


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


lowerCAmelCase    :     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				(_A					):







       """simple docstring"""







       for pegasus_name, hf_name in PATTERNS:
              _lowerCAmelCase    :							Optional[int]   			=       k.replace(_A   ,						_A					)
       return k






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







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







       """simple docstring"""







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






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







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

       # convert model
       _lowerCAmelCase    :							Optional[Any]   			=       get_tf_weights_as_numpy(_A					)
       _lowerCAmelCase    :							List[str]   			=       task_specific_params[f'summarization_{dataset}']
       if dataset == "large":
              _lowerCAmelCase    :							Any   			=       task_specific_params
       _lowerCAmelCase    :							List[str]   			=       convert_pegasus(_A   ,						_A					)
       torch_model.save_pretrained(_A					)
       _lowerCAmelCase    :							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__":
       lowerCAmelCase    :     Optional[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.""")
       lowerCAmelCase    :     Dict					=						parser.parse_args()
       if args.save_dir is None:
              lowerCAmelCase    :     int					=						Path(args.tf_ckpt_path).parent.name
              lowerCAmelCase    :     Optional[Any]					=						os.path.join("""pegasus""", dataset)
       convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





'''simple docstring'''







from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCAmelCase    :     Optional[Any]					=						logging.get_logger(__name__)

lowerCAmelCase    :     str					=						{
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "gptj"
     __magic_name__    			=    {
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }







     def __init__( self      ,	snake_case__=5_0400      ,	snake_case__=2048      ,	snake_case__=4096      ,	snake_case__=28      ,	snake_case__=16      ,	snake_case__=64      ,	snake_case__=None      ,	snake_case__="gelu_new"      ,	snake_case__=0.0      ,	snake_case__=0.0      ,	snake_case__=0.0      ,	snake_case__=1E-5      ,	snake_case__=0.02      ,	snake_case__=True      ,	snake_case__=5_0256      ,	snake_case__=5_0256      ,	snake_case__=False      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       vocab_size
            _lowerCAmelCase    :							Optional[int]   			=       n_positions
            _lowerCAmelCase    :							Optional[int]   			=       n_embd
            _lowerCAmelCase    :							Optional[int]   			=       n_layer
            _lowerCAmelCase    :							str   			=       n_head
            _lowerCAmelCase    :							Tuple   			=       n_inner
            _lowerCAmelCase    :							Tuple   			=       rotary_dim
            _lowerCAmelCase    :							Optional[int]   			=       activation_function
            _lowerCAmelCase    :							Any   			=       resid_pdrop
            _lowerCAmelCase    :							List[str]   			=       embd_pdrop
            _lowerCAmelCase    :							int   			=       attn_pdrop
            _lowerCAmelCase    :							Any   			=       layer_norm_epsilon
            _lowerCAmelCase    :							Optional[int]   			=       initializer_range
            _lowerCAmelCase    :							List[str]   			=       use_cache

            _lowerCAmelCase    :							Dict   			=       bos_token_id
            _lowerCAmelCase    :							Any   			=       eos_token_id

            super().__init__(
                bos_token_id=snake_case__      ,	eos_token_id=snake_case__      ,	tie_word_embeddings=snake_case__      ,	**snake_case__   )
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__ = "default"      ,	snake_case__ = None      ,	snake_case__ = False      ,	):

            '''simple docstring'''

            super().__init__(snake_case__      ,	task=snake_case__      ,	patching_specs=snake_case__      ,	use_past=snake_case__   )
            if not getattr(self._config      ,	'pad_token_id'      ,	snake_case__   ):
                   # TODO: how to do that better?
                   _lowerCAmelCase    :							Any   			=       0







     @property
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
            if self.use_past:
                   self.fill_with_past_key_values_(snake_case__      ,	direction='inputs'   )
                   _lowerCAmelCase    :							int   			=       {0: 'batch', 1: 'past_sequence + sequence'}
            else:
                   _lowerCAmelCase    :							int   			=       {0: 'batch', 1: 'sequence'}

            return common_inputs







     @property
     def a	( self   ):

            '''simple docstring'''

            return self._config.n_layer







     @property
     def a	( self   ):

            '''simple docstring'''

            return self._config.n_head







     def a	( self      ,	snake_case__      ,	snake_case__ = -1      ,	snake_case__ = -1      ,	snake_case__ = False      ,	snake_case__ = None      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       super(snake_case__      ,	self   ).generate_dummy_inputs(
                snake_case__      ,	batch_size=snake_case__      ,	seq_length=snake_case__      ,	is_pair=snake_case__      ,	framework=snake_case__   )

            # We need to order the input in the way they appears in the forward()
            _lowerCAmelCase    :							Any   			=       OrderedDict({'input_ids': common_inputs['input_ids']}   )

            # Need to add the past_keys
            if self.use_past:
                   if not is_torch_available():
                          raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
                   else:
                          import torch

                          _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[Any]   			=       common_inputs['input_ids'].shape
                          # Not using the same length for past_key_values
                          _lowerCAmelCase    :							Any   			=       seqlen + 2
                          _lowerCAmelCase    :							Optional[int]   			=       (
                              batch,
                              self.num_attention_heads,
                              past_key_values_length,
                              self._config.hidden_size // self.num_attention_heads,
                          )
                          _lowerCAmelCase    :							Tuple   			=       [
                              (torch.zeros(snake_case__   ), torch.zeros(snake_case__   )) for _ in range(self.num_layers   )
                          ]

            _lowerCAmelCase    :							Tuple   			=       common_inputs['attention_mask']
            if self.use_past:
                   _lowerCAmelCase    :							Any   			=       ordered_inputs['attention_mask'].dtype
                   _lowerCAmelCase    :							Union[str, Any]   			=       torch.cat(
                       [ordered_inputs['attention_mask'], torch.ones(snake_case__      ,	snake_case__      ,	dtype=snake_case__   )]      ,	dim=1   )

            return ordered_inputs







     @property
     def a	( self   ):

            '''simple docstring'''

            return 13

