





"""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


_snake_case     :		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 					A__				(			UpperCamelCase       ):
							for pegasus_name, hf_name in PATTERNS:
														A		=  k.replace(UpperCamelCase					,  UpperCamelCase       )
							return k






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  DEFAULTS.copy()
							cfg_kwargs.update(UpperCamelCase       )
							A		=  PegasusConfig(**UpperCamelCase       )
							A		=  PegasusForConditionalGeneration(UpperCamelCase       )
							A		=  torch_model.model.state_dict()
							A		=  {}
							for k, v in tf_weights.items():
														A		=  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:
																					A		=  v.T
														A		=  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
							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(UpperCamelCase       ) for k, v in sd.items() if k.endswith("bias"       ) and k not in mapping}
							mapping.update(**UpperCamelCase       )
							A,	A		=  torch_model.model.load_state_dict(UpperCamelCase					,  strict=UpperCamelCase       )
							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 					A__				(			UpperCamelCase="./ckpt/aeslc/model.ckpt-32000"       ):
							A		=  tf.train.list_variables(UpperCamelCase       )
							A		=  {}
							A		=  ["Adafactor", "global_step"]
							for name, shape in tqdm(UpperCamelCase					,  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(UpperCamelCase					,  UpperCamelCase       )
														A		=  array
							return tf_weights






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							# save tokenizer first
							A		=  Path(UpperCamelCase       ).parent.name
							A		=  task_specific_params[F"summarization_{dataset}"]["max_position_embeddings"]
							A		=  PegasusTokenizer.from_pretrained("sshleifer/pegasus"					,  model_max_length=UpperCamelCase       )
							assert tok.model_max_length == desired_max_model_length
							tok.save_pretrained(UpperCamelCase       )

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









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case     :		Union[str, Any]       						= {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case     :		int       						= [
		    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'EncodecModel',
		    'EncodecPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_encodec import (
		    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    EncodecConfig,
		)
		from .feature_extraction_encodec import EncodecFeatureExtractor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_encodec import (
				    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EncodecModel,
				    EncodecPreTrainedModel,
				)

else:
		import sys

		_snake_case     :		str       						= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


