



"""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


lowercase  							=					[
    # 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 	UpperCAmelCase     (		A	:						Dict       ):

		'''simple docstring'''


		for pegasus_name, hf_name in PATTERNS:
				_UpperCAmelCase							 =		k.replace(A     ,				A       )
		return k




def 	UpperCAmelCase     (		A	:						dict     ,				A	:						dict       ):

		'''simple docstring'''


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

		'''simple docstring'''


		_UpperCAmelCase							 =		tf.train.list_variables(A       )
		_UpperCAmelCase							 =		{}
		_UpperCAmelCase							 =		['Adafactor', 'global_step']
		for name, shape in tqdm(A     ,				desc='converting tf checkpoint to dict'       ):
				_UpperCAmelCase							 =		any(pat in name for pat in ignore_name       )
				if skip_key:
						continue
				_UpperCAmelCase							 =		tf.train.load_variable(A     ,				A       )
				_UpperCAmelCase							 =		array
		return tf_weights




def 	UpperCAmelCase     (		A	:						str     ,				A	:						str       ):

		'''simple docstring'''


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

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







"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}




class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					'''beit'''






							def __init__(  self	,       snake_case=8192	,       snake_case=768	,       snake_case=12	,       snake_case=12	,       snake_case=3072	,       snake_case="gelu"	,       snake_case=0.0	,       snake_case=0.0	,       snake_case=0.02	,       snake_case=1E-12	,       snake_case=224	,       snake_case=16	,       snake_case=3	,       snake_case=False	,       snake_case=False	,       snake_case=False	,       snake_case=False	,       snake_case=0.1	,       snake_case=0.1	,       snake_case=True	,       snake_case=[3, 5, 7, 11]	,       snake_case=[1, 2, 3, 6]	,       snake_case=True	,       snake_case=0.4	,       snake_case=256	,       snake_case=1	,       snake_case=False	,       snake_case=255	,       **snake_case	,       )			->							str:
									super().__init__(**snake_case						)

									_UpperCAmelCase							 =		vocab_size
									_UpperCAmelCase							 =		hidden_size
									_UpperCAmelCase							 =		num_hidden_layers
									_UpperCAmelCase							 =		num_attention_heads
									_UpperCAmelCase							 =		intermediate_size
									_UpperCAmelCase							 =		hidden_act
									_UpperCAmelCase							 =		hidden_dropout_prob
									_UpperCAmelCase							 =		attention_probs_dropout_prob
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		layer_norm_eps

									_UpperCAmelCase							 =		image_size
									_UpperCAmelCase							 =		patch_size
									_UpperCAmelCase							 =		num_channels
									_UpperCAmelCase							 =		use_mask_token
									_UpperCAmelCase							 =		use_absolute_position_embeddings
									_UpperCAmelCase							 =		use_relative_position_bias
									_UpperCAmelCase							 =		use_shared_relative_position_bias
									_UpperCAmelCase							 =		layer_scale_init_value
									_UpperCAmelCase							 =		drop_path_rate
									_UpperCAmelCase							 =		use_mean_pooling
									# decode head attributes (semantic segmentation)
									_UpperCAmelCase							 =		out_indices
									_UpperCAmelCase							 =		pool_scales
									# auxiliary head attributes (semantic segmentation)
									_UpperCAmelCase							 =		use_auxiliary_head
									_UpperCAmelCase							 =		auxiliary_loss_weight
									_UpperCAmelCase							 =		auxiliary_channels
									_UpperCAmelCase							 =		auxiliary_num_convs
									_UpperCAmelCase							 =		auxiliary_concat_input
									_UpperCAmelCase							 =		semantic_loss_ignore_index







class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					version.parse('''1.11'''    )






							@property
							def        lowerCamelCase_   (  self						)			->							Mapping[str, Mapping[int, str]]:
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]						)






							@property
							def        lowerCamelCase_   (  self						)			->							float:
									return 1E-4


