'''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=  [
    # 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 (     _a	:	Dict			):




    """simple docstring"""



    for pegasus_name, hf_name in PATTERNS:
        __UpperCamelCase	:	Union[str, Any]		    =  k.replace(_a				,     _a			)
    return k







def  _UpperCamelCase (     _a	:	dict				,     _a	:	dict			):




    """simple docstring"""



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




    """simple docstring"""



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







def  _UpperCamelCase (     _a	:	str				,     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  Path(_a			).parent.name
    __UpperCamelCase	:	List[str]		    =  task_specific_params[f"""summarization_{dataset}"""]['max_position_embeddings']
    __UpperCamelCase	:	Dict		    =  PegasusTokenizer.from_pretrained('sshleifer/pegasus'				,     model_max_length=_a			)
    assert tok.model_max_length == desired_max_model_length
    tok.save_pretrained(_a			)

    # convert model
    __UpperCamelCase	:	int		    =  get_tf_weights_as_numpy(_a			)
    __UpperCamelCase	:	List[str]		    =  task_specific_params[f"""summarization_{dataset}"""]
    if dataset == "large":
        __UpperCamelCase	:	Optional[int]		    =  task_specific_params
    __UpperCamelCase	:	int		    =  convert_pegasus(_a				,     _a			)
    torch_model.save_pretrained(_a			)
    __UpperCamelCase	:	int		    =  torch_model.state_dict()
    sd.pop('model.decoder.embed_positions.weight'			)
    sd.pop('model.encoder.embed_positions.weight'			)
    torch.save(_a				,     Path(_a			) / 'pytorch_model.bin'			)


if __name__ == "__main__":
  a=  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=  parser.parse_args()
  if args.save_dir is None:
    a=  Path(args.tf_ckpt_path).parent.name
    a=  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 OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						OpenAIGPTTokenizer
     SCREAMING_SNAKE_CASE__		   =						OpenAIGPTTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						False







     def lowerCAmelCase     (		self    ):
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __UpperCamelCase	:	List[Any]		    =  [
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             'w</w>',
             'r</w>',
             't</w>',
             'lo',
             'low',
             'er</w>',
             'low</w>',
             'lowest</w>',
             'newer</w>',
             'wider</w>',
             '<unk>',
         ]
         __UpperCamelCase	:	List[str]		    =  dict(zip(_lowerCamelCase     ,			range(len(_lowerCamelCase    )    )    )    )
         __UpperCamelCase	:	Union[str, Any]		    =  ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

         __UpperCamelCase	:	Optional[int]		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase	:	Tuple		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['merges_file']    )
         with open(self.vocab_file     ,			'w'    ) as fp:
             fp.write(json.dumps(_lowerCamelCase    )    )
         with open(self.merges_file     ,			'w'    ) as fp:
             fp.write('\n'.join(_lowerCamelCase    )    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return "lower newer", "lower newer"







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  OpenAIGPTTokenizer(self.vocab_file     ,			self.merges_file    )

         __UpperCamelCase	:	List[Any]		    =  'lower'
         __UpperCamelCase	:	Union[str, Any]		    =  ['low', 'er</w>']
         __UpperCamelCase	:	Tuple		    =  tokenizer.tokenize(_lowerCamelCase    )
         self.assertListEqual(_lowerCamelCase     ,			_lowerCamelCase    )

         __UpperCamelCase	:	List[str]		    =  tokens + ['<unk>']
         __UpperCamelCase	:	Optional[Any]		    =  [1_4, 1_5, 2_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=1_5    ):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
                 __UpperCamelCase	:	Dict		    =  self.rust_tokenizer_class.from_pretrained(_lowerCamelCase     ,			**_lowerCamelCase    )

                 # Simple input
                 __UpperCamelCase	:	int		    =  'This is a simple input'
                 __UpperCamelCase	:	Any		    =  ['This is a simple input 1', 'This is a simple input 2']
                 __UpperCamelCase	:	Union[str, Any]		    =  ('This is a simple input', 'This is a pair')
                 __UpperCamelCase	:	Any		    =  [
                     ('This is a simple input 1', 'This is a simple input 2'),
                     ('This is a simple pair 1', 'This is a simple pair 2'),
                 ]

                 # Simple input tests
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Simple input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Simple input
                 self.assertRaises(
                     _lowerCamelCase     ,			tokenizer_r.batch_encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'     ,			)

                 # Pair input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Pair input
                 self.assertRaises(_lowerCamelCase     ,			tokenizer_r.encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'    )

                 # Pair input
                 self.assertRaises(
                     _lowerCamelCase     ,			tokenizer_r.batch_encode_plus     ,			_lowerCamelCase     ,			max_length=_lowerCamelCase     ,			padding='max_length'     ,			)







     def lowerCAmelCase     (		self    ):
         pass






@require_ftfy
@require_spacy
@require_tokenizers
class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     pass



