



"""simple docstring"""




import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase		:				Tuple      		=get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class        snake_case__    (__lowercase ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:List[Any]      =					XGLMTokenizer
						__lowerCAmelCase					:Dict      =					XGLMTokenizerFast
						__lowerCAmelCase					:List[str]      =					True
						__lowerCAmelCase					:Optional[Any]      =					True

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									super().setUp()

									# We have a SentencePiece fixture for testing
									a__       :						List[str]							=  XGLMTokenizer(__lowercase ,       keep_accents=__lowercase  )
									tokenizer.save_pretrained(self.tmpdirname  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						str							=  """<pad>"""
									a__       :						List[str]							=  1

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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									a__       :						List[str]							=  list(self.get_tokenizer().get_vocab().keys()  )

									self.assertEqual(vocab_keys[0] ,       """<s>"""  )
									self.assertEqual(vocab_keys[1] ,       """<pad>"""  )
									self.assertEqual(len(__lowercase  ) ,       1_0_0_8  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									self.assertEqual(self.get_tokenizer().vocab_size ,       1_0_0_8  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__       :						List[str]							=  XGLMTokenizer(__lowercase ,       keep_accents=__lowercase  )

									a__       :						Dict							=  tokenizer.tokenize("""This is a test"""  )
									self.assertListEqual(__lowercase ,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]  )

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(__lowercase  ) ,       [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] ,       )

									a__       :						Union[str, Any]							=  tokenizer.tokenize("""I was born in 92000, and this is falsé."""  )
									self.assertListEqual(
									    __lowercase ,       [
									        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""",
									        """.""",
									    ] ,       )
									a__       :						Optional[int]							=  tokenizer.convert_tokens_to_ids(__lowercase  )
									self.assertListEqual(
									    __lowercase ,       [
									        value + tokenizer.fairseq_offset
									        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
									    ] ,       )

									a__       :						Tuple							=  tokenizer.convert_ids_to_tokens(__lowercase  )
									self.assertListEqual(
									    __lowercase ,       [
									        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 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									return XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									with tempfile.NamedTemporaryFile() as f:
												shutil.copyfile(__lowercase ,       f.name  )
												a__       :						Union[str, Any]							=  XGLMTokenizer(f.name ,       keep_accents=__lowercase  )
												a__       :						str							=  pickle.dumps(__lowercase  )
									pickle.loads(__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									if not self.test_rust_tokenizer:
												return

									a__       :						Optional[int]							=  self.get_tokenizer()
									a__       :						Optional[Any]							=  self.get_rust_tokenizer()

									a__       :						Dict							=  """I was born in 92000, and this is falsé."""

									a__       :						Any							=  tokenizer.tokenize(__lowercase  )
									a__       :						Optional[Any]							=  rust_tokenizer.tokenize(__lowercase  )
									self.assertListEqual(__lowercase ,       __lowercase  )

									a__       :						Tuple							=  tokenizer.encode(__lowercase ,       add_special_tokens=__lowercase  )
									a__       :						str							=  rust_tokenizer.encode(__lowercase ,       add_special_tokens=__lowercase  )
									self.assertListEqual(__lowercase ,       __lowercase  )

									a__       :						Tuple							=  self.get_rust_tokenizer()
									a__       :						Optional[int]							=  tokenizer.encode(__lowercase  )
									a__       :						Optional[int]							=  rust_tokenizer.encode(__lowercase  )
									self.assertListEqual(__lowercase ,       __lowercase  )

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__       :						str							=  """Hello World!"""
									a__       :						Optional[int]							=  [2, 3_1_2_2_7, 4_4_4_7, 3_5]

									self.assertListEqual(__lowercase ,       self.big_tokenizer.encode(__lowercase  )  )

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						Optional[int]							=  (
									    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
									    """ add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"""
									)
									# fmt: off
									a__       :						Optional[int]							=  [2, 1_0_1_8, 6_7, 1_1, 1_9_8_8, 2_6_1_7, 5_6_3_1, 2_7_8, 1_1, 3_4_0_7, 4_8, 7_1_6_3_0, 2_8_0_8_5, 4, 3_2_3_4, 1_5_7, 1_3, 6, 5, 6, 4, 3_5_2_6, 7_6_8, 1_5, 6_5_9, 5_7, 2_9_8, 3_9_8_3, 8_6_4, 1_2_9, 2_1, 6, 5, 1_3_6_7_5, 3_7_7, 6_5_2, 7_5_8_0, 1_0_3_4_1, 1_5_5, 2_8_1_7, 4_2_2, 1_6_6_6, 7, 1_6_7_4, 5_3, 1_1_3, 2_0_2_2_7_7, 1_7_8_9_2, 3_3, 6_0, 8_7, 4, 3_2_3_4, 1_5_7, 6_1, 2_6_6_7, 5_2_3_7_6, 1_9, 8_8, 2_3, 7_3_5]
									# fmt: on

									self.assertListEqual(__lowercase ,       self.big_tokenizer.encode(__lowercase  )  )

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						str							=  {
									    """input_ids""": [[2, 1_0_8_8_2_5, 1_1_6_3, 1_5, 8_8_0_1_0, 4_7_3, 1_5_8_9_8, 1_5_7, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 2_3_8_0_2_1, 1_1_6_3, 5_3, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 5_3_2_8_3, 1_8_2_3_9_6, 8, 1_8_5_6_6, 1_6, 3_6_7_3_3, 4_1_0_1, 8, 2_3_0, 2_4_4_0_1_7, 1_2_2_5_5_3, 7, 1_5, 1_3_2_5_9_7, 4, 2_9_3, 1_2_5_1_1, 7_6_1_0, 4, 3_4_1_4, 1_3_2_5_9_7, 9, 4, 3_2_3_6_1, 3_6_2, 4, 7_3_4, 2_8_5_1_2, 3_2_5_6_9, 1_8, 4, 3_2_3_6_1, 2_6_0_9_6, 1_4_9_8_2, 7_3, 1_8_7_1_5, 2_1_4_3_3, 2_3_5_2_6_1, 1_5, 4_9_2, 1_2_4_2_7, 1_6, 5_3, 1_8_7_1_5, 2_1_4_3_3, 6_5_4_5_4, 1_5, 2_3_6_5_9, 5_6_3, 1_6, 2_7_8, 5_9_7, 2_8_4_3, 5_9_5, 7_9_3_1, 1_8_2_3_9_6, 6_4_1_8_6, 2_2, 8_8_6, 5_9_5, 1_3_2_9_8_1, 5_3, 2_5_5_4_0, 3_4_4_9, 4_3_9_8_2, 3_9_9_0_1, 5_9_5_1, 8_7_8, 3_3_0, 4, 2_7_6_9_4, 8_0_2_6_9, 3_1_2, 5_3, 6_5_1_7, 1_1_7_8_0, 6_1_1, 2_0_4_0_8, 5], [2, 6, 1_3_2_5_9_7, 6_7, 4_2_8_9_7, 3_3, 5_9_2, 8, 1_6_3_7_2_9, 2_5_5_4_0, 3_6_1, 1_3_6_9_9_7, 1_0_9_5_1_4, 1_7_3_2_3_0, 7, 5_0_1, 6_0, 1_0_2_9_1_3, 1_9_6, 5_6_3_1, 2_3_5, 6_3_2_4_3, 4_7_3, 6, 2_3_1_7_5_7, 7_4, 5_2_7_7, 7_9_0_5, 5_3, 3_0_9_5, 3_7_3_1_7, 2_2, 4_5_4, 1_8_3_8_7_4, 5], [2, 2_6_8, 3_1_2_9_8, 4_6_5_3_0, 6, 1_3_2_9_3_5, 4_3_8_3_1, 7, 5_9_7, 3_2, 2_4, 3_6_8_8, 9_8_6_5, 5]],
									    """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, 1, 1, 1, 1, 1]]
									}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=__lowercase ,       model_name="""facebook/xglm-564M""" ,       padding=__lowercase ,       )









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_					(  _lowercase    :       List[str])							->	Union[str, Any]:







			"""simple docstring"""
			a__       :						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(_lowercase						,       _lowercase)

def      lowerCAmelCase_					(  _lowercase    :       List[Any])							->	Optional[Any]:







			"""simple docstring"""
			a__	,						a__       :						Union[str, Any]							=  emb.weight.shape
			a__       :						str							=  nn.Linear(_lowercase						,       _lowercase						,       bias=_lowercase)
			a__       :						Any							=  emb.weight.data
			return lin_layer

def      lowerCAmelCase_					(  _lowercase    :       int						,       _lowercase    :       int=None)							->	List[Any]:







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

def      lowerCAmelCase_					(  _lowercase    :       Tuple						,       _lowercase    :       Optional[int]						,       _lowercase    :       Any						,       _lowercase    :       Dict						,       _lowercase    :       str = WEIGHTS_NAME)							->	Tuple:







			"""simple docstring"""
			a__       :						Tuple							=  []
			a__       :						Optional[Any]							=  0
			os.makedirs(_lowercase						,       exist_ok=_lowercase)

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

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

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

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

			return metadata, index


if __name__ == "__main__":
		_lowercase		:				Any      		=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.",
		)
		_lowercase		:				Tuple      		=parser.parse_args()
		_lowercase		,					_lowercase		:				List[Any]      		=shard_on_the_fly(
		    args.nllb_moe_checkpoint_path,
		    args.pytorch_dump_folder_path,
		    128,
		    args.dtype,
		)

		_lowercase		:				int      		=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)
		_lowercase		:				List[str]      		=NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
		print("Done")
		model.save_pretrained(args.pytorch_dump_folder_path)




