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


__UpperCamelCase		:       Optional[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       A	(    _lowercase						):
						for pegasus_name, hf_name in PATTERNS:
												SCREAMING_SNAKE_CASE							:    int							=							k.replace(_lowercase		,  _lowercase						)
						return k
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							DEFAULTS.copy()
						cfg_kwargs.update(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							PegasusConfig(**_lowercase						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							PegasusForConditionalGeneration(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Dict							=							torch_model.model.state_dict()
						SCREAMING_SNAKE_CASE							:    Tuple							=							{}
						for k, v in tf_weights.items():
												SCREAMING_SNAKE_CASE							:    Dict							=							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:
																		SCREAMING_SNAKE_CASE							:    int							=							v.T
												SCREAMING_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
						SCREAMING_SNAKE_CASE							:    Any							=							torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1]						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							mapping['''shared.weight''']
						SCREAMING_SNAKE_CASE							:    Dict							=							mapping['''shared.weight''']
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							{k: torch.zeros_like(_lowercase						) for k, v in sd.items() if k.endswith('''bias'''						) and k not in mapping}
						mapping.update(**_lowercase						)
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Dict							=							torch_model.model.load_state_dict(_lowercase		,  strict=_lowercase						)
						SCREAMING_SNAKE_CASE							:    int							=							[
						    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	(    _lowercase="./ckpt/aeslc/model.ckpt-32000"						):
						SCREAMING_SNAKE_CASE							:    str							=							tf.train.list_variables(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Dict							=							{}
						SCREAMING_SNAKE_CASE							:    Tuple							=							['''Adafactor''', '''global_step''']
						for name, shape in tqdm(_lowercase		,  desc='''converting tf checkpoint to dict'''						):
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							any(pat in name for pat in ignore_name						)
												if skip_key:
																		continue
												SCREAMING_SNAKE_CASE							:    Tuple							=							tf.train.load_variable(_lowercase		,  _lowercase						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							array
						return tf_weights
def       A	(    _lowercase		,  _lowercase						):
						# save tokenizer first
						SCREAMING_SNAKE_CASE							:    int							=							Path(_lowercase						).parent.name
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							task_specific_params[f"""summarization_{dataset}"""]['''max_position_embeddings''']
						SCREAMING_SNAKE_CASE							:    Dict							=							PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''		,  model_max_length=_lowercase						)
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(_lowercase						)

						# convert model
						SCREAMING_SNAKE_CASE							:    List[Any]							=							get_tf_weights_as_numpy(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							task_specific_params[f"""summarization_{dataset}"""]
						if dataset == "large":
												SCREAMING_SNAKE_CASE							:    Tuple							=							task_specific_params
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							convert_pegasus(_lowercase		,  _lowercase						)
						torch_model.save_pretrained(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Dict							=							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__":
					__UpperCamelCase		:       Optional[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.')
					__UpperCamelCase		:       str									=     parser.parse_args()
					if args.save_dir is None:
										__UpperCamelCase		:       List[Any]									=     Path(args.tf_ckpt_path).parent.name
										__UpperCamelCase		:       Union[str, Any]									=     os.path.join('pegasus', dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)






from typing import TYPE_CHECKING

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


__UpperCamelCase		:       Tuple									=     {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     [
					    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SwiftFormerForImageClassification',
					    'SwiftFormerModel',
					    'SwiftFormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_swiftformer import (
					    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SwiftFormerConfig,
					    SwiftFormerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swiftformer import (
										    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwiftFormerForImageClassification,
										    SwiftFormerModel,
										    SwiftFormerPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase		:       List[Any]									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





