





"""simple docstring"""
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__UpperCamelCase     :						str						       =  (
    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
)

__UpperCamelCase     :						List[str]						       =  (
    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
)

__UpperCamelCase     :						Union[str, 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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Optional[int]  ,     _UpperCAmelCase					:    List[Any]			):
 for tf_name, hf_name in patterns:
  lowerCAmelCase    				= k.replace(_UpperCAmelCase  ,     _UpperCAmelCase			)
 return k

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    dict  ,     _UpperCAmelCase					:    dict			):
 lowerCAmelCase    				= BigBirdPegasusConfig(**_UpperCAmelCase			)
 lowerCAmelCase    				= BigBirdPegasusForConditionalGeneration(_UpperCAmelCase			)
 lowerCAmelCase    				= torch_model.state_dict()
 lowerCAmelCase    				= {}

 # separating decoder weights
 lowerCAmelCase    				= {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'			)}
 lowerCAmelCase    				= {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'			):
  lowerCAmelCase    				= [k.endswith(_UpperCAmelCase			) for ending in KEYS_TO_IGNORE]
  if any(_UpperCAmelCase			):
   continue
  lowerCAmelCase    				= DECODER_PATTERNS
  lowerCAmelCase    				= rename_state_dict_key(_UpperCAmelCase  ,     _UpperCAmelCase			)
  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']			):
   lowerCAmelCase    				= v.T
  lowerCAmelCase    				= torch.from_numpy(_UpperCAmelCase			)
  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'			):
  lowerCAmelCase    				= [k.endswith(_UpperCAmelCase			) for ending in KEYS_TO_IGNORE]
  if any(_UpperCAmelCase			):
   continue
  lowerCAmelCase    				= REMAINING_PATTERNS
  lowerCAmelCase    				= rename_state_dict_key(_UpperCAmelCase  ,     _UpperCAmelCase			)
  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']			):
   lowerCAmelCase    				= v.T
  lowerCAmelCase    				= torch.from_numpy(_UpperCAmelCase			)
  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}'

 lowerCAmelCase    				= mapping['model.embed_positions.weight']
 lowerCAmelCase    				= mapping.pop('model.embed_positions.weight'			)
 lowerCAmelCase				,lowerCAmelCase    				= torch_model.load_state_dict(_UpperCAmelCase  ,     strict=_UpperCAmelCase			)
 lowerCAmelCase    				= [
     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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[Any]			):
 lowerCAmelCase    				= tf.train.list_variables(_UpperCAmelCase			)
 lowerCAmelCase    				= {}
 lowerCAmelCase    				= ['global_step']
 for name, shape in tqdm(_UpperCAmelCase  ,     desc='converting tf checkpoint to dict'			):
  lowerCAmelCase    				= any(pat in name for pat in ignore_name			)
  if skip_key:
   continue
  lowerCAmelCase    				= tf.train.load_variable(_UpperCAmelCase  ,     _UpperCAmelCase			)
  lowerCAmelCase    				= array
 return tf_weights

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    dict			):
 lowerCAmelCase    				= get_tf_weights_as_numpy(_UpperCAmelCase			)
 lowerCAmelCase    				= convert_bigbird_pegasus(_UpperCAmelCase  ,     _UpperCAmelCase			)
 torch_model.save_pretrained(_UpperCAmelCase			)


if __name__ == "__main__":
       __UpperCamelCase     :						Optional[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.''')
       __UpperCamelCase     :						Optional[int]						       =  parser.parse_args()
       __UpperCamelCase     :						Optional[Any]						       =  {}
       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__ :       List[Any]				= logging.get_logger(__name__)

A__ :       Tuple				= {
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''instructblip_vision_model'''
       def __init__(  self			,					A_=1408			,					A_=6144			,					A_=39			,					A_=16			,					A_=224			,					A_=14			,					A_="gelu"			,					A_=1E-6			,					A_=0.0			,					A_=1E-10			,					A_=True			,					**A_			,					)  ->						Tuple:





         """simple docstring"""




         super().__init__(**A_							)

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



       @classmethod
       def 				lowercase_				(  cls			,					A_			,					**A_							)  ->						"PretrainedConfig":





         """simple docstring"""




         cls._set_token_in_kwargs(A_							)

         _lowercase			,					_lowercase:      str   			=  cls.get_config_dict(A_			,					**A_							)

         # get the vision config dict if we are loading from InstructBlipConfig
         if config_dict.get('''model_type'''							) == "instructblip":
           _lowercase:      Dict   			=  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(A_			,					**A_							)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''instructblip_qformer'''
       def __init__(  self			,					A_=3_0522			,					A_=768			,					A_=12			,					A_=12			,					A_=3072			,					A_="gelu"			,					A_=0.1			,					A_=0.1			,					A_=512			,					A_=0.02			,					A_=1E-12			,					A_=0			,					A_="absolute"			,					A_=2			,					A_=1408			,					**A_			,					)  ->						Optional[int]:





         """simple docstring"""




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

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



       @classmethod
       def 				lowercase_				(  cls			,					A_			,					**A_							)  ->						"PretrainedConfig":





         """simple docstring"""




         cls._set_token_in_kwargs(A_							)

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

         # get the qformer config dict if we are loading from InstructBlipConfig
         if config_dict.get('''model_type'''							) == "instructblip":
           _lowercase:      Tuple   			=  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(A_			,					**A_							)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''instructblip'''
       UpperCamelCase_ =		True
       def __init__(  self			,					A_=None			,					A_=None			,					A_=None			,					A_=32			,					**A_							)  ->						str:





         """simple docstring"""




         super().__init__(**A_							)

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

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

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

         _lowercase:      int   			=  InstructBlipVisionConfig(**A_							)
         _lowercase:      Optional[Any]   			=  InstructBlipQFormerConfig(**A_							)
         _lowercase:      Optional[Any]   			=  text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
         _lowercase:      Optional[Any]   			=  CONFIG_MAPPING[text_model_type](**A_							)

         _lowercase:      Tuple   			=  self.text_config.tie_word_embeddings
         _lowercase:      Dict   			=  self.text_config.is_encoder_decoder

         _lowercase:      List[Any]   			=  num_query_tokens
         _lowercase:      Any   			=  self.vision_config.hidden_size
         _lowercase:      List[Any]   			=  self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         _lowercase:      Union[str, Any]   			=  1.0
         _lowercase:      Dict   			=  0.02
       @classmethod
       def 				lowercase_				(  cls			,					A_			,					A_			,					A_			,					**A_			,					)  ->						str:





         """simple docstring"""




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



       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




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