
'''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_						(							__SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					List[str]=False , __SCREAMING_SNAKE_CASE   :					int=False , __SCREAMING_SNAKE_CASE   :					Dict="data2vec."				):



						"""simple docstring"""





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

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



						"""simple docstring"""





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

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

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

												lowercase_      :	int						=			gamma_a
												lowercase_      :	Dict						=			gamma_a

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

																		lowercase_      :	List[str]						=			table
																		lowercase_      :	List[Any]						=			index
def    snake_case_						(							):



						"""simple docstring"""





						lowercase_      :	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=224 , type=snake_case_ , help='''images input size'''				)
						parser.add_argument('''--beit_checkpoint''' , default='''''' , help='''beit checkpoint'''				)

						return parser.parse_args()
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					Any				):



						"""simple docstring"""





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

												def load(__SCREAMING_SNAKE_CASE   :					List[Any] , __SCREAMING_SNAKE_CASE   :					List[str]=""				):
																		lowercase_      :	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_				)

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

												lowercase_      :	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_				)				)

						lowercase_      :	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''': 1000,
												        '''use_mean_pooling''': True,
												        '''init_scale''': 0.001,
												        '''use_rel_pos_bias''': True,
												    }				)

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

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

						lowercase_      :	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:
																		lowercase_      :	List[str]						=			checkpoint_model[key]
																		lowercase_      :	Tuple						=			rel_pos_bias.size()
																		lowercase_      :	Union[str, Any]						=			model.state_dict()[key].size()
																		lowercase_      :	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    snake_case_						(							):



						"""simple docstring"""





						lowercase_      :	Optional[int]						=			get_args()

						lowercase_      :	Union[str, Any]						=			"ft1k" in args.hf_checkpoint_name
						lowercase_      :	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
						lowercase_      :	Union[str, Any]						=			DataaVecVisionConfig()
						if is_finetuned:
												lowercase_      :	Optional[Any]						=			True
												lowercase_      :	Tuple						=			False
												lowercase_      :	Optional[Any]						=			True
												lowercase_      :	Union[str, Any]						=			1000

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

						if is_large:
												lowercase_      :	Tuple						=			1024
												lowercase_      :	str						=			4096
												lowercase_      :	Tuple						=			24
												lowercase_      :	str						=			16

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

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

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

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

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

						read_in_q_k_v(snake_case_ , snake_case_ , hf_prefix=snake_case_ , has_lm_head=snake_case_				)
						lowercase_      :	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():
												lowercase_      :	int						=			hf_model(snake_case_				)

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

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

						print(F'''max_absolute_diff = {max_absolute_diff}'''				)
						lowercase_      :	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_tokenizers_available, is_torch_available


snake_case_      :					Optional[int]				      =     {
    'configuration_splinter': ['SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SplinterConfig'],
    'tokenization_splinter': ['SplinterTokenizer'],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SplinterForQuestionAnswering',
	    'SplinterForPreTraining',
	    'SplinterLayer',
	    'SplinterModel',
	    'SplinterPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
	from .tokenization_splinter import SplinterTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_splinter_fast import SplinterTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_splinter import (
		    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SplinterForPreTraining,
		    SplinterForQuestionAnswering,
		    SplinterLayer,
		    SplinterModel,
		    SplinterPreTrainedModel,
		)


else:
	import sys

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