





'''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   : int             =			[
    # 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_      (		lowercase__						)   ->Optional[Any]:
						for pegasus_name, hf_name in PATTERNS:
												_snake_case:   str  					=		k.replace(lowercase__				,  lowercase__						)
						return k





def       lowercase_      (		lowercase__				,  lowercase__						)   ->PegasusForConditionalGeneration:
						_snake_case:   Tuple  					=		DEFAULTS.copy()
						cfg_kwargs.update(lowercase__						)
						_snake_case:   str  					=		PegasusConfig(**lowercase__						)
						_snake_case:   Union[str, Any]  					=		PegasusForConditionalGeneration(lowercase__						)
						_snake_case:   Any  					=		torch_model.model.state_dict()
						_snake_case:   Any  					=		{}
						for k, v in tf_weights.items():
												_snake_case:   int  					=		rename_state_dict_key(lowercase__						)
												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:
																		_snake_case:   str  					=		v.T
												_snake_case:   Optional[int]  					=		torch.tensor(lowercase__				,  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
						_snake_case:   Optional[Any]  					=		torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]						)
						_snake_case:   Any  					=		mapping['shared.weight']
						_snake_case:   List[Any]  					=		mapping['shared.weight']
						_snake_case:   List[str]  					=		{k: torch.zeros_like(lowercase__						) for k, v in sd.items() if k.endswith('bias'						) and k not in mapping}
						mapping.update(**lowercase__						)
						_snake_case					,						_snake_case:   Dict  					=		torch_model.model.load_state_dict(lowercase__				,  strict=lowercase__						)
						_snake_case:   List[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_      (		lowercase__="./ckpt/aeslc/model.ckpt-32000"						)   ->Dict:
						_snake_case:   str  					=		tf.train.list_variables(lowercase__						)
						_snake_case:   Union[str, Any]  					=		{}
						_snake_case:   Dict  					=		['Adafactor', 'global_step']
						for name, shape in tqdm(lowercase__				,  desc='converting tf checkpoint to dict'						):
												_snake_case:   Dict  					=		any(pat in name for pat in ignore_name						)
												if skip_key:
																		continue
												_snake_case:   Any  					=		tf.train.load_variable(lowercase__				,  lowercase__						)
												_snake_case:   Optional[int]  					=		array
						return tf_weights





def       lowercase_      (		lowercase__				,  lowercase__						)   ->Any:
						# save tokenizer first
						_snake_case:   Optional[int]  					=		Path(lowercase__						).parent.name
						_snake_case:   Tuple  					=		task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
						_snake_case:   List[Any]  					=		PegasusTokenizer.from_pretrained('sshleifer/pegasus'				,  model_max_length=lowercase__						)
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(lowercase__						)

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


if __name__ == "__main__":
		A   : Optional[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   : Optional[int]             =			parser.parse_args()
		if args.save_dir is None:
				A   : List[Any]             =			Path(args.tf_ckpt_path).parent.name
				A   : Any             =			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_tf_available,
    is_torch_available,
    is_vision_available,
)


A   : Optional[Any]             =			{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : str             =			['DeiTFeatureExtractor']
		A   : Optional[int]             =			['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Optional[Any]             =			[
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			[
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

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





