





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   ):






    '''simple docstring'''





    def __init__(   self						:		List[str]			,						snake_case						:		List[str]			,						snake_case						:		Union[str, Any]			,						snake_case						:		Tuple							):

      """simple docstring"""





      super().__init__()
      __lowerCAmelCase			=				mega_args
      __lowerCAmelCase			=				nn.Embedding(snake_case			,						self.mega_args.encoder_embed_dim							)
      __lowerCAmelCase			=				nn.ModuleList([MegaEncoderLayer(self.mega_args							) for _ in range(snake_case							)]							)
      __lowerCAmelCase			=				depth





    def 	A		(   self						:		List[str]			,						snake_case						:		Union[str, Any]			,						snake_case						:		Tuple			,						snake_case						:		Optional[Any]=True			,						snake_case						:		Optional[int]=0							):

      """simple docstring"""





      if batch_first:
        __lowerCAmelCase			=				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:
        __lowerCAmelCase			=				1 - attention_mask

      # get token embeddings from IDs
      __lowerCAmelCase			=				self.embedding_layer(snake_case							)

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

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


class   __snake_case	(			nn.Module   ):






    '''simple docstring'''





    def __init__(   self						:		Dict			,						snake_case						:		List[str]			,						snake_case						:		Tuple			,						snake_case						:		int							):

      """simple docstring"""





      super().__init__()
      __lowerCAmelCase			=				MegaLM(snake_case			,						snake_case			,						snake_case							)
      __lowerCAmelCase			=				nn.Linear(mega_args.encoder_embed_dim			,						snake_case							)
      __lowerCAmelCase			=				nn.Dropout(p=0.1							)





    def 	A		(   self						:		Any			,						snake_case						:		Tuple			,						snake_case						:		List[Any]			,						snake_case						:		Tuple=True			,						snake_case						:		Any=0							):

      """simple docstring"""





      __lowerCAmelCase			=				self.mega(snake_case			,						snake_case			,						snake_case			,						snake_case							)
      return self.mlm_head(self.dropout(snake_case							)							)


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Any     ,							__lowercase:		Optional[Any]     ,							__lowercase:		List[str]):



  '''simple docstring'''






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

  # load the original encoder
  __lowerCAmelCase			=				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
  __lowerCAmelCase			=				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     ,							)

  __lowerCAmelCase			=				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
  __lowerCAmelCase			=				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
  __lowerCAmelCase			=				original_mlm.mega.encoders.state_dict()
  __lowerCAmelCase			=				{}
  for module_name in original_state_dict.keys():
    __lowerCAmelCase			=				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:
        __lowerCAmelCase			=				module_name.replace('''move.beta'''     ,							'''ema_gate.ema_expansion_matrix''')
      elif "mega_layer.beta" in module_name:
        __lowerCAmelCase			=				module_name.replace('''beta'''     ,							'''qk_bias''')
      else:
        __lowerCAmelCase			=				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:
        __lowerCAmelCase			=				module_name.replace('''move.gamma'''     ,							'''ema_gate.kernel_projection_matrix''')
      elif "mega_layer.gamma" in module_name:
        __lowerCAmelCase			=				module_name.replace('''gamma'''     ,							'''qk_weight''')
      else:
        __lowerCAmelCase			=				module_name.replace('''gamma'''     ,							'''g_param''')
        # alpha is used in EMA and positional bias; renaming to improve readability
    elif "move.alpha" in module_name:
      __lowerCAmelCase			=				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:
      __lowerCAmelCase			=				module_name.replace('''move.delta'''     ,							'''ema_gate.damping_factor''')
    # omega is only used in EMA; renaming to improve readability
    elif "omega" in module_name:
      __lowerCAmelCase			=				module_name.replace('''move.omega'''     ,							'''ema_gate.residual_weight''')

    if new_module_name:
      __lowerCAmelCase			=				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():
    __lowerCAmelCase			=				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
  __lowerCAmelCase			=				torch.randint(0     ,							hf_config.vocab_size     ,							size=(4, 2_56))
  __lowerCAmelCase			=				torch.ones_like(__lowerCamelCase)
  # mask a few tokens to make sure masking is applied appropriately :)
  __lowerCAmelCase			=				0

  # run forward passes
  __lowerCAmelCase			=				original_mlm(__lowerCamelCase     ,							__lowerCamelCase     ,							batch_first=__lowerCamelCase     ,							ignore_mask_value=0)
  __lowerCAmelCase			=				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
  __lowerCAmelCase			=				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''')
    __lowerCAmelCase			=				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)







import subprocess
from argparse import ArgumentParser
from typing import List, Union

from huggingface_hub.hf_api import HfFolder, create_repo, whoami
from requests.exceptions import HTTPError

from . import BaseTransformersCLICommand

class   __snake_case				(			_a   ):






    '''simple docstring'''





    @staticmethod
    def 	A		(   snake_case						:		ArgumentParser							):

      """simple docstring"""





      __lowerCAmelCase			=				parser.add_parser('''login'''			,						help='''Log in using the same credentials as on huggingface.co'''							)
      login_parser.set_defaults(func=lambda snake_case							: LoginCommand(snake_case							)							)
      __lowerCAmelCase			=				parser.add_parser('''whoami'''			,						help='''Find out which huggingface.co account you are logged in as.'''							)
      whoami_parser.set_defaults(func=lambda snake_case							: WhoamiCommand(snake_case							)							)
      __lowerCAmelCase			=				parser.add_parser('''logout'''			,						help='''Log out'''							)
      logout_parser.set_defaults(func=lambda snake_case							: LogoutCommand(snake_case							)							)

      # new system: git-based repo system
      __lowerCAmelCase			=				parser.add_parser(
          '''repo'''			,						help='''Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos.'''			,						)
      __lowerCAmelCase			=				repo_parser.add_subparsers(
          help='''Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands'''							)
      __lowerCAmelCase			=				repo_subparsers.add_parser(
          '''create'''			,						help='''Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co'''							)
      repo_create_parser.add_argument(
          '''name'''			,						type=snake_case			,						help='''Name for your model\'s repo. Will be namespaced under your username to build the model id.'''			,						)
      repo_create_parser.add_argument('''--organization'''			,						type=snake_case			,						help='''Optional: organization namespace.'''							)
      repo_create_parser.add_argument('''-y'''			,						'''--yes'''			,						action='''store_true'''			,						help='''Optional: answer Yes to the prompt'''							)
      repo_create_parser.set_defaults(func=lambda snake_case							: RepoCreateCommand(snake_case							)							)


class   __snake_case				:






    '''simple docstring'''
    _lowerCAmelCase     =	"""\u001b[1m"""
    _lowerCAmelCase     =	"""\u001b[31m"""
    _lowerCAmelCase     =	"""\u001b[90m"""
    _lowerCAmelCase     =	"""\u001b[0m"""





    @classmethod
    def 	A		(   cls						:		str			,						snake_case						:		Optional[int]							):

      """simple docstring"""





      return f'''{cls._bold}{s}{cls._reset}'''





    @classmethod
    def 	A		(   cls						:		Optional[Any]			,						snake_case						:		str							):

      """simple docstring"""





      return f'''{cls._bold}{cls._red}{s}{cls._reset}'''





    @classmethod
    def 	A		(   cls						:		Dict			,						snake_case						:		List[Any]							):

      """simple docstring"""





      return f'''{cls._gray}{s}{cls._reset}'''


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		List[List[Union[str, int]]]     ,							__lowercase:		List[str]):



  '''simple docstring'''






  __lowerCAmelCase			=				[max(len(str(__lowercase)) for x in col) for col in zip(*__lowercase     ,							__lowercase)]
  __lowerCAmelCase			=				('''{{:{}}} ''' * len(__lowercase)).format(*__lowercase)
  __lowerCAmelCase			=				[]
  lines.append(row_format.format(*__lowercase))
  lines.append(row_format.format(*['''-''' * w for w in col_widths]))
  for row in rows:
    lines.append(row_format.format(*__lowercase))
  return "\n".join(__lowercase)

class   __snake_case				:






    '''simple docstring'''





    def __init__(   self						:		List[str]			,						snake_case						:		List[str]							):

      """simple docstring"""





      __lowerCAmelCase			=				args


class   __snake_case				(			_a   ):






    '''simple docstring'''





    def 	A		(   self						:		List[Any]							):

      """simple docstring"""





      print(
          ANSI.red(
              '''ERROR! `huggingface-cli login` uses an outdated login mechanism '''
              '''that is not compatible with the Hugging Face Hub backend anymore. '''
              '''Please use `huggingface-cli login instead.'''							)							)


class   __snake_case				(			_a   ):






    '''simple docstring'''





    def 	A		(   self						:		int							):

      """simple docstring"""





      print(
          ANSI.red(
              '''WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use '''
              '''`huggingface-cli whoami` instead.'''							)							)
      __lowerCAmelCase			=				HfFolder.get_token()
      if token is None:
        print('''Not logged in'''							)
        exit()
      try:
        __lowerCAmelCase	,						__lowerCAmelCase			=				whoami(snake_case							)
        print(snake_case							)
        if orgs:
          print(ANSI.bold('''orgs: '''							)			,						''','''.join(snake_case							)							)
      except HTTPError as e:
        print(snake_case							)
        print(ANSI.red(e.response.text							)							)
        exit(1							)


class   __snake_case				(			_a   ):






    '''simple docstring'''





    def 	A		(   self						:		Any							):

      """simple docstring"""





      print(
          ANSI.red(
              '''ERROR! `transformers-cli logout` uses an outdated logout mechanism '''
              '''that is not compatible with the Hugging Face Hub backend anymore. '''
              '''Please use `huggingface-cli logout instead.'''							)							)


class   __snake_case				(			_a   ):






    '''simple docstring'''





    def 	A		(   self						:		Optional[int]							):

      """simple docstring"""





      print(
          ANSI.red(
              '''WARNING! Managing repositories through transformers-cli is deprecated. '''
              '''Please use `huggingface-cli` instead.'''							)							)
      __lowerCAmelCase			=				HfFolder.get_token()
      if token is None:
        print('''Not logged in'''							)
        exit(1							)
      try:
        __lowerCAmelCase			=				subprocess.check_output(['''git''', '''--version''']							).decode('''utf-8'''							)
        print(ANSI.gray(stdout.strip()							)							)
      except FileNotFoundError:
        print('''Looks like you do not have git installed, please install.'''							)

      try:
        __lowerCAmelCase			=				subprocess.check_output(['''git-lfs''', '''--version''']							).decode('''utf-8'''							)
        print(ANSI.gray(stdout.strip()							)							)
      except FileNotFoundError:
        print(
            ANSI.red(
                '''Looks like you do not have git-lfs installed, please install.'''
                ''' You can install from https://git-lfs.github.com/.'''
                ''' Then run `git lfs install` (you only have to do this once).'''							)							)
      print(''''''							)

      __lowerCAmelCase	,						__lowerCAmelCase			=				whoami(snake_case							)
      __lowerCAmelCase			=				self.args.organization if self.args.organization is not None else user
      __lowerCAmelCase			=				f'''{namespace}/{self.args.name}'''
      print(f'''You are about to create {ANSI.bold(snake_case							)}'''							)

      if not self.args.yes:
        __lowerCAmelCase			=				input('''Proceed? [Y/n] '''							).lower()
        if not (choice == "" or choice == "y" or choice == "yes"):
          print('''Abort'''							)
          exit()
      try:
        __lowerCAmelCase			=				create_repo(snake_case			,						name=self.args.name			,						organization=self.args.organization							)
      except HTTPError as e:
        print(snake_case							)
        print(ANSI.red(e.response.text							)							)
        exit(1							)
      print('''\nYour repo now lives at:'''							)
      print(f'''  {ANSI.bold(snake_case							)}'''							)
      print('''\nYou can clone it locally with the command below, and commit/push as usual.'''							)
      print(f'''\n  git clone {url}'''							)
      print(''''''							)
