
"""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     __magic_name__		(					UpperCamelCase     :		Dict     )     ->						int:
    for pegasus_name, hf_name in PATTERNS:
        a__		=			k.replace(UpperCamelCase							,		UpperCamelCase     )
    return k



def     __magic_name__		(					UpperCamelCase     :		dict							,		UpperCamelCase     :		dict     )     ->						PegasusForConditionalGeneration:
    a__		=			DEFAULTS.copy()
    cfg_kwargs.update(UpperCamelCase     )
    a__		=			PegasusConfig(**UpperCamelCase     )
    a__		=			PegasusForConditionalGeneration(UpperCamelCase     )
    a__		=			torch_model.model.state_dict()
    a__		=			{}
    for k, v in tf_weights.items():
        a__		=			rename_state_dict_key(UpperCamelCase     )
        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__		=			v.T
        a__		=			torch.tensor(UpperCamelCase							,		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__		=			torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]     )
    a__		=			mapping['shared.weight']
    a__		=			mapping['shared.weight']
    a__		=			{k: torch.zeros_like(UpperCamelCase     ) for k, v in sd.items() if k.endswith('bias'     ) and k not in mapping}
    mapping.update(**UpperCamelCase     )
    a__	,     a__		=			torch_model.model.load_state_dict(UpperCamelCase							,		strict=UpperCamelCase     )
    a__		=			[
        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     __magic_name__		(					UpperCamelCase     :		int="./ckpt/aeslc/model.ckpt-32000"     )     ->						Dict:
    a__		=			tf.train.list_variables(UpperCamelCase     )
    a__		=			{}
    a__		=			['Adafactor', 'global_step']
    for name, shape in tqdm(UpperCamelCase							,		desc='converting tf checkpoint to dict'     ):
        a__		=			any(pat in name for pat in ignore_name     )
        if skip_key:
            continue
        a__		=			tf.train.load_variable(UpperCamelCase							,		UpperCamelCase     )
        a__		=			array
    return tf_weights



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		str     )     ->						Any:
    # save tokenizer first
    a__		=			Path(UpperCamelCase     ).parent.name
    a__		=			task_specific_params[f'summarization_{dataset}']['max_position_embeddings']
    a__		=			PegasusTokenizer.from_pretrained('sshleifer/pegasus'							,		model_max_length=UpperCamelCase     )
    assert tok.model_max_length == desired_max_model_length
    tok.save_pretrained(UpperCamelCase     )

    # convert model
    a__		=			get_tf_weights_as_numpy(UpperCamelCase     )
    a__		=			task_specific_params[f'summarization_{dataset}']
    if dataset == "large":
        a__		=			task_specific_params
    a__		=			convert_pegasus(UpperCamelCase							,		UpperCamelCase     )
    torch_model.save_pretrained(UpperCamelCase     )
    a__		=			torch_model.state_dict()
    sd.pop('model.decoder.embed_positions.weight'     )
    sd.pop('model.encoder.embed_positions.weight'     )
    torch.save(UpperCamelCase							,		Path(UpperCamelCase     ) / '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		:       str     						=       parser.parse_args()
    if args.save_dir is None:
        a		:       Tuple     						=       Path(args.tf_ckpt_path).parent.name
        a		:       int     						=       os.path.join('pegasus', dataset)
    convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)




"""simple docstring"""




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class     lowercase(_lowercase    ,						_lowercase    ,						_lowercase       ):



     @register_to_config
     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = False     ,				)		->     Union[str, Any]:







         """simple docstring"""



         super().__init__()

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			False

         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )

         a__		=			TaConfig(
             vocab_size=__SCREAMING_SNAKE_CASE     ,				d_model=__SCREAMING_SNAKE_CASE     ,				num_heads=__SCREAMING_SNAKE_CASE     ,				d_kv=__SCREAMING_SNAKE_CASE     ,				d_ff=__SCREAMING_SNAKE_CASE     ,				dropout_rate=__SCREAMING_SNAKE_CASE     ,				feed_forward_proj=__SCREAMING_SNAKE_CASE     ,				is_decoder=__SCREAMING_SNAKE_CASE     ,				is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,				)

         a__		=			nn.ModuleList()
         for lyr_num in range(__SCREAMING_SNAKE_CASE   ):
             a__		=			TaBlock(__SCREAMING_SNAKE_CASE   )
             self.encoders.append(__SCREAMING_SNAKE_CASE   )

         a__		=			TaLayerNorm(__SCREAMING_SNAKE_CASE   )
         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			self.token_embedder(__SCREAMING_SNAKE_CASE   )

         a__		=			encoder_input_tokens.shape[1]
         a__		=			torch.arange(__SCREAMING_SNAKE_CASE     ,				device=encoder_input_tokens.device   )
         x += self.position_encoding(__SCREAMING_SNAKE_CASE   )

         a__		=			self.dropout_pre(__SCREAMING_SNAKE_CASE   )

         # inverted the attention mask
         a__		=			encoder_input_tokens.size()
         a__		=			self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         for lyr in self.encoders:
             a__		=			lyr(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )[0]
         a__		=			self.layer_norm(__SCREAMING_SNAKE_CASE   )

         return self.dropout_post(__SCREAMING_SNAKE_CASE   ), encoder_inputs_mask


