
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    (  snake_case_							:		int				,					snake_case_							:		List[str]=False				,					snake_case_							:		int=False				,					snake_case_							:		Dict="data2vec."				):



	'''simple docstring'''



	UpperCAmelCase:					Union[str, Any] 						=     "backbone." if is_semantic else ""

	UpperCAmelCase:					List[str] 						=     []
	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    (  snake_case_							:		Any				,					snake_case_							:		List[Any]				,					snake_case_							:		Tuple=False				,					snake_case_							:		List[str]=False				,					snake_case_							:		Tuple="data2vec_vision."				):



	'''simple docstring'''



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

		UpperCAmelCase:					List[Any] 						=     in_proj_weight[
		    : config.hidden_size, :
		]
		UpperCAmelCase:					Dict 						=     q_bias
		UpperCAmelCase:					Union[str, Any] 						=     in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		UpperCAmelCase:					Dict 						=     in_proj_weight[
		    -config.hidden_size :, :
		]
		UpperCAmelCase:					List[Any] 						=     v_bias

		# gamma_1 and gamma_2
		# we call them lambda because otherwise they are renamed when using .from_pretrained
		UpperCAmelCase:					Tuple 						=     state_dict.pop(F'{prefix}blocks.{i}.gamma_1'				)
		UpperCAmelCase:					List[Any] 						=     state_dict.pop(F'{prefix}blocks.{i}.gamma_2'				)

		UpperCAmelCase:					int 						=     gamma_a
		UpperCAmelCase:					Dict 						=     gamma_a

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

			UpperCAmelCase:					List[str] 						=     table
			UpperCAmelCase:					List[Any] 						=     index



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					Union[str, Any] 						=     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=2_2_4				,					type=snake_case_				,					help="images input size"				)
	parser.add_argument("--beit_checkpoint"				,					default=""				,					help="beit checkpoint"				)

	return parser.parse_args()



def      __UpperCAmelCase    (  snake_case_							:		Tuple				,					snake_case_							:		int				,					snake_case_							:		Any				):



	'''simple docstring'''



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

		def load(snake_case_							:		List[Any]				,					snake_case_							:		List[str]=""				):
			UpperCAmelCase:					Any 						=     {} 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_				)

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

		UpperCAmelCase:					Dict 						=     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_				)				)

	UpperCAmelCase:					str 						=     {
	    "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": 1_0_0_0,
		        "use_mean_pooling": True,
		        "init_scale": 0.001,
		        "use_rel_pos_bias": True,
		    }				)

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

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

	UpperCAmelCase:					Tuple 						=     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:
			UpperCAmelCase:					List[str] 						=     checkpoint_model[key]
			UpperCAmelCase						,   UpperCAmelCase:					Tuple 						=     rel_pos_bias.size()
			UpperCAmelCase						,   UpperCAmelCase:					Union[str, Any] 						=     model.state_dict()[key].size()
			UpperCAmelCase:					Dict 						=     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      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					Optional[int] 						=     get_args()

	UpperCAmelCase:					Union[str, Any] 						=     "ft1k" in args.hf_checkpoint_name
	UpperCAmelCase:					Any 						=     "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
	UpperCAmelCase:					Union[str, Any] 						=     DataaVecVisionConfig()
	if is_finetuned:
		UpperCAmelCase:					Optional[Any] 						=     True
		UpperCAmelCase:					Tuple 						=     False
		UpperCAmelCase:					Optional[Any] 						=     True
		UpperCAmelCase:					Union[str, Any] 						=     1_0_0_0

		UpperCAmelCase:					Dict 						=     "huggingface/label-files"
		UpperCAmelCase:					int 						=     "imagenet-1k-id2label.json"
		UpperCAmelCase:					str 						=     json.load(open(hf_hub_download(snake_case_				,					snake_case_				,					repo_type="dataset"				)				,					"r"				)				)
		UpperCAmelCase:					int 						=     {int(snake_case_				): v for k, v in idalabel.items()}
		UpperCAmelCase:					int 						=     idalabel
		UpperCAmelCase:					Dict 						=     {v: k for k, v in idalabel.items()}
	else:
		UpperCAmelCase:					Optional[int] 						=     False
		UpperCAmelCase:					Union[str, Any] 						=     True
		UpperCAmelCase:					Tuple 						=     False

	if is_large:
		UpperCAmelCase:					Tuple 						=     1_0_2_4
		UpperCAmelCase:					str 						=     4_0_9_6
		UpperCAmelCase:					Tuple 						=     2_4
		UpperCAmelCase:					str 						=     1_6

	# 2. Load Beit model
	UpperCAmelCase:					str 						=     load_beit_model(snake_case_				,					snake_case_				,					snake_case_				)
	orig_model.eval()

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

	UpperCAmelCase:					List[str] 						=     (pixel_values,) if is_finetuned else (pixel_values, None)
	with torch.no_grad():
		UpperCAmelCase:					int 						=     orig_model(*snake_case_				)

	# 4. Load HF Data2VecVision model
	if is_finetuned:
		UpperCAmelCase:					Optional[int] 						=     DataaVecVisionForImageClassification(snake_case_				)
		hf_model.eval()
		UpperCAmelCase:					Union[str, Any] 						=     False
		UpperCAmelCase:					List[Any] 						=     "data2vec_vision."
	else:
		UpperCAmelCase:					List[str] 						=     DataaVecVisionModel(snake_case_				)
		hf_model.eval()
		UpperCAmelCase:					List[Any] 						=     True
		UpperCAmelCase:					Any 						=     ""

	UpperCAmelCase:					Optional[int] 						=     create_rename_keys(snake_case_				,					hf_prefix=snake_case_				,					has_lm_head=snake_case_				)
	UpperCAmelCase:					str 						=     orig_model.state_dict()
	for src, dest in rename_keys:
		UpperCAmelCase:					List[Any] 						=     state_dict.pop(snake_case_				)
		UpperCAmelCase:					List[str] 						=     val

	read_in_q_k_v(snake_case_				,					snake_case_				,					hf_prefix=snake_case_				,					has_lm_head=snake_case_				)
	UpperCAmelCase						,   UpperCAmelCase:					List[str] 						=     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():
		UpperCAmelCase:					int 						=     hf_model(snake_case_				)

	UpperCAmelCase:					Tuple 						=     hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

	# 6. Compare
	UpperCAmelCase:					Dict 						=     torch.max(torch.abs(hf_output - orig_model_output				)				).item()

	print(F'max_absolute_diff = {max_absolute_diff}'				)
	UpperCAmelCase:					str 						=     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"


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_      :					Dict				      =     {
    'configuration_big_bird': ['BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BigBirdConfig', 'BigBirdOnnxConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					str				      =     ['BigBirdTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Any				      =     ['BigBirdTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BigBirdForCausalLM',
	    'BigBirdForMaskedLM',
	    'BigBirdForMultipleChoice',
	    'BigBirdForPreTraining',
	    'BigBirdForQuestionAnswering',
	    'BigBirdForSequenceClassification',
	    'BigBirdForTokenClassification',
	    'BigBirdLayer',
	    'BigBirdModel',
	    'BigBirdPreTrainedModel',
	    'load_tf_weights_in_big_bird',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Any				      =     [
	    'FlaxBigBirdForCausalLM',
	    'FlaxBigBirdForMaskedLM',
	    'FlaxBigBirdForMultipleChoice',
	    'FlaxBigBirdForPreTraining',
	    'FlaxBigBirdForQuestionAnswering',
	    'FlaxBigBirdForSequenceClassification',
	    'FlaxBigBirdForTokenClassification',
	    'FlaxBigBirdModel',
	    'FlaxBigBirdPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_big_bird import BigBirdTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_big_bird_fast import BigBirdTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_big_bird import (
		    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BigBirdForCausalLM,
		    BigBirdForMaskedLM,
		    BigBirdForMultipleChoice,
		    BigBirdForPreTraining,
		    BigBirdForQuestionAnswering,
		    BigBirdForSequenceClassification,
		    BigBirdForTokenClassification,
		    BigBirdLayer,
		    BigBirdModel,
		    BigBirdPreTrainedModel,
		    load_tf_weights_in_big_bird,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_big_bird import (
		    FlaxBigBirdForCausalLM,
		    FlaxBigBirdForMaskedLM,
		    FlaxBigBirdForMultipleChoice,
		    FlaxBigBirdForPreTraining,
		    FlaxBigBirdForQuestionAnswering,
		    FlaxBigBirdForSequenceClassification,
		    FlaxBigBirdForTokenClassification,
		    FlaxBigBirdModel,
		    FlaxBigBirdPreTrainedModel,
		)

else:
	import sys

	snake_case_      :					int				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
