






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			):



      """simple docstring"""
      def __init__(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				Union[str, Any]:
          super().__init__()
          UpperCamelCase	:Optional[int]     							=       mega_args
          UpperCamelCase	:str     							=       nn.Embedding(SCREAMING_SNAKE_CASE_   ,    self.mega_args.encoder_embed_dim			)
          UpperCamelCase	:List[Any]     							=       nn.ModuleList([MegaEncoderLayer(self.mega_args			) for _ in range(SCREAMING_SNAKE_CASE_			)]			)
          UpperCamelCase	:Dict     							=       depth




      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_=True   ,    SCREAMING_SNAKE_CASE_=0			)    ->				int:

          # Mega expects embeddings to be (time, batch, embedding size), but
          # Hugging Face returns tokens as (batch, time)
          if batch_first:
              UpperCamelCase	:Union[str, Any]     							=       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	:List[Any]     							=       1 - attention_mask

          # get token embeddings from IDs
          UpperCamelCase	:List[str]     							=       self.embedding_layer(SCREAMING_SNAKE_CASE_			)

          # pass through the Mega layers
          # input is (time, batch, encoder dim) and output is the same
          for encoder in self.encoders:
              UpperCamelCase	:List[Any]     							=       encoder(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

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







class       UpperCAmelCase_						( nn.Module			):



      """simple docstring"""
      def __init__(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				Optional[Any]:
          super().__init__()
          UpperCamelCase	:Tuple     							=       MegaLM(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Union[str, Any]     							=       nn.Linear(mega_args.encoder_embed_dim   ,    SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Any     							=       nn.Dropout(p=0.1			)




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







def   _A       (    SCREAMING_SNAKE_CASE__  :     Dict					,  SCREAMING_SNAKE_CASE__  :     Tuple					,  SCREAMING_SNAKE_CASE__  :     Optional[Any]	):
    with open(os.path.join(SCREAMING_SNAKE_CASE__					,  '''model_args.pkl'''	)					,  '''rb'''	) as f:
        UpperCamelCase	:Optional[Any]     							=       pkl.load(SCREAMING_SNAKE_CASE__	)

    # load the original encoder
    UpperCamelCase	:Dict     							=       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
    UpperCamelCase	:List[str]     							=       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__					,  )

    UpperCamelCase	:List[Any]     							=       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
    UpperCamelCase	:int     							=       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	:Union[str, Any]     							=       original_mlm.mega.encoders.state_dict()
    UpperCamelCase	:List[Any]     							=       {}
    for module_name in original_state_dict.keys():
        UpperCamelCase	:Union[str, Any]     							=       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	:Union[str, Any]     							=       module_name.replace('''move.beta'''					,  '''ema_gate.ema_expansion_matrix'''	)
            elif "mega_layer.beta" in module_name:
                UpperCamelCase	:Any     							=       module_name.replace('''beta'''					,  '''qk_bias'''	)
            else:
                UpperCamelCase	:Optional[Any]     							=       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	:Optional[int]     							=       module_name.replace('''move.gamma'''					,  '''ema_gate.kernel_projection_matrix'''	)
            elif "mega_layer.gamma" in module_name:
                UpperCamelCase	:List[str]     							=       module_name.replace('''gamma'''					,  '''qk_weight'''	)
            else:
                UpperCamelCase	:List[Any]     							=       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	:Dict     							=       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	:List[Any]     							=       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	:Dict     							=       module_name.replace('''move.omega'''					,  '''ema_gate.residual_weight'''	)

        if new_module_name:
            UpperCamelCase	:Optional[Any]     							=       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():
        UpperCamelCase	:Optional[Any]     							=       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
    UpperCamelCase	:Tuple     							=       torch.randint(0					,  hf_config.vocab_size					,  size=(4, 256)	)
    UpperCamelCase	:Optional[Any]     							=       torch.ones_like(SCREAMING_SNAKE_CASE__	)
    # mask a few tokens to make sure masking is applied appropriately :)
    UpperCamelCase	:List[Any]     							=       0

    # run forward passes
    UpperCamelCase	:str     							=       original_mlm(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  batch_first=SCREAMING_SNAKE_CASE__					,  ignore_mask_value=0	)
    UpperCamelCase	:int     							=       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
    UpperCamelCase	:str     							=       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'''	)
        UpperCamelCase	:Optional[int]     							=       AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__	)
        tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
 __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""",
 )

 __snake_case							 =  parser.parse_args()

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













def   _A       (    SCREAMING_SNAKE_CASE__  :     list[list[int]]	):
    if len(SCREAMING_SNAKE_CASE__	) == 0 or not all(len(SCREAMING_SNAKE_CASE__	) == 2 for pair in coordinates	):
        raise ValueError('''The program cannot work out a fitting polynomial.'''	)

    if len({tuple(SCREAMING_SNAKE_CASE__	) for pair in coordinates}	) != len(SCREAMING_SNAKE_CASE__	):
        raise ValueError('''The program cannot work out a fitting polynomial.'''	)

    UpperCamelCase	:str     							=       {x for x, _ in coordinates}
    if len(SCREAMING_SNAKE_CASE__	) == 1:
        return F'''x={coordinates[0][0]}'''

    if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
        raise ValueError('''The program cannot work out a fitting polynomial.'''	)

    UpperCamelCase	:List[Any]     							=       len(SCREAMING_SNAKE_CASE__	)

    UpperCamelCase	:Optional[int]     							=       0
    UpperCamelCase	:list[list[float]]     							=       []
    # put the x and x to the power values in a matrix
    while count_of_line < x:
        UpperCamelCase	:Tuple     							=       0
        UpperCamelCase	:Optional[int]     							=       coordinates[count_of_line][0]
        UpperCamelCase	:list[float]     							=       []
        while count_in_line < x:
            count_line.append(a ** (x - (count_in_line + 1))	)
            count_in_line += 1
        matrix.append(SCREAMING_SNAKE_CASE__	)
        count_of_line += 1

    UpperCamelCase	:Optional[int]     							=       0
    # put the y values into a vector
    UpperCamelCase	:list[float]     							=       []
    while count_of_line < x:
        vector.append(coordinates[count_of_line][1]	)
        count_of_line += 1

    UpperCamelCase	:str     							=       0

    while count < x:
        UpperCamelCase	:Optional[Any]     							=       0
        while zahlen < x:
            if count == zahlen:
                zahlen += 1
            if zahlen == x:
                break
            UpperCamelCase	:Union[str, Any]     							=       matrix[zahlen][count] / matrix[count][count]
            for counting_columns, item in enumerate(matrix[count]	):
                # manipulating all the values in the matrix
                matrix[zahlen][counting_columns] -= item * bruch
            # manipulating the values in the vector
            vector[zahlen] -= vector[count] * bruch
            zahlen += 1
        count += 1

    UpperCamelCase	:Optional[int]     							=       0
    # make solutions
    UpperCamelCase	:list[str]     							=       []
    while count < x:
        solution.append(str(vector[count] / matrix[count][count]	)	)
        count += 1

    UpperCamelCase	:Union[str, Any]     							=       0
    UpperCamelCase	:Any     							=       '''f(x)='''

    while count < x:
        UpperCamelCase	:list[str]     							=       solution[count].split('''E'''	)
        if len(SCREAMING_SNAKE_CASE__	) > 1:
            UpperCamelCase	:List[Any]     							=       F'''{remove_e[0]}*10^{remove_e[1]}'''
        solved += F'''x^{x - (count + 1)}*{solution[count]}'''
        if count + 1 != x:
            solved += "+"
        count += 1

    return solved


if __name__ == "__main__":
 print(points_to_polynomial([]))
 print(points_to_polynomial([[]]))
 print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
 print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
 print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
 print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
 print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
 print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
 print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
 print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))





