


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


lowercase_	   =     [
    # 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__					(  SCREAMING_SNAKE_CASE__						):
 for pegasus_name, hf_name in PATTERNS:
  __lowerCamelCase		:							Any		 =						k.replace(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
 return k
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[Any]		 =						DEFAULTS.copy()
 cfg_kwargs.update(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Any		 =						PegasusConfig(**SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							int		 =						PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Optional[int]		 =						torch_model.model.state_dict()
 __lowerCamelCase		:							Tuple		 =						{}
 for k, v in tf_weights.items():
  __lowerCamelCase		:							Union[str, Any]		 =						rename_state_dict_key(SCREAMING_SNAKE_CASE__						)
  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		:							Tuple		 =						v.T
  __lowerCamelCase		:							str		 =						torch.tensor(SCREAMING_SNAKE_CASE__      ,	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		:							Union[str, Any]		 =						torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]						)
 __lowerCamelCase		:							List[str]		 =						mapping['shared.weight']
 __lowerCamelCase		:							int		 =						mapping['shared.weight']
 __lowerCamelCase		:							int		 =						{k: torch.zeros_like(SCREAMING_SNAKE_CASE__						) for k, v in sd.items() if k.endswith('bias'						) and k not in mapping}
 mapping.update(**SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Union[str, Any]		 =						torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE__      ,	strict=SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Dict		 =						[
     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__					(  SCREAMING_SNAKE_CASE__="./ckpt/aeslc/model.ckpt-32000"						):
 __lowerCamelCase		:							int		 =						tf.train.list_variables(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							List[str]		 =						{}
 __lowerCamelCase		:							Dict		 =						['Adafactor', 'global_step']
 for name, shape in tqdm(SCREAMING_SNAKE_CASE__      ,	desc='converting tf checkpoint to dict'						):
  __lowerCamelCase		:							int		 =						any(pat in name for pat in ignore_name						)
  if skip_key:
   continue
  __lowerCamelCase		:							str		 =						tf.train.load_variable(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Any		 =						array
 return tf_weights
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 # save tokenizer first
 __lowerCamelCase		:							str		 =						Path(SCREAMING_SNAKE_CASE__						).parent.name
 __lowerCamelCase		:							Any		 =						task_specific_params[f'summarization_{dataset}']['max_position_embeddings']
 __lowerCamelCase		:							int		 =						PegasusTokenizer.from_pretrained('sshleifer/pegasus'      ,	model_max_length=SCREAMING_SNAKE_CASE__						)
 assert tok.model_max_length == desired_max_model_length
 tok.save_pretrained(SCREAMING_SNAKE_CASE__						)

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


if __name__ == "__main__":
       lowercase_	   =     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.')
       lowercase_	   =     parser.parse_args()
       if args.save_dir is None:
              lowercase_	   =     Path(args.tf_ckpt_path).parent.name
              lowercase_	   =     os.path.join('pegasus', dataset)
       convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
       import torch

if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm


lowercase_	   =     logging.get_logger(__name__)


@dataclass
class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       [
          """no_inference""",
          """no_cuda""",
          """no_tpu""",
          """no_speed""",
          """no_memory""",
          """no_env_print""",
          """no_multi_process""",
      ]




      def __init__(    self:	Any						,				**a:	Optional[Any]    ):
       for deprecated_arg in self.deprecated_args:
        if deprecated_arg in kwargs:
         __lowerCamelCase		:							str		 =						deprecated_arg[3:]
         setattr(self						,				a						,				not kwargs.pop(a    )    )
         logger.warning(
             F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
             F' {positive_arg}={kwargs[positive_arg]}'    )

       __lowerCamelCase		:							str		 =						kwargs.pop('torchscript'						,				self.torchscript    )
       __lowerCamelCase		:							int		 =						kwargs.pop('torch_xla_tpu_print_metrics'						,				self.torch_xla_tpu_print_metrics    )
       __lowerCamelCase		:							Dict		 =						kwargs.pop('fp16_opt_level'						,				self.fpaa_opt_level    )
       super().__init__(**a    )

      __snake_case 	=       field(default=__UpperCamelCase ,				metadata={"""help""": """Trace the models using torchscript"""}   )
      __snake_case 	=       field(default=__UpperCamelCase ,				metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}   )
      __snake_case 	=       field(
          default="""O1""" ,				metadata={
              """help""": (
                  """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
                  """See details at https://nvidia.github.io/apex/amp.html"""
              )
          } ,				)




      @cached_property
      def 					_snake_case     (    self:	Dict    ):
       requires_backends(self						,				['torch']    )
       logger.info('PyTorch: setting up devices'    )
       if not self.cuda:
        __lowerCamelCase		:							Dict		 =						torch.device('cpu'    )
        __lowerCamelCase		:							str		 =						0
       elif is_torch_tpu_available():
        __lowerCamelCase		:							Optional[int]		 =						xm.xla_device()
        __lowerCamelCase		:							Dict		 =						0
       else:
        __lowerCamelCase		:							Any		 =						torch.device('cuda' if torch.cuda.is_available() else 'cpu'    )
        __lowerCamelCase		:							Union[str, Any]		 =						torch.cuda.device_count()
       return device, n_gpu




      @property
      def 					_snake_case     (    self:	Optional[Any]    ):
       return is_torch_tpu_available() and self.tpu




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       requires_backends(self						,				['torch']    )
       # TODO(PVP): currently only single GPU is supported
       return torch.cuda.current_device()




      @property
      def 					_snake_case     (    self:	int    ):
       requires_backends(self						,				['torch']    )
       return self._setup_devices[0]




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       requires_backends(self						,				['torch']    )
       return self._setup_devices[1]




      @property
      def 					_snake_case     (    self:	List[Any]    ):
       return self.n_gpu > 0

