
"""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    _snake_case					(		snake_case__ :  List[str]  ):

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






def    _snake_case					(		snake_case__ :  dict						,   snake_case__ :  dict  ):
	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    _snake_case					(		snake_case__ :  List[Any]="./ckpt/aeslc/model.ckpt-32000"  ):
	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    _snake_case					(		snake_case__ :  str						,   snake_case__ :  str  ):
	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__":
		_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 typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Dict									=       ['image_processor', 'tokenizer']
       _snake_case      :	int									=       'BlipImageProcessor'
       _snake_case      :	Tuple									=       ('BertTokenizer', 'BertTokenizerFast')





       def __init__(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						List[str]	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			False
            super().__init__(lowerCAmelCase__			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			self.image_processor





       def __call__(				self  :						int			,					lowerCAmelCase__  :						ImageInput = None			,					lowerCAmelCase__  :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,					lowerCAmelCase__  :						bool = True			,					lowerCAmelCase__  :						Union[bool, str, PaddingStrategy] = False			,					lowerCAmelCase__  :						Union[bool, str, TruncationStrategy] = None			,					lowerCAmelCase__  :						Optional[int] = None			,					lowerCAmelCase__  :						int = 0			,					lowerCAmelCase__  :						Optional[int] = None			,					lowerCAmelCase__  :						Optional[bool] = None			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = False			,					lowerCAmelCase__  :						bool = True			,					lowerCAmelCase__  :						Optional[Union[str, TensorType]] = None			,					**lowerCAmelCase__  :						Optional[Any]			,					)       ->	BatchEncoding:

            '''simple docstring'''




            if images is None and text is None:
                 raise ValueError('''You have to specify either images or text.'''	)

            # Get only text
            if images is None:
                 _UpperCamelCase							       =			self.tokenizer
                 _UpperCamelCase							       =			self.tokenizer(
                     text=lowerCAmelCase__			,					add_special_tokens=lowerCAmelCase__			,					padding=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					stride=lowerCAmelCase__			,					pad_to_multiple_of=lowerCAmelCase__			,					return_attention_mask=lowerCAmelCase__			,					return_overflowing_tokens=lowerCAmelCase__			,					return_special_tokens_mask=lowerCAmelCase__			,					return_offsets_mapping=lowerCAmelCase__			,					return_token_type_ids=lowerCAmelCase__			,					return_length=lowerCAmelCase__			,					verbose=lowerCAmelCase__			,					return_tensors=lowerCAmelCase__			,					**lowerCAmelCase__			,					)
                 return text_encoding

            # add pixel_values
            _UpperCamelCase							       =			self.image_processor(lowerCAmelCase__			,					return_tensors=lowerCAmelCase__	)

            if text is not None:
                 _UpperCamelCase							       =			self.tokenizer(
                     text=lowerCAmelCase__			,					add_special_tokens=lowerCAmelCase__			,					padding=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					max_length=lowerCAmelCase__			,					stride=lowerCAmelCase__			,					pad_to_multiple_of=lowerCAmelCase__			,					return_attention_mask=lowerCAmelCase__			,					return_overflowing_tokens=lowerCAmelCase__			,					return_special_tokens_mask=lowerCAmelCase__			,					return_offsets_mapping=lowerCAmelCase__			,					return_token_type_ids=lowerCAmelCase__			,					return_length=lowerCAmelCase__			,					verbose=lowerCAmelCase__			,					return_tensors=lowerCAmelCase__			,					**lowerCAmelCase__			,					)
            else:
                 _UpperCamelCase							       =			None

            if text_encoding is not None:
                 encoding_image_processor.update(lowerCAmelCase__	)

            return encoding_image_processor





       def snake_case__		(				self  :						Any			,					*lowerCAmelCase__  :						Union[str, Any]			,					**lowerCAmelCase__  :						Dict	)       ->	List[str]:

            '''simple docstring'''




            return self.tokenizer.batch_decode(*lowerCAmelCase__			,					**lowerCAmelCase__	)





       def snake_case__		(				self  :						Tuple			,					*lowerCAmelCase__  :						int			,					**lowerCAmelCase__  :						Optional[int]	)       ->	Optional[int]:

            '''simple docstring'''




            return self.tokenizer.decode(*lowerCAmelCase__			,					**lowerCAmelCase__	)







       @property
       def snake_case__		(				self  :						Any	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer.model_input_names
            _UpperCamelCase							       =			self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



