



'''simple docstring'''






import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
						from PIL import Image
else:

						class 				SCREAMING_SNAKE_CASE:







							"""simple docstring"""






							@staticmethod
							def      A	(						*__snake_case			:     Optional[int]		,			**__snake_case			:     List[Any]		)			->		Dict:
														pass


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]  )      ->   str:
							UpperCAmelCase :       Optional[Any] 							=		hashlib.mda(image.tobytes()  )
							return m.hexdigest()[:10]


def  snake_case_     (					_lowerCAmelCase  :						Tuple  )      ->   Union[str, Any]:
							UpperCAmelCase :       Optional[int] 							=		np.array(__lowerCAmelCase  )
							UpperCAmelCase :       Tuple 							=		npimg.shape
							return {"hash": hashimage(__lowerCAmelCase  ), "shape": shape}




@is_pipeline_test
@require_vision
@require_torch
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       dict(
	    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
	lowerCamelCase__			 =       dict(
	    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)







	def      A	(						self			:     Optional[int]		,			__snake_case			:     Dict		,			__snake_case			:     Any		,			__snake_case			:     Union[str, Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       List[Any] 							=		MaskGenerationPipeline(model=lowerCamelCase__		,			image_processor=lowerCamelCase__		)
								return image_segmenter, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]







	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     List[str]		,			__snake_case			:     Any		)			->		List[Any]:
								pass







	@require_tf
	@unittest.skip('''Image segmentation not implemented in TF'''		)
	def      A	(						self			:     Any		)			->		Any:
								pass







	@slow
	@require_torch
	def      A	(						self			:     str		)			->		List[str]:
								UpperCAmelCase :       str 							=		pipeline('''mask-generation'''		,			model='''facebook/sam-vit-huge'''		)

								UpperCAmelCase :       List[str] 							=		image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''		,			points_per_batch=256		)

								# Shortening by hashing
								UpperCAmelCase :       List[str] 							=		[]
								for i, o in enumerate(outputs['''masks''']		):
															new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase__		), "scores": outputs["scores"][i]}]

								# fmt: off
								self.assertEqual(
								    nested_simplify(lowerCamelCase__		,			decimals=4		)		,			[
								        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.04_44},
								        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.0_21},
								        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.01_67},
								        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.01_32},
								        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.00_53},
								        {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (480, 640)}, '''scores''': 0.99_67},
								        {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (480, 640)}, '''scores''': 0.9_93},
								        {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (480, 640)}, '''scores''': 0.99_09},
								        {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (480, 640)}, '''scores''': 0.98_79},
								        {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (480, 640)}, '''scores''': 0.98_34},
								        {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (480, 640)}, '''scores''': 0.97_16},
								        {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (480, 640)}, '''scores''': 0.96_12},
								        {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (480, 640)}, '''scores''': 0.95_99},
								        {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (480, 640)}, '''scores''': 0.95_52},
								        {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (480, 640)}, '''scores''': 0.95_32},
								        {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (480, 640)}, '''scores''': 0.95_16},
								        {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (480, 640)}, '''scores''': 0.94_99},
								        {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (480, 640)}, '''scores''': 0.94_83},
								        {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (480, 640)}, '''scores''': 0.94_64},
								        {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (480, 640)}, '''scores''': 0.9_43},
								        {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (480, 640)}, '''scores''': 0.9_43},
								        {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (480, 640)}, '''scores''': 0.94_08},
								        {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (480, 640)}, '''scores''': 0.93_35},
								        {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (480, 640)}, '''scores''': 0.93_26},
								        {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (480, 640)}, '''scores''': 0.92_62},
								        {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (480, 640)}, '''scores''': 0.89_99},
								        {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (480, 640)}, '''scores''': 0.89_86},
								        {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (480, 640)}, '''scores''': 0.89_84},
								        {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (480, 640)}, '''scores''': 0.88_73},
								        {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (480, 640)}, '''scores''': 0.88_71}
								    ]		,			)
								# fmt: on







	@require_torch
	@slow
	def      A	(						self			:     Any		)			->		List[Any]:
								UpperCAmelCase :       Union[str, Any] 							=		"facebook/sam-vit-huge"
								UpperCAmelCase :       int 							=		pipeline('''mask-generation'''		,			model=lowerCamelCase__		)

								UpperCAmelCase :       Optional[Any] 							=		image_segmenter(
								    '''http://images.cocodataset.org/val2017/000000039769.jpg'''		,			pred_iou_thresh=1		,			points_per_batch=256		)

								# Shortening by hashing
								UpperCAmelCase :       Tuple 							=		[]
								for i, o in enumerate(outputs['''masks''']		):
															new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase__		), "scores": outputs["scores"][i]}]

								self.assertEqual(
								    nested_simplify(lowerCamelCase__		,			decimals=4		)		,			[
								        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (480, 640)}, '''scores''': 1.04_44},
								        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (480, 640)}, '''scores''': 1.02_10},
								        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (480, 640)}, '''scores''': 1.01_67},
								        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (480, 640)}, '''scores''': 1.01_32},
								        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (480, 640)}, '''scores''': 1.00_53},
								    ]		,			)









'''simple docstring'''


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      ):
		_UpperCAmelCase						:				List[str]	    =					[
		    "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        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase    ,			_UpperCAmelCase						:				Optional[Any]	    =					emb.weight.shape
		_UpperCAmelCase						:				str	    =					nn.Linear(__lowerCAmelCase					,       __lowerCAmelCase					,       bias=__lowerCAmelCase      )
		_UpperCAmelCase						:				Optional[int]	    =					emb.weight.data
		return lin_layer






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






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase = WEIGHTS_NAME      ):
		_UpperCAmelCase						:				Optional[int]	    =					[]
		_UpperCAmelCase						:				Optional[Any]	    =					0
		os.makedirs(__lowerCAmelCase					,       exist_ok=__lowerCAmelCase      )

		for expert in range(__lowerCAmelCase      ):
				_UpperCAmelCase						:				Tuple	    =					switch_checkpoint_path + F"""-rank-{expert}.pt"""
				if os.path.isfile(__lowerCAmelCase      ):
						_UpperCAmelCase						:				Tuple	    =					torch.load(__lowerCAmelCase      )["model"]
						remove_ignore_keys_(__lowerCAmelCase      )
						_UpperCAmelCase						:				Dict	    =					rename_fairseq_keys(__lowerCAmelCase					,       __lowerCAmelCase      )
						_UpperCAmelCase						:				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
		_UpperCAmelCase						:				Tuple	    =					os.path.join(__lowerCAmelCase					,       weights_name.replace(".bin"					,       F"""-{len(__lowerCAmelCase      )+1:05d}-of-???.bin"""      )      )
		_UpperCAmelCase						:				Union[str, Any]	    =					torch.load(switch_checkpoint_path + "-shared.pt"      )["model"]
		remove_ignore_keys_(__lowerCAmelCase      )
		_UpperCAmelCase						:				Union[str, Any]	    =					rename_fairseq_keys(__lowerCAmelCase					,       __lowerCAmelCase      )
		_UpperCAmelCase						:				Any	    =					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:
				_UpperCAmelCase						:				List[str]	    =					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
		_UpperCAmelCase						:				Union[str, Any]	    =					{}
		for idx, shard in enumerate(__lowerCAmelCase      ):
				_UpperCAmelCase						:				Tuple	    =					weights_name.replace(".bin"					,       F"""-{idx+1:05d}-of-{len(__lowerCAmelCase      ):05d}.bin"""      )
				_UpperCAmelCase						:				List[Any]	    =					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:
						_UpperCAmelCase						:				List[Any]	    =					shard_file

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

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

		return metadata, index


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

						lowerCamelCase__					=   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)
						lowerCamelCase__					=   NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print('Done')
						model.save_pretrained(args.pytorch_dump_folder_path)



