prob							,							initializer_range=self.initializer_range							,							)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Any       				=       MobileViTModel(config=_lowerCamelCase)
								model.to(_lowerCamelCase)
								model.eval()
								UpperCAmelCase__					:   int       				=       model(_lowerCamelCase)
								self.parent.assertEqual(
								    result.last_hidden_state.shape							,							(
								        self.batch_size,
								        self.last_hidden_size,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )							,							)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   str       				=       self.num_labels
								UpperCAmelCase__					:   str       				=       MobileViTForImageClassification(_lowerCamelCase)
								model.to(_lowerCamelCase)
								model.eval()
								UpperCAmelCase__					:   Tuple       				=       model(_lowerCamelCase							,							labels=_lowerCamelCase)
								self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels))




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__					:   Dict       				=       self.num_labels
								UpperCAmelCase__					:   int       				=       MobileViTForSemanticSegmentation(_lowerCamelCase)
								model.to(_lowerCamelCase)
								model.eval()
								UpperCAmelCase__					:   Tuple       				=       model(_lowerCamelCase)
								self.parent.assertEqual(
								    result.logits.shape							,							(
								        self.batch_size,
								        self.num_labels,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )							,							)
								UpperCAmelCase__					:   Tuple       				=       model(_lowerCamelCase							,							labels=_lowerCamelCase)
								self.parent.assertEqual(
								    result.logits.shape							,							(
								        self.batch_size,
								        self.num_labels,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )							,							)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       self.prepare_config_and_inputs()
								UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   Union[str, Any]       				=       config_and_inputs
								UpperCAmelCase__					:   Dict       				=       {"""pixel_values""": pixel_values}
								return config, inputs_dict


@require_torch
class 						_snake_case   (       a__    ,				a__    ,				unittest.TestCase		):
					lowerCAmelCase :Tuple						=   (
					    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)
					lowerCAmelCase :List[str]						=   (
					    {
					        '''feature-extraction''': MobileViTModel,
					        '''image-classification''': MobileViTForImageClassification,
					        '''image-segmentation''': MobileViTForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					lowerCAmelCase :List[Any]						=   False
					lowerCAmelCase :Dict						=   False
					lowerCAmelCase :Optional[int]						=   False
					lowerCAmelCase :Optional[int]						=   False




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       MobileViTModelTester(self)
								UpperCAmelCase__					:   Any       				=       MobileViTConfigTester(self							,							config_class=_lowerCamelCase							,							has_text_modality=_lowerCamelCase)




					def 			snake_case__  ( self):
								self.config_tester.run_common_tests()




					@unittest.skip(reason="""MobileViT does not use inputs_embeds""")
					def 			snake_case__  ( self):
								pass




					@unittest.skip(reason="""MobileViT does not support input and output embeddings""")
					def 			snake_case__  ( self):
								pass




					@unittest.skip(reason="""MobileViT does not output attentions""")
					def 			snake_case__  ( self):
								pass




					def 			snake_case__  ( self):
								UpperCAmelCase__				,    UpperCAmelCase__					:   Any       				=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase__					:   Union[str, Any]       				=       model_class(_lowerCamelCase)
											UpperCAmelCase__					:   Tuple       				=       inspect.signature(model.forward)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											UpperCAmelCase__					:   List[str]       				=       [*signature.parameters.keys()]

											UpperCAmelCase__					:   List[str]       				=       ["""pixel_values"""]
											self.assertListEqual(arg_names[:1]							,							_lowerCamelCase)




					@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
					def 			snake_case__  ( self):
								pass




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowerCamelCase)




					def 			snake_case__  ( self):
								def check_hidden_states_output(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
											UpperCAmelCase__					:   int       				=       model_class(_lowerCamelCase)
											model.to(_lowerCamelCase)
											model.eval()

											with torch.no_grad():
														UpperCAmelCase__					:   Optional[int]       				=       model(**self._prepare_for_class(_lowerCamelCase							,							_lowerCamelCase))

											UpperCAmelCase__					:   Any       				=       outputs.hidden_states

											UpperCAmelCase__					:   Union[str, Any]       				=       5
											self.assertEqual(len(_lowerCamelCase)							,							_lowerCamelCase)

											# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
											# with the width and height being successively divided by 2.
											UpperCAmelCase__					:   int       				=       2
											for i in range(len(_lowerCamelCase)):
														self.assertListEqual(
														    list(hidden_states[i].shape[-2:])							,							[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]							,							)
														divisor *= 2

											self.assertEqual(self.model_tester.output_stride							,							divisor // 2)

								UpperCAmelCase__				,    UpperCAmelCase__					:   str       				=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase__					:   Union[str, Any]       				=       True
											check_hidden_states_output(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											UpperCAmelCase__					:   Tuple       				=       True

											check_hidden_states_output(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Dict       				=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCamelCase)




					@slow
					def 			snake_case__  ( self):
								for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCAmelCase__					:   Optional[int]       				=       MobileViTModel.from_pretrained(_lowerCamelCase)
											self.assertIsNotNone(_lowerCamelCase)







def       _UpperCamelCase (						):
			UpperCAmelCase__					:   str       				=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
			return image


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




					@cached_property
					def 			snake_case__  ( self):
								return MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small""") if is_vision_available() else None




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Dict       				=       MobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small""").to(_lowerCamelCase)

								UpperCAmelCase__					:   Optional[int]       				=       self.default_image_processor
								UpperCAmelCase__					:   Dict       				=       prepare_img()
								UpperCAmelCase__					:   Optional[Any]       				=       image_processor(images=_lowerCamelCase							,							return_tensors="""pt""").to(_lowerCamelCase)

								# forward pass
								with torch.no_grad():
											UpperCAmelCase__					:   Tuple       				=       model(**_lowerCamelCase)

								# verify the logits
								UpperCAmelCase__					:   Optional[Any]       				=       torch.Size((1, 1000))
								self.assertEqual(outputs.logits.shape							,							_lowerCamelCase)

								UpperCAmelCase__					:   Optional[Any]       				=       torch.tensor([-1.9364, -1.2327, -0.4653]).to(_lowerCamelCase)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]							,							_lowerCamelCase							,							atol=1e-4))




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   str       				=       MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""")
								UpperCAmelCase__					:   Optional[Any]       				=       model.to(_lowerCamelCase)

								UpperCAmelCase__					:   Dict       				=       MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""")

								UpperCAmelCase__					:   List[Any]       				=       prepare_img()
								UpperCAmelCase__					:   Optional[int]       				=       image_processor(images=_lowerCamelCase							,							return_tensors="""pt""").to(_lowerCamelCase)

								# forward pass
								with torch.no_grad():
											UpperCAmelCase__					:   List[str]       				=       model(**_lowerCamelCase)
								UpperCAmelCase__					:   Optional[Any]       				=       outputs.logits

								# verify the logits
								UpperCAmelCase__					:   Optional[Any]       				=       torch.Size((1, 21, 32, 32))
								self.assertEqual(logits.shape							,							_lowerCamelCase)

								UpperCAmelCase__					:   Dict       				=       torch.tensor(
								    [
								        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
								        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
								        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
								    ]							,							device=_lowerCamelCase							,							)

								self.assertTrue(torch.allclose(logits[0, :3, :3, :3]							,							_lowerCamelCase							,							atol=1e-4))




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       MobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""")
								UpperCAmelCase__					:   Dict       				=       model.to(_lowerCamelCase)

								UpperCAmelCase__					:   Any       				=       MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small""")

								UpperCAmelCase__					:   Tuple       				=       prepare_img()
								UpperCAmelCase__					:   Any       				=       image_processor(images=_lowerCamelCase							,							return_tensors="""pt""").to(_lowerCamelCase)

								# forward pass
								with torch.no_grad():
											UpperCAmelCase__					:   List[Any]       				=       model(**_lowerCamelCase)

								UpperCAmelCase__					:   List[str]       				=       outputs.logits.detach().cpu()

								UpperCAmelCase__					:   str       				=       image_processor.post_process_semantic_segmentation(outputs=_lowerCamelCase							,							target_sizes=[(50, 60)])
								UpperCAmelCase__					:   Tuple       				=       torch.Size((50, 60))
								self.assertEqual(segmentation[0].shape							,							_lowerCamelCase)

								UpperCAmelCase__					:   Dict       				=       image_processor.post_process_semantic_segmentation(outputs=_lowerCamelCase)
								UpperCAmelCase__					:   Optional[int]       				=       torch.Size((32, 32))
								self.assertEqual(segmentation[0].shape							,							_lowerCamelCase)



'''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		=logging.get_logger(__name__)







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__=False	, UpperCamelCase__=False   ):
			UpperCAmelCase__					:   str       				=       """backbone.""" if is_semantic else """"""

			UpperCAmelCase__					:   Optional[Any]       				=       []
			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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=False	, UpperCamelCase__=False   ):
			for i in range(config.num_hidden_layers   ):
						UpperCAmelCase__					:   Optional[Any]       				=       """backbone.""" if is_semantic else """"""
						# queries, keys and values
						UpperCAmelCase__					:   Any       				=       state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''   )
						UpperCAmelCase__					:   List[str]       				=       state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''   )
						UpperCAmelCase__					:   int       				=       state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''   )

						UpperCAmelCase__					:   Optional[Any]       				=       in_proj_weight[
						    : config.hidden_size, :
						]
						UpperCAmelCase__					:   Any       				=       q_bias
						UpperCAmelCase__					:   Dict       				=       in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCAmelCase__					:   Any       				=       in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCAmelCase__					:   Any       				=       v_bias

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

						UpperCAmelCase__					:   Union[str, Any]       				=       gamma_a
						UpperCAmelCase__					:   str       				=       gamma_a







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   int       				=       dct.pop(UpperCamelCase__   )
			UpperCAmelCase__					:   Optional[Any]       				=       val







def       _UpperCamelCase (						):
			UpperCAmelCase__					:   Any       				=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase__					:   Dict       				=       Image.open(requests.get(UpperCamelCase__	, stream=UpperCamelCase__   ).raw   )
			return im







@torch.no_grad()
def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=False   ):
			UpperCAmelCase__					:   Optional[Any]       				=       False if """rvlcdip""" in checkpoint_url else True
			UpperCAmelCase__					:   Any       				=       BeitConfig(use_absolute_position_embeddings=UpperCamelCase__	, use_mask_token=UpperCamelCase__   )

			# size of the architecture
			if "large" in checkpoint_url or "dit-l" in checkpoint_url:
						UpperCAmelCase__					:   Optional[Any]       				=       1_0_2_4
						UpperCAmelCase__					:   Dict       				=       4_0_9_6
						UpperCAmelCase__					:   Any       				=       2_4
						UpperCAmelCase__					:   Tuple       				=       1_6

			# labels
			if "rvlcdip" in checkpoint_url:
						UpperCAmelCase__					:   int       				=       1_6
						UpperCAmelCase__					:   List[str]       				=       """huggingface/label-files"""
						UpperCAmelCase__					:   Optional[Any]       				=       """rvlcdip-id2label.json"""
						UpperCAmelCase__					:   Optional[int]       				=       json.load(open(hf_hub_download(UpperCamelCase__	, UpperCamelCase__	, repo_type="""dataset"""   )	, """r"""   )   )
						UpperCAmelCase__					:   Union[str, Any]       				=       {int(UpperCamelCase__   ): v for k, v in idalabel.items()}
						UpperCAmelCase__					:   Optional[Any]       				=       idalabel
						UpperCAmelCase__					:   List[Any]       				=       {v: k for k, v in idalabel.items()}

			# load state_dict of original model, remove and rename some keys
			UpperCAmelCase__					:   Optional[Any]       				=       torch.hub.load_state_dict_from_url(UpperCamelCase__	, map_location="""cpu"""   )["""model"""]

			UpperCAmelCase__					:   List[str]       				=       create_rename_keys(UpperCamelCase__	, has_lm_head=UpperCamelCase__   )
			for src, dest in rename_keys:
						rename_key(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )
			read_in_q_k_v(UpperCamelCase__	, UpperCamelCase__	, has_lm_head=UpperCamelCase__   )

			# load HuggingFace model
			UpperCAmelCase__					:   str       				=       BeitForMaskedImageModeling(UpperCamelCase__   ) if has_lm_head else BeitForImageClassification(UpperCamelCase__   )
			model.eval()
			model.load_state_dict(UpperCamelCase__   )

			# Check outputs on an image
			UpperCAmelCase__					:   List[str]       				=       BeitImageProcessor(
			    size=config.image_size	, resample=PILImageResampling.BILINEAR	, do_center_crop=UpperCamelCase__   )
			UpperCAmelCase__					:   List[str]       				=       prepare_img()

			UpperCAmelCase__					:   Optional[Any]       				=       image_processor(images=UpperCamelCase__	, return_tensors="""pt"""   )
			UpperCAmelCase__					:   Optional[Any]       				=       encoding["""pixel_values"""]

			UpperCAmelCase__					:   Optional[Any]       				=       model(UpperCamelCase__   )
			UpperCAmelCase__					:   int       				=       outputs.logits

			# verify logits
			UpperCAmelCase__					:   int       				=       [1, 1_6] if """rvlcdip""" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
			assert logits.shape == torch.Size(UpperCamelCase__   ), "Shape of logits not as expected"

			Path(UpperCamelCase__   ).mkdir(exist_ok=UpperCamelCase__   )
			print(f'''Saving model 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:
						if has_lm_head:
									UpperCAmelCase__					:   Union[str, Any]       				=       """dit-base""" if """base""" in checkpoint_url else """dit-large"""
						else:
									UpperCAmelCase__					:   Tuple       				=       """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(UpperCamelCase__	, UpperCamelCase__   )	, organization="""nielsr"""	, commit_message="""Add image processor"""	, use_temp_dir=UpperCamelCase__	, )
						model.push_to_hub(
						    repo_path_or_name=Path(UpperCamelCase__	, UpperCamelCase__   )	, organization="""nielsr"""	, commit_message="""Add model"""	, use_temp_dir=UpperCamelCase__	, )


if __name__ == "__main__":
		__A		=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		=parser.parse_args()
		convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)