
'''simple docstring'''

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


UpperCAmelCase__ :			str											=		logging.get_logger(__name__)

UpperCAmelCase__ :			Optional[Any]											=		{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Optional[int]   =  'data2vec-vision'





	def __init__(       self      :							Optional[Any]   ,				__magic_name__      :							Optional[int]=768   ,				__magic_name__      :							Any=12   ,				__magic_name__      :							int=12   ,				__magic_name__      :							str=3072   ,				__magic_name__      :							Optional[Any]="gelu"   ,				__magic_name__      :							Optional[int]=0.0   ,				__magic_name__      :							Optional[int]=0.0   ,				__magic_name__      :							List[Any]=0.02   ,				__magic_name__      :							Any=1E-12   ,				__magic_name__      :							Dict=224   ,				__magic_name__      :							Dict=16   ,				__magic_name__      :							List[Any]=3   ,				__magic_name__      :							Tuple=False   ,				__magic_name__      :							Optional[int]=False   ,				__magic_name__      :							Any=False   ,				__magic_name__      :							Optional[Any]=False   ,				__magic_name__      :							List[Any]=0.1   ,				__magic_name__      :							int=0.1   ,				__magic_name__      :							Tuple=True   ,				__magic_name__      :							Dict=[3, 5, 7, 11]   ,				__magic_name__      :							int=[1, 2, 3, 6]   ,				__magic_name__      :							Union[str, Any]=True   ,				__magic_name__      :							int=0.4   ,				__magic_name__      :							int=256   ,				__magic_name__      :							Union[str, Any]=1   ,				__magic_name__      :							Union[str, Any]=False   ,				__magic_name__      :							Optional[int]=255   ,				**__magic_name__      :							int   ,				):


				"""simple docstring"""

				super().__init__(**__magic_name__				)

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

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






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Any   =  version.parse('1.11'     )





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

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







	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[Any]				):


				"""simple docstring"""

				return 1E-4









import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
a			:			List[str]												=					logging.get_logger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)
			if "model" in sd.keys():
						__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)['''model''']

			# pop unnecessary weights
			__lowercase			       = [
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(_UpperCamelCase	)

			__lowercase			       = {
			    '''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:
									__lowercase			       = sd.pop(_UpperCamelCase	)

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

									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.q_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.k_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.v_proj.'''	)

									__lowercase			       = 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
									__lowercase       ,			__lowercase       ,			__lowercase			       = torch.split(_UpperCamelCase							,      depth // 3							,      dim=0	)

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

			return sd


@torch.no_grad()
def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = load_checkpoint(_UpperCamelCase	)

			if config is not None:
						__lowercase			       = OPTConfig.from_pretrained(_UpperCamelCase	)
			else:
						__lowercase			       = OPTConfig()

			__lowercase			       = OPTModel(_UpperCamelCase	).half().eval()
			model.load_state_dict(_UpperCamelCase	)

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


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			:			Any												=					parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


