



import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	LongformerTokenizer
     _SCREAMING_SNAKE_CASE						=	True
     _SCREAMING_SNAKE_CASE						=	LongformerTokenizerFast
     _SCREAMING_SNAKE_CASE						=	True






     def    lowercase_   (       self  :  Any					)   ->int:
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            snake_case__							:	Optional[int]   	=	[
                'l',
                'o',
                'w',
                'e',
                'r',
                's',
                't',
                'i',
                'd',
                'n',
                '\u0120',
                '\u0120l',
                '\u0120n',
                '\u0120lo',
                '\u0120low',
                'er',
                '\u0120lowest',
                '\u0120newer',
                '\u0120wider',
                '<unk>',
            ]
            snake_case__							:	List[str]   	=	dict(zip(_snake_case, range(len(_snake_case					)					)					)					)
            snake_case__							:	List[str]   	=	['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
            snake_case__							:	str   	=	{'unk_token': '<unk>'}

            snake_case__							:	int   	=	os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file']					)
            snake_case__							:	Union[str, Any]   	=	os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['merges_file']					)
            with open(self.vocab_file, 'w', encoding='utf-8'					) as fp:
                   fp.write(json.dumps(_snake_case					) + '\n'					)
            with open(self.merges_file, 'w', encoding='utf-8'					) as fp:
                   fp.write('\n'.join(_snake_case					)					)






     def    lowercase_   (       self  :  Optional[Any], **_snake_case  :  Dict					)   ->int:
            kwargs.update(self.special_tokens_map					)
            return self.tokenizer_class.from_pretrained(self.tmpdirname, **_snake_case					)






     def    lowercase_   (       self  :  Optional[Any], **_snake_case  :  List[Any]					)   ->Optional[int]:
            kwargs.update(self.special_tokens_map					)
            return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **_snake_case					)






     def    lowercase_   (       self  :  str, _snake_case  :  Dict					)   ->Any:
            snake_case__							:	Dict   	=	'lower newer'
            snake_case__							:	Optional[int]   	=	'lower newer'
            return input_text, output_text






     def    lowercase_   (       self  :  Dict					)   ->Optional[int]:
            snake_case__							:	str   	=	self.tokenizer_class(self.vocab_file, self.merges_file, **self.special_tokens_map					)
            snake_case__							:	Union[str, Any]   	=	'lower newer'
            snake_case__							:	Dict   	=	['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
            snake_case__							:	Tuple   	=	tokenizer.tokenize(_snake_case					)  # , add_prefix_space=True)
            self.assertListEqual(_snake_case, _snake_case					)

            snake_case__							:	Any   	=	tokens + [tokenizer.unk_token]
            snake_case__							:	Tuple   	=	[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case					), _snake_case					)






     def    lowercase_   (       self  :  List[str]					)   ->Any:
            snake_case__							:	Optional[int]   	=	self.get_tokenizer()

            self.assertListEqual(tokenizer.encode('Hello world!', add_special_tokens=_snake_case					), [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]					)
            self.assertListEqual(
                tokenizer.encode('Hello world! cécé herlolip 418', add_special_tokens=_snake_case					), [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2], )






     @slow
     def    lowercase_   (       self  :  Dict					)   ->Optional[int]:
            snake_case__							:	Union[str, Any]   	=	self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'					)

            snake_case__							:	List[Any]   	=	tokenizer.encode('sequence builders', add_special_tokens=_snake_case					)
            snake_case__							:	Any   	=	tokenizer.encode('multi-sequence build', add_special_tokens=_snake_case					)

            snake_case__							:	Union[str, Any]   	=	tokenizer.encode(
                'sequence builders', add_special_tokens=_snake_case, add_prefix_space=_snake_case					)
            snake_case__							:	List[str]   	=	tokenizer.encode(
                'sequence builders', 'multi-sequence build', add_special_tokens=_snake_case, add_prefix_space=_snake_case					)

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

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Tuple:
            snake_case__							:	List[str]   	=	self.get_tokenizer()

            snake_case__							:	List[Any]   	=	'Encode this sequence.'
            snake_case__							:	Optional[Any]   	=	tokenizer.byte_encoder[' '.encode('utf-8'					)[0]]

            # Testing encoder arguments
            snake_case__							:	List[str]   	=	tokenizer.encode(_snake_case, add_special_tokens=_snake_case, add_prefix_space=_snake_case					)
            snake_case__							:	int   	=	tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
            self.assertNotEqual(_snake_case, _snake_case					)

            snake_case__							:	List[Any]   	=	tokenizer.encode(_snake_case, add_special_tokens=_snake_case, add_prefix_space=_snake_case					)
            snake_case__							:	Union[str, Any]   	=	tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
            self.assertEqual(_snake_case, _snake_case					)

            tokenizer.add_special_tokens({'bos_token': '<s>'}					)
            snake_case__							:	List[str]   	=	tokenizer.encode(_snake_case, add_special_tokens=_snake_case					)
            snake_case__							:	List[Any]   	=	tokenizer.convert_ids_to_tokens(encoded[1]					)[0]
            self.assertNotEqual(_snake_case, _snake_case					)

            # Testing spaces after special tokens
            snake_case__							:	Optional[Any]   	=	'<mask>'
            tokenizer.add_special_tokens(
                {'mask_token': AddedToken(_snake_case, lstrip=_snake_case, rstrip=_snake_case					)}					)  # mask token has a left space
            snake_case__							:	str   	=	tokenizer.convert_tokens_to_ids(_snake_case					)

            snake_case__							:	str   	=	'Encode <mask> sequence'
            snake_case__							:	List[str]   	=	'Encode <mask>sequence'

            snake_case__							:	Dict   	=	tokenizer.encode(_snake_case					)
            snake_case__							:	Dict   	=	encoded.index(_snake_case					)
            snake_case__							:	int   	=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
            self.assertEqual(_snake_case, _snake_case					)

            snake_case__							:	Any   	=	tokenizer.encode(_snake_case					)
            snake_case__							:	List[str]   	=	encoded.index(_snake_case					)
            snake_case__							:	int   	=	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
            self.assertNotEqual(_snake_case, _snake_case					)






     def    lowercase_   (       self  :  Dict					)   ->List[str]:
            pass






     def    lowercase_   (       self  :  int					)   ->str:
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
                          snake_case__							:	str   	=	self.rust_tokenizer_class.from_pretrained(_snake_case, **_snake_case					)
                          snake_case__							:	str   	=	self.tokenizer_class.from_pretrained(_snake_case, **_snake_case					)
                          snake_case__							:	int   	=	'A, <mask> AllenNLP sentence.'
                          snake_case__							:	Any   	=	tokenizer_r.encode_plus(_snake_case, add_special_tokens=_snake_case, return_token_type_ids=_snake_case					)
                          snake_case__							:	List[Any]   	=	tokenizer_p.encode_plus(_snake_case, add_special_tokens=_snake_case, return_token_type_ids=_snake_case					)

                          # token_type_ids should put 0 everywhere
                          self.assertEqual(sum(tokens_r['token_type_ids']					), sum(tokens_p['token_type_ids']					)					)

                          # attention_mask should put 1 everywhere, so sum over length should be 1
                          self.assertEqual(
                              sum(tokens_r['attention_mask']					) / len(tokens_r['attention_mask']					), sum(tokens_p['attention_mask']					) / len(tokens_p['attention_mask']					), )

                          snake_case__							:	Dict   	=	tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
                          snake_case__							:	Optional[int]   	=	tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']					)

                          # Rust correctly handles the space before the mask while python doesnt
                          self.assertSequenceEqual(tokens_p['input_ids'], [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]					)
                          self.assertSequenceEqual(tokens_r['input_ids'], [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]					)

                          self.assertSequenceEqual(
                              _snake_case, ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)
                          self.assertSequenceEqual(
                              _snake_case, ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)






     def    lowercase_   (       self  :  Tuple					)   ->Optional[Any]:
            for trim_offsets, add_prefix_space in itertools.product([True, False], repeat=2					):
                   snake_case__							:	Optional[int]   	=	self.rust_tokenizer_class.from_pretrained(
                       self.tmpdirname, use_fast=_snake_case, add_prefix_space=_snake_case, trim_offsets=_snake_case					)

                   snake_case__							:	Optional[Any]   	=	json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
                   snake_case__							:	List[Any]   	=	json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()					)

                   self.assertEqual(pre_tokenizer_state['add_prefix_space'], _snake_case					)

                   self.assertEqual(post_processor_state['add_prefix_space'], _snake_case					)
                   self.assertEqual(post_processor_state['trim_offsets'], _snake_case					)






     def    lowercase_   (       self  :  List[Any]					)   ->Optional[int]:
            # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
            # `trim_offsets`
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
                          snake_case__							:	List[str]   	=	'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                          snake_case__							:	Union[str, Any]   	=	F'''{text_of_1_token} {text_of_1_token}'''

                          snake_case__							:	Union[str, Any]   	=	self.rust_tokenizer_class.from_pretrained(
                              _snake_case, use_fast=_snake_case, add_prefix_space=_snake_case, trim_offsets=_snake_case					)
                          snake_case__							:	Optional[int]   	=	tokenizer_r(_snake_case, return_offsets_mapping=_snake_case, add_special_tokens=_snake_case					)
                          self.assertEqual(encoding.offset_mapping[0], (0, len(_snake_case					))					)
                          self.assertEqual(
                              encoding.offset_mapping[1], (len(_snake_case					) + 1, len(_snake_case					) + 1 + len(_snake_case					)), )

                          snake_case__							:	Any   	=	self.rust_tokenizer_class.from_pretrained(
                              _snake_case, use_fast=_snake_case, add_prefix_space=_snake_case, trim_offsets=_snake_case					)
                          snake_case__							:	Any   	=	tokenizer_r(_snake_case, return_offsets_mapping=_snake_case, add_special_tokens=_snake_case					)
                          self.assertEqual(encoding.offset_mapping[0], (0, len(_snake_case					))					)
                          self.assertEqual(
                              encoding.offset_mapping[1], (len(_snake_case					) + 1, len(_snake_case					) + 1 + len(_snake_case					)), )

                          snake_case__							:	Dict   	=	self.rust_tokenizer_class.from_pretrained(
                              _snake_case, use_fast=_snake_case, add_prefix_space=_snake_case, trim_offsets=_snake_case					)
                          snake_case__							:	List[str]   	=	tokenizer_r(_snake_case, return_offsets_mapping=_snake_case, add_special_tokens=_snake_case					)
                          self.assertEqual(encoding.offset_mapping[0], (0, len(_snake_case					))					)
                          self.assertEqual(
                              encoding.offset_mapping[1], (len(_snake_case					), len(_snake_case					) + 1 + len(_snake_case					)), )

                          snake_case__							:	Any   	=	self.rust_tokenizer_class.from_pretrained(
                              _snake_case, use_fast=_snake_case, add_prefix_space=_snake_case, trim_offsets=_snake_case					)
                          snake_case__							:	List[Any]   	=	tokenizer_r(_snake_case, return_offsets_mapping=_snake_case, add_special_tokens=_snake_case					)
                          self.assertEqual(encoding.offset_mapping[0], (0, len(_snake_case					))					)
                          self.assertEqual(
                              encoding.offset_mapping[1], (len(_snake_case					), len(_snake_case					) + 1 + len(_snake_case					)), )

                          snake_case__							:	Optional[Any]   	=	F''' {text}'''

                          # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                          #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                          # )
                          # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                          # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                          # self.assertEqual(
                          #     encoding.offset_mapping[1],
                          #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                          # )

                          snake_case__							:	Tuple   	=	self.rust_tokenizer_class.from_pretrained(
                              _snake_case, use_fast=_snake_case, add_prefix_space=_snake_case, trim_offsets=_snake_case					)
                          snake_case__							:	int   	=	tokenizer_r(_snake_case, return_offsets_mapping=_snake_case, add_special_tokens=_snake_case					)
                          self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(_snake_case					))					)
                          self.assertEqual(
                              encoding.offset_mapping[1], (1 + len(_snake_case					) + 1, 1 + len(_snake_case					) + 1 + len(_snake_case					)), )

                          snake_case__							:	Any   	=	self.rust_tokenizer_class.from_pretrained(
                              _snake_case, use_fast=_snake_case, add_prefix_space=_snake_case, trim_offsets=_snake_case					)
                          snake_case__							:	List[str]   	=	tokenizer_r(_snake_case, return_offsets_mapping=_snake_case, add_special_tokens=_snake_case					)
                          self.assertEqual(encoding.offset_mapping[0], (0, 1 + len(_snake_case					))					)
                          self.assertEqual(
                              encoding.offset_mapping[1], (1 + len(_snake_case					), 1 + len(_snake_case					) + 1 + len(_snake_case					)), )

                          snake_case__							:	str   	=	self.rust_tokenizer_class.from_pretrained(
                              _snake_case, use_fast=_snake_case, add_prefix_space=_snake_case, trim_offsets=_snake_case					)
                          snake_case__							:	Union[str, Any]   	=	tokenizer_r(_snake_case, return_offsets_mapping=_snake_case, add_special_tokens=_snake_case					)
                          self.assertEqual(encoding.offset_mapping[0], (0, 1 + len(_snake_case					))					)
                          self.assertEqual(
                              encoding.offset_mapping[1], (1 + len(_snake_case					), 1 + len(_snake_case					) + 1 + len(_snake_case					)), )











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        lowercase_							(A : List[str]      ):
       snake_case__							:	Tuple   	=	[
           '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(A		,					A      )
def        lowercase_							(A : str      ):
       snake_case__	,				snake_case__							:	Union[str, Any]   	=	emb.weight.shape
       snake_case__							:	str   	=	nn.Linear(A		,					A		,					bias=A      )
       snake_case__							:	str   	=	emb.weight.data
       return lin_layer
def        lowercase_							(A : Optional[int]		,					A : Union[str, Any]=None      ):
       snake_case__							:	Any   	=	{}
       for old_key in state_dict.keys():
              snake_case__							:	Tuple   	=	old_key
              if "moe_layer.experts." in key:
                     if expert_idx is not None:
                            snake_case__							:	int   	=	key.replace('moe_layer.experts.0'		,					F'''ffn.experts.expert_{expert_idx}'''      )
                     else:
                            snake_case__							:	Any   	=	key.replace('moe_layer.experts.'		,					'ffn.experts.expert_'      )
              if "gate" in key:
                     snake_case__							:	Dict   	=	key.replace('.moe_layer.gate.wg'		,					'.ffn.router.classifier'      )
              if "fc2" and "experts" not in key:
                     snake_case__							:	str   	=	key.replace('.fc2.'		,					'.ffn.fc2.'      )
              if "fc1" and "experts" not in key:
                     snake_case__							:	str   	=	key.replace('.fc1.'		,					'.ffn.fc1.'      )
              if ".encoder_attn." in key:
                     snake_case__							:	Tuple   	=	key.replace('.encoder_attn.'		,					'.cross_attention.'      )
              if "encoder_attn_layer_norm" in key:
                     snake_case__							:	Tuple   	=	key.replace('encoder_attn_layer_norm'		,					'cross_attention_layer_norm'      )
              if "final_layer_norm" in key:
                     snake_case__							:	Optional[int]   	=	key.replace('final_layer_norm'		,					'ff_layer_norm'      )
              snake_case__							:	Dict   	=	state_dict[old_key]
       return new_dict
def        lowercase_							(A : List[Any]		,					A : Tuple		,					A : List[Any]		,					A : List[str]		,					A : str = WEIGHTS_NAME      ):
       snake_case__							:	Dict   	=	[]
       snake_case__							:	str   	=	0
       os.makedirs(A		,					exist_ok=A      )

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

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

    # Add the metadata
       snake_case__							:	int   	=	{'total_size': total_size}
       snake_case__							:	Dict   	=	{'metadata': metadata, 'weight_map': weight_map}

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

       return metadata, index


if __name__ == "__main__":
 a_   :int        =      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.",
 )
 a_   :Optional[Any]        =      parser.parse_args()
 a_  ,						a_   :Optional[Any]        =      shard_on_the_fly(
     args.nllb_moe_checkpoint_path,
     args.pytorch_dump_folder_path,
     128,
     args.dtype,
 )

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






