


"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Dict:
							snake_case_          =						[
							    """encoder.version""",
							    """decoder.version""",
							    """model.encoder.version""",
							    """model.decoder.version""",
							    """decoder.output_projection.weight""",
							    """_float_tensor""",
							    """encoder.embed_positions._float_tensor""",
							    """decoder.embed_positions._float_tensor""",
							]
							for k in ignore_keys:
														state_dict.pop(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Tuple:
							snake_case_    ,  snake_case_          =						emb.weight.shape
							snake_case_          =						nn.Linear(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	bias=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						emb.weight.data
							return lin_layer







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			str:
							snake_case_          =						torch.load(_SCREAMING_SNAKE_CASE			,	map_location="""cpu"""			)
							snake_case_          =						mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
							snake_case_          =						mam_aaa["""model"""]
							remove_ignore_keys_(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						state_dict["""encoder.embed_tokens.weight"""].shape[0]

							snake_case_          =						MaMaaaConfig(
							    vocab_size=_SCREAMING_SNAKE_CASE			,	max_position_embeddings=1_024			,	encoder_layers=args.encoder_layers			,	decoder_layers=args.decoder_layers			,	encoder_attention_heads=args.encoder_attention_heads			,	decoder_attention_heads=args.decoder_attention_heads			,	encoder_ffn_dim=args.encoder_ffn_embed_dim			,	decoder_ffn_dim=args.decoder_ffn_embed_dim			,	d_model=args.encoder_embed_dim			,	encoder_layerdrop=args.encoder_layerdrop			,	decoder_layerdrop=args.decoder_layerdrop			,	dropout=args.dropout			,	attention_dropout=args.attention_dropout			,	activation_dropout=args.activation_dropout			,	activation_function="""relu"""			,	)

							snake_case_          =						state_dict["""decoder.embed_tokens.weight"""]
							snake_case_          =						MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE			)
							model.model.load_state_dict(_SCREAMING_SNAKE_CASE			,	strict=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						make_linear_from_emb(model.model.shared			)

							return model


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
				parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      parser.parse_args()
				__SCREAMING_SNAKE_CASE							:      Any						=      convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
				model.save_pretrained(args.pytorch_dump_folder_path)







"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot"""  ,						metadata={"""help""": """Model name or path of model to be trained."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""./"""  ,						metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot-clean-train"""  ,						metadata={"""help""": """Name or path of training dataset."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot-clean-valid"""  ,						metadata={"""help""": """Name or path of validation dataset."""})
							__lowercase:						Optional[int]							 =      field(default=2  ,						metadata={"""help""": """Batch size for training."""})
							__lowercase:						Optional[int]							 =      field(default=2  ,						metadata={"""help""": """Batch size for evaluation."""})
							__lowercase:						Optional[float]							 =      field(default=0.1  ,						metadata={"""help""": """Value of weight decay."""})
							__lowercase:						Optional[int]							 =      field(
							    default=1_00_00  ,						metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""})
							__lowercase:						Optional[float]							 =      field(default=2E-4  ,						metadata={"""help""": """Learning rate fo training."""})
							__lowercase:						Optional[str]							 =      field(default="""cosine"""  ,						metadata={"""help""": """Learning rate."""})
							__lowercase:						Optional[int]							 =      field(
							    default=7_50  ,						metadata={"""help""": """Number of warmup steps in the learning rate schedule."""})
							__lowercase:						Optional[int]							 =      field(
							    default=16  ,						metadata={"""help""": """Number of gradient accumulation steps."""})
							__lowercase:						Optional[bool]							 =      field(
							    default=snake_case__  ,						metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""})
							__lowercase:						Optional[int]							 =      field(default=5_00_00  ,						metadata={"""help""": """Maximum number of training steps."""})
							__lowercase:						Optional[int]							 =      field(
							    default=-1  ,						metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""})
							__lowercase:						Optional[int]							 =      field(default=10_24  ,						metadata={"""help""": """Sequence lengths used for training."""})
							__lowercase:						Optional[int]							 =      field(default=1  ,						metadata={"""help""": """Training seed."""})
							__lowercase:						Optional[int]							 =      field(
							    default=10_24  ,						metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}  ,						)
							__lowercase:						Optional[str]							 =      field(
							    default=snake_case__  ,						metadata={"""help""": """States path if the training should continue from a checkpoint folder."""})
							__lowercase:						Optional[bool]							 =      field(default=snake_case__  ,						metadata={"""help""": """If True the data is pretokenized."""})





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot"""  ,						metadata={"""help""": """Model name or path of model to be evaluated."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot-clean-valid"""  ,						metadata={"""help""": """Name or path of validation dataset."""})
							__lowercase:						Optional[int]							 =      field(default=2  ,						metadata={"""help""": """Batch size used for evaluation."""})
							__lowercase:						Optional[int]							 =      field(
							    default=-1  ,						metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""})
							__lowercase:						Optional[int]							 =      field(default=10_24  ,						metadata={"""help""": """Length of sequences to be evaluated."""})
							__lowercase:						Optional[int]							 =      field(default=1  ,						metadata={"""help""": """Random seed used for evaluation."""})





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot"""  ,						metadata={"""help""": """Model name or path of model to be evaluated."""})
							__lowercase:						Optional[int]							 =      field(default=snake_case__  ,						metadata={"""help""": """Number of workers used for code evaluation."""})
							__lowercase:						Optional[int]							 =      field(
							    default=snake_case__  ,						metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}  ,						)
							__lowercase:						Optional[bool]							 =      field(
							    default=snake_case__  ,						metadata={"""help""": """Sample from the language model's output distribution."""})
							__lowercase:						Optional[float]							 =      field(default=0.2  ,						metadata={"""help""": """Sampling temperature used for generation."""})
							__lowercase:						Optional[int]							 =      field(default=2_56  ,						metadata={"""help""": """Maximum number of newly generated tokens."""})
							__lowercase:						Optional[int]							 =      field(default=0  ,						metadata={"""help""": """Top-k parameter used for generation."""})
							__lowercase:						Optional[float]							 =      field(default=0.9_5  ,						metadata={"""help""": """Top-p parameter used for nucleus sampling."""})
							__lowercase:						Optional[int]							 =      field(default=10  ,						metadata={"""help""": """Number of generations to run in parallel."""})
							__lowercase:						Optional[int]							 =      field(
							    default=2_00  ,						metadata={"""help""": """Number of completions to generate for each sample."""})
							__lowercase:						Optional[int]							 =      field(default=1  ,						metadata={"""help""": """Random seed used for evaluation."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""eval_results.json"""  ,						metadata={"""help""": """Random seed used for evaluation."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""0"""  ,						metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""})
							__lowercase:						Optional[int]							 =      field(
							    default=-1  ,						metadata={
							        """help""": (
							            """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"""
							            """ number corresponds to which GPU device id to run on."""
							        )
							    }  ,						)





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						Optional[int]							 =      field(
							    default=snake_case__  ,						metadata={
							        """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
							    }  ,						)
							__lowercase:						Optional[str]							 =      field(
							    default="""transformersbook/codeparrot"""  ,						metadata={"""help""": """Folder or name of dataset to process."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot-clean"""  ,						metadata={"""help""": """Folder to save processed processed dataset."""})
							__lowercase:						Optional[int]							 =      field(
							    default=10_00_00  ,						metadata={"""help""": """Number of files to save per JSON output file."""})
							__lowercase:						Optional[str]							 =      field(default="""content"""  ,						metadata={"""help""": """Column containing text data to process."""})
							__lowercase:						Optional[float]							 =      field(
							    default=10_00  ,						metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""})
							__lowercase:						Optional[float]							 =      field(
							    default=1_00  ,						metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""})
							__lowercase:						Optional[float]							 =      field(
							    default=0.2_5  ,						metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""})
							__lowercase:						Optional[float]							 =      field(
							    default=1.5  ,						metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""})
							__lowercase:						Optional[float]							 =      field(
							    default=0.7  ,						metadata={"""help""": """Probability for filtering config, test and uncommon files."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot"""  ,						metadata={"""help""": """Name or path to the tokenizer."""}  ,						)
							__lowercase:						Optional[bool]							 =      field(
							    default=snake_case__  ,						metadata={"""help""": """If True, near-duplicate samples are removed."""})
							__lowercase:						Optional[float]							 =      field(
							    default=0.8_5  ,						metadata={"""help""": """Jaccard threshold for near-duplicate samples."""})





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						Optional[str]							 =      field(
							    default="""gpt2"""  ,						metadata={"""help""": """Base tokenizer to build new tokenizer from."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""transformersbook/codeparrot-train"""  ,						metadata={"""help""": """Dataset to train tokenizer on."""})
							__lowercase:						Optional[str]							 =      field(default="""content"""  ,						metadata={"""help""": """Column containing text data to process."""})
							__lowercase:						Optional[int]							 =      field(default=20_00_00  ,						metadata={"""help""": """Number of examples to train tokenizer on."""})
							__lowercase:						Optional[int]							 =      field(
							    default=3_27_68  ,						metadata={"""help""": """Number of examples to train the tokenizer on."""})
							__lowercase:						Optional[str]							 =      field(default="""codeparrot"""  ,						metadata={"""help""": """Name of new tokenizer."""})
							__lowercase:						Optional[bool]							 =      field(default=snake_case__  ,						metadata={"""help""": """Push saved tokenizer to the hub."""})





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot"""  ,						metadata={"""help""": """Name or path to the tokenizer."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot-clean-train"""  ,						metadata={"""help""": """Name or path to the dataset to pretokenize."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""tokenized-codeparrot-train"""  ,						metadata={"""help""": """Repo name of the pretokenized data."""})
							__lowercase:						Optional[int]							 =      field(default=snake_case__  ,						metadata={"""help""": """Number of workers used for code evaluation."""})





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						Optional[str]							 =      field(
							    default="""gpt2-large"""  ,						metadata={"""help""": """Configuration to use for model initialization."""})
							__lowercase:						Optional[str]							 =      field(
							    default="""codeparrot/codeparrot"""  ,						metadata={"""help""": """Tokenizer attached to model."""})
							__lowercase:						Optional[str]							 =      field(default="""codeparrot"""  ,						metadata={"""help""": """Name of the created model."""})
							__lowercase:						Optional[bool]							 =      field(default=snake_case__  ,						metadata={"""help""": """Push saved tokenizer to the hub."""})



