



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_		:		str):





				'''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(_snake_case	,_snake_case)






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[int]):





				'''simple docstring'''

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






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		int=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__					:    str												=					key.replace('''moe_layer.experts.'''	,'''ffn.experts.expert_''')
								if "gate" in key:
												lowerCAmelCase__					:    int												=					key.replace('''.moe_layer.gate.wg'''	,'''.ffn.router.classifier''')
								if "fc2" and "experts" not in key:
												lowerCAmelCase__					:    Any												=					key.replace('''.fc2.'''	,'''.ffn.fc2.''')
								if "fc1" and "experts" not in key:
												lowerCAmelCase__					:    List[str]												=					key.replace('''.fc1.'''	,'''.ffn.fc1.''')
								if ".encoder_attn." in key:
												lowerCAmelCase__					:    List[str]												=					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__					:    str												=					key.replace('''final_layer_norm'''	,'''ff_layer_norm''')
								lowerCAmelCase__					:    List[str]												=					state_dict[old_key]
				return new_dict






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		Tuple	,lowerCamelCase_		:		str	,lowerCamelCase_		:		str = WEIGHTS_NAME):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[Any]												=					[]
				lowerCAmelCase__					:    Tuple												=					0
				os.makedirs(_snake_case	,exist_ok=_snake_case)

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

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

    # Add the metadata
				lowerCAmelCase__					:    str												=					{"total_size": total_size}
				lowerCAmelCase__					:    str												=					{"metadata": metadata, "weight_map": weight_map}

				with open(os.path.join(_snake_case	,_snake_case)	,'''w'''	,encoding='''utf-8''') as f:
								lowerCAmelCase__					:    Optional[int]												=					json.dumps(_snake_case	,indent=2	,sort_keys=_snake_case) + "\n"
								f.write(_snake_case)

				return metadata, index


if __name__ == "__main__":
							__snake_case    :		Dict  =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    :		Optional[int]  =parser.parse_args()
							__snake_case    :		Dict  =shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    1_2_8,
							    args.dtype,
							)

							__snake_case    :		Union[str, Any]  =NllbMoeConfig.from_pretrained(
							    'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
							)
							config.save_pretrained(args.pytorch_dump_folder_path)
							__snake_case    :		int  =NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print('Done')
							model.save_pretrained(args.pytorch_dump_folder_path)






import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case    :		int  =get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =XLMProphetNetTokenizer
		snake_case_     =False
		snake_case_     =True



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						super().setUp()

						# We have a SentencePiece fixture for testing
						lowerCAmelCase__					:    int												=					XLMProphetNetTokenizer(__lowerCamelCase		,keep_accents=__lowerCamelCase			)
						tokenizer.save_pretrained(self.tmpdirname			)



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					'''[PAD]'''
						lowerCAmelCase__					:    Tuple												=					0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase			)		,__lowerCamelCase			)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase			)		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					list(self.get_tokenizer().get_vocab().keys()			)

						self.assertEqual(vocab_keys[0]		,'''[PAD]'''			)
						self.assertEqual(vocab_keys[1]		,'''[CLS]'''			)
						self.assertEqual(vocab_keys[-1]		,'''j'''			)
						self.assertEqual(len(__lowerCamelCase			)		,10_12			)



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						self.assertEqual(self.get_tokenizer().vocab_size		,10_12			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					XLMProphetNetTokenizer(__lowerCamelCase		,keep_accents=__lowerCamelCase			)

						lowerCAmelCase__					:    Tuple												=					tokenizer.tokenize('''This is a test'''			)
						self.assertListEqual(__lowerCamelCase		,['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']			)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(__lowerCamelCase			)		,[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]		,)

						lowerCAmelCase__					:    Dict												=					tokenizer.tokenize('''I was born in 92000, and this is falsé.'''			)
						self.assertListEqual(
						    __lowerCamelCase		,[
						        SPIECE_UNDERLINE + '''I''',
						        SPIECE_UNDERLINE + '''was''',
						        SPIECE_UNDERLINE + '''b''',
						        '''or''',
						        '''n''',
						        SPIECE_UNDERLINE + '''in''',
						        SPIECE_UNDERLINE + '''''',
						        '''9''',
						        '''2''',
						        '''0''',
						        '''0''',
						        '''0''',
						        ''',''',
						        SPIECE_UNDERLINE + '''and''',
						        SPIECE_UNDERLINE + '''this''',
						        SPIECE_UNDERLINE + '''is''',
						        SPIECE_UNDERLINE + '''f''',
						        '''al''',
						        '''s''',
						        '''é''',
						        '''.''',
						    ]		,)
						lowerCAmelCase__					:    Optional[Any]												=					tokenizer.convert_tokens_to_ids(__lowerCamelCase			)
						self.assertListEqual(
						    __lowerCamelCase		,[
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, -9, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, -9, 4]
						    ]		,)

						lowerCAmelCase__					:    Optional[Any]												=					tokenizer.convert_ids_to_tokens(__lowerCamelCase			)
						self.assertListEqual(
						    __lowerCamelCase		,[
						        SPIECE_UNDERLINE + '''I''',
						        SPIECE_UNDERLINE + '''was''',
						        SPIECE_UNDERLINE + '''b''',
						        '''or''',
						        '''n''',
						        SPIECE_UNDERLINE + '''in''',
						        SPIECE_UNDERLINE + '''''',
						        '''[UNK]''',
						        '''2''',
						        '''0''',
						        '''0''',
						        '''0''',
						        ''',''',
						        SPIECE_UNDERLINE + '''and''',
						        SPIECE_UNDERLINE + '''this''',
						        SPIECE_UNDERLINE + '''is''',
						        SPIECE_UNDERLINE + '''f''',
						        '''al''',
						        '''s''',
						        '''[UNK]''',
						        '''.''',
						    ]		,)



		@cached_property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					'''Hello World!'''
						lowerCAmelCase__					:    str												=					[3_53_89, 66_72, 49, 2]
						self.assertListEqual(__lowerCamelCase		,self.big_tokenizer.encode(__lowerCamelCase			)			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					{'''input_ids''': [[1_10_73, 8_27_83, 18, 26, 8_27_83, 5_49, 5_15_40, 2_48, 1_72_09, 13_01, 2_17, 20, 21_51_86, 13_25, 1_47, 1_72_09, 13_01, 2_17, 20, 5_63_70, 53, 12_20_20, 20, 1_64_77, 27, 8_73_55, 45_48, 20, 47_28, 7_83_92, 17, 15_99_69, 18, 26, 2_44_91, 6_29, 15, 5_38, 2_27_04, 54_39, 15, 27_88, 2_44_91, 98_85, 15, 4_35_34, 6_05, 15, 8_14, 1_84_03, 3_32_00, 29, 15, 4_35_34, 2_44_58, 1_24_10, 1_11, 2_49_66, 8_36_69, 96_37, 14_40_68, 26, 8_50, 2_23_46, 27, 1_47, 2_49_66, 8_36_69, 8_34_90, 26, 3_91_13, 7_35, 27, 6_89, 6_56, 28_00, 13_39, 46_00, 53, 12_20_20, 11_57_85, 34, 8_16, 13_39, 4_68_87, 18, 1_47, 5_39_05, 19_51, 4_22_38, 4_11_70, 1_77_32, 8_34, 4_36, 15, 2_75_23, 9_87_33, 2_17, 1_47, 55_42, 49_81, 9_30, 1_73_47, 16, 2], [2_00_91, 6_29, 94, 8_27_86, 58, 4_90, 20, 15_28, 84, 5_39_05, 3_44, 8_05_92, 11_01_28, 1_88_22, 52_67, 13_06, 62, 15_25_37, 3_08, 79_97, 4_01, 12_44_27, 5_49, 3_54_42, 2_25, 1_09, 1_50_55, 2_57_48, 1_47, 71_19, 4_37_12, 34, 7_67, 13_53_66, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_92, 6_37_84, 11_94_66, 17, 14_78_08, 8_82_14, 18, 6_56, 81, 32, 32_96, 1_02_80, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=__lowerCamelCase		,model_name='''microsoft/xprophetnet-large-wiki100-cased'''		,revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''		,)

