


"""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


A  :     str    					=    [
    # 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   ):

			'''simple docstring'''




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


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    DEFAULTS.copy()
			cfg_kwargs.update(_UpperCamelCase   )
			__lowerCAmelCase       					=    PegasusConfig(**_UpperCamelCase   )
			__lowerCAmelCase       					=    PegasusForConditionalGeneration(_UpperCamelCase   )
			__lowerCAmelCase       					=    torch_model.model.state_dict()
			__lowerCAmelCase       					=    {}
			for k, v in tf_weights.items():
						__lowerCAmelCase       					=    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:
									__lowerCAmelCase       					=    v.T
						__lowerCAmelCase       					=    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
			__lowerCAmelCase       					=    torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]   )
			__lowerCAmelCase       					=    mapping["shared.weight"]
			__lowerCAmelCase       					=    mapping["shared.weight"]
			__lowerCAmelCase       					=    {k: torch.zeros_like(_UpperCamelCase   ) for k, v in sd.items() if k.endswith("bias"   ) and k not in mapping}
			mapping.update(**_UpperCamelCase   )
			__lowerCAmelCase					,			__lowerCAmelCase       					=    torch_model.model.load_state_dict(_UpperCamelCase					,				strict=_UpperCamelCase   )
			__lowerCAmelCase       					=    [
			    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="./ckpt/aeslc/model.ckpt-32000"   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    tf.train.list_variables(_UpperCamelCase   )
			__lowerCAmelCase       					=    {}
			__lowerCAmelCase       					=    ["Adafactor", "global_step"]
			for name, shape in tqdm(_UpperCamelCase					,				desc="converting tf checkpoint to dict"   ):
						__lowerCAmelCase       					=    any(pat in name for pat in ignore_name   )
						if skip_key:
									continue
						__lowerCAmelCase       					=    tf.train.load_variable(_UpperCamelCase					,				_UpperCamelCase   )
						__lowerCAmelCase       					=    array
			return tf_weights


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    Path(_UpperCamelCase   ).parent.name
			__lowerCAmelCase       					=    task_specific_params[f"summarization_{dataset}"]["max_position_embeddings"]
			__lowerCAmelCase       					=    PegasusTokenizer.from_pretrained("sshleifer/pegasus"					,				model_max_length=_UpperCamelCase   )
			assert tok.model_max_length == desired_max_model_length
			tok.save_pretrained(_UpperCamelCase   )

			# convert model
			__lowerCAmelCase       					=    get_tf_weights_as_numpy(_UpperCamelCase   )
			__lowerCAmelCase       					=    task_specific_params[f"summarization_{dataset}"]
			if dataset == "large":
						__lowerCAmelCase       					=    task_specific_params
			__lowerCAmelCase       					=    convert_pegasus(_UpperCamelCase					,				_UpperCamelCase   )
			torch_model.save_pretrained(_UpperCamelCase   )
			__lowerCAmelCase       					=    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__":
	A  :     int    					=    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.")
	A  :     List[Any]    					=    parser.parse_args()
	if args.save_dir is None:
		A  :     Tuple    					=    Path(args.tf_ckpt_path).parent.name
		A  :     Optional[Any]    					=    os.path.join("pegasus", dataset)
	convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A  :     Optional[int]    					=    logging.get_logger(__name__)

A  :     List[str]    					=    {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Any          ="""bit"""
							__UpperCAmelCase		:					Optional[int]          =["""preactivation""", """bottleneck"""]
							__UpperCAmelCase		:					List[str]          =["""SAME""", """VALID"""]

							def __init__(  self					,     __a=3					,     __a=64					,     __a=[2_56, 5_12, 10_24, 20_48]					,     __a=[3, 4, 6, 3]					,     __a="preactivation"					,     __a="relu"					,     __a=None					,     __a=32					,     __a=0.0					,     __a=False					,     __a=32					,     __a=1					,     __a=None					,     __a=None					,     **__a					,     ):
										super().__init__(**__a			)
										if layer_type not in self.layer_types:
													raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types			)}"			)
										if global_padding is not None:
													if global_padding.upper() in self.supported_padding:
																__lowerCAmelCase       					=    global_padding.upper()
													else:
																raise ValueError(f"Padding strategy {global_padding} not supported"			)
										__lowerCAmelCase       					=    num_channels
										__lowerCAmelCase       					=    embedding_size
										__lowerCAmelCase       					=    hidden_sizes
										__lowerCAmelCase       					=    depths
										__lowerCAmelCase       					=    layer_type
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    global_padding
										__lowerCAmelCase       					=    num_groups
										__lowerCAmelCase       					=    drop_path_rate
										__lowerCAmelCase       					=    embedding_dynamic_padding
										__lowerCAmelCase       					=    output_stride
										__lowerCAmelCase       					=    width_factor

										__lowerCAmelCase       					=    ["stem"] + [f"stage{idx}" for idx in range(1					,     len(__a			) + 1			)]
										__lowerCAmelCase					,			__lowerCAmelCase       					=    get_aligned_output_features_output_indices(
										    out_features=__a					,     out_indices=__a					,     stage_names=self.stage_names			)




