


'''simple docstring'''




import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
				import torch
				from torch import nn

				from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
				from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import DPTImageProcessor



class 			lowerCAmelCase					:



	def __init__(       self			,       snake_case__			,       snake_case__=2			,       snake_case__=32			,       snake_case__=16			,       snake_case__=3			,       snake_case__=True			,       snake_case__=True			,       snake_case__=32			,       snake_case__=4			,       snake_case__=[0, 1, 2, 3]			,       snake_case__=4			,       snake_case__=37			,       snake_case__="gelu"			,       snake_case__=0.1			,       snake_case__=0.1			,       snake_case__=0.0_2			,       snake_case__=3			,       snake_case__=False			,       snake_case__=None			,       ):
		lowerCAmelCase       :     Any   				=			parent
		lowerCAmelCase       :     Tuple   				=			batch_size
		lowerCAmelCase       :     Dict   				=			image_size
		lowerCAmelCase       :     Optional[Any]   				=			patch_size
		lowerCAmelCase       :     Optional[int]   				=			num_channels
		lowerCAmelCase       :     Tuple   				=			is_training
		lowerCAmelCase       :     List[Any]   				=			use_labels
		lowerCAmelCase       :     Any   				=			hidden_size
		lowerCAmelCase       :     str   				=			num_hidden_layers
		lowerCAmelCase       :     str   				=			backbone_out_indices
		lowerCAmelCase       :     Dict   				=			num_attention_heads
		lowerCAmelCase       :     Union[str, Any]   				=			intermediate_size
		lowerCAmelCase       :     Dict   				=			hidden_act
		lowerCAmelCase       :     List[str]   				=			hidden_dropout_prob
		lowerCAmelCase       :     Optional[Any]   				=			attention_probs_dropout_prob
		lowerCAmelCase       :     str   				=			initializer_range
		lowerCAmelCase       :     Union[str, Any]   				=			num_labels
		lowerCAmelCase       :     Union[str, Any]   				=			scope
		lowerCAmelCase       :     Optional[int]   				=			is_hybrid
		# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
		lowerCAmelCase       :     List[str]   				=			(image_size // patch_size) ** 2
		lowerCAmelCase       :     Dict   				=			num_patches + 1



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Optional[Any]   				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

		lowerCAmelCase       :     str   				=			None
		if self.use_labels:
			lowerCAmelCase       :     Any   				=			ids_tensor([self.batch_size, self.image_size, self.image_size]			,       self.num_labels     )

		lowerCAmelCase       :     Tuple   				=			self.get_config()

		return config, pixel_values, labels



	def 					lowercase  (       self     ):
		return DPTConfig(
		    image_size=self.image_size			,       patch_size=self.patch_size			,       num_channels=self.num_channels			,       hidden_size=self.hidden_size			,       num_hidden_layers=self.num_hidden_layers			,       backbone_out_indices=self.backbone_out_indices			,       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			,       is_decoder=lowerCamelCase_			,       initializer_range=self.initializer_range			,       is_hybrid=self.is_hybrid			,       )



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__     ):
		lowerCAmelCase       :     Union[str, Any]   				=			DPTModel(config=lowerCamelCase_     )
		model.to(lowerCamelCase_     )
		model.eval()
		lowerCAmelCase       :     Tuple   				=			model(lowerCamelCase_     )
		self.parent.assertEqual(result.last_hidden_state.shape			,       (self.batch_size, self.seq_length, self.hidden_size)     )



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__     ):
		lowerCAmelCase       :     Optional[Any]   				=			self.num_labels
		lowerCAmelCase       :     Dict   				=			DPTForDepthEstimation(lowerCamelCase_     )
		model.to(lowerCamelCase_     )
		model.eval()
		lowerCAmelCase       :     List[str]   				=			model(lowerCamelCase_     )
		self.parent.assertEqual(result.predicted_depth.shape			,       (self.batch_size, self.image_size, self.image_size)     )



	def 					lowercase  (       self			,       snake_case__			,       snake_case__			,       snake_case__     ):
		lowerCAmelCase       :     Any   				=			self.num_labels
		lowerCAmelCase       :     List[str]   				=			DPTForSemanticSegmentation(lowerCamelCase_     )
		model.to(lowerCamelCase_     )
		model.eval()
		lowerCAmelCase       :     Union[str, Any]   				=			model(lowerCamelCase_			,       labels=lowerCamelCase_     )
		self.parent.assertEqual(
		    result.logits.shape			,       (self.batch_size, self.num_labels, self.image_size, self.image_size)     )



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Any   				=			self.prepare_config_and_inputs()
		lowerCAmelCase				, lowerCAmelCase				, lowerCAmelCase       :     List[str]   				=			config_and_inputs
		lowerCAmelCase       :     int   				=			{'pixel_values': pixel_values}
		return config, inputs_dict



@require_torch
class 			lowerCAmelCase					(					A_   ,					A_   ,					unittest.TestCase				):
	_lowerCamelCase :       int         =			(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	_lowerCamelCase :       List[Any]         =			(
	    {
	        '''depth-estimation''': DPTForDepthEstimation,
	        '''feature-extraction''': DPTModel,
	        '''image-segmentation''': DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	_lowerCamelCase :       Tuple         =			False
	_lowerCamelCase :       List[str]         =			False
	_lowerCamelCase :       Tuple         =			False



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Dict   				=			DPTModelTester(self     )
		lowerCAmelCase       :     Union[str, Any]   				=			ConfigTester(self			,       config_class=lowerCamelCase_			,       has_text_modality=lowerCamelCase_			,       hidden_size=37     )



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



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



	def 					lowercase  (       self     ):
		lowerCAmelCase				, lowerCAmelCase       :     Optional[Any]   				=			self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowerCAmelCase       :     int   				=			model_class(lowerCamelCase_     )
			self.assertIsInstance(model.get_input_embeddings()			,       (nn.Module)     )
			lowerCAmelCase       :     List[str]   				=			model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(lowerCamelCase_			,       nn.Linear     )     )



	def 					lowercase  (       self     ):
		lowerCAmelCase				, lowerCAmelCase       :     Dict   				=			self.model_tester.prepare_config_and_inputs_for_common()

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

			lowerCAmelCase       :     str   				=			['pixel_values']
			self.assertListEqual(arg_names[:1]			,       lowerCamelCase_     )



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Optional[int]   				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCamelCase_     )



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     List[str]   				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase_     )



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     List[str]   				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase_     )



	def 					lowercase  (       self     ):
		for model_class in self.all_model_classes:
			if model_class.__name__ == "DPTForDepthEstimation":
				continue

			lowerCAmelCase				, lowerCAmelCase       :     Optional[int]   				=			self.model_tester.prepare_config_and_inputs_for_common()
			lowerCAmelCase       :     str   				=			True

			if model_class in get_values(lowerCamelCase_     ):
				continue

			lowerCAmelCase       :     List[str]   				=			model_class(lowerCamelCase_     )
			model.to(lowerCamelCase_     )
			model.train()
			lowerCAmelCase       :     str   				=			self._prepare_for_class(lowerCamelCase_			,       lowerCamelCase_			,       return_labels=lowerCamelCase_     )
			lowerCAmelCase       :     str   				=			model(**lowerCamelCase_     ).loss
			loss.backward()



	def 					lowercase  (       self     ):
		for model_class in self.all_model_classes:
			if model_class.__name__ == "DPTForDepthEstimation":
				continue

			lowerCAmelCase				, lowerCAmelCase       :     List[Any]   				=			self.model_tester.prepare_config_and_inputs_for_common()
			lowerCAmelCase       :     Union[str, Any]   				=			False
			lowerCAmelCase       :     List[str]   				=			True

			if model_class in get_values(lowerCamelCase_     ) or not model_class.supports_gradient_checkpointing:
				continue
			lowerCAmelCase       :     List[str]   				=			model_class(lowerCamelCase_     )
			model.to(lowerCamelCase_     )
			model.gradient_checkpointing_enable()
			model.train()
			lowerCAmelCase       :     int   				=			self._prepare_for_class(lowerCamelCase_			,       lowerCamelCase_			,       return_labels=lowerCamelCase_     )
			lowerCAmelCase       :     List[str]   				=			model(**lowerCamelCase_     ).loss
			loss.backward()



	def 					lowercase  (       self     ):
		lowerCAmelCase				, lowerCAmelCase       :     Optional[Any]   				=			self.model_tester.prepare_config_and_inputs_for_common()

		lowerCAmelCase       :     Dict   				=			_config_zero_init(lowerCamelCase_     )
		for model_class in self.all_model_classes:
			lowerCAmelCase       :     Tuple   				=			model_class(config=lowerCamelCase_     )
			# Skip the check for the backbone
			lowerCAmelCase       :     Optional[int]   				=			[]
			for name, module in model.named_modules():
				if module.__class__.__name__ == "DPTViTHybridEmbeddings":
					lowerCAmelCase       :     Any   				=			[f"{name}.{key}" for key in module.state_dict().keys()]
					break

			for name, param in model.named_parameters():
				if param.requires_grad:
					if name in backbone_params:
						continue
					self.assertIn(
					    ((param.data.mean() * 1e9).round() / 1e9).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     ):
		pass



	@slow
	def 					lowercase  (       self     ):
		for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowerCAmelCase       :     int   				=			DPTModel.from_pretrained(lowerCamelCase_     )
			self.assertIsNotNone(lowerCamelCase_     )


def       __UpperCamelCase     (     )     ->   Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase       :     Optional[int]   				=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
	return image






@require_torch
@require_vision
@slow
class 			lowerCAmelCase					(					unittest.TestCase				):



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Optional[int]   				=			DPTImageProcessor.from_pretrained('Intel/dpt-large'     )
		lowerCAmelCase       :     Tuple   				=			DPTForDepthEstimation.from_pretrained('Intel/dpt-large'     ).to(lowerCamelCase_     )

		lowerCAmelCase       :     List[Any]   				=			prepare_img()
		lowerCAmelCase       :     int   				=			image_processor(images=lowerCamelCase_			,       return_tensors='pt'     ).to(lowerCamelCase_     )

		# forward pass
		with torch.no_grad():
			lowerCAmelCase       :     Dict   				=			model(**lowerCamelCase_     )
			lowerCAmelCase       :     int   				=			outputs.predicted_depth

		# verify the predicted depth
		lowerCAmelCase       :     List[Any]   				=			torch.Size((1, 384, 384)     )
		self.assertEqual(predicted_depth.shape			,       lowerCamelCase_     )

		lowerCAmelCase       :     Tuple   				=			torch.tensor(
		    [[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]     ).to(lowerCamelCase_     )

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



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Optional[int]   				=			DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'     )
		lowerCAmelCase       :     List[str]   				=			DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'     ).to(lowerCamelCase_     )

		lowerCAmelCase       :     List[str]   				=			prepare_img()
		lowerCAmelCase       :     List[Any]   				=			image_processor(images=lowerCamelCase_			,       return_tensors='pt'     ).to(lowerCamelCase_     )

		# forward pass
		with torch.no_grad():
			lowerCAmelCase       :     Union[str, Any]   				=			model(**lowerCamelCase_     )

		# verify the logits
		lowerCAmelCase       :     Tuple   				=			torch.Size((1, 150, 480, 480)     )
		self.assertEqual(outputs.logits.shape			,       lowerCamelCase_     )

		lowerCAmelCase       :     List[str]   				=			torch.tensor(
		    [[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]     ).to(lowerCamelCase_     )

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



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Optional[Any]   				=			DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'     )
		lowerCAmelCase       :     int   				=			DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'     ).to(lowerCamelCase_     )

		lowerCAmelCase       :     str   				=			prepare_img()
		lowerCAmelCase       :     Tuple   				=			image_processor(images=lowerCamelCase_			,       return_tensors='pt'     ).to(lowerCamelCase_     )

		# forward pass
		with torch.no_grad():
			lowerCAmelCase       :     int   				=			model(**lowerCamelCase_     )

		lowerCAmelCase       :     Union[str, Any]   				=			outputs.logits.detach().cpu()

		lowerCAmelCase       :     str   				=			image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase_			,       target_sizes=[(500, 300)]     )
		lowerCAmelCase       :     List[Any]   				=			torch.Size((500, 300)     )
		self.assertEqual(segmentation[0].shape			,       lowerCamelCase_     )

		lowerCAmelCase       :     List[str]   				=			image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase_     )
		lowerCAmelCase       :     int   				=			torch.Size((480, 480)     )
		self.assertEqual(segmentation[0].shape			,       lowerCamelCase_     )

"""simple docstring"""


import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)

def 						UpperCamelCase				(	_lowerCAmelCase   :							List[str]				, _lowerCAmelCase   :							Optional[int]=False				, _lowerCAmelCase   :							Union[str, Any]=False				, _lowerCAmelCase   :							Any="data2vec."						):
    __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"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.norm2.weight""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")						)
        rename_keys.append(
            (f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")						)
        rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")						)

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

    if has_lm_head:
        # mask token + shared relative position bias + layernorm
        rename_keys.extend(
            [
                ("""mask_token""", f"""{hf_prefix}embeddings.mask_token"""),
                (
                    """rel_pos_bias.relative_position_bias_table""",
                    f"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
                ),
                (
                    """rel_pos_bias.relative_position_index""",
                    f"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
                ),
                ("""norm.weight""", """layernorm.weight"""),
                ("""norm.bias""", """layernorm.bias"""),
            ]						)
    elif is_semantic:
        # semantic segmentation classification heads
        rename_keys.extend(
            [
                ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
                ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
                ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
                ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
            ]						)
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ("""fc_norm.weight""", f"""{hf_prefix}pooler.layernorm.weight"""),
                ("""fc_norm.bias""", f"""{hf_prefix}pooler.layernorm.bias"""),
                ("""head.weight""", """classifier.weight"""),
                ("""head.bias""", """classifier.bias"""),
            ]						)

    return rename_keys

def 						UpperCamelCase				(	_lowerCAmelCase   :							str				, _lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							List[Any]=False				, _lowerCAmelCase   :							Optional[int]=False				, _lowerCAmelCase   :							Tuple="data2vec_vision."						):
    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

        # relative_position bias table + index
        if not has_lm_head:
            # each layer has its own relative position bias
            __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""						)
            __a			      =						state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""						)

            __a			      =						table
            __a			      =						index

def 						UpperCamelCase				(	):
    __a			      =						argparse.ArgumentParser(
        """Convert Data2VecVision to HF for image classification and pretraining"""				, add_help=_lowerCAmelCase						)
    parser.add_argument("""--hf_checkpoint_name"""				, type=_lowerCAmelCase						)
    parser.add_argument("""--input_size"""				, default=224				, type=_lowerCAmelCase				, help="""images input size"""						)
    parser.add_argument("""--beit_checkpoint"""				, default=""""""				, help="""beit checkpoint"""						)

    return parser.parse_args()

def 						UpperCamelCase				(	_lowerCAmelCase   :							Optional[int]				, _lowerCAmelCase   :							Union[str, Any]				, _lowerCAmelCase   :							Union[str, Any]						):
    def load_state_dict(_lowerCAmelCase   :							Tuple				, _lowerCAmelCase   :							str				, _lowerCAmelCase   :							Union[str, Any]=""				, _lowerCAmelCase   :							Union[str, Any]="relative_position_index"						):
        __a			      =						[]
        __a			      =						[]
        __a			      =						[]
        # copy state_dict so _load_from_state_dict can modify it
        __a			      =						getattr(_lowerCAmelCase				, """_metadata"""				, _lowerCAmelCase						)
        __a			      =						state_dict.copy()
        if metadata is not None:
            __a			      =						metadata

        def load(_lowerCAmelCase   :							List[Any]				, _lowerCAmelCase   :							List[str]=""						):
            __a			      =						{} if metadata is None else metadata.get(prefix[:-1]				, {}						)
            module._load_from_state_dict(
                _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase						)
            for name, child in module._modules.items():
                if child is not None:
                    load(_lowerCAmelCase				, prefix + name + """."""						)

        load(_lowerCAmelCase				, prefix=_lowerCAmelCase						)

        __a			      =						[]
        __a			      =						[]
        for key in missing_keys:
            __a			      =						True
            for ignore_key in ignore_missing.split("""|"""						):
                if ignore_key in key:
                    __a			      =						False
                    break
            if keep_flag:
                warn_missing_keys.append(_lowerCAmelCase						)
            else:
                ignore_missing_keys.append(_lowerCAmelCase						)

        __a			      =						warn_missing_keys

        if len(_lowerCAmelCase						) > 0:
            print(
                """Weights of {} not initialized from pretrained model: {}""".format(
                    model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print("""Weights from pretrained model not used in {}: {}""".format(model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print(
                """Ignored weights of {} not initialized from pretrained model: {}""".format(
                    model.__class__.__name__				, _lowerCAmelCase						)						)
        if len(_lowerCAmelCase						) > 0:
            print("""\n""".join(_lowerCAmelCase						)						)

    __a			      =						{
        """pretrained""": False,
        """use_shared_rel_pos_bias""": True,
        """use_abs_pos_emb""": False,
        """init_values""": 0.1,
    }

    if is_finetuned:
        model_kwargs.update(
            {
                """num_classes""": 1000,
                """use_mean_pooling""": True,
                """init_scale""": 0.001,
                """use_rel_pos_bias""": True,
            }						)

    __a			      =						create_model(
        """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""				, **_lowerCAmelCase				, )
    __a			      =						model.patch_embed.patch_size
    __a			      =						(args.input_size // patch_size[0], args.input_size // patch_size[1])
    __a			      =						torch.load(args.beit_checkpoint				, map_location="""cpu"""						)

    print(f"""Load ckpt from {args.beit_checkpoint}"""						)
    __a			      =						None
    for model_key in ("model", "module"):
        if model_key in checkpoint:
            __a			      =						checkpoint[model_key]
            print(f"""Load state_dict by model_key = {model_key}"""						)
            break

    __a			      =						list(checkpoint_model.keys()						)
    for key in all_keys:
        if "relative_position_index" in key:
            checkpoint_model.pop(_lowerCAmelCase						)

        if "relative_position_bias_table" in key:
            __a			      =						checkpoint_model[key]
            __a      ,  __a			      =						rel_pos_bias.size()
            __a      ,  __a			      =						model.state_dict()[key].size()
            __a			      =						model.patch_embed.patch_shape
            if dst_patch_shape[0] != dst_patch_shape[1]:
                raise NotImplementedError()

    load_state_dict(_lowerCAmelCase				, _lowerCAmelCase				, prefix=""""""						)

    return model

def 						UpperCamelCase				(	):
    __a			      =						get_args()

    __a			      =						"""ft1k""" in args.hf_checkpoint_name
    __a			      =						"""large""" in args.hf_checkpoint_name

    if is_finetuned:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
        # into this folder.
        import modeling_finetune  # noqa: F401
    else:
        # To convert Beit's data2vec_vision to HF you need to copy
        # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
        # into this folder
        # IMPORTANT: Note that for now we've only converted the down-stream
        # model and not the full pretrained model. This means for the integration
        # test you need to add a `return x` after the following line:
        # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
        # to make the integration test pass.
        import modeling_cyclical  # noqa: F401

    # 1. Create model config
    __a			      =						DataaVecVisionConfig()
    if is_finetuned:
        __a			      =						True
        __a			      =						False
        __a			      =						True
        __a			      =						1000

        __a			      =						"""huggingface/label-files"""
        __a			      =						"""imagenet-1k-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()}
    else:
        __a			      =						False
        __a			      =						True
        __a			      =						False

    if is_large:
        __a			      =						1024
        __a			      =						4096
        __a			      =						24
        __a			      =						16

    # 2. Load Beit model
    __a			      =						load_beit_model(_lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase						)
    orig_model.eval()

    # 3. Forward Beit model
    __a			      =						BeitImageProcessor(size=config.image_size				, do_center_crop=_lowerCAmelCase						)
    __a			      =						Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""						)
    __a			      =						image_processor(images=_lowerCAmelCase				, return_tensors="""pt"""						)
    __a			      =						encoding["""pixel_values"""]

    __a			      =						(pixel_values,) if is_finetuned else (pixel_values, None)
    with torch.no_grad():
        __a			      =						orig_model(*_lowerCAmelCase						)

    # 4. Load HF Data2VecVision model
    if is_finetuned:
        __a			      =						DataaVecVisionForImageClassification(_lowerCAmelCase						)
        hf_model.eval()
        __a			      =						False
        __a			      =						"""data2vec_vision."""
    else:
        __a			      =						DataaVecVisionModel(_lowerCAmelCase						)
        hf_model.eval()
        __a			      =						True
        __a			      =						""""""

    __a			      =						create_rename_keys(_lowerCAmelCase				, hf_prefix=_lowerCAmelCase				, has_lm_head=_lowerCAmelCase						)
    __a			      =						orig_model.state_dict()
    for src, dest in rename_keys:
        __a			      =						state_dict.pop(_lowerCAmelCase						)
        __a			      =						val

    read_in_q_k_v(_lowerCAmelCase				, _lowerCAmelCase				, hf_prefix=_lowerCAmelCase				, has_lm_head=_lowerCAmelCase						)
    __a      ,  __a			      =						hf_model.load_state_dict(_lowerCAmelCase				, strict=_lowerCAmelCase						)
    print("""HF missing"""				, _lowerCAmelCase						)
    print("""HF unexpected_keys"""				, _lowerCAmelCase						)

    # 5. Forward HF Data2VecVision model
    with torch.no_grad():
        __a			      =						hf_model(_lowerCAmelCase						)

    __a			      =						hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

    # 6. Compare
    __a			      =						torch.max(torch.abs(hf_output - orig_model_output						)						).item()

    print(f"""max_absolute_diff = {max_absolute_diff}"""						)
    __a			      =						torch.allclose(_lowerCAmelCase				, _lowerCAmelCase				, atol=1E-3						)
    print("""Do both models output the same tensors?"""				, """🔥""" if success else """💩"""						)
    if not success:
        raise Exception("""Something went wRoNg"""						)

    # 7. Save
    print(f"""Saving to {args.hf_checkpoint_name}"""						)
    hf_model.save_pretrained(args.hf_checkpoint_name						)
    image_processor.save_pretrained(args.hf_checkpoint_name						)


if __name__ == "__main__":
 main()
 # Run the following to convert checkpoints
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./pretrained_base.pt \
 #          --hf_checkpoint_name "./data2vec-vision-base"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./finetuned_base.pt \
 #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./pretrained_large.pt \
 #          --hf_checkpoint_name "./data2vec-vision-large"
 #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
 #          --beit_checkpoint ./finetuned_large.pt \
 #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"
