


"""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						(						lowercase	,   lowercase	,   lowercase=0		):







    """simple docstring"""
    # Format the message.
    if name is None:
        a					=None
    else:
        a					='''.''' * max(0	,   spaces - 2		) + '''# {:''' + str(50 - spaces		) + '''s}'''
        a					=fmt.format(lowercase		)

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







    """simple docstring"""
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    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(*lowercase		)
        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(*lowercase		)
        a					=param.transpose(0	,   1		).contiguous()
    a					=param.view(*lowercase		)
    return param
def _A						(						lowercase	,   lowercase	,   lowercase		):







    """simple docstring"""
    # The converted output model.
    a					={}

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

        # 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	,   lowercase	,   lowercase		)
            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(lowercase	,   lowercase	,   3	,   lowercase	,   lowercase		)
            # 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(lowercase	,   lowercase	,   3	,   lowercase	,   lowercase		)
            # 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						(						):







    """simple docstring"""
    # Create the argument parser.
    a					=argparse.ArgumentParser()
    parser.add_argument('''--print-checkpoint-structure'''	,   action='''store_true'''		)
    parser.add_argument(
        '''path_to_checkpoint'''	,   type=lowercase	,   help='''Path to the checkpoint file (.zip archive or direct .pt file)'''	,   )
    parser.add_argument(
        '''--config_file'''	,   default=''''''	,   type=lowercase	,   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(lowercase	,   map_location='''cpu'''		)
    else:
        a					=torch.load(args.path_to_checkpoint	,   map_location='''cpu'''		)

    a					=input_state_dict.get('''args'''	,   lowercase		)

    # 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=lowercase	,   resid_pdrop=0.1	,   embd_pdrop=0.1	,   attn_pdrop=0.1	,   layer_norm_epsilon=1E-5	,   initializer_range=0.02	,   summary_type='''cls_index'''	,   summary_use_proj=lowercase	,   summary_activation=lowercase	,   summary_proj_to_labels=lowercase	,   summary_first_dropout=0.1	,   scale_attn_weights=lowercase	,   use_cache=lowercase	,   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(lowercase	,   lowercase	,   lowercase		)

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

    # 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(lowercase		)
    a					=type(lowercase		).__name__
    a					=tokenizer_class

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

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

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


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

if __name__ == "__main__":
       main()

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



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_       :       int      						=  {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Dict      						=  ["""MBartTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       str      						=  ["""MBartTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[str]      						=  [
           """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """MBartForCausalLM""",
           """MBartForConditionalGeneration""",
           """MBartForQuestionAnswering""",
           """MBartForSequenceClassification""",
           """MBartModel""",
           """MBartPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       List[Any]      						=  [
           """TFMBartForConditionalGeneration""",
           """TFMBartModel""",
           """TFMBartPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       str      						=  [
           """FlaxMBartForConditionalGeneration""",
           """FlaxMBartForQuestionAnswering""",
           """FlaxMBartForSequenceClassification""",
           """FlaxMBartModel""",
           """FlaxMBartPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart import MBartTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart_fast import MBartTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mbart import (
                  MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MBartForCausalLM,
                  MBartForConditionalGeneration,
                  MBartForQuestionAnswering,
                  MBartForSequenceClassification,
                  MBartModel,
                  MBartPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_mbart import (
                  FlaxMBartForConditionalGeneration,
                  FlaxMBartForQuestionAnswering,
                  FlaxMBartForSequenceClassification,
                  FlaxMBartModel,
                  FlaxMBartPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase_       :       List[Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)