
"""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        _UpperCAmelCase							(						nn.Module							):




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




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_=True				,    lowercase_=0							)  ->   List[Any]:

          # Mega expects embeddings to be (time, batch, embedding size), but
          # Hugging Face returns tokens as (batch, time)
          if batch_first:
              UpperCAmelCase       =   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:
              UpperCAmelCase       =   1 - attention_mask

          # get token embeddings from IDs
          UpperCAmelCase       =   self.embedding_layer(lowercase_							)

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

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

class        _UpperCAmelCase							(						nn.Module							):




      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   str:
          super().__init__()
          UpperCAmelCase       =   MegaLM(lowercase_				,    lowercase_				,    lowercase_							)
          UpperCAmelCase       =   nn.Linear(mega_args.encoder_embed_dim				,    lowercase_							)
          UpperCAmelCase       =   nn.Dropout(p=0.1							)




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_=True				,    lowercase_=0							)  ->   Optional[Any]:
          UpperCAmelCase       =   self.mega(lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)
          return self.mlm_head(self.dropout(lowercase_							)							)




def       lowercase__  (   lowerCAmelCase			:   Any	,			lowerCAmelCase			:   str	,			lowerCAmelCase			:   List[str]	)	-> Optional[Any]:






    """simple docstring"""




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

    # load the original encoder
    UpperCAmelCase       =   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
    UpperCAmelCase       =   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	,			)

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

        if new_module_name:
            UpperCAmelCase       =   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():
        UpperCAmelCase       =   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
    UpperCAmelCase       =   torch.randint(0	,			hf_config.vocab_size	,			size=(4, 256)	)
    UpperCAmelCase       =   torch.ones_like(lowerCAmelCase	)
    # mask a few tokens to make sure masking is applied appropriately :)
    UpperCAmelCase       =   0

    # run forward passes
    UpperCAmelCase       =   original_mlm(lowerCAmelCase	,			lowerCAmelCase	,			batch_first=lowerCAmelCase	,			ignore_mask_value=0	)
    UpperCAmelCase       =   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
    UpperCAmelCase       =   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'	)
        UpperCAmelCase       =   AutoTokenizer.from_pretrained(lowerCAmelCase	)
        tokenizer.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_         =  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''',
   )

   SCREAMING_SNAKE_CASE_         =  parser.parse_args()

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





"""simple docstring"""

SCREAMING_SNAKE_CASE_         =  {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

SCREAMING_SNAKE_CASE_         =  [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

SCREAMING_SNAKE_CASE_         =  [0 for x in range(len(graph))]
SCREAMING_SNAKE_CASE_         =  []




def       lowercase__  (   lowerCAmelCase			:   Optional[int]	,			lowerCAmelCase			:   Any	)	-> List[str]:






    """simple docstring"""




    UpperCAmelCase       =   1
    while stack:
        UpperCAmelCase       =   stack.pop()
        print(lowerCAmelCase	,			clothes[current_clothing]	)
        order += 1




def       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Dict	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   1
    for v in graph[u]:
        if not visited[v]:
            depth_first_search(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)

    stack.append(lowerCAmelCase	)




def       lowercase__  (   lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   Dict	)	-> Optional[Any]:






    """simple docstring"""




    for v in range(len(lowerCAmelCase	)	):
        if not visited[v]:
            depth_first_search(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)



