

"""simple docstring"""


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    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Tuple:
		__lowerCAmelCase:	int   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location="cpu"			)
		if "model" in sd.keys():
				__lowerCAmelCase:	Union[str, Any]   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location="cpu"			)["model"]

		# pop unnecessary weights
		__lowerCAmelCase:	Optional[int]   				=      [
		    "decoder.version",
		    "decoder.output_projection.weight",
		]
		for key in keys_to_delete:
				if key in sd:
						sd.pop(__SCREAMING_SNAKE_CASE			)

		__lowerCAmelCase:	int   				=      {
		    "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:
						__lowerCAmelCase:	Tuple   				=      sd.pop(__SCREAMING_SNAKE_CASE			)

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

						__lowerCAmelCase:	Optional[Any]   				=      key.replace(".qkv_proj." ,			".q_proj."			)
						__lowerCAmelCase:	Dict   				=      key.replace(".qkv_proj." ,			".k_proj."			)
						__lowerCAmelCase:	List[str]   				=      key.replace(".qkv_proj." ,			".v_proj."			)

						__lowerCAmelCase:	Tuple   				=      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
						__lowerCAmelCase				,			__lowerCAmelCase				,			__lowerCAmelCase:	Dict   				=      torch.split(__SCREAMING_SNAKE_CASE ,			depth // 3 ,			dim=0			)

						__lowerCAmelCase:	str   				=      q
						__lowerCAmelCase:	Dict   				=      k
						__lowerCAmelCase:	Tuple   				=      v
						del sd[key]

		return sd






@torch.no_grad()
def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=None			)     ->     Union[str, Any]:
		__lowerCAmelCase:	Optional[Any]   				=      load_checkpoint(__SCREAMING_SNAKE_CASE			)

		if config is not None:
				__lowerCAmelCase:	Union[str, Any]   				=      OPTConfig.from_pretrained(__SCREAMING_SNAKE_CASE			)
		else:
				__lowerCAmelCase:	List[str]   				=      OPTConfig()

		__lowerCAmelCase:	Optional[Any]   				=      OPTModel(__SCREAMING_SNAKE_CASE			).half().eval()
		model.load_state_dict(__SCREAMING_SNAKE_CASE			)

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


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)





"""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


__A						       =      logging.get_logger(__name__)

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





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  """data2vec-vision"""

				def __init__(     self							:    Optional[int]					,  UpperCamelCase__							:    str=7_6_8					,  UpperCamelCase__							:    Any=1_2					,  UpperCamelCase__							:    str=1_2					,  UpperCamelCase__							:    Optional[Any]=3_0_7_2					,  UpperCamelCase__							:    Union[str, Any]="gelu"					,  UpperCamelCase__							:    List[Any]=0.0					,  UpperCamelCase__							:    Dict=0.0					,  UpperCamelCase__							:    Union[str, Any]=0.02					,  UpperCamelCase__							:    str=1e-12					,  UpperCamelCase__							:    Dict=2_2_4					,  UpperCamelCase__							:    List[Any]=1_6					,  UpperCamelCase__							:    Optional[Any]=3					,  UpperCamelCase__							:    List[str]=False					,  UpperCamelCase__							:    List[Any]=False					,  UpperCamelCase__							:    str=False					,  UpperCamelCase__							:    List[str]=False					,  UpperCamelCase__							:    int=0.1					,  UpperCamelCase__							:    List[Any]=0.1					,  UpperCamelCase__							:    Optional[Any]=True					,  UpperCamelCase__							:    List[Any]=[3, 5, 7, 1_1]					,  UpperCamelCase__							:    List[str]=[1, 2, 3, 6]					,  UpperCamelCase__							:    Union[str, Any]=True					,  UpperCamelCase__							:    Any=0.4					,  UpperCamelCase__							:    Union[str, Any]=2_5_6					,  UpperCamelCase__							:    List[Any]=1					,  UpperCamelCase__							:    str=False					,  UpperCamelCase__							:    Optional[int]=2_5_5					,  **UpperCamelCase__							:    Dict					,  )->       List[str]:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						__lowerCAmelCase:	List[str]   				=      hidden_size
						__lowerCAmelCase:	Union[str, Any]   				=      num_hidden_layers
						__lowerCAmelCase:	Dict   				=      num_attention_heads
						__lowerCAmelCase:	Optional[int]   				=      intermediate_size
						__lowerCAmelCase:	int   				=      hidden_act
						__lowerCAmelCase:	Union[str, Any]   				=      hidden_dropout_prob
						__lowerCAmelCase:	Any   				=      attention_probs_dropout_prob
						__lowerCAmelCase:	Dict   				=      initializer_range
						__lowerCAmelCase:	Any   				=      layer_norm_eps

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





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Dict       				=  version.parse("""1.11"""  )

				@property
				def 							lowercase_					(     self							:    Optional[Any])->       Mapping[str, Mapping[int, str]]:



						'''simple docstring'''


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




				@property
				def 							lowercase_					(     self							:    Any)->       float:



						'''simple docstring'''


						return 1e-4


