




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


UpperCamelCase__ :   Tuple						=							[
    # 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        __UpperCAmelCase						(							lowerCamelCase_ :     Any  )       ->  List[Any]:







			"""simple docstring"""





			for pegasus_name, hf_name in PATTERNS:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      k.replace(lowerCamelCase_  ,							lowerCamelCase_  )
			return k





def        __UpperCAmelCase						(							lowerCamelCase_ :     dict  ,							lowerCamelCase_ :     dict  )       ->  PegasusForConditionalGeneration:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Tuple					  =      DEFAULTS.copy()
			cfg_kwargs.update(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      PegasusConfig(**lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      PegasusForConditionalGeneration(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      torch_model.model.state_dict()
			SCREAMING_SNAKE_CASE_   : Dict					  =      {}
			for k, v in tf_weights.items():
						SCREAMING_SNAKE_CASE_   : str					  =      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:
									SCREAMING_SNAKE_CASE_   : str					  =      v.T
						SCREAMING_SNAKE_CASE_   : List[str]					  =      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
			SCREAMING_SNAKE_CASE_   : Dict					  =      torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]  )
			SCREAMING_SNAKE_CASE_   : int					  =      mapping['shared.weight']
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      mapping['shared.weight']
			SCREAMING_SNAKE_CASE_   : Any					  =      {k: torch.zeros_like(lowerCamelCase_  ) for k, v in sd.items() if k.endswith('bias'  ) and k not in mapping}
			mapping.update(**lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[int]					  =      torch_model.model.load_state_dict(lowerCamelCase_  ,							strict=lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      [
			    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        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]="./ckpt/aeslc/model.ckpt-32000"  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Dict					  =      tf.train.list_variables(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Dict					  =      {}
			SCREAMING_SNAKE_CASE_   : List[str]					  =      ['Adafactor', 'global_step']
			for name, shape in tqdm(lowerCamelCase_  ,							desc='converting tf checkpoint to dict'  ):
						SCREAMING_SNAKE_CASE_   : List[str]					  =      any(pat in name for pat in ignore_name  )
						if skip_key:
									continue
						SCREAMING_SNAKE_CASE_   : str					  =      tf.train.load_variable(lowerCamelCase_  ,							lowerCamelCase_  )
						SCREAMING_SNAKE_CASE_   : List[str]					  =      array
			return tf_weights





def        __UpperCAmelCase						(							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     str  )       ->  Any:







			"""simple docstring"""





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

			# convert model
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      get_tf_weights_as_numpy(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : str					  =      task_specific_params[F'summarization_{dataset}']
			if dataset == "large":
						SCREAMING_SNAKE_CASE_   : Tuple					  =      task_specific_params
			SCREAMING_SNAKE_CASE_   : Any					  =      convert_pegasus(lowerCamelCase_  ,							lowerCamelCase_  )
			torch_model.save_pretrained(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      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__":
						UpperCamelCase__ :   List[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.''')
						UpperCamelCase__ :   List[Any]						=							parser.parse_args()
						if args.save_dir is None:
												UpperCamelCase__ :   Optional[Any]						=							Path(args.tf_ckpt_path).parent.name
												UpperCamelCase__ :   List[str]						=							os.path.join('''pegasus''', dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








'''simple docstring'''





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= DebertaTokenizer
 A__				= True
 A__				= DebertaTokenizerFast
 def A_   (   self						:							Optional[int]  )      ->   str:







       '''simple docstring'''
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       __snake_case :   Union[str, Any]													=    [
           'l',
           'o',
           'w',
           'e',
           'r',
           's',
           't',
           'i',
           'd',
           'n',
           '\u0120',
           '\u0120l',
           '\u0120n',
           '\u0120lo',
           '\u0120low',
           'er',
           '\u0120lowest',
           '\u0120newer',
           '\u0120wider',
           '[UNK]',
       ]
       __snake_case :   List[str]													=    dict(zip(__a      ,     range(len(__a  )  )  )  )
       __snake_case :   int													=    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
       __snake_case :   Any													=    {'unk_token': '[UNK]'}

       __snake_case :   Union[str, Any]													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['vocab_file']  )
       __snake_case :   Dict													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['merges_file']  )
       with open(self.vocab_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write(json.dumps(__a  ) + '\n'  )
       with open(self.merges_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write('\n'.join(__a  )  )
 def A_   (   self						:							Optional[int]      ,     **__a						:							List[Any]  )      ->   Optional[Any]:







       '''simple docstring'''
       kwargs.update(self.special_tokens_map  )
       return self.tokenizer_class.from_pretrained(self.tmpdirname      ,     **__a  )
 def A_   (   self						:							Optional[int]      ,     __a						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    'lower newer'
       __snake_case :   List[Any]													=    'lower newer'
       return input_text, output_text
 def A_   (   self						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   str													=    self.get_tokenizer()
       __snake_case :   List[str]													=    'lower newer'
       __snake_case :   Tuple													=    ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
       __snake_case :   Optional[int]													=    tokenizer.tokenize(__a  )
       self.assertListEqual(__a      ,     __a  )

       __snake_case :   str													=    tokens + [tokenizer.unk_token]
       __snake_case :   Tuple													=    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__a  )      ,     __a  )
 def A_   (   self						:							List[Any]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   List[str]													=    self.get_tokenizer()
       __snake_case :   List[str]													=    tokenizer('Hello'      ,     'World'  )
       __snake_case :   int													=    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd['token_type_ids']      ,     __a  )
 @slow
 def A_   (   self						:							Dict  )      ->   str:







       '''simple docstring'''
       __snake_case :   str													=    self.tokenizer_class.from_pretrained('microsoft/deberta-base'  )

       __snake_case :   str													=    tokenizer.encode('sequence builders'      ,     add_special_tokens=__a  )
       __snake_case :   Any													=    tokenizer.encode('multi-sequence build'      ,     add_special_tokens=__a  )

       __snake_case :   List[Any]													=    tokenizer.encode(
           'sequence builders'      ,     add_special_tokens=__a      ,     add_prefix_space=__a  )
       __snake_case :   Union[str, Any]													=    tokenizer.encode(
           'sequence builders'      ,     'multi-sequence build'      ,     add_special_tokens=__a      ,     add_prefix_space=__a  )

       __snake_case :   int													=    tokenizer.build_inputs_with_special_tokens(__a  )
       __snake_case :   Tuple													=    tokenizer.build_inputs_with_special_tokens(__a      ,     __a  )

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode





 @slow
 def A_   (   self						:							Optional[Any]  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   int													=    [self.tokenizer_class]
       if self.test_rust_tokenizer:
             tokenizer_classes.append(self.rust_tokenizer_class  )

       for tokenizer_class in tokenizer_classes:
             __snake_case :   List[Any]													=    tokenizer_class.from_pretrained('microsoft/deberta-base'  )

             __snake_case :   int													=    [
                 'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
                 'ALBERT incorporates two parameter reduction techniques',
                 'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
                 ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
                 ' vocabulary embedding.',
             ]

             __snake_case :   Union[str, Any]													=    tokenizer(__a      ,     padding=__a  )
             __snake_case :   List[str]													=    [tokenizer.decode(__a      ,     skip_special_tokens=__a  ) for seq in encoding['input_ids']]

             # fmt: off
             __snake_case :   Optional[Any]													=    {
                 'input_ids': [
                     [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
                 ],
                 'token_type_ids': [
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                 ],
                 'attention_mask': [
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                 ]
             }
             # fmt: on

             __snake_case :   int													=    [
                 'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
                 'ALBERT incorporates two parameter reduction techniques',
                 'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
                 ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
                 ' vocabulary embedding.',
             ]

             self.assertDictEqual(encoding.data      ,     __a  )

             for expected, decoded in zip(__a      ,     __a  ):
                   self.assertEqual(__a      ,     __a  )



