




"""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							( lowerCamelCase      ,			lowerCamelCase=False      ,			lowerCamelCase=False      ,			lowerCamelCase="data2vec."		):
						'''simple docstring'''


						snake_case__       :					List[Any]										=					'''backbone.''' if is_semantic else ''''''

						snake_case__       :					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 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase=False      ,			lowerCamelCase=False      ,			lowerCamelCase="data2vec_vision."		):
						'''simple docstring'''


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

												snake_case__       :					Optional[int]										=					in_proj_weight[
												    : config.hidden_size, :
												]
												snake_case__       :					Tuple										=					q_bias
												snake_case__       :					Any										=					in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												snake_case__       :					Union[str, Any]										=					in_proj_weight[
												    -config.hidden_size :, :
												]
												snake_case__       :					Tuple										=					v_bias

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

												snake_case__       :					Optional[Any]										=					gamma_a
												snake_case__       :					Union[str, Any]										=					gamma_a

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

																		snake_case__       :					List[str]										=					table
																		snake_case__       :					List[str]										=					index

def 		lowerCAmelCase							( ):
						'''simple docstring'''


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

						return parser.parse_args()

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


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

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

												load(lowerCamelCase      ,			prefix=lowerCamelCase		)

												snake_case__       :					Union[str, Any]										=					[]
												snake_case__       :					str										=					[]
												for key in missing_keys:
																		snake_case__       :					Any										=					True
																		for ignore_key in ignore_missing.split('''|'''		):
																								if ignore_key in key:
																														snake_case__       :					Union[str, Any]										=					False
																														break
																		if keep_flag:
																								warn_missing_keys.append(lowerCamelCase		)
																		else:
																								ignore_missing_keys.append(lowerCamelCase		)

												snake_case__       :					str										=					warn_missing_keys

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

						snake_case__       :					Optional[int]										=					{
						    '''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,
												    }		)

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

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

						snake_case__       :					Tuple										=					list(checkpoint_model.keys()		)
						for key in all_keys:
												if "relative_position_index" in key:
																		checkpoint_model.pop(lowerCamelCase		)

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

						load_state_dict(lowerCamelCase      ,			lowerCamelCase      ,			prefix=''''''		)

						return model

def 		lowerCAmelCase							( ):
						'''simple docstring'''


						snake_case__       :					Optional[int]										=					get_args()

						snake_case__       :					str										=					'''ft1k''' in args.hf_checkpoint_name
						snake_case__       :					Dict										=					'''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
						snake_case__       :					List[Any]										=					DataaVecVisionConfig()
						if is_finetuned:
												snake_case__       :					Dict										=					True
												snake_case__       :					Tuple										=					False
												snake_case__       :					int										=					True
												snake_case__       :					Dict										=					1000

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

						if is_large:
												snake_case__       :					List[str]										=					1024
												snake_case__       :					Optional[Any]										=					4096
												snake_case__       :					int										=					24
												snake_case__       :					Optional[Any]										=					16

						# 2. Load Beit model
						snake_case__       :					Union[str, Any]										=					load_beit_model(lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase		)
						orig_model.eval()

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

						snake_case__       :					int										=					(pixel_values,) if is_finetuned else (pixel_values, None)
						with torch.no_grad():
												snake_case__       :					Optional[int]										=					orig_model(*lowerCamelCase		)

						# 4. Load HF Data2VecVision model
						if is_finetuned:
												snake_case__       :					Any										=					DataaVecVisionForImageClassification(lowerCamelCase		)
												hf_model.eval()
												snake_case__       :					str										=					False
												snake_case__       :					Any										=					'''data2vec_vision.'''
						else:
												snake_case__       :					Union[str, Any]										=					DataaVecVisionModel(lowerCamelCase		)
												hf_model.eval()
												snake_case__       :					int										=					True
												snake_case__       :					Union[str, Any]										=					''''''

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

						read_in_q_k_v(lowerCamelCase      ,			lowerCamelCase      ,			hf_prefix=lowerCamelCase      ,			has_lm_head=lowerCamelCase		)
						snake_case__			,	snake_case__       :					Dict										=					hf_model.load_state_dict(lowerCamelCase      ,			strict=lowerCamelCase		)
						print('''HF missing'''      ,			lowerCamelCase		)
						print('''HF unexpected_keys'''      ,			lowerCamelCase		)

						# 5. Forward HF Data2VecVision model
						with torch.no_grad():
												snake_case__       :					Union[str, Any]										=					hf_model(lowerCamelCase		)

						snake_case__       :					Optional[Any]										=					hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

						print(f'''max_absolute_diff = {max_absolute_diff}'''		)
						snake_case__       :					Union[str, Any]										=					torch.allclose(lowerCamelCase      ,			lowerCamelCase      ,			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"""





def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					Any										=					len(lowerCamelCase		)
						# Create a stack and push index of fist element to it
						snake_case__       :					str										=					[]
						st.append(0		)

						# Span value of first element is always 1
						snake_case__       :					Union[str, Any]										=					1

						# Calculate span values for rest of the elements
						for i in range(1      ,			lowerCamelCase		):
												# Pop elements from stack while stack is not
												# empty and top of stack is smaller than price[i]
												while len(lowerCamelCase		) > 0 and price[st[0]] <= price[i]:
																		st.pop()

												# If stack becomes empty, then price[i] is greater
												# than all elements on left of it, i.e. price[0],
												# price[1], ..price[i-1]. Else the price[i]  is
												# greater than elements after top of stack
												snake_case__       :					Dict										=					i + 1 if len(lowerCamelCase		) <= 0 else (i - st[0])

												# Push this element to stack
												st.append(lowerCamelCase		)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						for i in range(0      ,			lowerCamelCase		):
												print(arr[i]      ,			end=''' '''		)


# Driver program to test above function
_lowercase	      =    [10, 4, 5, 90, 1_20, 80]
_lowercase	      =    [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)

print_array(S, len(price))


