

'''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


UpperCamelCase_    						=       [
    # 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 				_lowerCAmelCase			(   __magic_name__						:     str						)				->  int:
						for pegasus_name, hf_name in PATTERNS:
												lowercase :							Dict    		=k.replace(__magic_name__   , __magic_name__						)
						return k






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






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     str						)				->  List[Any]:
						# save tokenizer first
						lowercase :							Tuple    		=Path(__magic_name__						).parent.name
						lowercase :							Optional[Any]    		=task_specific_params[f'''summarization_{dataset}''']['''max_position_embeddings''']
						lowercase :							List[Any]    		=PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''   , model_max_length=__magic_name__						)
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(__magic_name__						)

						# convert model
						lowercase :							Tuple    		=get_tf_weights_as_numpy(__magic_name__						)
						lowercase :							str    		=task_specific_params[f'''summarization_{dataset}''']
						if dataset == "large":
												lowercase :							Union[str, Any]    		=task_specific_params
						lowercase :							Any    		=convert_pegasus(__magic_name__   , __magic_name__						)
						torch_model.save_pretrained(__magic_name__						)
						lowercase :							Dict    		=torch_model.state_dict()
						sd.pop('''model.decoder.embed_positions.weight'''						)
						sd.pop('''model.encoder.embed_positions.weight'''						)
						torch.save(__magic_name__   , Path(__magic_name__						) / '''pytorch_model.bin'''						)


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








'''simple docstring'''





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCamelCase_    						=       logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    ['pixel_values']







				def __init__(       self     :		List[Any]		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		Union[int, float] = 1 / 255		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		int = 8		,					**UpperCAmelCase__     :		str		,					):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		=do_rescale
										lowercase :							List[Any]    		=rescale_factor
										lowercase :							Tuple    		=do_pad
										lowercase :							List[str]    		=pad_size







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		float		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		Optional[int]   ):
										'''simple docstring'''
										return rescale(UpperCAmelCase__		,					scale=UpperCAmelCase__		,					data_format=UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Union[str, Any]    		=get_image_size(UpperCAmelCase__   )
										lowercase :							Tuple    		=(old_height // size + 1) * size - old_height
										lowercase :							Tuple    		=(old_width // size + 1) * size - old_width

										return pad(UpperCAmelCase__		,					((0, pad_height), (0, pad_width))		,					mode='''symmetric'''		,					data_format=UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		ImageInput		,					UpperCAmelCase__     :		Optional[bool] = None		,					UpperCAmelCase__     :		Optional[float] = None		,					UpperCAmelCase__     :		Optional[bool] = None		,					UpperCAmelCase__     :		Optional[int] = None		,					UpperCAmelCase__     :		Optional[Union[str, TensorType]] = None		,					UpperCAmelCase__     :		Union[str, ChannelDimension] = ChannelDimension.FIRST		,					**UpperCAmelCase__     :		Optional[Any]		,					):
										'''simple docstring'''
										lowercase :							int    		=do_rescale if do_rescale is not None else self.do_rescale
										lowercase :							Tuple    		=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase :							int    		=do_pad if do_pad is not None else self.do_pad
										lowercase :							List[Any]    		=pad_size if pad_size is not None else self.pad_size

										lowercase :							Any    		=make_list_of_images(UpperCAmelCase__   )

										if not valid_images(UpperCAmelCase__   ):
																raise ValueError(
																    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																    '''torch.Tensor, tf.Tensor or jax.ndarray.'''   )

										if do_rescale and rescale_factor is None:
																raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

										# All transformations expect numpy arrays.
										lowercase :							Dict    		=[to_numpy_array(UpperCAmelCase__   ) for image in images]

										if do_rescale:
																lowercase :							Tuple    		=[self.rescale(image=UpperCAmelCase__		,					scale=UpperCAmelCase__   ) for image in images]

										if do_pad:
																lowercase :							Union[str, Any]    		=[self.pad(UpperCAmelCase__		,					size=UpperCAmelCase__   ) for image in images]

										lowercase :							Dict    		=[to_channel_dimension_format(UpperCAmelCase__		,					UpperCAmelCase__   ) for image in images]

										lowercase :							Any    		={'''pixel_values''': images}
										return BatchFeature(data=UpperCAmelCase__		,					tensor_type=UpperCAmelCase__   )





