

'''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 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_=False							,       snake_case_=False							,       snake_case_="data2vec."	):
	_lowercase							=     """backbone.""" if is_semantic else """"""

	_lowercase							=     []
	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 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_=False							,       snake_case_=False							,       snake_case_="data2vec_vision."	):
	for i in range(config.num_hidden_layers	):
		_lowercase							=     """backbone.""" if is_semantic else """"""
		# queries, keys and values
		_lowercase							=     state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""	)
		_lowercase							=     state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""	)
		_lowercase							=     state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""	)

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

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

		_lowercase							=     gamma_a
		_lowercase							=     gamma_a

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

			_lowercase							=     table
			_lowercase							=     index






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

	return parser.parse_args()






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

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

		load(snake_case_							,       prefix=snake_case_	)

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

		_lowercase							=     warn_missing_keys

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

	_lowercase							=     {
	    """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,
		    }	)

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

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

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

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

	load_state_dict(snake_case_							,       snake_case_							,       prefix=""""""	)

	return model






def 					_SCREAMING_SNAKE_CASE		(						):
	_lowercase							=     get_args()

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

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

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

	# 2. Load Beit model
	_lowercase							=     load_beit_model(snake_case_							,       snake_case_							,       snake_case_	)
	orig_model.eval()

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

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

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

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

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

	# 5. Forward HF Data2VecVision model
	with torch.no_grad():
		_lowercase							=     hf_model(snake_case_	)

	_lowercase							=     hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

	print(F"""max_absolute_diff = {max_absolute_diff}"""	)
	_lowercase							=     torch.allclose(snake_case_							,       snake_case_							,       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"







'''simple docstring'''







from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments






def 					_SCREAMING_SNAKE_CASE		(						):
	_lowercase							=     HfArgumentParser(snake_case_	)
	try:
		_lowercase							=     parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		_lowercase							=     """Arg --no_{0} is no longer used, please use --no-{0} instead."""
		_lowercase							=     """ """.join(str(snake_case_	).split(""" """	)[:-1]	)
		_lowercase							=     """"""
		_lowercase							=     eval(str(snake_case_	).split(""" """	)[-1]	)
		_lowercase							=     []
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]	)
			else:
				wrong_args.append(snake_case_	)
		if len(snake_case_	) > 0:
			_lowercase							=     full_error_msg + begin_error_msg + str(snake_case_	)
		raise ValueError(snake_case_	)

	_lowercase							=     PyTorchBenchmark(args=snake_case_	)
	benchmark.run()


if __name__ == "__main__":
							main()




