





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 	lowerCAmelCase__(       nn.Module			):






       '''simple docstring'''


       def __init__(					self			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)	->   List[Any]:
            super().__init__()
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							mega_args
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							nn.Embedding(__lowerCamelCase			,  self.mega_args.encoder_embed_dim							)
            _SCREAMING_SNAKE_CASE				:		str  			=							nn.ModuleList([MegaEncoderLayer(self.mega_args							) for _ in range(__lowerCamelCase							)]							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							depth







       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase=True			,  __lowerCamelCase=0							)	->   Any:

            # Mega expects embeddings to be (time, batch, embedding size), but
            # Hugging Face returns tokens as (batch, time)
            if batch_first:
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							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				:		Optional[int]  			=							1 - attention_mask

            # get token embeddings from IDs
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							self.embedding_layer(__lowerCamelCase							)

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

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





class 	lowerCAmelCase__(       nn.Module			):






       '''simple docstring'''


       def __init__(					self			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)	->   int:
            super().__init__()
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							MegaLM(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							nn.Linear(mega_args.encoder_embed_dim			,  __lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							nn.Dropout(p=0.1							)







       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase=True			,  __lowerCamelCase=0							)	->   Optional[Any]:
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.mega(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)
            return self.mlm_head(self.dropout(__lowerCamelCase							)							)






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase		):
     with open(os.path.join(__lowerCamelCase,				"model_args.pkl"		),				"rb"		) as f:
          _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							pkl.load(__lowerCamelCase		)

     # load the original encoder
     _SCREAMING_SNAKE_CASE				:		str  			=							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				:		Dict  			=							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				:		Optional[int]  			=							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				:		List[str]  			=							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				:		List[str]  			=							original_mlm.mega.encoders.state_dict()
     _SCREAMING_SNAKE_CASE				:		Dict  			=							{}
     for module_name in original_state_dict.keys():
          _SCREAMING_SNAKE_CASE				:		List[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:
                    _SCREAMING_SNAKE_CASE				:		int  			=							module_name.replace("move.beta",				"ema_gate.ema_expansion_matrix"		)
               elif "mega_layer.beta" in module_name:
                    _SCREAMING_SNAKE_CASE				:		int  			=							module_name.replace("beta",				"qk_bias"		)
               else:
                    _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							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				:		Dict  			=							module_name.replace("move.gamma",				"ema_gate.kernel_projection_matrix"		)
               elif "mega_layer.gamma" in module_name:
                    _SCREAMING_SNAKE_CASE				:		Tuple  			=							module_name.replace("gamma",				"qk_weight"		)
               else:
                    _SCREAMING_SNAKE_CASE				:		Dict  			=							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				:		Tuple  			=							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				:		List[str]  			=							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				:		Tuple  			=							module_name.replace("move.omega",				"ema_gate.residual_weight"		)

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

     # run forward passes
     _SCREAMING_SNAKE_CASE				:		Dict  			=							original_mlm(__lowerCamelCase,				__lowerCamelCase,				batch_first=__lowerCamelCase,				ignore_mask_value=0		)
     _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							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				:		Optional[int]  			=							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				:		List[str]  			=							AutoTokenizer.from_pretrained(__lowerCamelCase		)
          tokenizer.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
       UpperCamelCase__          =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__          =parser.parse_args()

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






# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       @classmethod
       def 			UpperCamelCase_	(					cls							)	->   Tuple:
            return cls







       @slow
       @require_torch_gpu
       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.get_auto_remove_tmp_dir()
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							F"""
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        """.split()

            # we want this test to run even if there is only one GPU, but if there are more we use them all
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							get_gpu_count()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							F"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
            _SCREAMING_SNAKE_CASE				:		int  			=							[sys.executable] + distributed_args + args
            execute_subprocess_async(__lowerCamelCase			,  env=self.get_env()							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							os.path.join(__lowerCamelCase			,  "test_bleu.json"							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							load_json(__lowerCamelCase							)
            # print(metrics)
            self.assertGreaterEqual(metrics["bleu"]			,  2_5							)