



'''simple docstring'''

import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


_A				:    Optional[Any]     						=[
    # tf -> hf
    ('''/''', '''.'''),
    ('''layer_''', '''layers.'''),
    ('''kernel''', '''weight'''),
    ('''beta''', '''bias'''),
    ('''gamma''', '''weight'''),
    ('''pegasus''', '''model'''),
]
_A				:    List[str]     						=[
    ('''.output.dense''', '''.fc2'''),
    ('''intermediate.LayerNorm''', '''final_layer_norm'''),
    ('''intermediate.dense''', '''fc1'''),
]

_A				:    int     						=(
    INIT_COMMON
    + [
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.out_proj'''),
        ('''attention.self''', '''self_attn'''),
        ('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
        ('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
        ('''attention.encdec''', '''encoder_attn'''),
        ('''key''', '''k_proj'''),
        ('''value''', '''v_proj'''),
        ('''query''', '''q_proj'''),
        ('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

_A				:    Any     						=(
    INIT_COMMON
    + [
        ('''embeddings.word_embeddings''', '''shared.weight'''),
        ('''embeddings.position_embeddings''', '''embed_positions.weight'''),
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.output'''),
        ('''attention.self''', '''self_attn.self'''),
        ('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

_A				:    Optional[Any]     						=[
    '''encdec/key/bias''',
    '''encdec/query/bias''',
    '''encdec/value/bias''',
    '''self/key/bias''',
    '''self/query/bias''',
    '''self/value/bias''',
    '''encdec_output/dense/bias''',
    '''attention/output/dense/bias''',
]







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			str:
 for tf_name, hf_name in patterns:
  _lowercase					:       List[str]       =		k.replace(_lowercase,				_lowercase					)
 return k







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			BigBirdPegasusForConditionalGeneration:
 _lowercase					:       List[str]       =		BigBirdPegasusConfig(**_lowercase					)
 _lowercase					:       List[Any]       =		BigBirdPegasusForConditionalGeneration(_lowercase					)
 _lowercase					:       Any       =		torch_model.state_dict()
 _lowercase					:       List[str]       =		{}

 # separating decoder weights
 _lowercase					:       Tuple       =		{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'					)}
 _lowercase					:       Dict       =		{k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'					)}

 for k, v in tqdm(decoder_weights.items(),				'tf -> hf conversion'					):
  _lowercase					:       Tuple       =		[k.endswith(_lowercase					) for ending in KEYS_TO_IGNORE]
  if any(_lowercase					):
   continue
  _lowercase					:       str       =		DECODER_PATTERNS
  _lowercase					:       Optional[Any]       =		rename_state_dict_key(_lowercase,				_lowercase					)
  if new_k not in state_dict:
   raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})'''					)
  if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']					):
   _lowercase					:       Union[str, Any]       =		v.T
  _lowercase					:       str       =		torch.from_numpy(_lowercase					)
  assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

 for k, v in tqdm(remaining_weights.items(),				'tf -> hf conversion'					):
  _lowercase					:       str       =		[k.endswith(_lowercase					) for ending in KEYS_TO_IGNORE]
  if any(_lowercase					):
   continue
  _lowercase					:       Tuple       =		REMAINING_PATTERNS
  _lowercase					:       Optional[Any]       =		rename_state_dict_key(_lowercase,				_lowercase					)
  if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
   raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})'''					)
  if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']					):
   _lowercase					:       List[Any]       =		v.T
  _lowercase					:       Optional[Any]       =		torch.from_numpy(_lowercase					)
  if k != "pegasus/embeddings/position_embeddings":
   assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

 _lowercase					:       Any       =		mapping['model.embed_positions.weight']
 _lowercase					:       Optional[Any]       =		mapping.pop('model.embed_positions.weight'					)
 _lowercase					:       Any       =		torch_model.load_state_dict(_lowercase,				strict=_lowercase					)
 _lowercase					:       Any       =		[
     k
     for k in missing
     if k
     not in [
         'final_logits_bias',
         'model.encoder.embed_tokens.weight',
         'model.decoder.embed_tokens.weight',
         'lm_head.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				(							_lowercase					) ->			Dict:
 _lowercase					:       Tuple       =		tf.train.list_variables(_lowercase					)
 _lowercase					:       Union[str, Any]       =		{}
 _lowercase					:       Optional[Any]       =		['global_step']
 for name, shape in tqdm(_lowercase,				desc='converting tf checkpoint to dict'					):
  _lowercase					:       str       =		any(pat in name for pat in ignore_name					)
  if skip_key:
   continue
  _lowercase					:       Optional[int]       =		tf.train.load_variable(_lowercase,				_lowercase					)
  _lowercase					:       str       =		array
 return tf_weights







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			Optional[Any]:
 _lowercase					:       int       =		get_tf_weights_as_numpy(_lowercase					)
 _lowercase					:       Dict       =		convert_bigbird_pegasus(_lowercase,				_lowercase					)
 torch_model.save_pretrained(_lowercase					)


if __name__ == "__main__":
  _A				:    List[Any]     						=argparse.ArgumentParser()
  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				:    int     						=parser.parse_args()
  _A				:    int     						={}
  convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)





'''simple docstring'''

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A				:    int     						=logging.get_logger(__name__)

_A				:    Union[str, Any]     						={
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip_vision_model"""




					def __init__(  self				:	Union[str, Any]     ,  UpperCamelCase_				:	str=1408     ,  UpperCamelCase_				:	Tuple=6144     ,  UpperCamelCase_				:	Union[str, Any]=39     ,  UpperCamelCase_				:	Optional[Any]=16     ,  UpperCamelCase_				:	str=224     ,  UpperCamelCase_				:	Dict=14     ,  UpperCamelCase_				:	Dict="gelu"     ,  UpperCamelCase_				:	int=1E-6     ,  UpperCamelCase_				:	int=0.0     ,  UpperCamelCase_				:	List[str]=1E-10     ,  UpperCamelCase_				:	str=True     ,  **UpperCamelCase_				:	Dict     ,  )  ->       Any:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		hidden_size
						_lowercase					:       Optional[Any]       =		intermediate_size
						_lowercase					:       Optional[int]       =		num_hidden_layers
						_lowercase					:       str       =		num_attention_heads
						_lowercase					:       Tuple       =		patch_size
						_lowercase					:       Dict       =		image_size
						_lowercase					:       Optional[int]       =		initializer_range
						_lowercase					:       List[Any]       =		attention_dropout
						_lowercase					:       int       =		layer_norm_eps
						_lowercase					:       Optional[int]       =		hidden_act
						_lowercase					:       str       =		qkv_bias




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	List[Any]     ,  UpperCamelCase_				:	Union[str, os.PathLike]     ,  **UpperCamelCase_				:	List[str]					)  ->       "PretrainedConfig":



						'''simple docstring'''





						cls._set_token_in_kwargs(UpperCamelCase_					)

						_lowercase  ,    _lowercase					:       Tuple       =		cls.get_config_dict(UpperCamelCase_     ,  **UpperCamelCase_					)

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
							_lowercase					:       Any       =		config_dict['vision_config']

						if "model_type" in config_dict and hasattr(cls     ,  'model_type'					) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
							    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''					)

						return cls.from_dict(UpperCamelCase_     ,  **UpperCamelCase_					)






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip_qformer"""




					def __init__(  self				:	Tuple     ,  UpperCamelCase_				:	Union[str, Any]=3_0522     ,  UpperCamelCase_				:	Union[str, Any]=768     ,  UpperCamelCase_				:	Tuple=12     ,  UpperCamelCase_				:	Optional[Any]=12     ,  UpperCamelCase_				:	List[str]=3072     ,  UpperCamelCase_				:	List[str]="gelu"     ,  UpperCamelCase_				:	Union[str, Any]=0.1     ,  UpperCamelCase_				:	List[str]=0.1     ,  UpperCamelCase_				:	Any=512     ,  UpperCamelCase_				:	Union[str, Any]=0.02     ,  UpperCamelCase_				:	List[Any]=1E-12     ,  UpperCamelCase_				:	Optional[Any]=0     ,  UpperCamelCase_				:	str="absolute"     ,  UpperCamelCase_				:	List[Any]=2     ,  UpperCamelCase_				:	Any=1408     ,  **UpperCamelCase_				:	Dict     ,  )  ->       Any:



						'''simple docstring'''





						super().__init__(pad_token_id=UpperCamelCase_     ,  **UpperCamelCase_					)

						_lowercase					:       Dict       =		vocab_size
						_lowercase					:       Optional[Any]       =		hidden_size
						_lowercase					:       Any       =		num_hidden_layers
						_lowercase					:       List[Any]       =		num_attention_heads
						_lowercase					:       Optional[int]       =		hidden_act
						_lowercase					:       Union[str, Any]       =		intermediate_size
						_lowercase					:       List[Any]       =		hidden_dropout_prob
						_lowercase					:       Dict       =		attention_probs_dropout_prob
						_lowercase					:       Any       =		max_position_embeddings
						_lowercase					:       Optional[int]       =		initializer_range
						_lowercase					:       Tuple       =		layer_norm_eps
						_lowercase					:       List[str]       =		position_embedding_type
						_lowercase					:       str       =		cross_attention_frequency
						_lowercase					:       int       =		encoder_hidden_size




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	List[Any]     ,  UpperCamelCase_				:	Union[str, os.PathLike]     ,  **UpperCamelCase_				:	List[str]					)  ->       "PretrainedConfig":



						'''simple docstring'''





						cls._set_token_in_kwargs(UpperCamelCase_					)

						_lowercase  ,    _lowercase					:       List[str]       =		cls.get_config_dict(UpperCamelCase_     ,  **UpperCamelCase_					)

						# get the qformer config dict if we are loading from InstructBlipConfig
						if config_dict.get('model_type'					) == "instructblip":
							_lowercase					:       Optional[int]       =		config_dict['qformer_config']

						if "model_type" in config_dict and hasattr(cls     ,  'model_type'					) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    F'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
							    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''					)

						return cls.from_dict(UpperCamelCase_     ,  **UpperCamelCase_					)






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""instructblip"""
					A_						    =						True




					def __init__(  self				:	Any     ,  UpperCamelCase_				:	List[Any]=None     ,  UpperCamelCase_				:	Tuple=None     ,  UpperCamelCase_				:	Dict=None     ,  UpperCamelCase_				:	Union[str, Any]=32     ,  **UpperCamelCase_				:	int					)  ->       List[str]:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

						if vision_config is None:
							_lowercase					:       Any       =		{}
							logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'					)

						if qformer_config is None:
							_lowercase					:       List[Any]       =		{}
							logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'					)

						if text_config is None:
							_lowercase					:       List[Any]       =		{}
							logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'					)

						_lowercase					:       List[Any]       =		InstructBlipVisionConfig(**UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		InstructBlipQFormerConfig(**UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		text_config['model_type'] if 'model_type' in text_config else 'opt'
						_lowercase					:       int       =		CONFIG_MAPPING[text_model_type](**UpperCamelCase_					)

						_lowercase					:       str       =		self.text_config.tie_word_embeddings
						_lowercase					:       int       =		self.text_config.is_encoder_decoder

						_lowercase					:       Tuple       =		num_query_tokens
						_lowercase					:       str       =		self.vision_config.hidden_size
						_lowercase					:       Union[str, Any]       =		self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
						_lowercase					:       List[Any]       =		1.0
						_lowercase					:       int       =		0.02




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	Tuple     ,  UpperCamelCase_				:	InstructBlipVisionConfig     ,  UpperCamelCase_				:	InstructBlipQFormerConfig     ,  UpperCamelCase_				:	PretrainedConfig     ,  **UpperCamelCase_				:	Dict     ,  )  ->       List[str]:



						'''simple docstring'''





						return cls(
						    vision_config=vision_config.to_dict()     ,  qformer_config=qformer_config.to_dict()     ,  text_config=text_config.to_dict()     ,  **UpperCamelCase_     ,  )




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		copy.deepcopy(self.__dict__					)
						_lowercase					:       Optional[int]       =		self.vision_config.to_dict()
						_lowercase					:       Optional[Any]       =		self.qformer_config.to_dict()
						_lowercase					:       Tuple       =		self.text_config.to_dict()
						_lowercase					:       Dict       =		self.__class__.model_type
						return output
