




'''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 		_lowercase		(	lowercase_						,							lowercase_=False						,							lowercase_=False						,							lowercase_="data2vec."  ):



		'''simple docstring'''







		_snake_case	       =       "backbone." if is_semantic else ""

		_snake_case	       =       []
		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 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_=False						,							lowercase_=False						,							lowercase_="data2vec_vision."  ):



		'''simple docstring'''







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

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

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

				_snake_case	       =       gamma_a
				_snake_case	       =       gamma_a

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

						_snake_case	       =       table
						_snake_case	       =       index






def 		_lowercase		(	):



		'''simple docstring'''







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

		return parser.parse_args()






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		def load_state_dict(lowercase_						,							lowercase_						,							lowercase_=""						,							lowercase_="relative_position_index"  ):
				_snake_case	       =       []
				_snake_case	       =       []
				_snake_case	       =       []
				# copy state_dict so _load_from_state_dict can modify it
				_snake_case	       =       getattr(__UpperCamelCase						,							"_metadata"						,							__UpperCamelCase  )
				_snake_case	       =       state_dict.copy()
				if metadata is not None:
						_snake_case	       =       metadata

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

				load(__UpperCamelCase						,							prefix=__UpperCamelCase  )

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

				_snake_case	       =       warn_missing_keys

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

		_snake_case	       =       {
		    "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,
				    }  )

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

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

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

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

		load_state_dict(__UpperCamelCase						,							__UpperCamelCase						,							prefix=""  )

		return model






def 		_lowercase		(	):



		'''simple docstring'''







		_snake_case	       =       get_args()

		_snake_case	       =       "ft1k" in args.hf_checkpoint_name
		_snake_case	       =       "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
		_snake_case	       =       DataaVecVisionConfig()
		if is_finetuned:
				_snake_case	       =       True
				_snake_case	       =       False
				_snake_case	       =       True
				_snake_case	       =       1000

				_snake_case	       =       "huggingface/label-files"
				_snake_case	       =       "imagenet-1k-id2label.json"
				_snake_case	       =       json.load(open(hf_hub_download(__UpperCamelCase						,							__UpperCamelCase						,							repo_type="dataset"  )						,							"r"  )  )
				_snake_case	       =       {int(__UpperCamelCase  ): v for k, v in idalabel.items()}
				_snake_case	       =       idalabel
				_snake_case	       =       {v: k for k, v in idalabel.items()}
		else:
				_snake_case	       =       False
				_snake_case	       =       True
				_snake_case	       =       False

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

		# 2. Load Beit model
		_snake_case	       =       load_beit_model(__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase  )
		orig_model.eval()

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

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

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

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

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

		# 5. Forward HF Data2VecVision model
		with torch.no_grad():
				_snake_case	       =       hf_model(__UpperCamelCase  )

		_snake_case	       =       hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

		print(f'''max_absolute_diff = {max_absolute_diff}'''  )
		_snake_case	       =       torch.allclose(__UpperCamelCase						,							__UpperCamelCase						,							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"











import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ConvNextImageProcessor
class       __a					( unittest.TestCase     ):


     """simple docstring"""


     def __init__(	self			: List[str]				,_UpperCamelCase			: int				,_UpperCamelCase			: int=7				,_UpperCamelCase			: int=3				,_UpperCamelCase			: List[str]=1_8				,_UpperCamelCase			: Dict=3_0				,_UpperCamelCase			: Tuple=4_0_0				,_UpperCamelCase			: Optional[int]=True				,_UpperCamelCase			: int=None				,_UpperCamelCase			: str=0.875				,_UpperCamelCase			: Tuple=True				,_UpperCamelCase			: Dict=[0.5, 0.5, 0.5]				,_UpperCamelCase			: str=[0.5, 0.5, 0.5]				,)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=size if size is not None else {"""shortest_edge""": 2_0}
         SCREAMING_SNAKE_CASE__								=parent
         SCREAMING_SNAKE_CASE__								=batch_size
         SCREAMING_SNAKE_CASE__								=num_channels
         SCREAMING_SNAKE_CASE__								=image_size
         SCREAMING_SNAKE_CASE__								=min_resolution
         SCREAMING_SNAKE_CASE__								=max_resolution
         SCREAMING_SNAKE_CASE__								=do_resize
         SCREAMING_SNAKE_CASE__								=size
         SCREAMING_SNAKE_CASE__								=crop_pct
         SCREAMING_SNAKE_CASE__								=do_normalize
         SCREAMING_SNAKE_CASE__								=image_mean
         SCREAMING_SNAKE_CASE__								=image_std




     def   __A				(	self			: Dict						)		->   str:


         '''simple docstring'''





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }






@require_torch
@require_vision
class       __a					( __lowerCamelCase							,     unittest.TestCase     ):


     """simple docstring"""


     _A				:    Dict									=  ConvNextImageProcessor if is_vision_available() else None




     def   __A				(	self			: List[str]						)		->   Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=ConvNextImageProcessingTester(self						)




     @property
     def   __A				(	self			: str						)		->   Optional[int]:


         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()




     def   __A				(	self			: Optional[int]						)		->   Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""do_resize"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""size"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""crop_pct"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""do_normalize"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""image_mean"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""image_std"""						)						)




     def   __A				(	self			: Tuple						)		->   Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class.from_dict(self.image_processor_dict						)
         self.assertEqual(image_processor.size				,{"""shortest_edge""": 2_0}						)

         SCREAMING_SNAKE_CASE__								=self.image_processing_class.from_dict(self.image_processor_dict				,size=4_2						)
         self.assertEqual(image_processor.size				,{"""shortest_edge""": 4_2}						)




     def   __A				(	self			: Any						)		->   List[str]:


         '''simple docstring'''





         pass




     def   __A				(	self			: Optional[int]						)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         SCREAMING_SNAKE_CASE__								=prepare_image_inputs(self.image_processor_tester				,equal_resolution=_UpperCamelCase						)
         for image in image_inputs:
             self.assertIsInstance(_UpperCamelCase				,Image.Image						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=image_processing(image_inputs[0]				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)




     def   __A				(	self			: str						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random numpy tensors
         SCREAMING_SNAKE_CASE__								=prepare_image_inputs(self.image_processor_tester				,equal_resolution=_UpperCamelCase				,numpify=_UpperCamelCase						)
         for image in image_inputs:
             self.assertIsInstance(_UpperCamelCase				,np.ndarray						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=image_processing(image_inputs[0]				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)




     def   __A				(	self			: List[Any]						)		->   Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random PyTorch tensors
         SCREAMING_SNAKE_CASE__								=prepare_image_inputs(self.image_processor_tester				,equal_resolution=_UpperCamelCase				,torchify=_UpperCamelCase						)
         for image in image_inputs:
             self.assertIsInstance(_UpperCamelCase				,torch.Tensor						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=image_processing(image_inputs[0]				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape				,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)






