
'''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   :					List[Any] , __SCREAMING_SNAKE_CASE   :					Tuple=False , __SCREAMING_SNAKE_CASE   :					Dict=False , __SCREAMING_SNAKE_CASE   :					Any="data2vec."				):



						"""simple docstring"""





						lowercase_      :	List[str]						=			'''backbone.''' if is_semantic else ''''''

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



						"""simple docstring"""





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

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

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

												lowercase_      :	Any						=			gamma_a
												lowercase_      :	List[str]						=			gamma_a

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

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



						"""simple docstring"""





						lowercase_      :	str						=			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    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Optional[Any] , __SCREAMING_SNAKE_CASE   :					Optional[int] , __SCREAMING_SNAKE_CASE   :					Optional[int]				):



						"""simple docstring"""





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

												def load(__SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					List[Any]=""				):
																		lowercase_      :	Optional[Any]						=			{} 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_				)

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

												lowercase_      :	Optional[int]						=			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_				)				)

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

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

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



						"""simple docstring"""





						lowercase_      :	Optional[int]						=			get_args()

						lowercase_      :	List[Any]						=			'''ft1k''' in args.hf_checkpoint_name
						lowercase_      :	str						=			'''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_      :	Optional[int]						=			DataaVecVisionConfig()
						if is_finetuned:
												lowercase_      :	int						=			True
												lowercase_      :	Any						=			False
												lowercase_      :	Optional[int]						=			True
												lowercase_      :	str						=			1000

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

						if is_large:
												lowercase_      :	Any						=			1024
												lowercase_      :	Union[str, Any]						=			4096
												lowercase_      :	int						=			24
												lowercase_      :	Union[str, Any]						=			16

						# 2. Load Beit model
						lowercase_      :	Any						=			load_beit_model(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_				)
						orig_model.eval()

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

						lowercase_      :	Optional[int]						=			(pixel_values,) if is_finetuned else (pixel_values, None)
						with torch.no_grad():
												lowercase_      :	Union[str, Any]						=			orig_model(*lowerCAmelCase_				)

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

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

						read_in_q_k_v(lowerCAmelCase_ , lowerCAmelCase_ , hf_prefix=lowerCAmelCase_ , has_lm_head=lowerCAmelCase_				)
						lowercase_		,					lowercase_      :	str						=			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():
												lowercase_      :	int						=			hf_model(lowerCAmelCase_				)

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

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

						print(F'''max_absolute_diff = {max_absolute_diff}'''				)
						lowercase_      :	List[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'''







import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    UpperCamelCase__	     =      copy.deepcopy(lowerCAmelCase_						)
    UpperCamelCase__	     =      sorted(lowerCAmelCase_						)
    UpperCamelCase__	     =      [bisect.bisect_right(lowerCAmelCase_       ,	lowerCAmelCase_						) for y in x]
    return quantized





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_=0						):
    UpperCamelCase__	     =      np.arange(start=3       ,	stop=lowerCAmelCase_       ,	step=4						).tolist() if k > 0 else [10]
    UpperCamelCase__	     =      _quantize(lowerCAmelCase_       ,	lowerCAmelCase_						)
    # count number of elements per group
    UpperCamelCase__	     =      np.unique(lowerCAmelCase_       ,	return_counts=lowerCAmelCase_						)[1]
    UpperCamelCase__	     =      [0] + bins + [np.inf]
    logger.info("Using {} as bins for aspect lengths quantization".format(lowerCAmelCase_						)						)
    logger.info("Count of instances per bin: {}".format(lowerCAmelCase_						)						)
    return groups





class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       if not isinstance(__UpperCamelCase  ,    __UpperCamelCase			):
           raise ValueError(
               "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(__UpperCamelCase			)			)
       UpperCamelCase__	     =      sampler
       UpperCamelCase__	     =      group_ids
       UpperCamelCase__	     =      batch_size


   def __iter__(	self			):
       UpperCamelCase__	     =      defaultdict(__UpperCamelCase			)
       UpperCamelCase__	     =      defaultdict(__UpperCamelCase			)

       UpperCamelCase__	     =      0
       for idx in self.sampler:
           UpperCamelCase__	     =      self.group_ids[idx]
           buffer_per_group[group_id].append(__UpperCamelCase			)
           samples_per_group[group_id].append(__UpperCamelCase			)
           if len(buffer_per_group[group_id]			) == self.batch_size:
               yield buffer_per_group[group_id]  # TODO
               num_batches += 1
               del buffer_per_group[group_id]
           assert len(buffer_per_group[group_id]			) < self.batch_size

       # now we have run out of elements that satisfy
       # the group criteria, let's return the remaining
       # elements so that the size of the sampler is
       # deterministic
       UpperCamelCase__	     =      len(self			)
       UpperCamelCase__	     =      expected_num_batches - num_batches
       if num_remaining > 0:
           # for the remaining batches, group the batches by similar lengths
           UpperCamelCase__	     =      []
           for group_id, idxs in sorted(buffer_per_group.items()  ,    key=lambda __UpperCamelCase			: x[0]			):
               batch_idx.extend(__UpperCamelCase			)
               if len(__UpperCamelCase			) >= self.batch_size:
                   yield batch_idx[: self.batch_size]
                   UpperCamelCase__	     =      batch_idx[self.batch_size :]
                   num_remaining -= 1
           if len(__UpperCamelCase			) > 0:
               yield batch_idx
               num_remaining -= 1
       assert num_remaining == 0







   def __len__(	self			):
       return (len(self.sampler			) + self.batch_size - 1) // self.batch_size




