





"""simple docstring"""




import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 					_A	(		SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Dict="attention"						):





  """simple docstring"""



  a__     : Any  =params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
  a__     : Optional[int]  =params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
  a__     : str  =params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
  a__     : List[Any]  =params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
  return k, o, q, v
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       Tuple=False						):





  """simple docstring"""



  if split_mlp_wi:
    a__     : Dict  =params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
    a__     : Union[str, Any]  =params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
    a__     : List[str]  =(wi_a, wi_a)
  else:
    a__     : Union[str, Any]  =params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

  a__     : Union[str, Any]  =params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
  return wi, wo
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       List[str]	,	SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       Optional[int]						):





  """simple docstring"""



  return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']
def 					_A	(		SCREAMING_SNAKE_CASE					:       dict	,	*, SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       bool						):





  """simple docstring"""



  a__     : Dict  =traverse_util.flatten_dict(variables["target"]						)
  a__     : Tuple  ={"/".join(SCREAMING_SNAKE_CASE						): v for k, v in old.items()}

  # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
  a__     : int  ="encoder/layers_0/mlp/wi_0/kernel" in old
  print("Split MLP:"	,	SCREAMING_SNAKE_CASE						)

  a__     : List[Any]  =collections.OrderedDict()

  # Shared embeddings.
  a__     : Tuple  =old["token_embedder/embedding"]

  # Encoder.
  for i in range(SCREAMING_SNAKE_CASE						):
    # Block i, layer 0 (Self Attention).
    a__     : int  =tax_layer_norm_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"encoder"	,	"pre_attention_layer_norm"						)
    a__     : Dict  =tax_attention_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"encoder"	,	"attention"						)
    a__     : List[str]  =layer_norm
    a__     : Optional[Any]  =k.T
    a__     : List[Any]  =o.T
    a__     : Union[str, Any]  =q.T
    a__     : str  =v.T

    # Block i, layer 1 (MLP).
    a__     : int  =tax_layer_norm_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"encoder"	,	"pre_mlp_layer_norm"						)
    a__     : int  =tax_mlp_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"encoder"	,	SCREAMING_SNAKE_CASE						)
    a__     : Tuple  =layer_norm
    if split_mlp_wi:
      a__     : Optional[Any]  =wi[0].T
      a__     : Dict  =wi[1].T
    else:
      a__     : Dict  =wi.T
    a__     : Dict  =wo.T

  a__     : Any  =old[
      "encoder/relpos_bias/rel_embedding"
  ].T
  a__     : List[Any]  =old["encoder/encoder_norm/scale"]

  if not is_encoder_only:
    # Decoder.
    for i in range(SCREAMING_SNAKE_CASE						):
      # Block i, layer 0 (Self Attention).
      a__     : Union[str, Any]  =tax_layer_norm_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"decoder"	,	"pre_self_attention_layer_norm"						)
      a__     : Any  =tax_attention_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"decoder"	,	"self_attention"						)
      a__     : Union[str, Any]  =layer_norm
      a__     : List[Any]  =k.T
      a__     : Optional[int]  =o.T
      a__     : Any  =q.T
      a__     : Optional[int]  =v.T

      # Block i, layer 1 (Cross Attention).
      a__     : Optional[int]  =tax_layer_norm_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"decoder"	,	"pre_cross_attention_layer_norm"						)
      a__     : Optional[Any]  =tax_attention_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"decoder"	,	"encoder_decoder_attention"						)
      a__     : Optional[int]  =layer_norm
      a__     : Union[str, Any]  =k.T
      a__     : Optional[Any]  =o.T
      a__     : str  =q.T
      a__     : Any  =v.T

      # Block i, layer 2 (MLP).
      a__     : Tuple  =tax_layer_norm_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"decoder"	,	"pre_mlp_layer_norm"						)
      a__     : Union[str, Any]  =tax_mlp_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"decoder"	,	SCREAMING_SNAKE_CASE						)
      a__     : str  =layer_norm
      if split_mlp_wi:
        a__     : Tuple  =wi[0].T
        a__     : List[str]  =wi[1].T
      else:
        a__     : Tuple  =wi.T
      a__     : Union[str, Any]  =wo.T

    a__     : Union[str, Any]  =old["decoder/decoder_norm/scale"]
    a__     : str  =old[
        "decoder/relpos_bias/rel_embedding"
    ].T

    # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
    if "decoder/logits_dense/kernel" in old:
      a__     : List[str]  =old["decoder/logits_dense/kernel"].T

  return new
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       bool						):





  """simple docstring"""



  a__     : List[Any]  =collections.OrderedDict([(k, torch.from_numpy(v.copy()						)) for (k, v) in converted_params.items()]						)

  # Add what is missing.
  if "encoder.embed_tokens.weight" not in state_dict:
    a__     : Optional[Any]  =state_dict["shared.weight"]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      a__     : List[Any]  =state_dict["shared.weight"]

    if "lm_head.weight" not in state_dict:  # For old 1.0 models.
      print("Using shared word embeddings as lm_head."						)
      a__     : int  =state_dict["shared.weight"]

  return state_dict
def 					_A	(		SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       Any						):





  """simple docstring"""



  a__     : Dict  =checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE						)
  a__     : str  =convert_tax_to_pytorch(SCREAMING_SNAKE_CASE	,	num_layers=config.num_layers	,	is_encoder_only=SCREAMING_SNAKE_CASE						)
  a__     : List[str]  =make_state_dict(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  model.load_state_dict(SCREAMING_SNAKE_CASE	,	strict=SCREAMING_SNAKE_CASE						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       bool = False						):





  """simple docstring"""



  a__     : Optional[Any]  =TaConfig.from_json_file(SCREAMING_SNAKE_CASE						)
  print(f'''Building PyTorch model from configuration: {config}'''						)
  # Non-v1.1 checkpoints could also use T5Model, but this works for all.
  # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
  if is_encoder_only:
    a__     : Optional[Any]  =TaEncoderModel(SCREAMING_SNAKE_CASE						)
  else:
    a__     : Optional[Any]  =TaForConditionalGeneration(SCREAMING_SNAKE_CASE						)

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)

  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
  model.save_pretrained(SCREAMING_SNAKE_CASE						)

  # Verify that we can load the checkpoint.
  model.from_pretrained(SCREAMING_SNAKE_CASE						)
  print("Done"						)


if __name__ == "__main__":
     UpperCAmelCase     :     Any							=	argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
     # Required parameters
     parser.add_argument(
         """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
     )
     parser.add_argument(
         """--config_file""",
         default=None,
         type=str,
         required=True,
         help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     parser.add_argument(
         """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
     )
     UpperCAmelCase     :     List[Any]							=	parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )













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


UpperCAmelCase     :     List[Any]							=	logging.get_logger(__name__)

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







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Dict            =      """instructblip_vision_model"""







      def __init__(   self		,				lowerCAmelCase__=1_4_0_8		,				lowerCAmelCase__=6_1_4_4		,				lowerCAmelCase__=3_9		,				lowerCAmelCase__=1_6		,				lowerCAmelCase__=2_2_4		,				lowerCAmelCase__=1_4		,				lowerCAmelCase__="gelu"		,				lowerCAmelCase__=1E-6		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=1E-10		,				lowerCAmelCase__=True		,				**lowerCAmelCase__		,				)       ->						Optional[Any]:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__							)

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







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						"PretrainedConfig":



        '''simple docstring'''




        cls._set_token_in_kwargs(lowerCAmelCase__							)

        a__ ,							a__     : Optional[Any]  =cls.get_config_dict(lowerCAmelCase__		,				**lowerCAmelCase__							)

        # get the vision config dict if we are loading from InstructBlipConfig
        if config_dict.get("model_type"							) == "instructblip":
          a__     : 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(lowerCAmelCase__		,				**lowerCAmelCase__							)







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Dict            =      """instructblip_qformer"""







      def __init__(   self		,				lowerCAmelCase__=3_0_5_2_2		,				lowerCAmelCase__=7_6_8		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=3_0_7_2		,				lowerCAmelCase__="gelu"		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=5_1_2		,				lowerCAmelCase__=0.02		,				lowerCAmelCase__=1E-12		,				lowerCAmelCase__=0		,				lowerCAmelCase__="absolute"		,				lowerCAmelCase__=2		,				lowerCAmelCase__=1_4_0_8		,				**lowerCAmelCase__		,				)       ->						Dict:



        '''simple docstring'''




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

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







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						"PretrainedConfig":



        '''simple docstring'''




        cls._set_token_in_kwargs(lowerCAmelCase__							)

        a__ ,							a__     : str  =cls.get_config_dict(lowerCAmelCase__		,				**lowerCAmelCase__							)

        # get the qformer config dict if we are loading from InstructBlipConfig
        if config_dict.get("model_type"							) == "instructblip":
          a__     : 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(lowerCAmelCase__		,				**lowerCAmelCase__							)







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Dict            =      """instructblip"""
      _lowercase    : List[Any]            =      True







      def __init__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=3_2		,				**lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__							)

        if vision_config is None:
          a__     : List[Any]  ={}
          logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."							)

        if qformer_config is None:
          a__     : Tuple  ={}
          logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."							)

        if text_config is None:
          a__     : Dict  ={}
          logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)."							)

        a__     : Dict  =InstructBlipVisionConfig(**lowerCAmelCase__							)
        a__     : Union[str, Any]  =InstructBlipQFormerConfig(**lowerCAmelCase__							)
        a__     : Tuple  =text_config["model_type"] if "model_type" in text_config else "opt"
        a__     : List[str]  =CONFIG_MAPPING[text_model_type](**lowerCAmelCase__							)

        a__     : Union[str, Any]  =self.text_config.tie_word_embeddings
        a__     : Optional[Any]  =self.text_config.is_encoder_decoder

        a__     : str  =num_query_tokens
        a__     : List[Any]  =self.vision_config.hidden_size
        a__     : str  =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
        a__     : List[Any]  =1.0
        a__     : List[str]  =0.02







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__		,				)       ->						int:



        '''simple docstring'''




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







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



        '''simple docstring'''




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






