



'''simple docstring'''




from importlib import import_module

from .logging import get_logger


lowerCamelCase				:Union[str, Any]							       =   get_logger(__name__)







class       _lowerCAmelCase				:


	def __init__(self			, lowercase			, lowercase=None				):
				A_      :		List[Any]      =	attrs or []
				if module is not None:
							for key in module.__dict__:
										if key in attrs or not key.startswith("""__"""				):
													setattr(self			, lowerCAmelCase__			, getattr(lowerCAmelCase__			, lowerCAmelCase__				)				)
				A_      :		Dict      =	module._original_module if isinstance(lowerCAmelCase__			, _PatchedModuleObj				) else module







class       _lowerCAmelCase				:
	__SCREAMING_SNAKE_CASE  :						Union[str, Any]						     =   []


	def __init__(self			, lowercase			, lowercase			, lowercase			, lowercase=None				):
				A_      :		Optional[Any]      =	obj
				A_      :		Optional[int]      =	target
				A_      :		Any      =	new
				A_      :		Optional[int]      =	target.split("""."""				)[0]
				A_      :		Optional[int]      =	{}
				A_      :		Tuple      =	attrs or []


	def __enter__(self				):
				*A_,       A_      :		Tuple      =	self.target.split("""."""				)

				# Patch modules:
				# it's used to patch attributes of submodules like "os.path.join";
				# in this case we need to patch "os" and "os.path"

				for i in range(len(lowerCAmelCase__				)				):
							try:
										A_      :		List[str]      =	import_module(""".""".join(submodules[: i + 1]				)				)
							except ModuleNotFoundError:
										continue
							# We iterate over all the globals in self.obj in case we find "os" or "os.path"
							for attr in self.obj.__dir__():
										A_      :		List[Any]      =	getattr(self.obj			, lowerCAmelCase__				)
										# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
										# This allows to patch renamed modules like "from os import path as ospath".
										if obj_attr is submodule or (
										    (isinstance(lowerCAmelCase__			, _PatchedModuleObj				) and obj_attr._original_module is submodule)
										):
													A_      :		Union[str, Any]      =	obj_attr
													# patch at top level
													setattr(self.obj			, lowerCAmelCase__			, _PatchedModuleObj(lowerCAmelCase__			, attrs=self.attrs				)				)
													A_      :		Optional[Any]      =	getattr(self.obj			, lowerCAmelCase__				)
													# construct lower levels patches
													for key in submodules[i + 1 :]:
																setattr(lowerCAmelCase__			, lowerCAmelCase__			, _PatchedModuleObj(getattr(lowerCAmelCase__			, lowerCAmelCase__			, lowerCAmelCase__				)			, attrs=self.attrs				)				)
																A_      :		str      =	getattr(lowerCAmelCase__			, lowerCAmelCase__				)
													# finally set the target attribute
													setattr(lowerCAmelCase__			, lowerCAmelCase__			, self.new				)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

				if submodules:  # if it's an attribute of a submodule like "os.path.join"
							try:
										A_      :		List[Any]      =	getattr(import_module(""".""".join(lowerCAmelCase__				)				)			, lowerCAmelCase__				)
							except (AttributeError, ModuleNotFoundError):
										return
							# We iterate over all the globals in self.obj in case we find "os.path.join"
							for attr in self.obj.__dir__():
										# We don't check for the name of the global, but rather if its value *is* "os.path.join".
										# This allows to patch renamed attributes like "from os.path import join as pjoin".
										if getattr(self.obj			, lowerCAmelCase__				) is attr_value:
													A_      :		Any      =	getattr(self.obj			, lowerCAmelCase__				)
													setattr(self.obj			, lowerCAmelCase__			, self.new				)
				elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
							A_      :		List[Any]      =	globals()["""__builtins__"""][target_attr]
							setattr(self.obj			, lowerCAmelCase__			, self.new				)
				else:
							raise RuntimeError(F'Tried to patch attribute {target_attr} instead of a submodule.'				)


	def __exit__(self			, *lowercase				):




				for attr in list(self.original				):
							setattr(self.obj			, lowerCAmelCase__			, self.original.pop(lowerCAmelCase__				)				)


	def        _a      (self				):
				self.__enter__()
				self._active_patches.append(self				)


	def        _a      (self				):




				try:
							self._active_patches.remove(self				)
				except ValueError:
							# If the patch hasn't been started this will fail
							return None

				return self.__exit__()






'''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


lowercase__		:     Dict			=							[
    # 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    a__							(							lowercase   :	List[str] )						->						List[Any]:





     """simple docstring"""

     for pegasus_name, hf_name in PATTERNS:
          _UpperCamelCase							       =			k.replace(lowercase,   lowercase )
     return k







def    a__							(							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    a__							(							lowercase   :	List[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    a__							(							lowercase   :	str,   lowercase   :	str )						->						Optional[int]:





     """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__":
   lowercase__		:     List[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.')
   lowercase__		:     Optional[int]			=							parser.parse_args()
   if args.save_dir is None:
      lowercase__		:     Tuple			=							Path(args.tf_ckpt_path).parent.name
      lowercase__		:     Optional[Any]			=							os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)



