

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__								=		[
    # 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_     (							UpperCAmelCase_							:     Union[str, Any]	)						->				Union[str, Any]:




	'''simple docstring'''




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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     dict							,       UpperCAmelCase_							:     dict	)						->				PegasusForConditionalGeneration:




	'''simple docstring'''




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




	'''simple docstring'''




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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     str	)						->				Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						Path(UpperCAmelCase_	).parent.name
	_UpperCamelCase   :		Tuple			=						task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
	_UpperCamelCase   :		List[Any]			=						PegasusTokenizer.from_pretrained('sshleifer/pegasus'							,       model_max_length=UpperCAmelCase_	)
	assert tok.model_max_length == desired_max_model_length
	tok.save_pretrained(UpperCAmelCase_	)

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


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







from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




