'''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     snake_case__							(      _A: Any				,       _A: Dict=False				,       _A: str=False				,       _A: Any="data2vec."			)   ->     int:

		'''simple docstring'''


		lowerCAmelCase        =  """backbone.""" if is_semantic else """"""

		lowerCAmelCase        =  []
		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     snake_case__							(      _A: Optional[Any]				,       _A: Dict				,       _A: Optional[int]=False				,       _A: int=False				,       _A: int="data2vec_vision."			)   ->     Union[str, Any]:

		'''simple docstring'''


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

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

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

				lowerCAmelCase        =  gamma_a
				lowerCAmelCase        =  gamma_a

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

						lowerCAmelCase        =  table
						lowerCAmelCase        =  index






def     snake_case__							(      )   ->     Dict:

		'''simple docstring'''


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

		return parser.parse_args()






def     snake_case__							(      _A: str				,       _A: Tuple				,       _A: Tuple			)   ->     List[str]:

		'''simple docstring'''


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

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

				load(_A				,       prefix=_A			)

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

				lowerCAmelCase        =  warn_missing_keys

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

		lowerCAmelCase        =  {
		    """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,
				    }			)

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

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

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

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

		load_state_dict(_A				,       _A				,       prefix=""""""			)

		return model






def     snake_case__							(      )   ->     Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase        =  get_args()

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

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

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

		# 2. Load Beit model
		lowerCAmelCase        =  load_beit_model(_A				,       _A				,       _A			)
		orig_model.eval()

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

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

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

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

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

		# 5. Forward HF Data2VecVision model
		with torch.no_grad():
				lowerCAmelCase        =  hf_model(_A			)

		lowerCAmelCase        =  hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

		print(f"max_absolute_diff = {max_absolute_diff}"			)
		lowerCAmelCase        =  torch.allclose(_A				,       _A				,       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'''




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()






def     snake_case__							(      _A: Dict				,       _A: str				,       _A: int			)   ->     Any:

		'''simple docstring'''


		lowerCAmelCase        =  FNetConfig.from_json_file(_A			)
		print(f"Building PyTorch model from configuration: {config}"			)
		lowerCAmelCase        =  FNetForPreTraining(_A			)

		lowerCAmelCase        =  restore_checkpoint(_A				,       _A			)
		lowerCAmelCase        =  checkpoint_dict["""target"""]

		# Embeddings
		# Position IDs
		lowerCAmelCase        =  fnet_pretraining_model.state_dict()

		lowerCAmelCase        =  state_dict["""fnet.embeddings.position_ids"""]
		lowerCAmelCase        =  {"""fnet.embeddings.position_ids""": position_ids}
		# Embedding Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]			)

		# Encoder Layers
		for layer in range(config.num_hidden_layers			):
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""mixing_layer_norm"""]["""scale"""]			)
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""mixing_layer_norm"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""output_layer_norm"""]["""scale"""]			)
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""output_layer_norm"""]["""bias"""]			)

		# Pooler Layers
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]			)

		# Masked LM Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_output"""]["""output_bias"""]			)
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]			)

		# Seq Relationship Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""classification"""]["""output_kernel"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""classification"""]["""output_bias"""]			)

		# Load State Dict
		fnet_pretraining_model.load_state_dict(_A			)

		# Save PreTrained
		print(f"Saving pretrained model to {save_path}"			)
		fnet_pretraining_model.save_pretrained(_A			)


if __name__ == "__main__":
						__lowercase				   =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--fnet_config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        '''The config json file corresponding to the pre-trained FNet model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
						__lowercase				   =  parser.parse_args()
						convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




