






'''simple docstring'''
from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


__magic_name__		:     Union[str, Any]					=    logging.get_logger(__name__)



def     A__						(	A_    ,      A_		)     ->				str:
					try:
										with open(__snake_case    ,      "rb"		) as flax_state_f:
															_lowercase			= from_bytes(__snake_case    ,      flax_state_f.read()		)
					except UnpicklingError as e:
										try:
															with open(__snake_case		) as f:
																				if f.read().startswith("version"		):
																									raise OSError(
																									    "You seem to have cloned a repository without having git-lfs installed. Please"
																									    " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
																									    " folder you cloned."		)
																				else:
																									raise ValueError from e
										except (UnicodeDecodeError, ValueError):
															raise EnvironmentError(F"""Unable to convert {model_file} to Flax deserializable object. """		)

					return load_flax_weights_in_pytorch_model(__snake_case    ,      __snake_case		)



def     A__						(	A_    ,      A_		)     ->				Union[str, Any]:

					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    "Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"
										    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
										    " instructions."		)
										raise

					# check if we have bf16 weights
					_lowercase			= flatten_dict(jax.tree_util.tree_map(lambda A_		: x.dtype == jnp.bfloataa    ,      __snake_case		)		).values()
					if any(__snake_case		):
										# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

										# and bf16 is not fully supported in PT yet.
										logger.warning(
										    "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
										    "before loading those in PyTorch model."		)
										_lowercase			= jax.tree_util.tree_map(
										    lambda A_		: params.astype(np.floataa		) if params.dtype == jnp.bfloataa else params    ,      __snake_case		)

					_lowercase			= ""

					_lowercase			= flatten_dict(__snake_case    ,      sep="."		)
					_lowercase			= pt_model.state_dict()

					# keep track of unexpected & missing keys
					_lowercase			= []
					_lowercase			= set(pt_model_dict.keys()		)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										_lowercase			= flax_key_tuple.split("."		)

										if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
															_lowercase			= flax_key_tuple_array[:-1] + ["weight"]
															_lowercase			= jnp.transpose(__snake_case    ,      (3, 2, 0, 1)		)
										elif flax_key_tuple_array[-1] == "kernel":
															_lowercase			= flax_key_tuple_array[:-1] + ["weight"]
															_lowercase			= flax_tensor.T
										elif flax_key_tuple_array[-1] == "scale":
															_lowercase			= flax_key_tuple_array[:-1] + ["weight"]

										if "time_embedding" not in flax_key_tuple_array:
															for i, flax_key_tuple_string in enumerate(__snake_case		):
																				_lowercase			= (
																				    flax_key_tuple_string.replace("_0"    ,      ".0"		)
																				    .replace("_1"    ,      ".1"		)
																				    .replace("_2"    ,      ".2"		)
																				    .replace("_3"    ,      ".3"		)
																				    .replace("_4"    ,      ".4"		)
																				    .replace("_5"    ,      ".5"		)
																				    .replace("_6"    ,      ".6"		)
																				    .replace("_7"    ,      ".7"		)
																				    .replace("_8"    ,      ".8"		)
																				    .replace("_9"    ,      ".9"		)
																				)

										_lowercase			= ".".join(__snake_case		)

										if flax_key in pt_model_dict:
															if flax_tensor.shape != pt_model_dict[flax_key].shape:
																				raise ValueError(
																				    F"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
																				    F"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""		)
															else:
																				# add weight to pytorch dict
																				_lowercase			= np.asarray(__snake_case		) if not isinstance(__snake_case    ,      np.ndarray		) else flax_tensor
																				_lowercase			= torch.from_numpy(__snake_case		)
																				# remove from missing keys
																				missing_keys.remove(__snake_case		)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(__snake_case		)

					pt_model.load_state_dict(__snake_case		)

					# re-transform missing_keys to list
					_lowercase			= list(__snake_case		)

					if len(__snake_case		) > 0:
										logger.warning(
										    "Some weights of the Flax model were not used when initializing the PyTorch model"
										    F""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
										    F""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
										    " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
										    F""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
										    " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
										    " FlaxBertForSequenceClassification model)."		)
					if len(__snake_case		) > 0:
										logger.warning(
										    F"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
										    F""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
										    " use it for predictions and inference."		)

					return pt_model









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase      :       List[Any]    =						{
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase      :       List[Any]    =						['''OwlViTFeatureExtractor''']
 lowerCamelCase      :       str    =						['''OwlViTImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase      :       List[str]    =						[
     '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''OwlViTModel''',
     '''OwlViTPreTrainedModel''',
     '''OwlViTTextModel''',
     '''OwlViTVisionModel''',
     '''OwlViTForObjectDetection''',
 ]

if TYPE_CHECKING:
 from .configuration_owlvit import (
     OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     OwlViTConfig,
     OwlViTOnnxConfig,
     OwlViTTextConfig,
     OwlViTVisionConfig,
 )
 from .processing_owlvit import OwlViTProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_owlvit import OwlViTFeatureExtractor
  from .image_processing_owlvit import OwlViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_owlvit import (
      OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      OwlViTForObjectDetection,
      OwlViTModel,
      OwlViTPreTrainedModel,
      OwlViTTextModel,
      OwlViTVisionModel,
  )

else:
 import sys

 lowerCamelCase      :       List[str]    =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






