

import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
a_      =     logging.get_logger(__name__)

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   List[Any]					  =							torch.load(_a				,      map_location="cpu")
	if "model" in sd.keys():
		SCREAMING_SNAKE_CASE :   Any					  =							torch.load(_a				,      map_location="cpu")["model"]

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

	SCREAMING_SNAKE_CASE :   Any					  =							{
	    "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:
			SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd.pop(_a)

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

			SCREAMING_SNAKE_CASE :   Tuple					  =							key.replace(".qkv_proj."				,      ".q_proj.")
			SCREAMING_SNAKE_CASE :   str					  =							key.replace(".qkv_proj."				,      ".k_proj.")
			SCREAMING_SNAKE_CASE :   Optional[Any]					  =							key.replace(".qkv_proj."				,      ".v_proj.")

			SCREAMING_SNAKE_CASE :   int					  =							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
			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							torch.split(_a				,      depth // 3				,      dim=0)

			SCREAMING_SNAKE_CASE :   str					  =							q
			SCREAMING_SNAKE_CASE :   Optional[int]					  =							k
			SCREAMING_SNAKE_CASE :   List[str]					  =							v
			del sd[key]

	return sd

@torch.no_grad()
def 			lowerCamelCase__			(       _a				,      _a				,      _a=None):
	SCREAMING_SNAKE_CASE :   str					  =							load_checkpoint(_a)

	if config is not None:
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							OPTConfig.from_pretrained(_a)
	else:
		SCREAMING_SNAKE_CASE :   str					  =							OPTConfig()

	SCREAMING_SNAKE_CASE :   Tuple					  =							OPTModel(_a).half().eval()
	model.load_state_dict(_a)

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


if __name__ == "__main__":
			a_      =     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_      =     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


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

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





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Union[str, Any]		      =     '''data2vec-vision'''






				def __init__(  self			, _lowerCAmelCase=768			, _lowerCAmelCase=12			, _lowerCAmelCase=12			, _lowerCAmelCase=3072			, _lowerCAmelCase="gelu"			, _lowerCAmelCase=0.0			, _lowerCAmelCase=0.0			, _lowerCAmelCase=0.02			, _lowerCAmelCase=1e-12			, _lowerCAmelCase=224			, _lowerCAmelCase=16			, _lowerCAmelCase=3			, _lowerCAmelCase=False			, _lowerCAmelCase=False			, _lowerCAmelCase=False			, _lowerCAmelCase=False			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.1			, _lowerCAmelCase=True			, _lowerCAmelCase=[3, 5, 7, 11]			, _lowerCAmelCase=[1, 2, 3, 6]			, _lowerCAmelCase=True			, _lowerCAmelCase=0.4			, _lowerCAmelCase=256			, _lowerCAmelCase=1			, _lowerCAmelCase=False			, _lowerCAmelCase=255			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				str           =   hidden_size
										__SCREAMING_SNAKE_CASE:				Tuple           =   num_hidden_layers
										__SCREAMING_SNAKE_CASE:				Tuple           =   num_attention_heads
										__SCREAMING_SNAKE_CASE:				int           =   intermediate_size
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   hidden_act
										__SCREAMING_SNAKE_CASE:				int           =   hidden_dropout_prob
										__SCREAMING_SNAKE_CASE:				str           =   attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE:				Any           =   initializer_range
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   layer_norm_eps

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   image_size
										__SCREAMING_SNAKE_CASE:				List[str]           =   patch_size
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   num_channels
										__SCREAMING_SNAKE_CASE:				List[Any]           =   use_mask_token
										__SCREAMING_SNAKE_CASE:				Tuple           =   use_absolute_position_embeddings
										__SCREAMING_SNAKE_CASE:				Any           =   use_relative_position_bias
										__SCREAMING_SNAKE_CASE:				Dict           =   use_shared_relative_position_bias
										__SCREAMING_SNAKE_CASE:				Any           =   layer_scale_init_value
										__SCREAMING_SNAKE_CASE:				List[Any]           =   drop_path_rate
										__SCREAMING_SNAKE_CASE:				int           =   use_mean_pooling
										# decode head attributes (semantic segmentation)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   out_indices
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   pool_scales
										# auxiliary head attributes (semantic segmentation)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   use_auxiliary_head
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   auxiliary_loss_weight
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   auxiliary_channels
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   auxiliary_num_convs
										__SCREAMING_SNAKE_CASE:				Any           =   auxiliary_concat_input
										__SCREAMING_SNAKE_CASE:				List[str]           =   semantic_loss_ignore_index





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     version.parse('''1.11'''			)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




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






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										return 1e-4



