


'''simple docstring'''







import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


A				:					Any    						=			[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]       ):




       for pegasus_name, hf_name in PATTERNS:
              _A					:     str     = k.replace(lowerCamelCase__  ,lowerCamelCase__       )
       return k






def 				lowerCAmelCase__	(    lowerCamelCase					:      dict  ,lowerCamelCase					:      dict       ):
       _A					:     Optional[int]     = DEFAULTS.copy()
       cfg_kwargs.update(lowerCamelCase__       )
       _A					:     List[str]     = PegasusConfig(**lowerCamelCase__       )
       _A					:     Union[str, Any]     = PegasusForConditionalGeneration(lowerCamelCase__       )
       _A					:     List[Any]     = torch_model.model.state_dict()
       _A					:     List[str]     = {}
       for k, v in tf_weights.items():
              _A					:     Tuple     = rename_state_dict_key(lowerCamelCase__       )
              if new_k not in sd:
                     raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'       )

              if "dense" in k or "proj" in new_k:
                     _A					:     List[str]     = v.T
              _A					:     Any     = torch.tensor(lowerCamelCase__  ,dtype=sd[new_k].dtype       )
              assert v.shape == sd[new_k].shape, F'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
       # make sure embedding.padding_idx is respected
       _A					:     Optional[Any]     = torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]       )
       _A					:     List[Any]     = mapping['shared.weight']
       _A					:     Dict     = mapping['shared.weight']
       _A					:     Any     = {k: torch.zeros_like(lowerCamelCase__       ) for k, v in sd.items() if k.endswith('bias'       ) and k not in mapping}
       mapping.update(**lowerCamelCase__       )
       _A  ,			_A					:     Optional[int]     = torch_model.model.load_state_dict(lowerCamelCase__  ,strict=lowerCamelCase__       )
       _A					:     Optional[int]     = [
           k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
       ]
       assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
       assert extra == [], F'no matches found for the following tf keys {extra}'
       return torch_model






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]="./ckpt/aeslc/model.ckpt-32000"       ):
       _A					:     List[Any]     = tf.train.list_variables(lowerCamelCase__       )
       _A					:     Dict     = {}
       _A					:     List[Any]     = ['Adafactor', 'global_step']
       for name, shape in tqdm(lowerCamelCase__  ,desc='converting tf checkpoint to dict'       ):
              _A					:     List[str]     = any(pat in name for pat in ignore_name       )
              if skip_key:
                     continue
              _A					:     str     = tf.train.load_variable(lowerCamelCase__  ,lowerCamelCase__       )
              _A					:     List[Any]     = array
       return tf_weights






def 				lowerCAmelCase__	(    lowerCamelCase					:      str  ,lowerCamelCase					:      str       ):
       _A					:     Optional[Any]     = Path(lowerCamelCase__       ).parent.name
       _A					:     Union[str, Any]     = task_specific_params[F'summarization_{dataset}']['max_position_embeddings']
       _A					:     int     = PegasusTokenizer.from_pretrained('sshleifer/pegasus'  ,model_max_length=lowerCamelCase__       )
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(lowerCamelCase__       )

       # convert model
       _A					:     Tuple     = get_tf_weights_as_numpy(lowerCamelCase__       )
       _A					:     str     = task_specific_params[F'summarization_{dataset}']
       if dataset == "large":
              _A					:     List[Any]     = task_specific_params
       _A					:     str     = convert_pegasus(lowerCamelCase__  ,lowerCamelCase__       )
       torch_model.save_pretrained(lowerCamelCase__       )
       _A					:     str     = torch_model.state_dict()
       sd.pop('model.decoder.embed_positions.weight'       )
       sd.pop('model.encoder.embed_positions.weight'       )
       torch.save(lowerCamelCase__  ,Path(lowerCamelCase__       ) / 'pytorch_model.bin'       )


if __name__ == "__main__":
   A				:					Any    						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
   parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
   A				:					Dict    						=			parser.parse_args()
   if args.save_dir is None:
      A				:					Optional[int]    						=			Path(args.tf_ckpt_path).parent.name
      A				:					Dict    						=			os.path.join('''pegasus''', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)










'''simple docstring'''







from dataclasses import dataclass, field
from typing import Optional


@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be trained."}	)
    a													=   field(
        default="./"    , metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-train"    , metadata={"help": "Name or path of training dataset."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-valid"    , metadata={"help": "Name or path of validation dataset."}	)
    a													=   field(default=2    , metadata={"help": "Batch size for training."}	)
    a													=   field(default=2    , metadata={"help": "Batch size for evaluation."}	)
    a													=   field(default=0.1    , metadata={"help": "Value of weight decay."}	)
    a													=   field(
        default=1_0000    , metadata={"help": "Size of buffer used to shuffle streaming dataset."}	)
    a													=   field(default=2E-4    , metadata={"help": "Learning rate fo training."}	)
    a													=   field(default="cosine"    , metadata={"help": "Learning rate."}	)
    a													=   field(
        default=750    , metadata={"help": "Number of warmup steps in the learning rate schedule."}	)
    a													=   field(
        default=16    , metadata={"help": "Number of gradient accumulation steps."}	)
    a													=   field(
        default=a_    , metadata={"help": "Use gradient checkpointing to reduce memory footprint."}	)
    a													=   field(default=5_0000    , metadata={"help": "Maximum number of training steps."}	)
    a													=   field(
        default=-1    , metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}	)
    a													=   field(default=1024    , metadata={"help": "Sequence lengths used for training."}	)
    a													=   field(default=1    , metadata={"help": "Training seed."}	)
    a													=   field(
        default=1024    , metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}    , )
    a													=   field(
        default=a_    , metadata={"help": "States path if the training should continue from a checkpoint folder."}	)
    a													=   field(default=a_    , metadata={"help": "If True the data is pretokenized."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be evaluated."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-valid"    , metadata={"help": "Name or path of validation dataset."}	)
    a													=   field(default=2    , metadata={"help": "Batch size used for evaluation."}	)
    a													=   field(
        default=-1    , metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}	)
    a													=   field(default=1024    , metadata={"help": "Length of sequences to be evaluated."}	)
    a													=   field(default=1    , metadata={"help": "Random seed used for evaluation."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Model name or path of model to be evaluated."}	)
    a													=   field(default=a_    , metadata={"help": "Number of workers used for code evaluation."}	)
    a													=   field(
        default=a_    , metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}    , )
    a													=   field(
        default=a_    , metadata={"help": "Sample from the language model's output distribution."}	)
    a													=   field(default=0.2    , metadata={"help": "Sampling temperature used for generation."}	)
    a													=   field(default=256    , metadata={"help": "Maximum number of newly generated tokens."}	)
    a													=   field(default=0    , metadata={"help": "Top-k parameter used for generation."}	)
    a													=   field(default=0.95    , metadata={"help": "Top-p parameter used for nucleus sampling."}	)
    a													=   field(default=10    , metadata={"help": "Number of generations to run in parallel."}	)
    a													=   field(
        default=200    , metadata={"help": "Number of completions to generate for each sample."}	)
    a													=   field(default=1    , metadata={"help": "Random seed used for evaluation."}	)
    a													=   field(
        default="eval_results.json"    , metadata={"help": "Random seed used for evaluation."}	)
    a													=   field(
        default="0"    , metadata={"help": "Allow `code_eval` to execute Python code on machine"}	)
    a													=   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 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default=a_    , metadata={
            "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
        }    , )
    a													=   field(
        default="transformersbook/codeparrot"    , metadata={"help": "Folder or name of dataset to process."}	)
    a													=   field(
        default="codeparrot-clean"    , metadata={"help": "Folder to save processed processed dataset."}	)
    a													=   field(
        default=10_0000    , metadata={"help": "Number of files to save per JSON output file."}	)
    a													=   field(default="content"    , metadata={"help": "Column containing text data to process."}	)
    a													=   field(
        default=1000    , metadata={"help": "Maximum line length in file, otherwise file is filtered."}	)
    a													=   field(
        default=100    , metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}	)
    a													=   field(
        default=0.25    , metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}	)
    a													=   field(
        default=1.5    , metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}	)
    a													=   field(
        default=0.7    , metadata={"help": "Probability for filtering config, test and uncommon files."}	)
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Name or path to the tokenizer."}    , )
    a													=   field(
        default=a_    , metadata={"help": "If True, near-duplicate samples are removed."}	)
    a													=   field(
        default=0.85    , metadata={"help": "Jaccard threshold for near-duplicate samples."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="gpt2"    , metadata={"help": "Base tokenizer to build new tokenizer from."}	)
    a													=   field(
        default="transformersbook/codeparrot-train"    , metadata={"help": "Dataset to train tokenizer on."}	)
    a													=   field(default="content"    , metadata={"help": "Column containing text data to process."}	)
    a													=   field(default=20_0000    , metadata={"help": "Number of examples to train tokenizer on."}	)
    a													=   field(
        default=3_2768    , metadata={"help": "Number of examples to train the tokenizer on."}	)
    a													=   field(default="codeparrot"    , metadata={"help": "Name of new tokenizer."}	)
    a													=   field(default=a_    , metadata={"help": "Push saved tokenizer to the hub."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Name or path to the tokenizer."}	)
    a													=   field(
        default="codeparrot/codeparrot-clean-train"    , metadata={"help": "Name or path to the dataset to pretokenize."}	)
    a													=   field(
        default="tokenized-codeparrot-train"    , metadata={"help": "Repo name of the pretokenized data."}	)
    a													=   field(default=a_    , metadata={"help": "Number of workers used for code evaluation."}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        default="gpt2-large"    , metadata={"help": "Configuration to use for model initialization."}	)
    a													=   field(
        default="codeparrot/codeparrot"    , metadata={"help": "Tokenizer attached to model."}	)
    a													=   field(default="codeparrot"    , metadata={"help": "Name of the created model."}	)
    a													=   field(default=a_    , metadata={"help": "Push saved tokenizer to the hub."}	)






