



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        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							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(_lowerCamelCase       ,				_lowerCamelCase						)






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase     ,					_lowerCAmelCase  :							Any				     =					emb.weight.shape
							_lowerCAmelCase  :							Optional[int]				     =					nn.Linear(_lowerCamelCase       ,				_lowerCamelCase       ,				bias=_lowerCamelCase						)
							_lowerCAmelCase  :							Tuple				     =					emb.weight.data
							return lin_layer






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=None						):





							'''simple docstring'''




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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase = WEIGHTS_NAME						):





							'''simple docstring'''




							_lowerCAmelCase  :							int				     =					[]
							_lowerCAmelCase  :							Any				     =					0
							os.makedirs(_lowerCamelCase       ,				exist_ok=_lowerCamelCase						)

							for expert in range(_lowerCamelCase						):
														_lowerCAmelCase  :							str				     =					switch_checkpoint_path + F"-rank-{expert}.pt"
														if os.path.isfile(_lowerCamelCase						):
																					_lowerCAmelCase  :							int				     =					torch.load(_lowerCamelCase						)["model"]
																					remove_ignore_keys_(_lowerCamelCase						)
																					_lowerCAmelCase  :							List[Any]				     =					rename_fairseq_keys(_lowerCamelCase       ,				_lowerCamelCase						)
																					_lowerCAmelCase  :							List[str]				     =					os.path.join(
																					    _lowerCamelCase       ,				weights_name.replace(".bin"       ,				F"-{len(_lowerCamelCase						)+1:05d}-of-???.bin"						)						)
																					torch.save(_lowerCamelCase       ,				_lowerCamelCase						)
																					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(_lowerCamelCase						)[0]].dtype						)

    # Add the last block
							_lowerCAmelCase  :							List[str]				     =					os.path.join(_lowerCamelCase       ,				weights_name.replace(".bin"       ,				F"-{len(_lowerCamelCase						)+1:05d}-of-???.bin"						)						)
							_lowerCAmelCase  :							Optional[Any]				     =					torch.load(switch_checkpoint_path + "-shared.pt"						)["model"]
							remove_ignore_keys_(_lowerCamelCase						)
							_lowerCAmelCase  :							Dict				     =					rename_fairseq_keys(_lowerCamelCase       ,				_lowerCamelCase						)
							_lowerCAmelCase  :							List[str]				     =					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(_lowerCamelCase						) == 1:
														_lowerCAmelCase  :							List[Any]				     =					os.path.join(_lowerCamelCase       ,				_lowerCamelCase						)
														torch.save(_lowerCamelCase       ,				_lowerCamelCase						)
														return {weights_name: sharded_state_dicts[0]}, None
							else:
														torch.save(_lowerCamelCase       ,				_lowerCamelCase						)
							# Otherwise, let's build the index
							_lowerCAmelCase  :							List[Any]				     =					{}
							for idx, shard in enumerate(_lowerCamelCase						):
														_lowerCAmelCase  :							str				     =					weights_name.replace(".bin"       ,				F"-{idx+1:05d}-of-{len(_lowerCamelCase						):05d}.bin"						)
														_lowerCAmelCase  :							Optional[int]				     =					os.path.join(_lowerCamelCase       ,				weights_name.replace(".bin"       ,				F"-{idx+1:05d}-of-???.bin"						)						)
														os.rename(_lowerCamelCase       ,				os.path.join(_lowerCamelCase       ,				_lowerCamelCase						)						)
														for key in shard:
																					_lowerCAmelCase  :							Optional[int]				     =					shard_file

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

							with open(os.path.join(_lowerCamelCase       ,				_lowerCamelCase						)       ,				"w"       ,				encoding="utf-8"						) as f:
														_lowerCAmelCase  :							Tuple				     =					json.dumps(_lowerCamelCase       ,				indent=2       ,				sort_keys=_lowerCamelCase						) + "\n"
														f.write(_lowerCamelCase						)

							return metadata, index


if __name__ == "__main__":
						_snake_case	=						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	=						parser.parse_args()
						_snake_case,		_snake_case	=						shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						_snake_case	=						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	=						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("Done")
						model.save_pretrained(args.pytorch_dump_folder_path)





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if (ksize % 2) == 0:
														_lowerCAmelCase  :							str				     =					ksize + 1
							_lowerCAmelCase  :							List[str]				     =					np.zeros((ksize, ksize)       ,				dtype=np.floataa						)

							# each value
							for y in range(_lowerCamelCase						):
														for x in range(_lowerCamelCase						):
																					# distance from center
																					_lowerCAmelCase  :							int				     =					x - ksize // 2
																					_lowerCAmelCase  :							Dict				     =					y - ksize // 2

																					# degree to radiant
																					_lowerCAmelCase  :							List[Any]				     =					theta / 180 * np.pi
																					_lowerCAmelCase  :							int				     =					np.cos(_theta						)
																					_lowerCAmelCase  :							Optional[int]				     =					np.sin(_theta						)

																					# get kernel x
																					_lowerCAmelCase  :							int				     =					cos_theta * px + sin_theta * py

																					# get kernel y
																					_lowerCAmelCase  :							str				     =					-sin_theta * px + cos_theta * py

																					# fill kernel
																					_lowerCAmelCase  :							Union[str, Any]				     =					np.exp(
																					    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)						) * np.cos(2 * np.pi * _x / lambd + psi						)

							return gabor


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						# read original image
						_snake_case	=						imread("../image_data/lena.jpg")
						# turn image in gray scale value
						_snake_case	=						cvtColor(img, COLOR_BGR2GRAY)

						# Apply multiple Kernel to detect edges
						_snake_case	=						np.zeros(gray.shape[:2])
						for theta in [0, 30, 60, 90, 120, 150]:
												_snake_case	=						gabor_filter_kernel(10, 8, theta, 10, 0, 0)
												out += filteraD(gray, CV_8UC3, kernel_aa)
						_snake_case	=						out / out.max() * 255
						_snake_case	=						out.astype(np.uinta)

						imshow("Original", gray)
						imshow("Gabor filter with 20x20 mask and 6 directions", out)

						waitKey(0)
