

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


a_      =     logging.get_logger(__name__)

a_      =     {
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='roformer'
			def __init__(      self				:			Dict   ,   a				:			Any=5_0000   ,   a				:			List[Any]=None   ,   a				:			str=768   ,   a				:			str=12   ,   a				:			Tuple=12   ,   a				:			Optional[Any]=3072   ,   a				:			List[str]="gelu"   ,   a				:			List[Any]=0.1   ,   a				:			Union[str, Any]=0.1   ,   a				:			Tuple=1536   ,   a				:			List[str]=2   ,   a				:			Tuple=0.02   ,   a				:			Any=1e-12   ,   a				:			Optional[int]=0   ,   a				:			Union[str, Any]=False   ,   a				:			int=True   ,   **a				:			str   ,   )				->							int:

				"""simple docstring"""

				super().__init__(pad_token_id=a   ,   **a						)

				SCREAMING_SNAKE_CASE :   str					  =							vocab_size
				SCREAMING_SNAKE_CASE :   int					  =							hidden_size if embedding_size is None else embedding_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   int					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_act
				SCREAMING_SNAKE_CASE :   int					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   int					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   Any					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   List[str]					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   List[str]					  =							rotary_value
				SCREAMING_SNAKE_CASE :   int					  =							use_cache






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task == "multiple-choice":
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{0: "batch", 1: "choice", 2: "sequence"}
				else:
					SCREAMING_SNAKE_CASE :   str					  =							{0: "batch", 1: "sequence"}
				SCREAMING_SNAKE_CASE :   List[Any]					  =							{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]						)