






import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin




class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		List[Any]									=BlenderbotSmallTokenizer
       a    :		Optional[Any]									=False





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

         UpperCamelCase_: List[Any]   				=    ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
         UpperCamelCase_: Any   				=    dict(zip(_lowerCamelCase  ,      range(len(_lowerCamelCase							)							)							)							)

         UpperCamelCase_: Optional[Any]   				=    ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
         UpperCamelCase_: List[Any]   				=    {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

         UpperCamelCase_: Any   				=    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'  ,      encoding='utf-8'							) as fp:
           fp.write(json.dumps(_lowerCamelCase							) + '\n'							)
         with open(self.merges_file  ,      'w'  ,      encoding='utf-8'							) as fp:
           fp.write('\n'.join(_lowerCamelCase							)							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         kwargs.update(self.special_tokens_map							)
         return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Any   				=    'adapt act apte'
         UpperCamelCase_: Union[str, Any]   				=    'adapt act apte'
         return input_text, output_text





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    BlenderbotSmallTokenizer(self.vocab_file  ,      self.merges_file  ,      **self.special_tokens_map							)
         UpperCamelCase_: List[str]   				=    'adapt act apte'
         UpperCamelCase_: Optional[Any]   				=    ['adapt', 'act', 'ap@@', 'te']
         UpperCamelCase_: Dict   				=    tokenizer.tokenize(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase  ,      _lowerCamelCase							)

         UpperCamelCase_: Any   				=    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

         UpperCamelCase_: str   				=    [0, 1, 2, 3, 4, 5]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: int   				=    BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'							)
         assert tok('sam'							).input_ids == [1_3_8_4]
         UpperCamelCase_: Optional[Any]   				=    'I am a small frog.'
         UpperCamelCase_: Optional[int]   				=    tok([src_text]  ,      padding=_lowerCamelCase  ,      truncation=_lowerCamelCase							)['input_ids']
         UpperCamelCase_: Union[str, Any]   				=    tok.batch_decode(_lowerCamelCase  ,      skip_special_tokens=_lowerCamelCase  ,      clean_up_tokenization_spaces=_lowerCamelCase							)[0]
         assert src_text != decoded  # I wish it did!
         assert decoded == "i am a small frog ."





       def       _a   ( self							):
         UpperCamelCase_: List[str]   				=    BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'							)
         UpperCamelCase_: int   				=    'I am a small frog .'
         UpperCamelCase_: Union[str, Any]   				=    '.'
         UpperCamelCase_: List[Any]   				=    tok(_lowerCamelCase							)['input_ids']
         UpperCamelCase_: int   				=    tok(_lowerCamelCase							)['input_ids']

         assert encoded[-1] == encoded_dot[0]







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_		:		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   snake_case (UpperCAmelCase__  )	->    str:
  for pegasus_name, hf_name in PATTERNS:
    UpperCamelCase_: List[str]   				=    k.replace(UpperCAmelCase__     ,   UpperCAmelCase__  )
  return k

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  # save tokenizer first
  UpperCamelCase_: Any   				=    Path(UpperCAmelCase__  ).parent.name
  UpperCamelCase_: Tuple   				=    task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
  UpperCamelCase_: Optional[Any]   				=    PegasusTokenizer.from_pretrained('sshleifer/pegasus'     ,   model_max_length=UpperCAmelCase__  )
  assert tok.model_max_length == desired_max_model_length
  tok.save_pretrained(UpperCAmelCase__  )

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