






from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

lowerCamelCase      :       List[str]    =						{
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				"""efficientnet"""


      def __init__(						self       ,    __UpperCamelCase = 3       ,    __UpperCamelCase = 6_0_0       ,    __UpperCamelCase = 2.0       ,    __UpperCamelCase = 3.1       ,    __UpperCamelCase = 8       ,    __UpperCamelCase = [3, 3, 5, 3, 5, 5, 3]       ,    __UpperCamelCase = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]       ,    __UpperCamelCase = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]       ,    __UpperCamelCase = []       ,    __UpperCamelCase = [1, 2, 2, 2, 1, 2, 1]       ,    __UpperCamelCase = [1, 2, 2, 3, 3, 4, 1]       ,    __UpperCamelCase = [1, 6, 6, 6, 6, 6, 6]       ,    __UpperCamelCase = 0.2_5       ,    __UpperCamelCase = "swish"       ,    __UpperCamelCase = 2_5_6_0       ,    __UpperCamelCase = "mean"       ,    __UpperCamelCase = 0.0_2       ,    __UpperCamelCase = 0.0_0_1       ,    __UpperCamelCase = 0.9_9       ,    __UpperCamelCase = 0.5       ,    __UpperCamelCase = 0.2       ,    **__UpperCamelCase       ,    )->						Tuple:
           super().__init__(**__UpperCamelCase						)

           __lowerCAmelCase             =       num_channels
           __lowerCAmelCase             =       image_size
           __lowerCAmelCase             =       width_coefficient
           __lowerCAmelCase             =       depth_coefficient
           __lowerCAmelCase             =       depth_divisor
           __lowerCAmelCase             =       kernel_sizes
           __lowerCAmelCase             =       in_channels
           __lowerCAmelCase             =       out_channels
           __lowerCAmelCase             =       depthwise_padding
           __lowerCAmelCase             =       strides
           __lowerCAmelCase             =       num_block_repeats
           __lowerCAmelCase             =       expand_ratios
           __lowerCAmelCase             =       squeeze_expansion_ratio
           __lowerCAmelCase             =       hidden_act
           __lowerCAmelCase             =       hidden_dim
           __lowerCAmelCase             =       pooling_type
           __lowerCAmelCase             =       initializer_range
           __lowerCAmelCase             =       batch_norm_eps
           __lowerCAmelCase             =       batch_norm_momentum
           __lowerCAmelCase             =       dropout_rate
           __lowerCAmelCase             =       drop_connect_rate
           __lowerCAmelCase             =       sum(__UpperCamelCase						) * 4



class 				_UpperCamelCase      (a_      ):
      snake_case_	=				version.parse("""1.11"""      )


      @property
      def 			__UpperCAmelCase		(						self						)->						Mapping[str, Mapping[int, str]]:
           return OrderedDict(
               [
                   ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
               ]						)


      @property
      def 			__UpperCAmelCase		(						self						)->						float:
           return 1e-5














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


lowerCamelCase      :       int    =						logging.get_logger(__name__)





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





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       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             =       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             =       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             =       pt_tuple_key[:-1] + ("embedding",)
          return renamed_pt_tuple_key, pt_tensor

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

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

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

     # old PyTorch layer norm bias
     __lowerCAmelCase             =       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    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case=42							):
     # Step 1: Convert pytorch tensor to numpy
     __lowerCAmelCase             =       {k: v.numpy() for k, v in pt_state_dict.items()}

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

     __lowerCAmelCase             =       flatten_dict(__snake_case							)
     __lowerCAmelCase             =       {}

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

          # Correctly rename weight parameters
          __lowerCAmelCase      ,			__lowerCAmelCase             =       rename_key_and_reshape_tensor(__snake_case       ,			__snake_case       ,			__snake_case							)

          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             =       jnp.asarray(__snake_case							)

     return unflatten_dict(__snake_case							)






