

"""simple docstring"""

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


_UpperCamelCase		     =    logging.get_logger(__name__)



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						str:
 lowerCAmelCase__   :				Dict 			=				r"\w+[.]\d+"
 lowerCAmelCase__   :				Optional[int] 			=				re.findall(lowercase__					,					lowercase__   )
 for pat in pats:
  lowerCAmelCase__   :				Any 			=				key.replace(lowercase__					,					"_".join(pat.split("."   )   )   )
 return key



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						Tuple:
 lowerCAmelCase__   :				Optional[int] 			=				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)
 ):
  lowerCAmelCase__   :				Union[str, Any] 			=				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:
  lowerCAmelCase__   :				Optional[Any] 			=				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:
  lowerCAmelCase__   :				List[Any] 			=				pt_tuple_key[:-1] + ("embedding",)
  return renamed_pt_tuple_key, pt_tensor

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

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

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

 # old PyTorch layer norm bias
 lowerCAmelCase__   :				Tuple 			=				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       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__=4_2   )			->						Optional[int]:
 # Step 1: Convert pytorch tensor to numpy
 lowerCAmelCase__   :				List[str] 			=				{k: v.numpy() for k, v in pt_state_dict.items()}

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

 lowerCAmelCase__   :				Optional[Any] 			=				flatten_dict(lowercase__   )
 lowerCAmelCase__   :				Any 			=				{}

 # Need to change some parameters name to match Flax names
 for pt_key, pt_tensor in pt_state_dict.items():
  lowerCAmelCase__   :				int 			=				rename_key(lowercase__   )
  lowerCAmelCase__   :				List[Any] 			=				tuple(renamed_pt_key.split("."   )   )

  # Correctly rename weight parameters
  lowerCAmelCase__			,						lowerCAmelCase__   :				Dict 			=				rename_key_and_reshape_tensor(lowercase__					,					lowercase__					,					lowercase__   )

  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
  lowerCAmelCase__   :				Union[str, Any] 			=				jnp.asarray(lowercase__   )

 return unflatten_dict(lowercase__   )









"""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


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      Union[str, Any]    =						'beit'






       def __init__(   self			,					snake_case=8_192			,					snake_case=768			,					snake_case=12			,					snake_case=12			,					snake_case=3_072			,					snake_case="gelu"			,					snake_case=0.0			,					snake_case=0.0			,					snake_case=0.02			,					snake_case=1e-12			,					snake_case=224			,					snake_case=16			,					snake_case=3			,					snake_case=False			,					snake_case=False			,					snake_case=False			,					snake_case=False			,					snake_case=0.1			,					snake_case=0.1			,					snake_case=True			,					snake_case=[3, 5, 7, 11]			,					snake_case=[1, 2, 3, 6]			,					snake_case=True			,					snake_case=0.4			,					snake_case=256			,					snake_case=1			,					snake_case=False			,					snake_case=255			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(**snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				vocab_size
        lowerCAmelCase__   :				int 			=				hidden_size
        lowerCAmelCase__   :				str 			=				num_hidden_layers
        lowerCAmelCase__   :				Any 			=				num_attention_heads
        lowerCAmelCase__   :				List[str] 			=				intermediate_size
        lowerCAmelCase__   :				int 			=				hidden_act
        lowerCAmelCase__   :				Optional[Any] 			=				hidden_dropout_prob
        lowerCAmelCase__   :				str 			=				attention_probs_dropout_prob
        lowerCAmelCase__   :				Optional[int] 			=				initializer_range
        lowerCAmelCase__   :				Dict 			=				layer_norm_eps

        lowerCAmelCase__   :				int 			=				image_size
        lowerCAmelCase__   :				Union[str, Any] 			=				patch_size
        lowerCAmelCase__   :				Dict 			=				num_channels
        lowerCAmelCase__   :				Optional[Any] 			=				use_mask_token
        lowerCAmelCase__   :				Dict 			=				use_absolute_position_embeddings
        lowerCAmelCase__   :				Any 			=				use_relative_position_bias
        lowerCAmelCase__   :				List[Any] 			=				use_shared_relative_position_bias
        lowerCAmelCase__   :				Dict 			=				layer_scale_init_value
        lowerCAmelCase__   :				Optional[int] 			=				drop_path_rate
        lowerCAmelCase__   :				Optional[Any] 			=				use_mean_pooling
        # decode head attributes (semantic segmentation)
        lowerCAmelCase__   :				Optional[int] 			=				out_indices
        lowerCAmelCase__   :				List[Any] 			=				pool_scales
        # auxiliary head attributes (semantic segmentation)
        lowerCAmelCase__   :				List[Any] 			=				use_auxiliary_head
        lowerCAmelCase__   :				Optional[int] 			=				auxiliary_loss_weight
        lowerCAmelCase__   :				List[str] 			=				auxiliary_channels
        lowerCAmelCase__   :				Optional[Any] 			=				auxiliary_num_convs
        lowerCAmelCase__   :				Union[str, Any] 			=				auxiliary_concat_input
        lowerCAmelCase__   :				List[str] 			=				semantic_loss_ignore_index






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      List[str]    =						version.parse('1.11'       )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return 1e-4






