


"""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


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

a__    :							str            =					{
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Dict    =      "levit"




	def __init__(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    List[Any]=2_2_4      ,      UpperCAmelCase__      :    List[Any]=3      ,      UpperCAmelCase__      :    Tuple=3      ,      UpperCAmelCase__      :    List[str]=2      ,      UpperCAmelCase__      :    Union[str, Any]=1      ,      UpperCAmelCase__      :    int=1_6      ,      UpperCAmelCase__      :    Dict=[1_2_8, 2_5_6, 3_8_4]      ,      UpperCAmelCase__      :    str=[4, 8, 1_2]      ,      UpperCAmelCase__      :    Optional[Any]=[4, 4, 4]      ,      UpperCAmelCase__      :    Optional[Any]=[1_6, 1_6, 1_6]      ,      UpperCAmelCase__      :    Optional[Any]=0      ,      UpperCAmelCase__      :    str=[2, 2, 2]      ,      UpperCAmelCase__      :    Tuple=[2, 2, 2]      ,      UpperCAmelCase__      :    List[str]=0.02      ,      **UpperCAmelCase__      :    str      ,      )	->     str:
						super().__init__(**UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    image_size
						__SCREAMING_SNAKE_CASE						       =    num_channels
						__SCREAMING_SNAKE_CASE						       =    kernel_size
						__SCREAMING_SNAKE_CASE						       =    stride
						__SCREAMING_SNAKE_CASE						       =    padding
						__SCREAMING_SNAKE_CASE						       =    hidden_sizes
						__SCREAMING_SNAKE_CASE						       =    num_attention_heads
						__SCREAMING_SNAKE_CASE						       =    depths
						__SCREAMING_SNAKE_CASE						       =    key_dim
						__SCREAMING_SNAKE_CASE						       =    drop_path_rate
						__SCREAMING_SNAKE_CASE						       =    patch_size
						__SCREAMING_SNAKE_CASE						       =    attention_ratio
						__SCREAMING_SNAKE_CASE						       =    mlp_ratio
						__SCREAMING_SNAKE_CASE						       =    initializer_range
						__SCREAMING_SNAKE_CASE						       =    [
						    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
						    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
						]




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  List[str]    =      version.parse("1.11")




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




	@property
	def   UpperCAmelCase_				(			self      :    Dict       )	->     float:
						return 1E-4




"""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


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




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


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

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

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

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

					# old PyTorch layer norm bias
					__SCREAMING_SNAKE_CASE						       =    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__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=42       ):




					'''simple docstring'''


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

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

					__SCREAMING_SNAKE_CASE						       =    flatten_dict(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    {}

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

										# Correctly rename weight parameters
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    rename_key_and_reshape_tensor(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )

										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
										__SCREAMING_SNAKE_CASE						       =    jnp.asarray(lowerCAmelCase_       )

					return unflatten_dict(lowerCAmelCase_       )
