


'''simple docstring'''






import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


a_					:    Optional[Any]          =    get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
 from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

a_					:    Tuple          =    2_5_6_0_4_7
a_					:    str          =    2_5_6_1_4_5




@require_sentencepiece
@require_tokenizers
class  a     (     _SCREAMING_SNAKE_CASE  ,				unittest.TestCase       ):
 _lowerCAmelCase 					=	NllbTokenizer
 _lowerCAmelCase 					=	NllbTokenizerFast
 _lowerCAmelCase 					=	True
 _lowerCAmelCase 					=	True
 _lowerCAmelCase 					=	{}
 def 							__UpperCAmelCase		( self						)					->							Any:
      super().setUp()

      # We have a SentencePiece fixture for testing
      _a			      =							NllbTokenizer(__magic_name__      ,				keep_accents=__magic_name__						)
      tokenizer.save_pretrained(self.tmpdirname						)
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      _a			      =							NllbTokenizer(__magic_name__      ,				keep_accents=__magic_name__						)

      _a			      =							tokenizer.tokenize('This is a test'						)
      self.assertListEqual(__magic_name__      ,				['▁This', '▁is', '▁a', '▁t', 'est']						)

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

      _a			      =							tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
      self.assertListEqual(
          __magic_name__      ,				[
              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			      =							tokenizer.convert_tokens_to_ids(__magic_name__						)
      self.assertListEqual(
          __magic_name__      ,				[
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
          ]      ,				)

      _a			      =							tokenizer.convert_ids_to_tokens(__magic_name__						)
      self.assertListEqual(
          __magic_name__      ,				[
              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>',
              '.',
          ]      ,				)
 def 							__UpperCAmelCase		( self						)					->							Any:
      _a			      =							(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'						):
                _a			      =							self.rust_tokenizer_class.from_pretrained(__magic_name__      ,				**__magic_name__						)
                _a			      =							self.tokenizer_class.from_pretrained(__magic_name__      ,				**__magic_name__						)

                _a			      =							tempfile.mkdtemp()

                _a			      =							tokenizer_r.save_pretrained(__magic_name__						)
                _a			      =							tokenizer_p.save_pretrained(__magic_name__						)

                # Checks it save with the same files + the tokenizer.json file for the fast one
                self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files						)						)
                _a			      =							tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f						)
                self.assertSequenceEqual(__magic_name__      ,				__magic_name__						)

                # Checks everything loads correctly in the same way
                _a			      =							tokenizer_r.from_pretrained(__magic_name__						)
                _a			      =							tokenizer_p.from_pretrained(__magic_name__						)

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(__magic_name__      ,				__magic_name__						)						)

                shutil.rmtree(__magic_name__						)

                # Save tokenizer rust, legacy_format=True
                _a			      =							tempfile.mkdtemp()

                _a			      =							tokenizer_r.save_pretrained(__magic_name__      ,				legacy_format=__magic_name__						)
                _a			      =							tokenizer_p.save_pretrained(__magic_name__						)

                # Checks it save with the same files
                self.assertSequenceEqual(__magic_name__      ,				__magic_name__						)

                # Checks everything loads correctly in the same way
                _a			      =							tokenizer_r.from_pretrained(__magic_name__						)
                _a			      =							tokenizer_p.from_pretrained(__magic_name__						)

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(__magic_name__      ,				__magic_name__						)						)

                shutil.rmtree(__magic_name__						)

                # Save tokenizer rust, legacy_format=False
                _a			      =							tempfile.mkdtemp()

                _a			      =							tokenizer_r.save_pretrained(__magic_name__      ,				legacy_format=__magic_name__						)
                _a			      =							tokenizer_p.save_pretrained(__magic_name__						)

                # Checks it saved the tokenizer.json file
                self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files						)						)

                # Checks everything loads correctly in the same way
                _a			      =							tokenizer_r.from_pretrained(__magic_name__						)
                _a			      =							tokenizer_p.from_pretrained(__magic_name__						)

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(__magic_name__      ,				__magic_name__						)						)

                shutil.rmtree(__magic_name__						)
 @require_torch
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      if not self.test_seqaseq:
           return

      _a			      =							self.get_tokenizers()
      for tokenizer in tokenizers:
           with self.subTest(f'{tokenizer.__class__.__name__}'						):
                # Longer text that will definitely require truncation.
                _a			      =							[
                    ' UN Chief Says There Is No Military Solution in Syria',
                    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
                    ' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
                    ' will only worsen the violence and misery for millions of people.',
                ]
                _a			      =							[
                    'Şeful ONU declară că nu există o soluţie militară în Siria',
                    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
                    ' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
                    ' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
                ]
                try:
                     _a			      =							tokenizer.prepare_seqaseq_batch(
                         src_texts=__magic_name__      ,				tgt_texts=__magic_name__      ,				max_length=3      ,				max_target_length=10      ,				return_tensors='pt'      ,				src_lang='eng_Latn'      ,				tgt_lang='ron_Latn'      ,				)
                except NotImplementedError:
                     return
                self.assertEqual(batch.input_ids.shape[1]      ,				3						)
                self.assertEqual(batch.labels.shape[1]      ,				10						)
                # max_target_length will default to max_length if not specified
                _a			      =							tokenizer.prepare_seqaseq_batch(
                    __magic_name__      ,				tgt_texts=__magic_name__      ,				max_length=3      ,				return_tensors='pt'						)
                self.assertEqual(batch.input_ids.shape[1]      ,				3						)
                self.assertEqual(batch.labels.shape[1]      ,				3						)

                _a			      =							tokenizer.prepare_seqaseq_batch(
                    src_texts=__magic_name__      ,				max_length=3      ,				max_target_length=10      ,				return_tensors='pt'						)
                self.assertEqual(batch_encoder_only.input_ids.shape[1]      ,				3						)
                self.assertEqual(batch_encoder_only.attention_mask.shape[1]      ,				3						)
                self.assertNotIn('decoder_input_ids'      ,				__magic_name__						)
 @unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'						)
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      pass




 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'						):
                _a			      =							[AddedToken('<special>'      ,				lstrip=__magic_name__						)]

                _a			      =							self.rust_tokenizer_class.from_pretrained(
                    __magic_name__      ,				additional_special_tokens=__magic_name__      ,				**__magic_name__						)
                _a			      =							tokenizer_r.encode('Hey this is a <special> token'						)

                _a			      =							tokenizer_r.encode('<special>'      ,				add_special_tokens=__magic_name__						)[0]

                self.assertTrue(special_token_id in r_output						)

                if self.test_slow_tokenizer:
                     _a			      =							self.rust_tokenizer_class.from_pretrained(
                         __magic_name__      ,				additional_special_tokens=__magic_name__      ,				**__magic_name__      ,				)
                     _a			      =							self.tokenizer_class.from_pretrained(
                         __magic_name__      ,				additional_special_tokens=__magic_name__      ,				**__magic_name__						)

                     _a			      =							tokenizer_p.encode('Hey this is a <special> token'						)

                     _a			      =							tokenizer_cr.encode('Hey this is a <special> token'						)

                     self.assertEqual(__magic_name__      ,				__magic_name__						)
                     self.assertEqual(__magic_name__      ,				__magic_name__						)
                     self.assertTrue(special_token_id in p_output						)
                     self.assertTrue(special_token_id in cr_output						)





@require_torch
@require_sentencepiece
@require_tokenizers
class  a     (     unittest.TestCase       ):
 _lowerCAmelCase 					=	"""facebook/nllb-200-distilled-600M"""
 _lowerCAmelCase 					=	[
     """ UN Chief Says There Is No Military Solution in Syria""",
     """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
 ]
 _lowerCAmelCase 					=	[
     """Şeful ONU declară că nu există o soluţie militară în Siria""",
     """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
     """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
     """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
 ]
 _lowerCAmelCase 					=	[
     2_5_6_0_4_7,
     1_6_2_9_7,
     1_3_4_4_0_8,
     8_1_6_5,
     2_4_8_0_6_6,
     1_4_7_3_4,
     9_5_0,
     1_1_3_5,
     1_0_5_7_2_1,
     3_5_7_3,
     8_3,
     2_7_3_5_2,
     1_0_8,
     4_9_4_8_6,
     2,
 ]
 @classmethod
 def 							__UpperCAmelCase		( cls						)					->							Union[str, Any]:
      _a			      =							NllbTokenizer.from_pretrained(
          cls.checkpoint_name      ,				src_lang='eng_Latn'      ,				tgt_lang='ron_Latn'						)
      _a			      =							1
      return cls
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']      ,				25_60_01						)
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']      ,				25_60_02						)
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']      ,				25_60_57						)
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      _a			      =							self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
      self.assertListEqual(self.expected_src_tokens      ,				__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							int:
      self.assertIn(__magic_name__      ,				self.tokenizer.all_special_ids						)
      # fmt: off
      _a			      =							[RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
      # fmt: on

      _a			      =							self.tokenizer.decode(__magic_name__      ,				skip_special_tokens=__magic_name__						)
      _a			      =							self.tokenizer.decode(generated_ids[1:]      ,				skip_special_tokens=__magic_name__						)
      self.assertEqual(__magic_name__      ,				__magic_name__						)
      self.assertNotIn(self.tokenizer.eos_token      ,				__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							str:
      _a			      =							['this is gunna be a long sentence ' * 20]
      assert isinstance(src_text[0]      ,				__magic_name__						)
      _a			      =							10
      _a			      =							self.tokenizer(__magic_name__      ,				max_length=__magic_name__      ,				truncation=__magic_name__						).input_ids[0]
      self.assertEqual(ids[-1]      ,				2						)
      self.assertEqual(ids[0]      ,				__magic_name__						)
      self.assertEqual(len(__magic_name__						)      ,				__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']						)      ,				[25_62_03, 3]						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							tempfile.mkdtemp()
      _a			      =							self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(__magic_name__						)
      _a			      =							NllbTokenizer.from_pretrained(__magic_name__						)
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,				__magic_name__						)
 @require_torch
 def 							__UpperCAmelCase		( self						)					->							str:
      _a			      =							self.tokenizer(
          self.src_text      ,				text_target=self.tgt_text      ,				padding=__magic_name__      ,				truncation=__magic_name__      ,				max_length=len(self.expected_src_tokens						)      ,				return_tensors='pt'      ,				)
      _a			      =							shift_tokens_right(
          batch['labels']      ,				self.tokenizer.pad_token_id      ,				self.tokenizer.lang_code_to_id['ron_Latn']						)

      self.assertIsInstance(__magic_name__      ,				__magic_name__						)

      self.assertEqual((2, 15)      ,				batch.input_ids.shape						)
      self.assertEqual((2, 15)      ,				batch.attention_mask.shape						)
      _a			      =							batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens      ,				__magic_name__						)
      self.assertEqual(__magic_name__      ,				batch.decoder_input_ids[0, 0]						)  # EOS
      # Test that special tokens are reset
      self.assertEqual(self.tokenizer.prefix_tokens      ,				[EN_CODE]						)
      self.assertEqual(self.tokenizer.suffix_tokens      ,				[self.tokenizer.eos_token_id]						)
 def 							__UpperCAmelCase		( self						)					->							Dict:
      _a			      =							self.tokenizer(self.src_text      ,				padding=__magic_name__      ,				truncation=__magic_name__      ,				max_length=3      ,				return_tensors='pt'						)
      _a			      =							self.tokenizer(
          text_target=self.tgt_text      ,				padding=__magic_name__      ,				truncation=__magic_name__      ,				max_length=10      ,				return_tensors='pt'						)
      _a			      =							targets['input_ids']
      _a			      =							shift_tokens_right(
          __magic_name__      ,				self.tokenizer.pad_token_id      ,				decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]      ,				)

      self.assertEqual(batch.input_ids.shape[1]      ,				3						)
      self.assertEqual(batch.decoder_input_ids.shape[1]      ,				10						)
 @require_torch
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      _a			      =							self.tokenizer._build_translation_inputs(
          'A test'      ,				return_tensors='pt'      ,				src_lang='eng_Latn'      ,				tgt_lang='fra_Latn'						)

      self.assertEqual(
          nested_simplify(__magic_name__						)      ,				{
              # A, test, EOS, en_XX
              'input_ids': [[25_60_47, 70, 73_56, 2]],
              'attention_mask': [[1, 1, 1, 1]],
              # ar_AR
              'forced_bos_token_id': 25_60_57,
          }      ,				)




 @require_torch
 def 							__UpperCAmelCase		( self						)					->							Dict:
      _a			      =							True
      _a			      =							self.tokenizer(
          'UN Chief says there is no military solution in Syria'      ,				src_lang='eng_Latn'      ,				tgt_lang='fra_Latn'						)
      self.assertEqual(
          inputs.input_ids      ,				[1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47]						)

      _a			      =							False
      _a			      =							self.tokenizer(
          'UN Chief says there is no military solution in Syria'      ,				src_lang='eng_Latn'      ,				tgt_lang='fra_Latn'						)
      self.assertEqual(
          inputs.input_ids      ,				[25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2]						)





'''simple docstring'''






# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig






def 	_A				(lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[int]=0		)   ->  Optional[int]:




     '''simple docstring'''



     if name is None:
          _a			      =							None
     else:
          _a			      =							'.' * max(0				,						spaces - 2		) + '# {:' + str(50 - spaces		) + 's}'
          _a			      =							fmt.format(lowerCAmelCase__		)

     # Print and recurse (if needed).
     if isinstance(lowerCAmelCase__				,						lowerCAmelCase__		):
          if msg is not None:
               print(lowerCAmelCase__		)
          for k in val.keys():
               recursive_print(lowerCAmelCase__				,						val[k]				,						spaces + 2		)
     elif isinstance(lowerCAmelCase__				,						torch.Tensor		):
          print(lowerCAmelCase__				,						':'				,						val.size()		)
     else:
          print(lowerCAmelCase__				,						':'				,						lowerCAmelCase__		)






def 	_A				(lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :List[str]		)   ->  Any:




     '''simple docstring'''



     _a			      =							param.size()
     if checkpoint_version == 1.0:
          # version 1.0 stores [num_heads * hidden_size * num_splits, :]
          _a			      =							(num_heads, hidden_size, num_splits) + input_shape[1:]
          _a			      =							param.view(*lowerCAmelCase__		)
          _a			      =							param.transpose(0				,						2		)
          _a			      =							param.transpose(1				,						2		).contiguous()
     elif checkpoint_version >= 2.0:
          # other versions store [num_heads * num_splits * hidden_size, :]
          _a			      =							(num_heads, num_splits, hidden_size) + input_shape[1:]
          _a			      =							param.view(*lowerCAmelCase__		)
          _a			      =							param.transpose(0				,						1		).contiguous()
     _a			      =							param.view(*lowerCAmelCase__		)
     return param






def 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :List[str]		)   ->  Tuple:




     '''simple docstring'''



     _a			      =							{}

     # old versions did not store training args
     _a			      =							input_state_dict.get('args'				,						lowerCAmelCase__		)
     if ds_args is not None:
          # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
          # from pprint import pprint
          # pprint(vars(ds_args))

          _a			      =							ds_args.padded_vocab_size
          _a			      =							ds_args.max_position_embeddings
          _a			      =							ds_args.hidden_size
          _a			      =							ds_args.num_layers
          _a			      =							ds_args.num_attention_heads
          _a			      =							ds_args.ffn_hidden_size
          # pprint(config)

     # The number of heads.
     _a			      =							config.n_head
     # The hidden_size per head.
     _a			      =							config.n_embd // config.n_head
     # Megatron-LM checkpoint version
     if "checkpoint_version" in input_state_dict.keys():
          _a			      =							input_state_dict['checkpoint_version']
     else:
          _a			      =							0.0

     # The model.
     _a			      =							input_state_dict['model']
     # The language model.
     _a			      =							model['language_model']
     # The embeddings.
     _a			      =							lm['embedding']

     # The word embeddings.
     _a			      =							embeddings['word_embeddings']['weight']
     # Truncate the embedding table to vocab_size rows.
     _a			      =							word_embeddings[: config.vocab_size, :]
     _a			      =							word_embeddings

     # The position embeddings.
     _a			      =							embeddings['position_embeddings']['weight']
     # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
     _a			      =							pos_embeddings.size(0		)
     if n_positions != config.n_positions:
          raise ValueError(
              f'pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'		)
     # Store the position embeddings.
     _a			      =							pos_embeddings

     # The transformer.
     _a			      =							lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

     # The regex to extract layer names.
     _a			      =							re.compile(r'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'		)

     # The simple map of names for "automated" rules.
     _a			      =							{
         'attention.dense': '.attn.c_proj.',
         'self_attention.dense': '.attn.c_proj.',
         'mlp.dense_h_to_4h': '.mlp.c_fc.',
         'mlp.dense_4h_to_h': '.mlp.c_proj.',
     }

     # Extract the layers.
     for key, val in transformer.items():
          # Match the name.
          _a			      =							layer_re.match(lowerCAmelCase__		)

          # Stop if that's not a layer
          if m is None:
               break

          # The index of the layer.
          _a			      =							int(m.group(1		)		)
          # The name of the operation.
          _a			      =							m.group(2		)
          # Is it a weight or a bias?
          _a			      =							m.group(3		)

          # The name of the layer.
          _a			      =							f'transformer.h.{layer_idx}'

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith('layernorm'		):
               _a			      =							'ln_1' if op_name.startswith('input'		) else 'ln_2'
               _a			      =							val

          # Transpose the QKV matrix.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "weight":
               # Insert a tensor of 1x1xDxD bias.
               _a			      =							torch.tril(torch.ones((n_positions, n_positions)				,						dtype=torch.floataa		)		).view(
                   1				,						1				,						lowerCAmelCase__				,						lowerCAmelCase__		)
               _a			      =							causal_mask

               # Insert a "dummy" tensor for masked_bias.
               _a			      =							torch.tensor(-1E4				,						dtype=torch.floataa		)
               _a			      =							masked_bias

               _a			      =							fix_query_key_value_ordering(lowerCAmelCase__				,						lowerCAmelCase__				,						3				,						lowerCAmelCase__				,						lowerCAmelCase__		)
               # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
               _a			      =							out_val.transpose(0				,						1		).contiguous()
               # Store.
               _a			      =							out_val

          # Transpose the bias.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "bias":
               _a			      =							fix_query_key_value_ordering(lowerCAmelCase__				,						lowerCAmelCase__				,						3				,						lowerCAmelCase__				,						lowerCAmelCase__		)
               # Store. No change of shape.
               _a			      =							out_val

          # Transpose the weights.
          elif weight_or_bias == "weight":
               _a			      =							megatron_to_transformers[op_name]
               _a			      =							val.transpose(0				,						1		)

          # Copy the bias.
          elif weight_or_bias == "bias":
               _a			      =							megatron_to_transformers[op_name]
               _a			      =							val

    # DEBUG.
     assert config.n_layer == layer_idx + 1

     # The final layernorm.
     _a			      =							transformer['final_layernorm.weight']
     _a			      =							transformer['final_layernorm.bias']

     # For LM head, transformers' wants the matrix to weight embeddings.
     _a			      =							word_embeddings

     # It should be done!
     return output_state_dict






def 	_A				()   ->  Any:




     '''simple docstring'''



     _a			      =							argparse.ArgumentParser()
     parser.add_argument('--print-checkpoint-structure'				,						action='store_true'		)
     parser.add_argument(
         'path_to_checkpoint'				,						type=lowerCAmelCase__				,						help='Path to the checkpoint file (.zip archive or direct .pt file)'				,						)
     parser.add_argument(
         '--config_file'				,						default=''				,						type=lowerCAmelCase__				,						help='An optional config json file describing the pre-trained model.'				,						)
     _a			      =							parser.parse_args()

     # Extract the basename.
     _a			      =							os.path.dirname(args.path_to_checkpoint		)

     # Load the model.
     # the .zip is very optional, let's keep it for backward compatibility
     print(f'Extracting PyTorch state dictionary from {args.path_to_checkpoint}'		)
     if args.path_to_checkpoint.endswith('.zip'		):
          with zipfile.ZipFile(args.path_to_checkpoint				,						'r'		) as checkpoint:
               with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'		) as pytorch_dict:
                    _a			      =							torch.load(lowerCAmelCase__				,						map_location='cpu'		)
     else:
          _a			      =							torch.load(args.path_to_checkpoint				,						map_location='cpu'		)

     _a			      =							input_state_dict.get('args'				,						lowerCAmelCase__		)

     # Read the config, or default to the model released by NVIDIA.
     if args.config_file == "":
          if ds_args is not None:
               if ds_args.bias_gelu_fusion:
                    _a			      =							'gelu_fast'
               elif ds_args.openai_gelu:
                    _a			      =							'gelu_new'
               else:
                    _a			      =							'gelu'
          else:
               # in the very early days this used to be "gelu_new"
               _a			      =							'gelu_new'

          # Spell out all parameters in case the defaults change.
          _a			      =							GPTaConfig(
              vocab_size=5_02_57				,						n_positions=10_24				,						n_embd=10_24				,						n_layer=24				,						n_head=16				,						n_inner=40_96				,						activation_function=lowerCAmelCase__				,						resid_pdrop=0.1				,						embd_pdrop=0.1				,						attn_pdrop=0.1				,						layer_norm_epsilon=1E-5				,						initializer_range=0.0_2				,						summary_type='cls_index'				,						summary_use_proj=lowerCAmelCase__				,						summary_activation=lowerCAmelCase__				,						summary_proj_to_labels=lowerCAmelCase__				,						summary_first_dropout=0.1				,						scale_attn_weights=lowerCAmelCase__				,						use_cache=lowerCAmelCase__				,						bos_token_id=5_02_56				,						eos_token_id=5_02_56				,						)
     else:
          _a			      =							GPTaConfig.from_json_file(args.config_file		)

     _a			      =							['GPT2LMHeadModel']

     # Convert.
     print('Converting'		)
     _a			      =							convert_megatron_checkpoint(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		)

     # Print the structure of converted state dict.
     if args.print_checkpoint_structure:
          recursive_print(lowerCAmelCase__				,						lowerCAmelCase__		)

     # Add tokenizer class info to config
     # see https://github.com/huggingface/transformers/issues/13906)
     if ds_args is not None:
          _a			      =							ds_args.tokenizer_type
          if tokenizer_type == "GPT2BPETokenizer":
               _a			      =							'gpt2'
          elif tokenizer_type == "PretrainedFromHF":
               _a			      =							ds_args.tokenizer_name_or_path
          else:
               raise ValueError(f'Unrecognized tokenizer_type {tokenizer_type}'		)
     else:
          _a			      =							'gpt2'

     _a			      =							AutoTokenizer.from_pretrained(lowerCAmelCase__		)
     _a			      =							type(lowerCAmelCase__		).__name__
     _a			      =							tokenizer_class

     # Store the config to file.
     print('Saving config'		)
     config.save_pretrained(lowerCAmelCase__		)

     # Save tokenizer based on args
     print(f'Adding {tokenizer_class} tokenizer files'		)
     tokenizer.save_pretrained(lowerCAmelCase__		)

     # Store the state_dict to file.
     _a			      =							os.path.join(lowerCAmelCase__				,						'pytorch_model.bin'		)
     print(f'Saving checkpoint to "{output_checkpoint_file}"'		)
     torch.save(lowerCAmelCase__				,						lowerCAmelCase__		)


####################################################################################################

if __name__ == "__main__":
 main()

####################################################################################################

