


'''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


UpperCAmelCase_      :		Tuple          =		logging.get_logger(__name__)


def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   try:
      with open(_lowerCamelCase		,	'rb'      ) as flax_state_f:
         _a					:  Dict						=  from_bytes(_lowerCamelCase		,	flax_state_f.read()      )
   except UnpicklingError as e:
      try:
         with open(_lowerCamelCase      ) 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(_lowerCamelCase		,	_lowerCamelCase      )







def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   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
   _a					:  List[str]						=  flatten_dict(jax.tree_util.tree_map(lambda A      : x.dtype == jnp.bfloataa		,	_lowerCamelCase      )      ).values()
   if any(_lowerCamelCase      ):
      # 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.'      )
      _a					:  Optional[Any]						=  jax.tree_util.tree_map(
          lambda A      : params.astype(np.floataa      ) if params.dtype == jnp.bfloataa else params		,	_lowerCamelCase      )

   _a					:  Dict						=  ''

   _a					:  Any						=  flatten_dict(_lowerCamelCase		,	sep='.'      )
   _a					:  Any						=  pt_model.state_dict()

   # keep track of unexpected & missing keys
   _a					:  Tuple						=  []
   _a					:  str						=  set(pt_model_dict.keys()      )

   for flax_key_tuple, flax_tensor in flax_state_dict.items():
      _a					:  List[str]						=  flax_key_tuple.split('.'      )

      if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
         _a					:  int						=  flax_key_tuple_array[:-1] + ['weight']
         _a					:  Tuple						=  jnp.transpose(_lowerCamelCase		,	(3, 2, 0, 1)      )
      elif flax_key_tuple_array[-1] == "kernel":
         _a					:  str						=  flax_key_tuple_array[:-1] + ['weight']
         _a					:  List[str]						=  flax_tensor.T
      elif flax_key_tuple_array[-1] == "scale":
         _a					:  Any						=  flax_key_tuple_array[:-1] + ['weight']

      if "time_embedding" not in flax_key_tuple_array:
         for i, flax_key_tuple_string in enumerate(_lowerCamelCase      ):
            _a					:  Optional[Any]						=  (
                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'      )
            )

      _a					:  Union[str, Any]						=  '.'.join(_lowerCamelCase      )

      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
            _a					:  List[Any]						=  np.asarray(_lowerCamelCase      ) if not isinstance(_lowerCamelCase		,	np.ndarray      ) else flax_tensor
            _a					:  Dict						=  torch.from_numpy(_lowerCamelCase      )
            # remove from missing keys
            missing_keys.remove(_lowerCamelCase      )
      else:
         # weight is not expected by PyTorch model
         unexpected_keys.append(_lowerCamelCase      )

   pt_model.load_state_dict(_lowerCamelCase      )

   # re-transform missing_keys to list
   _a					:  int						=  list(_lowerCamelCase      )

   if len(_lowerCamelCase      ) > 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(_lowerCamelCase      ) > 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








'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase_							:	int	     =		logging.get_logger(__name__)

UpperCAmelCase_							:	Dict	     =		{
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}





class      lowerCAmelCase (			__lowerCAmelCase					, __lowerCAmelCase):
       __lowercase	:   List[Any]										=      '''swin'''

       __lowercase	:   str										=      {
           '''num_attention_heads''': '''num_heads''',
           '''num_hidden_layers''': '''num_layers''',
       }


       def __init__( self     ,	__SCREAMING_SNAKE_CASE=224     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=96     ,	__SCREAMING_SNAKE_CASE=[2, 2, 6, 2]     ,	__SCREAMING_SNAKE_CASE=[3, 6, 12, 24]     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=4.0     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=0.0     ,	__SCREAMING_SNAKE_CASE=0.1     ,	__SCREAMING_SNAKE_CASE="gelu"     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=0.02     ,	__SCREAMING_SNAKE_CASE=1E-5     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	int:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE	)

            __snake_case            =	image_size
            __snake_case            =	patch_size
            __snake_case            =	num_channels
            __snake_case            =	embed_dim
            __snake_case            =	depths
            __snake_case            =	len(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	num_heads
            __snake_case            =	window_size
            __snake_case            =	mlp_ratio
            __snake_case            =	qkv_bias
            __snake_case            =	hidden_dropout_prob
            __snake_case            =	attention_probs_dropout_prob
            __snake_case            =	drop_path_rate
            __snake_case            =	hidden_act
            __snake_case            =	use_absolute_embeddings
            __snake_case            =	layer_norm_eps
            __snake_case            =	initializer_range
            __snake_case            =	encoder_stride
            # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
            # this indicates the channel dimension after the last stage of the model
            __snake_case            =	int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE	) - 1)	)
            __snake_case            =	['''stem'''] + [F'''stage{idx}''' for idx in range(1     ,	len(__SCREAMING_SNAKE_CASE	) + 1	)]
            __snake_case       ,     __snake_case            =	get_aligned_output_features_output_indices(
                out_features=__SCREAMING_SNAKE_CASE     ,	out_indices=__SCREAMING_SNAKE_CASE     ,	stage_names=self.stage_names	)





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   Optional[int]										=      version.parse('''1.11''')


       @property
       def    lowerCAmelCase   ( self	)   ->	Mapping[str, Mapping[int, str]]:



            '''simple docstring'''




            return OrderedDict(
                [
                    ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                ]	)




       @property
       def    lowerCAmelCase   ( self	)   ->	float:



            '''simple docstring'''




            return 1E-4






