
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


A_		:     Tuple        =     logging.get_logger(__name__)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Dict					):






 '''simple docstring'''

 a						=		r"\w+[.]\d+"
 a						=		re.findall(UpperCAmelCase__ ,					UpperCAmelCase__					)
 for pat in pats:
  a						=		key.replace(UpperCAmelCase__ ,					"_".join(pat.split("."					)					)					)
 return key


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int ,					UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :Union[str, Any]					):






 '''simple docstring'''

 a						=		pt_tuple_key[:-1] + ("scale",)
 if (
     any("norm" in str_ for str_ in pt_tuple_key					)
     and (pt_tuple_key[-1] == "bias")
     and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
     and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
 ):
  a						=		pt_tuple_key[:-1] + ("scale",)
  return renamed_pt_tuple_key, pt_tensor
 elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
  a						=		pt_tuple_key[:-1] + ("scale",)
  return renamed_pt_tuple_key, pt_tensor

 # embedding
 if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
  a						=		pt_tuple_key[:-1] + ("embedding",)
  return renamed_pt_tuple_key, pt_tensor

 # conv layer
 a						=		pt_tuple_key[:-1] + ("kernel",)
 if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
  a						=		pt_tensor.transpose(2 ,					3 ,					1 ,					0					)
  return renamed_pt_tuple_key, pt_tensor

 # linear layer
 a						=		pt_tuple_key[:-1] + ("kernel",)
 if pt_tuple_key[-1] == "weight":
  a						=		pt_tensor.T
  return renamed_pt_tuple_key, pt_tensor

 # old PyTorch layer norm weight
 a						=		pt_tuple_key[:-1] + ("weight",)
 if pt_tuple_key[-1] == "gamma":
  return renamed_pt_tuple_key, pt_tensor

 # old PyTorch layer norm bias
 a						=		pt_tuple_key[:-1] + ("bias",)
 if pt_tuple_key[-1] == "beta":
  return renamed_pt_tuple_key, pt_tensor

 return pt_tuple_key, pt_tensor




def 	UpperCAmelCase__      (      UpperCAmelCase__ :Dict ,					UpperCAmelCase__ :Optional[Any] ,					UpperCAmelCase__ :Union[str, Any]=42					):






 '''simple docstring'''

 a						=		{k: v.numpy() for k, v in pt_state_dict.items()}

 # Step 2: Since the model is stateless, get random Flax params
 a						=		flax_model.init_weights(PRNGKey(UpperCAmelCase__					)					)

 a						=		flatten_dict(UpperCAmelCase__					)
 a						=		{}

 # Need to change some parameters name to match Flax names
 for pt_key, pt_tensor in pt_state_dict.items():
  a						=		rename_key(UpperCAmelCase__					)
  a						=		tuple(renamed_pt_key.split("."					)					)

  # Correctly rename weight parameters
  a		,  a						=		rename_key_and_reshape_tensor(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

  if flax_key in random_flax_state_dict:
   if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
    raise ValueError(
        F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
        F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""					)

        # also add unexpected weight so that warning is thrown
  a						=		jnp.asarray(UpperCAmelCase__					)

 return unflatten_dict(UpperCAmelCase__					)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_		:     Optional[int]        =     {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_		:     List[Any]        =     [
         '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''InstructBlipQFormerModel''',
         '''InstructBlipPreTrainedModel''',
         '''InstructBlipForConditionalGeneration''',
         '''InstructBlipVisionModel''',
     ]

if TYPE_CHECKING:
     from .configuration_instructblip import (
         INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         InstructBlipConfig,
         InstructBlipQFormerConfig,
         InstructBlipVisionConfig,
     )
     from .processing_instructblip import InstructBlipProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_instructblip import (
              INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              InstructBlipForConditionalGeneration,
              InstructBlipPreTrainedModel,
              InstructBlipQFormerModel,
              InstructBlipVisionModel,
          )

else:
     import sys

     A_		:     Tuple        =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

