


import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
A							:					Optional[int]     						=     logging.get_logger(__name__)





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Optional[Any]:


						'''simple docstring'''






						__snake_case				=		torch.load(_lowerCAmelCase      ,	map_location="cpu" )
						if "model" in sd.keys():
												__snake_case				=		torch.load(_lowerCAmelCase      ,	map_location="cpu" )["model"]

						# pop unnecessary weights
						__snake_case				=		[
						    "decoder.version",
						    "decoder.output_projection.weight",
						]
						for key in keys_to_delete:
												if key in sd:
																		sd.pop(_lowerCAmelCase )

						__snake_case				=		{
						    "decoder.project_in_dim.weight": "decoder.project_in.weight",
						    "decoder.project_out_dim.weight": "decoder.project_out.weight",
						    "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
						    "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
						}
						for old_key, new_key in keys_to_rename.items():
												if old_key in sd:
																		__snake_case				=		sd.pop(_lowerCAmelCase )

						__snake_case				=		list(sd.keys() )
						for key in keys:
												if ".qkv_proj." in key:
																		__snake_case				=		sd[key]
																		# We split QKV in separate Q,K,V

																		__snake_case				=		key.replace(".qkv_proj."      ,	".q_proj." )
																		__snake_case				=		key.replace(".qkv_proj."      ,	".k_proj." )
																		__snake_case				=		key.replace(".qkv_proj."      ,	".v_proj." )

																		__snake_case				=		value.shape[0]
																		assert depth % 3 == 0
																		# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
																		# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
																		__snake_case		,					__snake_case		,					__snake_case				=		torch.split(_lowerCAmelCase      ,	depth // 3      ,	dim=0 )

																		__snake_case				=		q
																		__snake_case				=		k
																		__snake_case				=		v
																		del sd[key]

						return sd





@torch.no_grad()
def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase=None )						->	List[str]:


						'''simple docstring'''






						__snake_case				=		load_checkpoint(_lowerCAmelCase )

						if config is not None:
												__snake_case				=		OPTConfig.from_pretrained(_lowerCAmelCase )
						else:
												__snake_case				=		OPTConfig()

						__snake_case				=		OPTModel(_lowerCAmelCase ).half().eval()
						model.load_state_dict(_lowerCAmelCase )

						# Check results
						Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
						model.save_pretrained(_lowerCAmelCase )


if __name__ == "__main__":
	A							:					List[Any]     						=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--fairseq_path',
	    type=str,
	    help=(
	        'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
	        ' https://huggingface.co/models?other=opt_metasq'
	    ),
	)
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
	A							:					Union[str, Any]     						=     parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A							:					Union[str, Any]     						=     logging.get_logger(__name__)

A							:					Tuple     						=     {
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""data2vec-vision"""


					def __init__(						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[str]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Tuple=1_2     ,    SCREAMING_SNAKE_CASE	:		int=1_2     ,    SCREAMING_SNAKE_CASE	:		List[str]=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu"     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		Any=0.02     ,    SCREAMING_SNAKE_CASE	:		List[str]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2_2_4     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_6     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=False     ,    SCREAMING_SNAKE_CASE	:		Any=False     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		int=False     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.1     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Any=True     ,    SCREAMING_SNAKE_CASE	:		List[str]=[3, 5, 7, 1_1]     ,    SCREAMING_SNAKE_CASE	:		int=[1, 2, 3, 6]     ,    SCREAMING_SNAKE_CASE	:		List[str]=True     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.4     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=2_5_6     ,    SCREAMING_SNAKE_CASE	:		Tuple=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False     ,    SCREAMING_SNAKE_CASE	:		List[Any]=2_5_5     ,    **SCREAMING_SNAKE_CASE	:		Any     ,    )			->	List[str]:




											'''simple docstring'''

											super().__init__(**SCREAMING_SNAKE_CASE						)

											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		initializer_range
											__snake_case				=		layer_norm_eps

											__snake_case				=		image_size
											__snake_case				=		patch_size
											__snake_case				=		num_channels
											__snake_case				=		use_mask_token
											__snake_case				=		use_absolute_position_embeddings
											__snake_case				=		use_relative_position_bias
											__snake_case				=		use_shared_relative_position_bias
											__snake_case				=		layer_scale_init_value
											__snake_case				=		drop_path_rate
											__snake_case				=		use_mean_pooling
											# decode head attributes (semantic segmentation)
											__snake_case				=		out_indices
											__snake_case				=		pool_scales
											# auxiliary head attributes (semantic segmentation)
											__snake_case				=		use_auxiliary_head
											__snake_case				=		auxiliary_loss_weight
											__snake_case				=		auxiliary_channels
											__snake_case				=		auxiliary_num_convs
											__snake_case				=		auxiliary_concat_input
											__snake_case				=		semantic_loss_ignore_index






class     UpperCamelCase(  _a   ):
					snake_case_		:				Any										=	version.parse("""1.11"""   )


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											return OrderedDict(
											    [
											        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
											    ]						)






					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	float:




											'''simple docstring'''

											return 1e-4





