


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		(				UpperCAmelCase_		:	List[str]				,						UpperCAmelCase_		:	Tuple=False				,						UpperCAmelCase_		:	int=False				,						UpperCAmelCase_		:	Optional[Any]="data2vec."		):




						"""simple docstring"""





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

						a		:Union[str, Any]											= []
						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		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	int				,						UpperCAmelCase_		:	Tuple=False				,						UpperCAmelCase_		:	str=False				,						UpperCAmelCase_		:	List[str]="data2vec_vision."		):




						"""simple docstring"""





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

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

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

												a		:str											= gamma_a
												a		:Union[str, Any]											= gamma_a

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

																		a		:str											= table
																		a		:int											= index
def        __lowerCamelCase		(				):




						"""simple docstring"""





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

						return parser.parse_args()
def        __lowerCamelCase		(				UpperCAmelCase_		:	Tuple				,						UpperCAmelCase_		:	Optional[int]				,						UpperCAmelCase_		:	Union[str, Any]		):




						"""simple docstring"""





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

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

												load(UpperCAmelCase_				,						prefix=UpperCAmelCase_		)

												a		:str											= []
												a		:Tuple											= []
												for key in missing_keys:
																		a		:Tuple											= True
																		for ignore_key in ignore_missing.split('''|'''		):
																								if ignore_key in key:
																														a		:Optional[int]											= False
																														break
																		if keep_flag:
																								warn_missing_keys.append(UpperCAmelCase_		)
																		else:
																								ignore_missing_keys.append(UpperCAmelCase_		)

												a		:int											= warn_missing_keys

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

						a		: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,
												    }		)

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

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

						a		:Dict											= list(checkpoint_model.keys()		)
						for key in all_keys:
												if "relative_position_index" in key:
																		checkpoint_model.pop(UpperCAmelCase_		)

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

						load_state_dict(UpperCAmelCase_				,						UpperCAmelCase_				,						prefix=''''''		)

						return model
def        __lowerCamelCase		(				):




						"""simple docstring"""





						a		:List[str]											= get_args()

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

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

						if is_large:
												a		:Optional[int]											= 1024
												a		:int											= 4096
												a		:Any											= 24
												a		:int											= 16

						# 2. Load Beit model
						a		:Any											= load_beit_model(UpperCAmelCase_				,						UpperCAmelCase_				,						UpperCAmelCase_		)
						orig_model.eval()

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

						a		:List[str]											= (pixel_values,) if is_finetuned else (pixel_values, None)
						with torch.no_grad():
												a		:Tuple											= orig_model(*UpperCAmelCase_		)

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

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

						read_in_q_k_v(UpperCAmelCase_				,						UpperCAmelCase_				,						hf_prefix=UpperCAmelCase_				,						has_lm_head=UpperCAmelCase_		)
						a		:Any											= hf_model.load_state_dict(UpperCAmelCase_				,						strict=UpperCAmelCase_		)
						print('''HF missing'''				,						UpperCAmelCase_		)
						print('''HF unexpected_keys'''				,						UpperCAmelCase_		)

						# 5. Forward HF Data2VecVision model
						with torch.no_grad():
												a		:int											= hf_model(UpperCAmelCase_		)

						a		:Union[str, Any]											= hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

						print(F'''max_absolute_diff = {max_absolute_diff}'''		)
						a		:List[Any]											= torch.allclose(UpperCAmelCase_				,						UpperCAmelCase_				,						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"









import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


snake_case       :					Dict        =  logging.get_logger(__name__)







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'semantic'




 def __init__(	self      ,						_lowerCamelCase=1_0000      ,						_lowerCamelCase=True      ,						_lowerCamelCase=768      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=0.7      ,						_lowerCamelCase=True      ,						_lowerCamelCase=1_0048      ,						_lowerCamelCase=12_9595      ,						_lowerCamelCase=12_9599      ,						_lowerCamelCase=1_0000      ,						_lowerCamelCase=256      ,						_lowerCamelCase=49.9      ,						**_lowerCamelCase      ,						):
       super().__init__(
           temperature=_lowerCamelCase      ,						do_sample=_lowerCamelCase      ,						eos_token_id=_lowerCamelCase      ,						renormalize_logits=_lowerCamelCase      ,						max_new_tokens=_lowerCamelCase      ,						output_scores=_lowerCamelCase      ,						return_dict_in_generate=_lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						output_attentions=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       a		:Dict											= text_encoding_offset
       a		:Any											= text_pad_token
       a		:List[Any]											= eos_token_id
       a		:str											= semantic_infer_token
       a		:Any											= semantic_vocab_size
       a		:Any											= max_input_semantic_length
       a		:Dict											= semantic_rate_hz







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'coarse_acoustics'




 def __init__(	self      ,						_lowerCamelCase=True      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=0.7      ,						_lowerCamelCase=True      ,						_lowerCamelCase=1_2048      ,						_lowerCamelCase=75      ,						_lowerCamelCase=2      ,						_lowerCamelCase=1_2050      ,						_lowerCamelCase=256      ,						_lowerCamelCase = 630      ,						_lowerCamelCase = 60      ,						**_lowerCamelCase      ,						):
       super().__init__(
           temperature=_lowerCamelCase      ,						do_sample=_lowerCamelCase      ,						renormalize_logits=_lowerCamelCase      ,						output_scores=_lowerCamelCase      ,						return_dict_in_generate=_lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						output_attentions=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       a		:Optional[Any]											= coarse_semantic_pad_token
       a		:Dict											= coarse_rate_hz
       a		:List[str]											= n_coarse_codebooks
       a		:List[Any]											= coarse_infer_token
       a		:Optional[Any]											= max_coarse_input_length
       a		:Any											= max_coarse_history
       a		:Optional[int]											= sliding_window_len







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'fine_acoustics'




 def __init__(	self      ,						_lowerCamelCase=0.5      ,						_lowerCamelCase=512      ,						_lowerCamelCase=1024      ,						_lowerCamelCase=8      ,						**_lowerCamelCase      ,						):
       super().__init__(temperature=_lowerCamelCase					)

       a		:Union[str, Any]											= max_fine_history_length
       a		:int											= max_fine_input_length
       a		:str											= n_fine_codebooks







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'bark'
 SCREAMING_SNAKE_CASE__   = True




 def __init__(	self      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase=2_4000      ,						_lowerCamelCase=1024      ,						**_lowerCamelCase      ,						):
       if semantic_config is None:
             a		:str											= {}
             logger.info('''semantic_config is None. initializing the semantic model with default values.'''					)

       if coarse_acoustics_config is None:
             a		:List[str]											= {}
             logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''					)

       if fine_acoustics_config is None:
             a		:Any											= {}
             logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''					)

       a		:Any											= BarkSemanticGenerationConfig(**_lowerCamelCase					)
       a		:int											= BarkCoarseGenerationConfig(**_lowerCamelCase					)
       a		:Optional[int]											= BarkFineGenerationConfig(**_lowerCamelCase					)

       a		:Tuple											= sample_rate
       a		:Dict											= codebook_size




 @classmethod
 def  SCREAMING_SNAKE_CASE__       (	cls      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						**_lowerCamelCase      ,						):
       return cls(
           semantic_config=semantic_config.to_dict()      ,						coarse_acoustics_config=coarse_acoustics_config.to_dict()      ,						fine_acoustics_config=fine_acoustics_config.to_dict()      ,						**_lowerCamelCase      ,						)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= copy.deepcopy(self.__dict__					)

       a		:List[Any]											= self.semantic_config.to_dict()
       a		:Union[str, Any]											= self.coarse_acoustics_config.to_dict()
       a		:int											= self.fine_acoustics_config.to_dict()

       a		:Tuple											= self.__class__.model_type
       return output





