

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    __snake_case	(     nn.Module							):

  def __init__(       self    ,		_A    ,		_A    ,		_A):
      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     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A=True    ,		_A=0):

      # 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    __snake_case	(     nn.Module							):

  def __init__(       self    ,		_A    ,		_A    ,		_A):
      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     lowerCAmelCase__  (       self    ,		_A    ,		_A    ,		_A=True    ,		_A=0):
      SCREAMING_SNAKE_CASE_						=    self.mega(_A    ,		_A    ,		_A    ,		_A)
      return self.mlm_head(self.dropout(_A))






def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		int			, _SCREAMING_SNAKE_CASE       :		Any			, _SCREAMING_SNAKE_CASE       :		str							):





    """simple docstring"""


    with open(os.path.join(_SCREAMING_SNAKE_CASE			, 'model_args.pkl'							)			, 'rb'							) as f:
        SCREAMING_SNAKE_CASE_						=    pkl.load(_SCREAMING_SNAKE_CASE							)

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

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

    SCREAMING_SNAKE_CASE_						=    MegaForMaskedLM(_SCREAMING_SNAKE_CASE							).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(_SCREAMING_SNAKE_CASE							) != 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(_SCREAMING_SNAKE_CASE							)

    # 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(_SCREAMING_SNAKE_CASE							)							)

    # load the MLM head weights directly
    print(
        'HF Mega MLM layer:'			, hf_mlm.mlm_head.load_state_dict(
            torch.load(os.path.join(_SCREAMING_SNAKE_CASE			, '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(_SCREAMING_SNAKE_CASE							)
    # mask a few tokens to make sure masking is applied appropriately :)
    SCREAMING_SNAKE_CASE_						=    0

    # run forward passes
    SCREAMING_SNAKE_CASE_						=    original_mlm(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, batch_first=_SCREAMING_SNAKE_CASE			, ignore_mask_value=0							)
    SCREAMING_SNAKE_CASE_						=    hf_mlm(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)[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(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, atol=1E-3							)

    if success:
        print('Yay!'							)
        hf_mlm.save_pretrained(_SCREAMING_SNAKE_CASE							)
    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(_SCREAMING_SNAKE_CASE							)
        tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
       UpperCamelCase__	:   List[Any]								=     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",
       )

       UpperCamelCase__	:   Any								=     parser.parse_args()

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





from __future__ import annotations


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    [0] * len(_SCREAMING_SNAKE_CASE							)
    for i in range(1			, len(_SCREAMING_SNAKE_CASE							)							):
        SCREAMING_SNAKE_CASE_						=    duration_times[i - 1] + waiting_times[i - 1]
    return waiting_times


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]			, _SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return [
        duration_time + waiting_times[i]
        for i, duration_time in enumerate(_SCREAMING_SNAKE_CASE							)
    ]


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
       # process id's
       UpperCamelCase__	:   List[str]								=     [1, 2, 3]

       # ensure that we actually have processes
       if len(processes) == 0:
              print("Zero amount of processes")
              raise SystemExit(0)

       # duration time of all processes
       UpperCamelCase__	:   Any								=     [19, 8, 9]

       # ensure we can match each id to a duration time
       if len(duration_times) != len(processes):
              print("Unable to match all id's with their duration time")
              raise SystemExit(0)

       # get the waiting times and the turnaround times
       UpperCamelCase__	:   List[str]								=     calculate_waiting_times(duration_times)
       UpperCamelCase__	:   Union[str, Any]								=     calculate_turnaround_times(duration_times, waiting_times)

       # get the average times
       UpperCamelCase__	:   str								=     calculate_average_waiting_time(waiting_times)
       UpperCamelCase__	:   List[Any]								=     calculate_average_turnaround_time(turnaround_times)

       # print all the results
       print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
       for i, process in enumerate(processes):
              print(
                  F'{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'
                  F'{turnaround_times[i]}'
              )
       print(F'Average waiting time = {average_waiting_time}')
       print(F'Average turn around time = {average_turnaround_time}')


