




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:	Optional[int]				):





					'''simple docstring'''






					for pegasus_name, hf_name in PATTERNS:
										__magic_name__							:	str    							=							k.replace(__A							,__A				)
					return k







def 		lowercase__   (		__A:	dict							,__A:	dict				):





					'''simple docstring'''






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





					'''simple docstring'''






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







def 		lowercase__   (		__A:	str							,__A:	str				):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							Path(__A				).parent.name
					__magic_name__							:	Dict    							=							task_specific_params[F'''summarization_{dataset}''']['''max_position_embeddings''']
					__magic_name__							:	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
					__magic_name__							:	Any    							=							get_tf_weights_as_numpy(__A				)
					__magic_name__							:	List[Any]    							=							task_specific_params[F'''summarization_{dataset}''']
					if dataset == "large":
										__magic_name__							:	Tuple    							=							task_specific_params
					__magic_name__							:	List[str]    							=							convert_pegasus(__A							,__A				)
					torch_model.save_pretrained(__A				)
					__magic_name__							:	Optional[Any]    							=							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   :						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   :						Any				=			parser.parse_args()
	if args.save_dir is None:
		__lowerCamelCase   :						str				=			Path(args.tf_ckpt_path).parent.name
		__lowerCamelCase   :						Dict				=			os.path.join('''pegasus''', dataset)
	convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)









# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__lowerCamelCase   :						int				=			logging.get_logger(__name__)

__lowerCamelCase   :						Dict[Optional[str], Type[Formatter]]				=			{}
__lowerCamelCase   :						Dict[Optional[str], str]				=			{}
__lowerCamelCase   :						Dict[Optional[str], Exception]				=			{}







def 		lowercase__   (		__A:	type							,__A:	Optional[str]							,__A:	Optional[List[str]] = None							,):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							aliases if aliases is not None else []
					if format_type in _FORMAT_TYPES:
										logger.warning(
										    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''				)
					__magic_name__							:	Tuple    							=							formatter_cls
					for alias in set(aliases + [format_type]				):
										if alias in _FORMAT_TYPES_ALIASES:
															logger.warning(
															    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''				)
										__magic_name__							:	Optional[int]    							=							format_type







def 		lowercase__   (		__A:	Exception							,__A:	Optional[str]							,__A:	Optional[List[str]] = None				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							aliases if aliases is not None else []
					for alias in set(aliases + [format_type]				):
										__magic_name__							:	List[str]    							=							unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
	__lowerCamelCase   :						str				=			ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
	_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
	__lowerCamelCase   :						int				=			ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
	_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
	__lowerCamelCase   :						str				=			ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])







def 		lowercase__   (		__A:	Optional[str]				):





					'''simple docstring'''






					if format_type in _FORMAT_TYPES_ALIASES:
										return _FORMAT_TYPES_ALIASES[format_type]
					else:
										return format_type







def 		lowercase__   (		__A:	Optional[str]							,**__A:	Any				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							get_format_type_from_alias(__A				)
					if format_type in _FORMAT_TYPES:
										return _FORMAT_TYPES[format_type](**__A				)
					if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
										raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
					else:
										raise ValueError(
										    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None				)}, but got \'{format_type}\''''				)



