
"""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


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		):
	for pegasus_name, hf_name in PATTERNS:
		__lowerCAmelCase      :						List[Any]						      =				k.replace(_UpperCamelCase	,  _UpperCamelCase		)
	return k
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						List[Any]						      =				DEFAULTS.copy()
	cfg_kwargs.update(_UpperCamelCase		)
	__lowerCAmelCase      :						List[Any]						      =				PegasusConfig(**_UpperCamelCase		)
	__lowerCAmelCase      :						Dict						      =				PegasusForConditionalGeneration(_UpperCamelCase		)
	__lowerCAmelCase      :						Optional[int]						      =				torch_model.model.state_dict()
	__lowerCAmelCase      :						Union[str, Any]						      =				{}
	for k, v in tf_weights.items():
		__lowerCAmelCase      :						str						      =				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      :						Tuple						      =				v.T
		__lowerCAmelCase      :						List[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
	__lowerCAmelCase      :						Optional[Any]						      =				torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]		)
	__lowerCAmelCase      :						List[str]						      =				mapping['shared.weight']
	__lowerCAmelCase      :						List[Any]						      =				mapping['shared.weight']
	__lowerCAmelCase      :						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		)
	__lowerCAmelCase      ,       __lowerCAmelCase      :						Union[str, Any]						      =				torch_model.model.load_state_dict(_UpperCamelCase	,  strict=_UpperCamelCase		)
	__lowerCAmelCase      :						Tuple						      =				[
	    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"		):
	__lowerCAmelCase      :						Dict						      =				tf.train.list_variables(_UpperCamelCase		)
	__lowerCAmelCase      :						Optional[Any]						      =				{}
	__lowerCAmelCase      :						Union[str, Any]						      =				['Adafactor', 'global_step']
	for name, shape in tqdm(_UpperCamelCase	,  desc='converting tf checkpoint to dict'		):
		__lowerCAmelCase      :						Optional[int]						      =				any(pat in name for pat in ignore_name		)
		if skip_key:
			continue
		__lowerCAmelCase      :						int						      =				tf.train.load_variable(_UpperCamelCase	,  _UpperCamelCase		)
		__lowerCAmelCase      :						int						      =				array
	return tf_weights
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	# save tokenizer first
	__lowerCAmelCase      :						List[str]						      =				Path(_UpperCamelCase		).parent.name
	__lowerCAmelCase      :						int						      =				task_specific_params[F"summarization_{dataset}"]['max_position_embeddings']
	__lowerCAmelCase      :						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
	__lowerCAmelCase      :						List[Any]						      =				get_tf_weights_as_numpy(_UpperCamelCase		)
	__lowerCAmelCase      :						List[Any]						      =				task_specific_params[F"summarization_{dataset}"]
	if dataset == "large":
		__lowerCAmelCase      :						str						      =				task_specific_params
	__lowerCAmelCase      :						Optional[Any]						      =				convert_pegasus(_UpperCamelCase	,  _UpperCamelCase		)
	torch_model.save_pretrained(_UpperCamelCase		)
	__lowerCAmelCase      :						Optional[Any]						      =				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)

"""simple docstring"""







lowerCamelCase__        =		"""0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
							from .utils import rich