



'''simple docstring'''





import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







class        UpperCAmelCase_			(			_a						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						ProphetNetTokenizer
    lowercase   =						False




    def lowerCamelCase			(       self   :	Dict		):
          super().setUp()

          snake_case__    :       Any       		= [
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          snake_case__    :       int       		= os.path.join(self.tmpdirname					,	VOCAB_FILES_NAMES["""vocab_file"""]		)
          with open(self.vocab_file					,	"""w"""					,	encoding="""utf-8"""		) as vocab_writer:
                vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	Any		):
          snake_case__    :       Optional[int]       		= "UNwant\u00E9d,running"
          snake_case__    :       Optional[int]       		= "unwanted, running"
          return input_text, output_text




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       Union[str, Any]       		= self.tokenizer_class(self.vocab_file		)

          snake_case__    :       int       		= tokenizer.tokenize("""UNwant\u00E9d,running"""		)
          self.assertListEqual(__UpperCAmelCase					,	["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]		)
          self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase		)					,	[9, 6, 7, 12, 10, 11]		)




    def lowerCamelCase			(       self   :	Dict		):
          snake_case__    :       str       		= BasicTokenizer()

          self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""		)					,	["""ah""", """\u535A""", """\u63A8""", """zz"""]		)




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       str       		= BasicTokenizer(do_lower_case=__UpperCAmelCase		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """		)					,	["""hello""", """!""", """how""", """are""", """you""", """?"""]		)
          self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		)					,	["""hello"""]		)




    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       Any       		= BasicTokenizer(do_lower_case=__UpperCAmelCase					,	strip_accents=__UpperCAmelCase		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)					,	["""hällo""", """!""", """how""", """are""", """you""", """?"""]		)
          self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		)					,	["""h\u00E9llo"""]		)




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Tuple       		= BasicTokenizer(do_lower_case=__UpperCAmelCase					,	strip_accents=__UpperCAmelCase		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)					,	["""hallo""", """!""", """how""", """are""", """you""", """?"""]		)
          self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		)					,	["""hello"""]		)




    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       int       		= BasicTokenizer(do_lower_case=__UpperCAmelCase		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)					,	["""hallo""", """!""", """how""", """are""", """you""", """?"""]		)
          self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""		)					,	["""hello"""]		)




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       Dict       		= BasicTokenizer(do_lower_case=__UpperCAmelCase		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """		)					,	["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)




    def lowerCamelCase			(       self   :	Optional[int]		):
          snake_case__    :       Optional[Any]       		= BasicTokenizer(do_lower_case=__UpperCAmelCase					,	strip_accents=__UpperCAmelCase		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)					,	["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)




    def lowerCamelCase			(       self   :	int		):
          snake_case__    :       Optional[Any]       		= BasicTokenizer(do_lower_case=__UpperCAmelCase					,	strip_accents=__UpperCAmelCase		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """		)					,	["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]		)




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       Optional[int]       		= BasicTokenizer(do_lower_case=__UpperCAmelCase					,	never_split=["""[UNK]"""]		)

          self.assertListEqual(
              tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""		)					,	["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]		)




    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       Any       		= ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

          snake_case__    :       List[str]       		= {}
          for i, token in enumerate(__UpperCAmelCase		):
                snake_case__    :       str       		= i
          snake_case__    :       int       		= WordpieceTokenizer(vocab=__UpperCAmelCase					,	unk_token="""[UNK]"""		)

          self.assertListEqual(tokenizer.tokenize(""""""		)					,	[]		)

          self.assertListEqual(tokenizer.tokenize("""unwanted running"""		)					,	["""un""", """##want""", """##ed""", """runn""", """##ing"""]		)

          self.assertListEqual(tokenizer.tokenize("""unwantedX running"""		)					,	["""[UNK]""", """runn""", """##ing"""]		)




    @require_torch
    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       List[str]       		= self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""		)

          snake_case__    :       Any       		= ["A long paragraph for summarization.", "Another paragraph for summarization."]
          snake_case__    :       Optional[Any]       		= [1_037, 2_146, 20_423, 2_005, 7_680, 7_849, 3_989, 1_012, 102]
          snake_case__    :       Optional[int]       		= tokenizer(__UpperCAmelCase					,	padding=__UpperCAmelCase					,	return_tensors="""pt"""		)
          self.assertIsInstance(__UpperCAmelCase					,	__UpperCAmelCase		)
          snake_case__    :       Optional[int]       		= list(batch.input_ids.numpy()[0]		)
          self.assertListEqual(__UpperCAmelCase					,	__UpperCAmelCase		)

          self.assertEqual((2, 9)					,	batch.input_ids.shape		)
          self.assertEqual((2, 9)					,	batch.attention_mask.shape		)




    def lowerCamelCase			(       self   :	List[str]		):
          self.assertTrue(_is_whitespace(""" """		)		)
          self.assertTrue(_is_whitespace("""\t"""		)		)
          self.assertTrue(_is_whitespace("""\r"""		)		)
          self.assertTrue(_is_whitespace("""\n"""		)		)
          self.assertTrue(_is_whitespace("""\u00A0"""		)		)

          self.assertFalse(_is_whitespace("""A"""		)		)
          self.assertFalse(_is_whitespace("""-"""		)		)




    def lowerCamelCase			(       self   :	List[str]		):
          self.assertTrue(_is_control("""\u0005"""		)		)

          self.assertFalse(_is_control("""A"""		)		)
          self.assertFalse(_is_control(""" """		)		)
          self.assertFalse(_is_control("""\t"""		)		)
          self.assertFalse(_is_control("""\r"""		)		)




    def lowerCamelCase			(       self   :	Dict		):
          self.assertTrue(_is_punctuation("""-"""		)		)
          self.assertTrue(_is_punctuation("""$"""		)		)
          self.assertTrue(_is_punctuation("""`"""		)		)
          self.assertTrue(_is_punctuation("""."""		)		)

          self.assertFalse(_is_punctuation("""A"""		)		)
          self.assertFalse(_is_punctuation(""" """		)		)




    @slow
    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       List[str]       		= self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""		)

          snake_case__    :       Dict       		= tokenizer.encode("""sequence builders"""					,	add_special_tokens=__UpperCAmelCase		)
          snake_case__    :       int       		= tokenizer.encode("""multi-sequence build"""					,	add_special_tokens=__UpperCAmelCase		)

          snake_case__    :       Optional[Any]       		= tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase		)
          snake_case__    :       Optional[int]       		= tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase					,	__UpperCAmelCase		)

          assert encoded_sentence == text + [102]
          assert encoded_pair == text + [102] + text_a + [102]





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     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List[Any]:
				a__	:							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(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Optional[Any]:
				a__						,   a__	:							Optional[int]      						= emb.weight.shape
				a__	:							Union[str, Any]      						= nn.Linear(__UpperCamelCase	,				__UpperCamelCase	,				bias=__UpperCamelCase )
				a__	:							str      						= emb.weight.data
				return lin_layer






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






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = WEIGHTS_NAME )	->						Dict:
				a__	:							Any      						= []
				a__	:							List[str]      						= 0
				os.makedirs(__UpperCamelCase	,				exist_ok=__UpperCamelCase )

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

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

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

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

				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,
						    1_28,
						    args.dtype,
						)

						lowerCamelCase		       =      NllbMoeConfig.from_pretrained(
						    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
						)
						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)


