






"""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			:    List[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 						lowercase__(A						)    ->Optional[Any]:




						"""simple docstring"""



						for pegasus_name, hf_name in PATTERNS:
												lowercase__    :				List[str]=	k.replace(A ,		A						)
						return k





def 						lowercase__(A ,		A						)    ->PegasusForConditionalGeneration:




						"""simple docstring"""



						lowercase__    :				List[str]=	DEFAULTS.copy()
						cfg_kwargs.update(A						)
						lowercase__    :				Union[str, Any]=	PegasusConfig(**A						)
						lowercase__    :				Optional[int]=	PegasusForConditionalGeneration(A						)
						lowercase__    :				str=	torch_model.model.state_dict()
						lowercase__    :				Dict=	{}
						for k, v in tf_weights.items():
												lowercase__    :				Optional[Any]=	rename_state_dict_key(A						)
												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:
																		lowercase__    :				Optional[int]=	v.T
												lowercase__    :				int=	torch.tensor(A ,		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
						lowercase__    :				Dict=	torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]						)
						lowercase__    :				int=	mapping["shared.weight"]
						lowercase__    :				List[str]=	mapping["shared.weight"]
						lowercase__    :				Tuple=	{k: torch.zeros_like(A						) for k, v in sd.items() if k.endswith("bias"						) and k not in mapping}
						mapping.update(**A						)
						lowercase__,     lowercase__    :				Union[str, Any]=	torch_model.model.load_state_dict(A ,		strict=A						)
						lowercase__    :				Union[str, Any]=	[
						    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 						lowercase__(A="./ckpt/aeslc/model.ckpt-32000"						)    ->Dict:




						"""simple docstring"""



						lowercase__    :				Any=	tf.train.list_variables(A						)
						lowercase__    :				Union[str, Any]=	{}
						lowercase__    :				Tuple=	["Adafactor", "global_step"]
						for name, shape in tqdm(A ,		desc="converting tf checkpoint to dict"						):
												lowercase__    :				Tuple=	any(pat in name for pat in ignore_name						)
												if skip_key:
																		continue
												lowercase__    :				int=	tf.train.load_variable(A ,		A						)
												lowercase__    :				Optional[Any]=	array
						return tf_weights





def 						lowercase__(A ,		A						)    ->Optional[int]:




						"""simple docstring"""



						lowercase__    :				str=	Path(A						).parent.name
						lowercase__    :				Dict=	task_specific_params[f'''summarization_{dataset}''']["max_position_embeddings"]
						lowercase__    :				Any=	PegasusTokenizer.from_pretrained("sshleifer/pegasus" ,		model_max_length=A						)
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(A						)

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


if __name__ == "__main__":
			a			:    Union[str, 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.""")
			a			:    Union[str, Any]				 =		parser.parse_args()
			if args.save_dir is None:
						a			:    Optional[int]				 =		Path(args.tf_ckpt_path).parent.name
						a			:    Tuple				 =		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


a			:    List[str]				 =		{
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a			:    List[str]				 =		[
			    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XLMRobertaXLForCausalLM""",
			    """XLMRobertaXLForMaskedLM""",
			    """XLMRobertaXLForMultipleChoice""",
			    """XLMRobertaXLForQuestionAnswering""",
			    """XLMRobertaXLForSequenceClassification""",
			    """XLMRobertaXLForTokenClassification""",
			    """XLMRobertaXLModel""",
			    """XLMRobertaXLPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta_xl import (
			    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaXLConfig,
			    XLMRobertaXLOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaXLForCausalLM,
						    XLMRobertaXLForMaskedLM,
						    XLMRobertaXLForMultipleChoice,
						    XLMRobertaXLForQuestionAnswering,
						    XLMRobertaXLForSequenceClassification,
						    XLMRobertaXLForTokenClassification,
						    XLMRobertaXLModel,
						    XLMRobertaXLPreTrainedModel,
						)

else:
			import sys

			a			:    List[str]				 =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)






