


import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    Any											=     [
							    "encoder.version",
							    "decoder.version",
							    "model.encoder.version",
							    "model.decoder.version",
							    "decoder.output_projection.weight",
							    "_float_tensor",
							    "encoder.embed_positions._float_tensor",
							    "decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(_A							,			_A			)







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case				,    __snake_case	:    int											=     emb.weight.shape
							__snake_case	:    str											=     nn.Linear(_A							,			_A							,			bias=_A			)
							__snake_case	:    str											=     emb.weight.data
							return lin_layer







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=None			):
							__snake_case	:    Optional[int]											=     {}
							for old_key in state_dict.keys():
														__snake_case	:    List[Any]											=     old_key
														if "moe_layer.experts." in key:
																					if expert_idx is not None:
																												__snake_case	:    Tuple											=     key.replace("moe_layer.experts.0"							,			F'ffn.experts.expert_{expert_idx}'			)
																					else:
																												__snake_case	:    Tuple											=     key.replace("moe_layer.experts."							,			"ffn.experts.expert_"			)
														if "gate" in key:
																					__snake_case	:    List[str]											=     key.replace(".moe_layer.gate.wg"							,			".ffn.router.classifier"			)
														if "fc2" and "experts" not in key:
																					__snake_case	:    Optional[int]											=     key.replace(".fc2."							,			".ffn.fc2."			)
														if "fc1" and "experts" not in key:
																					__snake_case	:    Union[str, Any]											=     key.replace(".fc1."							,			".ffn.fc1."			)
														if ".encoder_attn." in key:
																					__snake_case	:    List[Any]											=     key.replace(".encoder_attn."							,			".cross_attention."			)
														if "encoder_attn_layer_norm" in key:
																					__snake_case	:    int											=     key.replace("encoder_attn_layer_norm"							,			"cross_attention_layer_norm"			)
														if "final_layer_norm" in key:
																					__snake_case	:    Union[str, Any]											=     key.replace("final_layer_norm"							,			"ff_layer_norm"			)
														__snake_case	:    Tuple											=     state_dict[old_key]
							return new_dict







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = WEIGHTS_NAME			):
							__snake_case	:    List[Any]											=     []
							__snake_case	:    Any											=     0
							os.makedirs(_A							,			exist_ok=_A			)

							for expert in range(_A			):
														__snake_case	:    Optional[Any]											=     switch_checkpoint_path + F'-rank-{expert}.pt'
														if os.path.isfile(_A			):
																					__snake_case	:    Tuple											=     torch.load(_A			)["model"]
																					remove_ignore_keys_(_A			)
																					__snake_case	:    Optional[int]											=     rename_fairseq_keys(_A							,			_A			)
																					__snake_case	:    List[str]											=     os.path.join(
																					    _A							,			weights_name.replace(".bin"							,			F'-{len(_A			)+1:05d}-of-???.bin'			)			)
																					torch.save(_A							,			_A			)
																					sharded_state_dicts.append(expert_state.keys()			)
																					total_size += sum([value.numel() for key, value in expert_state.items()]			) * dtype_byte_size(
																					    expert_state[list(_A			)[0]].dtype			)

    # Add the last block
							__snake_case	:    Any											=     os.path.join(_A							,			weights_name.replace(".bin"							,			F'-{len(_A			)+1:05d}-of-???.bin'			)			)
							__snake_case	:    Any											=     torch.load(switch_checkpoint_path + "-shared.pt"			)["model"]
							remove_ignore_keys_(_A			)
							__snake_case	:    int											=     rename_fairseq_keys(_A							,			_A			)
							__snake_case	:    Optional[int]											=     shared_weights["decoder.embed_tokens.weight"]
							sharded_state_dicts.append(shared_weights.keys()			)

							# If we only have the shared weights (dummy model/experts saved on the same file)
							if len(_A			) == 1:
														__snake_case	:    List[Any]											=     os.path.join(_A							,			_A			)
														torch.save(_A							,			_A			)
														return {weights_name: sharded_state_dicts[0]}, None
							else:
														torch.save(_A							,			_A			)
							# Otherwise, let's build the index
							__snake_case	:    Union[str, Any]											=     {}
							for idx, shard in enumerate(_A			):
														__snake_case	:    Tuple											=     weights_name.replace(".bin"							,			F'-{idx+1:05d}-of-{len(_A			):05d}.bin'			)
														__snake_case	:    str											=     os.path.join(_A							,			weights_name.replace(".bin"							,			F'-{idx+1:05d}-of-???.bin'			)			)
														os.rename(_A							,			os.path.join(_A							,			_A			)			)
														for key in shard:
																					__snake_case	:    List[Any]											=     shard_file

    # Add the metadata
							__snake_case	:    List[Any]											=     {"total_size": total_size}
							__snake_case	:    Optional[Any]											=     {"metadata": metadata, "weight_map": weight_map}

							with open(os.path.join(_A							,			_A			)							,			"w"							,			encoding="utf-8"			) as f:
														__snake_case	:    Union[str, Any]											=     json.dumps(_A							,			indent=2							,			sort_keys=_A			) + "\n"
														f.write(_A			)

							return metadata, index


if __name__ == "__main__":
			_snake_case  :	str			      =       argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--nllb_moe_checkpoint_path",
			    default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
			    type=str,
			    required=False,
			    help="Path to a directory containing a folder per layer. Follows the original Google format.",
			)
			parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
			parser.add_argument(
			    "--pytorch_dump_folder_path",
			    default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
			    type=str,
			    required=False,
			    help="Path to the output pytorch model.",
			)
			_snake_case  :	Tuple			      =       parser.parse_args()
			_snake_case  :	List[Any]			      =       shard_on_the_fly(
			    args.nllb_moe_checkpoint_path,
			    args.pytorch_dump_folder_path,
			    128,
			    args.dtype,
			)

			_snake_case  :	str			      =       NllbMoeConfig.from_pretrained(
			    "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
			)
			config.save_pretrained(args.pytorch_dump_folder_path)
			_snake_case  :	Optional[int]			      =       NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
			print("Done")
			model.save_pretrained(args.pytorch_dump_folder_path)





import unittest

from transformers import DonutProcessor


_snake_case  :	Dict			      =       "naver-clova-ix/donut-base"

class        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Dict       )			->					int:
														__snake_case	:    Any											=     DonutProcessor.from_pretrained(lowerCamelCase       )






							def 					__snake_case      (							self :						str       )			->					List[Any]:
														__snake_case	:    Union[str, Any]											=     {
														    "name": "John Doe",
														    "age": "99",
														    "city": "Atlanta",
														    "state": "GA",
														    "zip": "30301",
														    "phone": "123-4567",
														    "nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
														}

														__snake_case	:    Union[str, Any]											=     (
														    "<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
														    "<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
														    "<s_nicknames><s_nickname>Johnny</s_nickname>"
														    "<sep/><s_nickname>JD</s_nickname></s_nicknames>"
														)
														__snake_case	:    Tuple											=     self.processor.tokenajson(lowerCamelCase       )

														self.assertDictEqual(lowerCamelCase					,			lowerCamelCase       )

