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

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


if __name__ == "__main__":
	lowerCamelCase_    :		Optional[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_    :		Dict							=	parser.parse_args()
	if args.save_dir is None:
		lowerCamelCase_    :		Optional[Any]							=	Path(args.tf_ckpt_path).parent.name
		lowerCamelCase_    :		int							=	os.path.join("""pegasus""", dataset)
	convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    :		Tuple							=	logging.get_logger(__name__)

lowerCamelCase_    :		List[Any]							=	{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}


class 						a__							(       __snake_case		):
			A__    :       Dict        =      'fnet'



			def __init__(						self				,						UpperCAmelCase=3_2_0_0_0				,						UpperCAmelCase=7_6_8				,						UpperCAmelCase=1_2				,						UpperCAmelCase=3_0_7_2				,						UpperCAmelCase="gelu_new"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=4				,						UpperCAmelCase=0.02				,						UpperCAmelCase=1e-12				,						UpperCAmelCase=False				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=3				,						UpperCAmelCase=1				,						UpperCAmelCase=2				,						**UpperCAmelCase				,						)			->   Dict:
								super().__init__(pad_token_id=UpperCAmelCase				,						bos_token_id=UpperCAmelCase				,						eos_token_id=UpperCAmelCase				,						**UpperCAmelCase   )

								__a		 =       vocab_size
								__a		 =       max_position_embeddings
								__a		 =       hidden_size
								__a		 =       num_hidden_layers
								__a		 =       intermediate_size
								__a		 =       hidden_act
								__a		 =       hidden_dropout_prob
								__a		 =       initializer_range
								__a		 =       type_vocab_size
								__a		 =       layer_norm_eps
								__a		 =       use_tpu_fourier_optimizations
								__a		 =       tpu_short_seq_length




