




import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer







class __snake_case     (     tf.keras.layers.Layer						):







   def __init__( self						:		Optional[int]   ,		_snake_case						:		List   ,		_snake_case						:		bool   ,		_snake_case						:		int = None   ,		_snake_case						:		int = None   ,		_snake_case						:		int = None   ,		_snake_case						:		str = "longest"   ,		_snake_case						:		bool = True   ,		_snake_case						:		int = 512   ,		_snake_case						:		int = None   ,		_snake_case						:		bool = True   ,		_snake_case						:		bool = True   ,		_snake_case						:		bool = True   ,		**_snake_case						:		Any   ,		):

        """simple docstring"""


        super().__init__()
        if use_fast_bert_tokenizer:
             UpperCAmelCase_		=				FastBertTokenizer(
                 _snake_case   ,		token_out_type=tf.intaa   ,		lower_case_nfd_strip_accents=_snake_case   ,		**_snake_case)
        else:
             UpperCAmelCase_		=				tf.lookup.StaticVocabularyTable(
                 tf.lookup.KeyValueTensorInitializer(
                     keys=_snake_case   ,		key_dtype=tf.string   ,		values=tf.range(tf.size(_snake_case   ,		out_type=tf.intaa)   ,		dtype=tf.intaa)   ,		value_dtype=tf.intaa   ,		)   ,		num_oov_buckets=1   ,		)
             UpperCAmelCase_		=				BertTokenizerLayer(
                 _snake_case   ,		token_out_type=tf.intaa   ,		lower_case=_snake_case   ,		**_snake_case)

        UpperCAmelCase_		=				vocab_list
        UpperCAmelCase_		=				do_lower_case
        UpperCAmelCase_		=				cls_token_id or vocab_list.index('''[CLS]''')
        UpperCAmelCase_		=				sep_token_id or vocab_list.index('''[SEP]''')
        UpperCAmelCase_		=				pad_token_id or vocab_list.index('''[PAD]''')
        UpperCAmelCase_		=				ShrinkLongestTrimmer(max_length - 3   ,		axis=1)  # Allow room for special tokens
        UpperCAmelCase_		=				max_length
        UpperCAmelCase_		=				padding
        UpperCAmelCase_		=				truncation
        UpperCAmelCase_		=				pad_to_multiple_of
        UpperCAmelCase_		=				return_token_type_ids
        UpperCAmelCase_		=				return_attention_mask







   @classmethod
   def lowerCamelCase		( cls						:		Optional[Any]   ,		_snake_case						:		"PreTrainedTokenizerBase"   ,		**_snake_case						:		str):  # noqa: F821

        """simple docstring"""


        UpperCAmelCase_		=				kwargs.pop('''do_lower_case'''   ,		_snake_case)
        UpperCAmelCase_		=				tokenizer.do_lower_case if do_lower_case is None else do_lower_case
        UpperCAmelCase_		=				kwargs.pop('''cls_token_id'''   ,		_snake_case)
        UpperCAmelCase_		=				tokenizer.cls_token_id if cls_token_id is None else cls_token_id
        UpperCAmelCase_		=				kwargs.pop('''sep_token_id'''   ,		_snake_case)
        UpperCAmelCase_		=				tokenizer.sep_token_id if sep_token_id is None else sep_token_id
        UpperCAmelCase_		=				kwargs.pop('''pad_token_id'''   ,		_snake_case)
        UpperCAmelCase_		=				tokenizer.pad_token_id if pad_token_id is None else pad_token_id

        UpperCAmelCase_		=				tokenizer.get_vocab()
        UpperCAmelCase_		=				sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]   ,		key=lambda _snake_case: x[1])
        UpperCAmelCase_		=				[entry[0] for entry in vocab]
        return cls(
            vocab_list=_snake_case   ,		do_lower_case=_snake_case   ,		cls_token_id=_snake_case   ,		sep_token_id=_snake_case   ,		pad_token_id=_snake_case   ,		**_snake_case   ,		)







   @classmethod
   def lowerCamelCase		( cls						:		List[str]   ,		_snake_case						:		Union[str, os.PathLike]   ,		*_snake_case						:		Union[str, Any]   ,		**_snake_case						:		Union[str, Any]):

        """simple docstring"""


        try:
             UpperCAmelCase_		=				BertTokenizer.from_pretrained(_snake_case   ,		*_snake_case   ,		**_snake_case)
        except:  # noqa: E722
             from .tokenization_bert_fast import BertTokenizerFast

             UpperCAmelCase_		=				BertTokenizerFast.from_pretrained(_snake_case   ,		*_snake_case   ,		**_snake_case)
        return cls.from_tokenizer(_snake_case   ,		**_snake_case)







   def lowerCamelCase		( self						:		int   ,		_snake_case						:		str):

        """simple docstring"""


        if self.do_lower_case:
             UpperCAmelCase_		=				case_fold_utfa(_snake_case)
        UpperCAmelCase_		=				self.tf_tokenizer.tokenize(_snake_case)
        return tokens.merge_dims(1   ,		-1)







   def lowerCamelCase		( self						:		int   ,		_snake_case						:		str   ,		_snake_case						:		str=None   ,		_snake_case						:		Union[str, Any]=None   ,		_snake_case						:		str=None   ,		_snake_case						:		int=None   ,		_snake_case						:		Optional[int]=None   ,		_snake_case						:		Optional[Any]=None   ,		_snake_case						:		List[str]=None   ,		):

        """simple docstring"""


        if padding is None:
             UpperCAmelCase_		=				self.padding
        if padding not in ("longest", "max_length"):
             raise ValueError('''Padding must be either \'longest\' or \'max_length\'!''')
        if max_length is not None and text_pair is not None:
             # Because we have to instantiate a Trimmer to do it properly
             raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!''')
        if max_length is None:
             UpperCAmelCase_		=				self.max_length
        if truncation is None:
             UpperCAmelCase_		=				self.truncation
        if pad_to_multiple_of is None:
             UpperCAmelCase_		=				self.pad_to_multiple_of
        if return_token_type_ids is None:
             UpperCAmelCase_		=				self.return_token_type_ids
        if return_attention_mask is None:
             UpperCAmelCase_		=				self.return_attention_mask
        if not isinstance(_snake_case   ,		tf.Tensor):
             UpperCAmelCase_		=				tf.convert_to_tensor(_snake_case)
        if text_pair is not None and not isinstance(_snake_case   ,		tf.Tensor):
             UpperCAmelCase_		=				tf.convert_to_tensor(_snake_case)
        if text_pair is not None:
             if text.shape.rank > 1:
                  raise ValueError('''text argument should not be multidimensional when a text pair is supplied!''')
             if text_pair.shape.rank > 1:
                  raise ValueError('''text_pair should not be multidimensional!''')
        if text.shape.rank == 2:
             UpperCAmelCase_	,			UpperCAmelCase_		=				text[:, 0], text[:, 1]
        UpperCAmelCase_		=				self.unpaired_tokenize(_snake_case)
        if text_pair is None:  # Unpaired text
             if truncation:
                  UpperCAmelCase_		=				text[:, : max_length - 2]  # Allow room for special tokens
             UpperCAmelCase_	,			UpperCAmelCase_		=				combine_segments(
                 (text,)   ,		start_of_sequence_id=self.cls_token_id   ,		end_of_segment_id=self.sep_token_id)
        else:  # Paired text
             UpperCAmelCase_		=				self.unpaired_tokenize(_snake_case)
             if truncation:
                  UpperCAmelCase_	,			UpperCAmelCase_		=				self.paired_trimmer.trim([text, text_pair])
             UpperCAmelCase_	,			UpperCAmelCase_		=				combine_segments(
                 (text, text_pair)   ,		start_of_sequence_id=self.cls_token_id   ,		end_of_segment_id=self.sep_token_id)
        if padding == "longest":
             UpperCAmelCase_		=				input_ids.bounding_shape(axis=1)
             if pad_to_multiple_of is not None:
                  # No ceiling division in tensorflow, so we negate floordiv instead
                  UpperCAmelCase_		=				pad_to_multiple_of * (-tf.math.floordiv(-pad_length   ,		_snake_case))
        else:
             UpperCAmelCase_		=				max_length

        UpperCAmelCase_	,			UpperCAmelCase_		=				pad_model_inputs(_snake_case   ,		max_seq_length=_snake_case   ,		pad_value=self.pad_token_id)
        UpperCAmelCase_		=				{'''input_ids''': input_ids}
        if return_attention_mask:
             UpperCAmelCase_		=				attention_mask
        if return_token_type_ids:
             UpperCAmelCase_	,			UpperCAmelCase_		=				pad_model_inputs(
                 _snake_case   ,		max_seq_length=_snake_case   ,		pad_value=self.pad_token_id)
             UpperCAmelCase_		=				token_type_ids
        return output







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        return {
            "vocab_list": self.vocab_list,
            "do_lower_case": self.do_lower_case,
            "cls_token_id": self.cls_token_id,
            "sep_token_id": self.sep_token_id,
            "pad_token_id": self.pad_token_id,
        }












# 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_bert/convert_megatron_bert_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 MegatronBertConfig


def   A		(__A  :       List[Any]  ,							__A  :       str  ,							__A  :       Optional[int]=0			)  -> Dict:







     """simple docstring"""


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

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


def   A		(__A  :       Any  ,							__A  :       str  ,							__A  :       Tuple  ,							__A  :       Optional[int]  ,							__A  :       Any			)  -> Union[str, Any]:







     """simple docstring"""


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


def   A		(__A  :       Optional[Any]  ,							__A  :       Tuple  ,							__A  :       Optional[int]			)  -> Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_		=				{}

     # old versions did not store training args
     UpperCAmelCase_		=				input_state_dict.get('''args'''  ,							__A			)
     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))

          UpperCAmelCase_		=				ds_args.tokenizer_type
          UpperCAmelCase_		=				ds_args.padded_vocab_size
          UpperCAmelCase_		=				ds_args.max_position_embeddings
          UpperCAmelCase_		=				ds_args.hidden_size
          UpperCAmelCase_		=				ds_args.num_layers
          UpperCAmelCase_		=				ds_args.num_attention_heads
          UpperCAmelCase_		=				ds_args.ffn_hidden_size if '''ffn_hidden_size''' in ds_args else 4 * ds_args.hidden_size
          # pprint(config)

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

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

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

     # The position embeddings.
     UpperCAmelCase_		=				embeddings['''position_embeddings''']['''weight''']
     assert pos_embeddings.size(0			) == config.max_position_embeddings and pos_embeddings.size(1			) == config.hidden_size
     # Store the position embeddings.
     UpperCAmelCase_		=				pos_embeddings

     # The token-type embeddings.
     UpperCAmelCase_		=				embeddings['''tokentype_embeddings''']['''weight''']
     # Store the position embeddings.
     UpperCAmelCase_		=				tokentype_embeddings

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

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

     # The simple map of names for "automated" rules.
     UpperCAmelCase_		=				{
         '''attention.dense''': '''.attention.output.dense.''',
         '''self_attention.dense''': '''.attention.output.dense.''',
         '''mlp.dense_h_to_4h''': '''.intermediate.dense.''',
         '''mlp.dense_4h_to_h''': '''.output.dense.''',
     }

     # Keep track of the attention/query/value tensor.
     UpperCAmelCase_		=				None

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

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

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

          # The name of the layer.
          UpperCAmelCase_		=				F"""bert.encoder.layer.{layer_idx}"""

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith('''layernorm'''			):
               UpperCAmelCase_		=				'''attention.ln''' if op_name.startswith('''input'''			) else '''ln'''
               UpperCAmelCase_		=				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":
               # Make sure the QKV pointer is nil.
               assert attention_qkv_weight is None, ""

               UpperCAmelCase_		=				fix_query_key_value_ordering(__A  ,							__A  ,							3  ,							__A  ,							__A			)
               # Store the tensor as we need the bias as well to interleave QKV and biases.
               UpperCAmelCase_		=				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":
               # Make sure we read the weight tensor.
               assert attention_qkv_weight is not None, ""

               # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
               UpperCAmelCase_		=				attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
               UpperCAmelCase_		=				attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
               UpperCAmelCase_		=				attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

               UpperCAmelCase_		=				fix_query_key_value_ordering(__A  ,							__A  ,							3  ,							__A  ,							__A			)
               # Split the bias.
               UpperCAmelCase_		=				out_val[0 * config.hidden_size : 1 * config.hidden_size]
               UpperCAmelCase_		=				out_val[1 * config.hidden_size : 2 * config.hidden_size]
               UpperCAmelCase_		=				out_val[2 * config.hidden_size : 3 * config.hidden_size]

               # Store.
               UpperCAmelCase_		=				q
               UpperCAmelCase_		=				q_bias
               UpperCAmelCase_		=				k
               UpperCAmelCase_		=				k_bias
               UpperCAmelCase_		=				v
               UpperCAmelCase_		=				v_bias

               # Clear the stored tensor.
               UpperCAmelCase_		=				None

          # Copy weights and biases as is.
          elif weight_or_bias in ["weight", "bias"]:
               UpperCAmelCase_		=				megatron_to_transformers[op_name]
               UpperCAmelCase_		=				val

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

     # The pooler.
     UpperCAmelCase_		=				lm['''pooler''']

     # Store the matrix and the bias.
     UpperCAmelCase_		=				pooler['''dense.weight''']
     UpperCAmelCase_		=				pooler['''dense.bias''']

     # The LM head from Megatron (for RACE).
     UpperCAmelCase_		=				model['''lm_head''']

     # The transform matrix.
     UpperCAmelCase_		=				lm_head['''dense.weight''']
     UpperCAmelCase_		=				lm_head['''dense.bias''']

     # The transform LN.
     UpperCAmelCase_		=				lm_head['''layernorm.weight''']
     UpperCAmelCase_		=				lm_head['''layernorm.bias''']

     # For the decoder, we replicate the weights.
     UpperCAmelCase_		=				word_embeddings
     UpperCAmelCase_		=				lm_head['''bias''']

     # The classifier from Megatron (for MLNI).
     UpperCAmelCase_		=				model['''binary_head''']

     # Store the classifier.
     UpperCAmelCase_		=				binary_head['''weight''']
     UpperCAmelCase_		=				binary_head['''bias''']

     # It should be done!
     return output_state_dict


def   A		()  -> Dict:







     """simple docstring"""


     UpperCAmelCase_		=				argparse.ArgumentParser()
     parser.add_argument('''--print-checkpoint-structure'''  ,							action='''store_true'''			)
     parser.add_argument('''path_to_checkpoint'''  ,							type=__A  ,							help='''Path to the ZIP file containing the checkpoint'''			)
     parser.add_argument(
         '''--config_file'''  ,							default=''''''  ,							type=__A  ,							help='''An optional config json file describing the pre-trained model.'''  ,							)
     UpperCAmelCase_		=				parser.parse_args()

     # Extract the basename.
     UpperCAmelCase_		=				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:
                    UpperCAmelCase_		=				torch.load(__A  ,							map_location='''cpu'''			)
     else:
          UpperCAmelCase_		=				torch.load(args.path_to_checkpoint  ,							map_location='''cpu'''			)

     if args.config_file == "":
          # Default config of megatron-bert 345m
          UpperCAmelCase_		=				MegatronBertConfig()

          # different megatron-bert-*-345m models have different vocab sizes, so override the default
          # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
          UpperCAmelCase_		=				input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
     else:
          UpperCAmelCase_		=				MegatronBertConfig.from_json_file(args.config_file			)

     # Convert.
     print('''Converting'''			)
     UpperCAmelCase_		=				convert_megatron_checkpoint(__A  ,							__A  ,							__A			)

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

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

     # Store the state_dict to file.
     UpperCAmelCase_		=				os.path.join(__A  ,							'''pytorch_model.bin'''			)
     print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""			)
     torch.save(__A  ,							__A			)


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

if __name__ == "__main__":
    main()

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






