
'''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


_snake_case				:   Dict     =				logging.get_logger(__name__)



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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   Optional[int]  ):
						_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 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   List[str]=42  ):
						# Step 1: Convert pytorch tensor to numpy
						_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(_SCREAMING_SNAKE_CASE  )  )

						_SCREAMING_SNAKE_CASE				= flatten_dict(_SCREAMING_SNAKE_CASE  )
						_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(_SCREAMING_SNAKE_CASE  )
												_SCREAMING_SNAKE_CASE				= tuple(renamed_pt_key.split("."  )  )

												# Correctly rename weight parameters
												_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= rename_key_and_reshape_tensor(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_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
												_SCREAMING_SNAKE_CASE				= jnp.asarray(_SCREAMING_SNAKE_CASE  )

						return unflatten_dict(_SCREAMING_SNAKE_CASE  )

'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


_snake_case				:   List[Any]     =				logging.get_logger(__name__)


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								warnings.warn(
								    "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
								    " use DPTImageProcessor instead."					,						UpperCamelCase					,						)
								super().__init__(*UpperCamelCase					,						**UpperCamelCase      )