

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


_lowerCAmelCase					:			Union[str, 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        UpperCamelCase_(							_snake_case   :  int	):




			"""simple docstring"""






			for pegasus_name, hf_name in PATTERNS:
						__a        =k.replace(_snake_case       ,     _snake_case	)
			return k






def        UpperCamelCase_(							_snake_case   :  dict       ,     _snake_case   :  dict	):




			"""simple docstring"""






			__a        =DEFAULTS.copy()
			cfg_kwargs.update(_snake_case	)
			__a        =PegasusConfig(**_snake_case	)
			__a        =PegasusForConditionalGeneration(_snake_case	)
			__a        =torch_model.model.state_dict()
			__a        ={}
			for k, v in tf_weights.items():
						__a        =rename_state_dict_key(_snake_case	)
						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(_snake_case       ,     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(_snake_case	) for k, v in sd.items() if k.endswith('bias'	) and k not in mapping}
			mapping.update(**_snake_case	)
			__a      ,    __a        =torch_model.model.load_state_dict(_snake_case       ,     strict=_snake_case	)
			__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        UpperCamelCase_(							_snake_case   :  Optional[Any]="./ckpt/aeslc/model.ckpt-32000"	):




			"""simple docstring"""






			__a        =tf.train.list_variables(_snake_case	)
			__a        ={}
			__a        =['Adafactor', 'global_step']
			for name, shape in tqdm(_snake_case       ,     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(_snake_case       ,     _snake_case	)
						__a        =array
			return tf_weights






def        UpperCamelCase_(							_snake_case   :  str       ,     _snake_case   :  str	):




			"""simple docstring"""






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

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


if __name__ == "__main__":
						_lowerCAmelCase					:			Dict  				=			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.")
						_lowerCAmelCase					:			Union[str, Any]  				=			parser.parse_args()
						if args.save_dir is None:
												_lowerCAmelCase					:			Optional[int]  				=			Path(args.tf_ckpt_path).parent.name
												_lowerCAmelCase					:			Optional[int]  				=			os.path.join("pegasus", dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						['image_processor', 'tokenizer']
			SCREAMING_SNAKE_CASE							   =						'BridgeTowerImageProcessor'
			SCREAMING_SNAKE_CASE							   =						('RobertaTokenizer', 'RobertaTokenizerFast')





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> Optional[int]:




						'''simple docstring'''
						super().__init__(__snake_case					,  __snake_case					)





			def __call__(		self					,  __snake_case					,  __snake_case = None					,  __snake_case = True					,  __snake_case = False					,  __snake_case = None					,  __snake_case = None					,  __snake_case = 0					,  __snake_case = None					,  __snake_case = None					,  __snake_case = None					,  __snake_case = False					,  __snake_case = False					,  __snake_case = False					,  __snake_case = False					,  __snake_case = True					,  __snake_case = None					,  **__snake_case					,  )   -> BatchEncoding:




						'''simple docstring'''
						__a        =self.tokenizer(
						    text=__snake_case					,  add_special_tokens=__snake_case					,  padding=__snake_case					,  truncation=__snake_case					,  max_length=__snake_case					,  stride=__snake_case					,  pad_to_multiple_of=__snake_case					,  return_token_type_ids=__snake_case					,  return_attention_mask=__snake_case					,  return_overflowing_tokens=__snake_case					,  return_special_tokens_mask=__snake_case					,  return_offsets_mapping=__snake_case					,  return_length=__snake_case					,  verbose=__snake_case					,  return_tensors=__snake_case					,  **__snake_case					,  )
						# add pixel_values + pixel_mask
						__a        =self.image_processor(
						    __snake_case					,  return_tensors=__snake_case					,  do_normalize=__snake_case					,  do_center_crop=__snake_case					,  **__snake_case					)
						encoding.update(__snake_case					)

						return encoding





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> str:




						'''simple docstring'''
						return self.tokenizer.batch_decode(*__snake_case					,  **__snake_case					)





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> Optional[Any]:




						'''simple docstring'''
						return self.tokenizer.decode(*__snake_case					,  **__snake_case					)





			@property
			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =self.tokenizer.model_input_names
						__a        =self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)






