





'''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__							:							int =[
    # 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 				lowercase__   (   __lowercase						:  Dict     ) ->      Tuple:



       """simple docstring"""






       for pegasus_name, hf_name in PATTERNS:
              __UpperCamelCase       =  k.replace(__lowercase				,  __lowercase     )
       return k
def 				lowercase__   (   __lowercase						:  dict				,  __lowercase						:  dict     ) ->      PegasusForConditionalGeneration:



       """simple docstring"""






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



       """simple docstring"""






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



       """simple docstring"""






       __UpperCamelCase       =  Path(__lowercase     ).parent.name
       __UpperCamelCase       =  task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
       __UpperCamelCase       =  PegasusTokenizer.from_pretrained('sshleifer/pegasus'				,  model_max_length=__lowercase     )
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(__lowercase     )

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


if __name__ == "__main__":
    a__							:							Union[str, 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__							:							int =parser.parse_args()
    if args.save_dir is None:
        a__							:							str =Path(args.tf_ckpt_path).parent.name
        a__							:							str =os.path.join('''pegasus''', dataset)
    convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)







'''simple docstring'''
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=["image_processor", "tokenizer"]
 SCREAMING_SNAKE_CASE_	:					List[Any]					="BlipImageProcessor"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=("BertTokenizer", "BertTokenizerFast")






 def __init__(       self       :							Dict							,	__A       :							Optional[int]							,	__A       :							List[Any]	):
        __UpperCamelCase       =  False
        super().__init__(__A							,	__A	)
        __UpperCamelCase       =  self.image_processor






 def __call__(       self       :							List[Any]							,	__A       :							ImageInput = None							,	__A       :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,	__A       :							bool = True							,	__A       :							Union[bool, str, PaddingStrategy] = False							,	__A       :							Union[bool, str, TruncationStrategy] = None							,	__A       :							Optional[int] = None							,	__A       :							int = 0							,	__A       :							Optional[int] = None							,	__A       :							Optional[bool] = None							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = True							,	__A       :							Optional[Union[str, TensorType]] = None							,	**__A       :							List[Any]							,	):
        if images is None and text is None:
               raise ValueError('You have to specify either images or text.'	)

        # Get only text
        if images is None:
               __UpperCamelCase       =  self.tokenizer
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
               return text_encoding

        # add pixel_values
        __UpperCamelCase       =  self.image_processor(__A							,	return_tensors=__A	)

        if text is not None:
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
        else:
               __UpperCamelCase       =  None

        if text_encoding is not None:
               encoding_image_processor.update(__A	)

        return encoding_image_processor






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							Dict							,	**__A       :							Optional[int]	):
        return self.tokenizer.batch_decode(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							List[str]							,	**__A       :							Dict	):
        return self.tokenizer.decode(*__A							,	**__A	)






 @property
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  self.tokenizer.model_input_names
        __UpperCamelCase       =  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
