





'''simple docstring'''

import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
      from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
      raise ImportError("""You need to install the version of fairseq from the Mega repo!""")


class       a_  (       nn.Module ):

    '''simple docstring'''


    def __init__(	self				,	A				,	A				,	A )	->     Dict:
        super().__init__()
        _SCREAMING_SNAKE_CASE             =    mega_args
        _SCREAMING_SNAKE_CASE             =    nn.Embedding(A				,	self.mega_args.encoder_embed_dim )
        _SCREAMING_SNAKE_CASE             =    nn.ModuleList([MegaEncoderLayer(self.mega_args ) for _ in range(A )] )
        _SCREAMING_SNAKE_CASE             =    depth





    def     snake_case_(	self				,	A				,	A				,	A=True				,	A=0 )	->     Optional[int]:

        # Mega expects embeddings to be (time, batch, embedding size), but
        # Hugging Face returns tokens as (batch, time)
        if batch_first:
            _SCREAMING_SNAKE_CASE             =    input_ids.T

        # to make things more confusing, Mega expects the attention mask to
        # be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
        # which is the opposite of what HF returns
        if ignore_mask_value == 0:
            _SCREAMING_SNAKE_CASE             =    1 - attention_mask

        # get token embeddings from IDs
        _SCREAMING_SNAKE_CASE             =    self.embedding_layer(A )

        # pass through the Mega layers
        # input is (time, batch, encoder dim) and output is the same
        for encoder in self.encoders:
            _SCREAMING_SNAKE_CASE             =    encoder(A				,	A )

        # return according to the shape specified
        if batch_first:
            # (T, B, H) --> (B, T, H)
            return torch.transpose(A				,	0				,	1 )
        else:
            return embeds




class       a_  (       nn.Module ):

    '''simple docstring'''


    def __init__(	self				,	A				,	A				,	A )	->     Any:
        super().__init__()
        _SCREAMING_SNAKE_CASE             =    MegaLM(A				,	A				,	A )
        _SCREAMING_SNAKE_CASE             =    nn.Linear(mega_args.encoder_embed_dim				,	A )
        _SCREAMING_SNAKE_CASE             =    nn.Dropout(p=0.1 )





    def     snake_case_(	self				,	A				,	A				,	A=True				,	A=0 )	->     Optional[int]:
        _SCREAMING_SNAKE_CASE             =    self.mega(A				,	A				,	A				,	A )
        return self.mlm_head(self.dropout(A ) )





def lowerCamelCase		(  __lowerCamelCase				:					Any      , __lowerCamelCase				:					Optional[Any]      , __lowerCamelCase				:					List[str]					)		->Dict:
    with open(os.path.join(__lowerCamelCase      , """model_args.pkl"""					)      , """rb"""					) as f:
        _SCREAMING_SNAKE_CASE             =    pkl.load(__lowerCamelCase					)

    # load the original encoder
    _SCREAMING_SNAKE_CASE             =    OriginalMegaForMaskedLM(**__lowerCamelCase					).eval()

    # load its weights
    print(
        """Original Mega encoder:"""      , original_mlm.mega.load_state_dict(
            torch.load(os.path.join(__lowerCamelCase      , """encoder_weights.pt"""					)      , map_location="""cpu"""					)					)      , )
    print(
        """Original Mega MLM layer:"""      , original_mlm.mlm_head.load_state_dict(
            torch.load(os.path.join(__lowerCamelCase      , """mlm_head_weights.pt"""					)      , map_location="""cpu"""					)					)      , )

    # create a new config from the old one
    _SCREAMING_SNAKE_CASE             =    MegaConfig(
        num_hidden_layers=mega_original_args["""depth"""]      , vocab_size=mega_original_args["""vocab_size"""]      , hidden_size=mega_original_args["""mega_args"""].encoder_embed_dim      , shared_representation_size=mega_original_args["""mega_args"""].encoder_z_dim      , intermediate_size=mega_original_args["""mega_args"""].encoder_hidden_dim      , ema_projection_size=mega_original_args["""mega_args"""].encoder_n_dim      , dropout_prob=mega_original_args["""mega_args"""].dropout      , attention_probs_dropout_prob=mega_original_args["""mega_args"""].attention_dropout      , hidden_dropout_prob=mega_original_args["""mega_args"""].hidden_dropout      , activation=mega_original_args["""mega_args"""].activation_fn      , attention_activation=mega_original_args["""mega_args"""].attention_activation_fn      , bidirectional=mega_original_args["""mega_args"""].bidirectional      , use_chunking=mega_original_args["""mega_args"""].encoder_chunk_size > 0      , chunk_size=mega_original_args["""mega_args"""].encoder_chunk_size      , truncation=mega_original_args["""mega_args"""].truncation_length      , normalization_type=mega_original_args["""mega_args"""].normalization_type      , normalize_before_mega=__lowerCamelCase      , norm_affine=__lowerCamelCase      , use_feature_dropout=mega_original_args["""mega_args"""].feature_dropout      , relative_positional_bias=mega_original_args["""mega_args"""].rel_pos_bias      , max_positions=mega_original_args["""mega_args"""].max_source_positions      , nffn_hidden_size=mega_original_args["""mega_args"""].encoder_ffn_embed_dim      , normalize_before_ffn=mega_original_args["""mega_args"""].normalize_before      , nffn_activation_dropout_prob=0.0      , add_token_type_embeddings=__lowerCamelCase      , add_lm_hidden_dense_layer=__lowerCamelCase      , )

    _SCREAMING_SNAKE_CASE             =    MegaForMaskedLM(__lowerCamelCase					).eval()

    # the originl checkpoint just uses nn.Embedding for the word embeddings
    # we use a wrapper module for embeddings to add support for positional embeddings
    _SCREAMING_SNAKE_CASE             =    original_mlm.mega.embedding_layer.weight

    # modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
    # ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
    # also renaming previously confusing parameter names
    _SCREAMING_SNAKE_CASE             =    original_mlm.mega.encoders.state_dict()
    _SCREAMING_SNAKE_CASE             =    {}
    for module_name in original_state_dict.keys():
        _SCREAMING_SNAKE_CASE             =    None
        # have to handle gamma, beta, and alpha differently due to their use
        # in multiple modules within the original repository;
        # beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
        # the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
        if "beta" in module_name:
            # EMA sub-layers were always called "move" in the original repo
            if "move.beta" in module_name:
                _SCREAMING_SNAKE_CASE             =    module_name.replace("""move.beta"""      , """ema_gate.ema_expansion_matrix"""					)
            elif "mega_layer.beta" in module_name:
                _SCREAMING_SNAKE_CASE             =    module_name.replace("""beta"""      , """qk_bias"""					)
            else:
                _SCREAMING_SNAKE_CASE             =    module_name.replace("""beta"""      , """b_param"""					)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
        elif "gamma" in module_name:
            if "move.gamma" in module_name:
                _SCREAMING_SNAKE_CASE             =    module_name.replace("""move.gamma"""      , """ema_gate.kernel_projection_matrix"""					)
            elif "mega_layer.gamma" in module_name:
                _SCREAMING_SNAKE_CASE             =    module_name.replace("""gamma"""      , """qk_weight"""					)
            else:
                _SCREAMING_SNAKE_CASE             =    module_name.replace("""gamma"""      , """g_param"""					)
        # alpha is used in EMA and positional bias; renaming to improve readability
        elif "move.alpha" in module_name:
            _SCREAMING_SNAKE_CASE             =    module_name.replace("""move.alpha"""      , """ema_gate.decay_factor"""					)
        # delta is only used in EMA; renaming to improve readability
        elif "move.delta" in module_name:
            _SCREAMING_SNAKE_CASE             =    module_name.replace("""move.delta"""      , """ema_gate.damping_factor"""					)
        # omega is only used in EMA; renaming to improve readability
        elif "omega" in module_name:
            _SCREAMING_SNAKE_CASE             =    module_name.replace("""move.omega"""      , """ema_gate.residual_weight"""					)

        if new_module_name:
            _SCREAMING_SNAKE_CASE             =    new_module_name

    if len(__lowerCamelCase					) != 0:
        print(F'Renaming these keys: {updated_keys.keys()}'					)
    else:
        print("""No need to rename state dict entries"""					)
    for old, new in updated_keys.items():
        _SCREAMING_SNAKE_CASE             =    original_state_dict.pop(__lowerCamelCase					)

    # now attempt to load the state dictionary with updated names
    # note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
    print("""HF Mega encoder:"""      , hf_mlm.mega.layers.load_state_dict(__lowerCamelCase					)					)

    # load the MLM head weights directly
    print(
        """HF Mega MLM layer:"""      , hf_mlm.mlm_head.load_state_dict(
            torch.load(os.path.join(__lowerCamelCase      , """mlm_head_weights.pt"""					)      , map_location="""cpu"""					)					)      , )

    # test on a randomly generated input sequence
    _SCREAMING_SNAKE_CASE             =    torch.randint(0      , hf_config.vocab_size      , size=(4, 256)					)
    _SCREAMING_SNAKE_CASE             =    torch.ones_like(__lowerCamelCase					)
    # mask a few tokens to make sure masking is applied appropriately :)
    _SCREAMING_SNAKE_CASE             =    0

    # run forward passes
    _SCREAMING_SNAKE_CASE             =    original_mlm(__lowerCamelCase      , __lowerCamelCase      , batch_first=__lowerCamelCase      , ignore_mask_value=0					)
    _SCREAMING_SNAKE_CASE             =    hf_mlm(__lowerCamelCase      , __lowerCamelCase					)[0]

    # print shapes and diff
    print(F'original output {original_output.shape}'					)
    print(F'hf output {hf_output.shape}'					)
    print(F'max diff: {(original_output - hf_output).max()}'					)  # 0.0
    _SCREAMING_SNAKE_CASE             =    torch.allclose(__lowerCamelCase      , __lowerCamelCase      , atol=1e-3					)

    if success:
        print("""Yay!"""					)
        hf_mlm.save_pretrained(__lowerCamelCase					)
    else:
        raise RuntimeError(F'Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'					)

    if includes_tokenizer:
        print("""Transferring tokenizer"""					)
        _SCREAMING_SNAKE_CASE             =    AutoTokenizer.from_pretrained(__lowerCamelCase					)
        tokenizer.save_pretrained(__lowerCamelCase					)


if __name__ == "__main__":
      lowercase_			       =				argparse.ArgumentParser()

      parser.add_argument(
          """--pretrained_checkpoint_path""",
          default=None,
          type=str,
          required=True,
          help="""Point to the directory containing your model weights using the official Mega repo""",
      )

      parser.add_argument(
          """--output_path""", default=None, type=str, required=True, help="""Location to save the Hugging Face version"""
      )

      parser.add_argument(
          """--includes_tokenizer""",
          action="""store_true""",
          help="""Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo""",
      )

      lowercase_			       =				parser.parse_args()

      convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)










'''simple docstring'''

# Copyright 2022 The HuggingFace Team. 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.
import os
from typing import List





def lowerCamelCase		(  __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					str					)		->Tuple:
    if name != "training_function" and name != "main":
        raise ValueError(F'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''					)
    _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    [], False
    for line in lines:
        if not found_start and F'def {name}' in line:
            _SCREAMING_SNAKE_CASE             =    True
            good_lines.append(__lowerCamelCase					)
            continue
        if found_start:
            if name == "training_function" and "def main" in line:
                return good_lines
            if name == "main" and "if __name__" in line:
                return good_lines
            good_lines.append(__lowerCamelCase					)





def lowerCamelCase		(  __lowerCamelCase				:					List[str]					)		->List[str]:
    return [line for line in lines if not line.lstrip().startswith("""#"""					) and line != "\n"]






def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					str      , __lowerCamelCase				:					bool      , __lowerCamelCase				:					str = None					)		->List[str]:
    with open(__lowerCamelCase      , """r"""					) as f:
        _SCREAMING_SNAKE_CASE             =    f.readlines()
    with open(os.path.abspath(os.path.join("""examples"""      , """nlp_example.py"""					)					)      , """r"""					) as f:
        _SCREAMING_SNAKE_CASE             =    f.readlines()
    with open(__lowerCamelCase      , """r"""					) as f:
        _SCREAMING_SNAKE_CASE             =    f.readlines()
    if secondary_filename is not None:
        with open(__lowerCamelCase      , """r"""					) as f:
            _SCREAMING_SNAKE_CASE             =    f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
    if parser_only:
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
        if secondary_filename is not None:
            _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
    else:
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
        _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
        if secondary_filename is not None:
            _SCREAMING_SNAKE_CASE             =    clean_lines(
                get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)

    _SCREAMING_SNAKE_CASE             =    """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

    # Specific code in our script that differs from the full version, aka what is new
    _SCREAMING_SNAKE_CASE             =    []
    _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
    _SCREAMING_SNAKE_CASE             =    iter(__lowerCamelCase					)
    for i in range(len(__lowerCamelCase					) - 1					):
        if i not in passed_idxs:
            _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
            if (line not in full_file_func) and (line.lstrip() != _dl_line):
                if "TESTING_MOCKED_DATALOADERS" not in line:
                    new_feature_code.append(__lowerCamelCase					)
                    passed_idxs.append(__lowerCamelCase					)
                else:
                    # Skip over the `config['num_epochs'] = 2` statement
                    _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)

    # Extract out just the new parts from the full_file_training_func
    _SCREAMING_SNAKE_CASE             =    []
    _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
    for i, line in enumerate(__lowerCamelCase					):
        if i not in passed_idxs:
            if (line not in full_file_func) and (line.lstrip() != _dl_line):
                if "TESTING_MOCKED_DATALOADERS" not in line:
                    new_full_example_parts.append(__lowerCamelCase					)
                    passed_idxs.append(__lowerCamelCase					)

    # Finally, get the overall diff
    _SCREAMING_SNAKE_CASE             =    [line for line in new_feature_code if line not in new_full_example_parts]
    if secondary_filename is not None:
        _SCREAMING_SNAKE_CASE             =    [line for line in full_file_contents if line not in secondary_file_func]
        _SCREAMING_SNAKE_CASE             =    [line for line in diff_from_example if line not in diff_from_two]

    return diff_from_example



