
"""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        lowerCAmelCase_       (		snake_case_		:      Optional[Any]							,   snake_case_		:      Union[str, Any]=False							,   snake_case_		:      Any=False							,   snake_case_		:      Tuple="data2vec."      )       ->Tuple:
			lowerCamelCase__     :					str		="backbone." if is_semantic else ""

			lowerCamelCase__     :					Optional[int]		=[]
			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        lowerCAmelCase_       (		snake_case_		:      int							,   snake_case_		:      str							,   snake_case_		:      Optional[int]=False							,   snake_case_		:      List[Any]=False							,   snake_case_		:      int="data2vec_vision."      )       ->Union[str, Any]:
			for i in range(config.num_hidden_layers      ):
						lowerCamelCase__     :					Union[str, Any]		="backbone." if is_semantic else ""
						# queries, keys and values
						lowerCamelCase__     :					Union[str, Any]		=state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""      )
						lowerCamelCase__     :					Dict		=state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""      )
						lowerCamelCase__     :					int		=state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""      )

						lowerCamelCase__     :					str		=in_proj_weight[
						    : config.hidden_size, :
						]
						lowerCamelCase__     :					Optional[Any]		=q_bias
						lowerCamelCase__     :					List[str]		=in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						lowerCamelCase__     :					List[Any]		=in_proj_weight[
						    -config.hidden_size :, :
						]
						lowerCamelCase__     :					Any		=v_bias

						# gamma_1 and gamma_2
						# we call them lambda because otherwise they are renamed when using .from_pretrained
						lowerCamelCase__     :					List[str]		=state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""      )
						lowerCamelCase__     :					Optional[Any]		=state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""      )

						lowerCamelCase__     :					Dict		=gamma_a
						lowerCamelCase__     :					Dict		=gamma_a

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

									lowerCamelCase__     :					Union[str, Any]		=table
									lowerCamelCase__     :					List[str]		=index






def        lowerCAmelCase_       (		)       ->Union[str, Any]:
			lowerCamelCase__     :					str		=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        lowerCAmelCase_       (		snake_case_		:      Union[str, Any]							,   snake_case_		:      Dict							,   snake_case_		:      List[Any]      )       ->Optional[Any]:
			def load_state_dict(snake_case_		:      Any							,   snake_case_		:      Any							,   snake_case_		:      List[Any]=""							,   snake_case_		:      Dict="relative_position_index"      ):
						lowerCamelCase__     :					str		=[]
						lowerCamelCase__     :					Optional[int]		=[]
						lowerCamelCase__     :					Optional[Any]		=[]
						# copy state_dict so _load_from_state_dict can modify it
						lowerCamelCase__     :					int		=getattr(snake_case__							,   '_metadata'							,   snake_case__      )
						lowerCamelCase__     :					Optional[Any]		=state_dict.copy()
						if metadata is not None:
									lowerCamelCase__     :					Optional[int]		=metadata

						def load(snake_case_		:      List[Any]							,   snake_case_		:      Optional[int]=""      ):
									lowerCamelCase__     :					Optional[int]		={} 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__      )

						lowerCamelCase__     :					Optional[int]		=[]
						lowerCamelCase__     :					Optional[Any]		=[]
						for key in missing_keys:
									lowerCamelCase__     :					Dict		=True
									for ignore_key in ignore_missing.split('|'      ):
												if ignore_key in key:
															lowerCamelCase__     :					Optional[int]		=False
															break
									if keep_flag:
												warn_missing_keys.append(snake_case__      )
									else:
												ignore_missing_keys.append(snake_case__      )

						lowerCamelCase__     :					Optional[int]		=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__      )      )

			lowerCamelCase__     :					List[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,
						    }      )

			lowerCamelCase__     :					Dict		=create_model(
			    'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'							,   **snake_case__							,   )
			lowerCamelCase__     :					Optional[int]		=model.patch_embed.patch_size
			lowerCamelCase__     :					List[Any]		=(args.input_size // patch_size[0], args.input_size // patch_size[1])
			lowerCamelCase__     :					Union[str, Any]		=torch.load(args.beit_checkpoint							,   map_location='cpu'      )

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

			lowerCamelCase__     :					Any		=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:
									lowerCamelCase__     :					List[Any]		=checkpoint_model[key]
									lowerCamelCase__     :					List[Any]		=rel_pos_bias.size()
									lowerCamelCase__     :					Optional[Any]		=model.state_dict()[key].size()
									lowerCamelCase__     :					Optional[int]		=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        lowerCAmelCase_       (		)       ->int:
			lowerCamelCase__     :					Any		=get_args()

			lowerCamelCase__     :					str		="ft1k" in args.hf_checkpoint_name
			lowerCamelCase__     :					Tuple		="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__     :					List[Any]		=DataaVecVisionConfig()
			if is_finetuned:
						lowerCamelCase__     :					int		=True
						lowerCamelCase__     :					Any		=False
						lowerCamelCase__     :					Dict		=True
						lowerCamelCase__     :					Optional[int]		=1_0_0_0

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

			if is_large:
						lowerCamelCase__     :					Tuple		=1_0_2_4
						lowerCamelCase__     :					str		=4_0_9_6
						lowerCamelCase__     :					Optional[int]		=2_4
						lowerCamelCase__     :					Any		=1_6

			# 2. Load Beit model
			lowerCamelCase__     :					Dict		=load_beit_model(snake_case__							,   snake_case__							,   snake_case__      )
			orig_model.eval()

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

			lowerCamelCase__     :					str		=(pixel_values,) if is_finetuned else (pixel_values, None)
			with torch.no_grad():
						lowerCamelCase__     :					int		=orig_model(*snake_case__      )

			# 4. Load HF Data2VecVision model
			if is_finetuned:
						lowerCamelCase__     :					Tuple		=DataaVecVisionForImageClassification(snake_case__      )
						hf_model.eval()
						lowerCamelCase__     :					List[Any]		=False
						lowerCamelCase__     :					List[str]		="data2vec_vision."
			else:
						lowerCamelCase__     :					List[Any]		=DataaVecVisionModel(snake_case__      )
						hf_model.eval()
						lowerCamelCase__     :					List[Any]		=True
						lowerCamelCase__     :					int		=""

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

			read_in_q_k_v(snake_case__							,   snake_case__							,   hf_prefix=snake_case__							,   has_lm_head=snake_case__      )
			lowerCamelCase__     :					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():
						lowerCamelCase__     :					List[Any]		=hf_model(snake_case__      )

			lowerCamelCase__     :					Any		=hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

			# 6. Compare
			lowerCamelCase__     :					List[Any]		=torch.max(torch.abs(hf_output - orig_model_output      )      ).item()

			print(f"""max_absolute_diff = {max_absolute_diff}"""      )
			lowerCamelCase__     :					List[Any]		=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 math import cos, sin






def        lowerCAmelCase_       (		snake_case_		:      float      )       ->list[list[float]]:
			lowerCamelCase__     :					List[Any]		=float(snake_case_      )
			return [[scaling_factor * int(x == y      ) for x in range(2      )] for y in range(2      )]






def        lowerCAmelCase_       (		snake_case_		:      float      )       ->list[list[float]]:
			lowerCamelCase__	,    lowerCamelCase__     :					Union[str, Any]		=cos(snake_case_      ), sin(snake_case_      )
			return [[c, -s], [s, c]]






def        lowerCAmelCase_       (		snake_case_		:      float      )       ->list[list[float]]:
			lowerCamelCase__	,    lowerCamelCase__     :					Optional[Any]		=cos(snake_case_      ), sin(snake_case_      )
			lowerCamelCase__     :					Dict		=c * s
			return [[c * c, cs], [cs, s * s]]






def        lowerCAmelCase_       (		snake_case_		:      float      )       ->list[list[float]]:
			lowerCamelCase__	,    lowerCamelCase__     :					str		=cos(snake_case_      ), sin(snake_case_      )
			lowerCamelCase__     :					Dict		=c * s
			return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(f"""    {scaling(5) = }""")
print(f"""  {rotation(45) = }""")
print(f"""{projection(45) = }""")

print(f"""{reflection(45) = }""")