


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"""


import flax.linen as nn
import jax
import jax.numpy as jnp
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    nn.Conv(
										    self.out_channels					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a			):
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    hidden_states.shape
										__lowerCAmelCase       					=    jax.image.resize(
										    __a					,     shape=(batch, height * 2, width * 2, channels)					,     method="nearest"					,     )
										__lowerCAmelCase       					=    self.conv(__a			)
										return hidden_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    nn.Conv(
										    self.out_channels					,     kernel_size=(3, 3)					,     strides=(2, 2)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a			):
										# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
										# hidden_states = jnp.pad(hidden_states, pad_width=pad)
										__lowerCAmelCase       					=    self.conv(__a			)
										return hidden_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							__UpperCAmelCase		:					int
							__UpperCAmelCase		:					int          =None
							__UpperCAmelCase		:					float          =0.0
							__UpperCAmelCase		:					bool          =None
							__UpperCAmelCase		:					jnp.dtype          =jnp.floataa

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.in_channels if self.out_channels is None else self.out_channels

										__lowerCAmelCase       					=    nn.GroupNorm(num_groups=32					,     epsilon=1e-5			)
										__lowerCAmelCase       					=    nn.Conv(
										    __a					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )

										__lowerCAmelCase       					=    nn.Dense(__a					,     dtype=self.dtype			)

										__lowerCAmelCase       					=    nn.GroupNorm(num_groups=32					,     epsilon=1e-5			)
										__lowerCAmelCase       					=    nn.Dropout(self.dropout_prob			)
										__lowerCAmelCase       					=    nn.Conv(
										    __a					,     kernel_size=(3, 3)					,     strides=(1, 1)					,     padding=((1, 1), (1, 1))					,     dtype=self.dtype					,     )

										__lowerCAmelCase       					=    self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

										__lowerCAmelCase       					=    None
										if use_nin_shortcut:
													__lowerCAmelCase       					=    nn.Conv(
													    __a					,     kernel_size=(1, 1)					,     strides=(1, 1)					,     padding="VALID"					,     dtype=self.dtype					,     )



							def __call__(  self					,     __a					,     __a					,     __a=True			):
										__lowerCAmelCase       					=    hidden_states
										__lowerCAmelCase       					=    self.norma(__a			)
										__lowerCAmelCase       					=    nn.swish(__a			)
										__lowerCAmelCase       					=    self.conva(__a			)

										__lowerCAmelCase       					=    self.time_emb_proj(nn.swish(__a			)			)
										__lowerCAmelCase       					=    jnp.expand_dims(jnp.expand_dims(__a					,     1			)					,     1			)
										__lowerCAmelCase       					=    hidden_states + temb

										__lowerCAmelCase       					=    self.norma(__a			)
										__lowerCAmelCase       					=    nn.swish(__a			)
										__lowerCAmelCase       					=    self.dropout(__a					,     __a			)
										__lowerCAmelCase       					=    self.conva(__a			)

										if self.conv_shortcut is not None:
													__lowerCAmelCase       					=    self.conv_shortcut(__a			)

										return hidden_states + residual




