


'''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 				__magic_name__(    lowerCamelCase,					lowerCamelCase=False,					lowerCamelCase=False,					lowerCamelCase="data2vec."):
						__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 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase=False,					lowerCamelCase=False,					lowerCamelCase="data2vec_vision."):
						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 				__magic_name__(    ):
						__lowerCAmelCase											=					argparse.ArgumentParser(
						    '''Convert Data2VecVision to HF for image classification and pretraining''',					add_help=SCREAMING_SNAKE_CASE_)
						parser.add_argument('''--hf_checkpoint_name''',					type=SCREAMING_SNAKE_CASE_)
						parser.add_argument('''--input_size''',					default=2_2_4,					type=SCREAMING_SNAKE_CASE_,					help='''images input size''')
						parser.add_argument('''--beit_checkpoint''',					default='''''',					help='''beit checkpoint''')

						return parser.parse_args()

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase):
						def load_state_dict(lowerCamelCase,					lowerCamelCase,					lowerCamelCase="",					lowerCamelCase="relative_position_index"):
												__lowerCAmelCase											=					[]
												__lowerCAmelCase											=					[]
												__lowerCAmelCase											=					[]
												# copy state_dict so _load_from_state_dict can modify it
												__lowerCAmelCase											=					getattr(SCREAMING_SNAKE_CASE_,					'''_metadata''',					SCREAMING_SNAKE_CASE_)
												__lowerCAmelCase											=					state_dict.copy()
												if metadata is not None:
																		__lowerCAmelCase											=					metadata

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

												load(SCREAMING_SNAKE_CASE_,					prefix=SCREAMING_SNAKE_CASE_)

												__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(SCREAMING_SNAKE_CASE_)
																		else:
																								ignore_missing_keys.append(SCREAMING_SNAKE_CASE_)

												__lowerCAmelCase											=					warn_missing_keys

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

						__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''': 1_0_0_0,
												        '''use_mean_pooling''': True,
												        '''init_scale''': 0.0_01,
												        '''use_rel_pos_bias''': True,
												    })

						__lowerCAmelCase											=					create_model(
						    '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224''',					**SCREAMING_SNAKE_CASE_,					)
						__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(SCREAMING_SNAKE_CASE_)

												if "relative_position_bias_table" in key:
																		__lowerCAmelCase											=					checkpoint_model[key]
																		__lowerCAmelCase											=					rel_pos_bias.size()
																		__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(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					prefix='''''')

						return model

def 				__magic_name__(    ):
						__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											=					1_0_0_0

												__lowerCAmelCase											=					'huggingface/label-files'
												__lowerCAmelCase											=					'imagenet-1k-id2label.json'
												__lowerCAmelCase											=					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					repo_type='''dataset'''),					'''r'''))
												__lowerCAmelCase											=					{int(SCREAMING_SNAKE_CASE_): 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											=					1_0_2_4
												__lowerCAmelCase											=					4_0_9_6
												__lowerCAmelCase											=					2_4
												__lowerCAmelCase											=					1_6

						# 2. Load Beit model
						__lowerCAmelCase											=					load_beit_model(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_)
						orig_model.eval()

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

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

						# 4. Load HF Data2VecVision model
						if is_finetuned:
												__lowerCAmelCase											=					DataaVecVisionForImageClassification(SCREAMING_SNAKE_CASE_)
												hf_model.eval()
												__lowerCAmelCase											=					False
												__lowerCAmelCase											=					'data2vec_vision.'
						else:
												__lowerCAmelCase											=					DataaVecVisionModel(SCREAMING_SNAKE_CASE_)
												hf_model.eval()
												__lowerCAmelCase											=					True
												__lowerCAmelCase											=					''

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

						read_in_q_k_v(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					hf_prefix=SCREAMING_SNAKE_CASE_,					has_lm_head=SCREAMING_SNAKE_CASE_)
						__lowerCAmelCase											=					hf_model.load_state_dict(SCREAMING_SNAKE_CASE_,					strict=SCREAMING_SNAKE_CASE_)
						print('''HF missing''',					SCREAMING_SNAKE_CASE_)
						print('''HF unexpected_keys''',					SCREAMING_SNAKE_CASE_)

						# 5. Forward HF Data2VecVision model
						with torch.no_grad():
												__lowerCAmelCase											=					hf_model(SCREAMING_SNAKE_CASE_)

						__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(SCREAMING_SNAKE_CASE_,					SCREAMING_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'''




_A										= '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_A										= [{'type': 'code', 'content': INSTALL_CONTENT}]
_A										= {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




