
"""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						=			logging.get_logger(__name__)
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      List[str]          =   r'\w+[.]\d+'
		lowerCamelCase					:      str          =   re.findall(a_,						a_      )
		for pat in pats:
				lowerCamelCase					:      List[Any]          =   key.replace(a_,						'_'.join(pat.split('.'      )      )      )
		return key
def 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      str          =   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					:      Optional[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					:      List[str]          =   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					:      Optional[Any]          =   pt_tuple_key[:-1] + ('embedding',)
				return renamed_pt_tuple_key, pt_tensor

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

		# linear layer
		lowerCamelCase					:      Dict          =   pt_tuple_key[:-1] + ('kernel',)
		if pt_tuple_key[-1] == "weight":
				lowerCamelCase					:      List[Any]          =   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					:      int          =   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 (		a_,						a_,						a_=42      ):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   {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(a_      )      )

		lowerCamelCase					:      Optional[Any]          =   flatten_dict(a_      )
		lowerCamelCase					:      int          =   {}

		# Need to change some parameters name to match Flax names
		for pt_key, pt_tensor in pt_state_dict.items():
				lowerCamelCase					:      Optional[Any]          =   rename_key(a_      )
				lowerCamelCase					:      Any          =   tuple(renamed_pt_key.split('.'      )      )

				# Correctly rename weight parameters
				lowerCamelCase					:      Optional[Any]          =   rename_key_and_reshape_tensor(a_,						a_,						a_      )

				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					:      str          =   jnp.asarray(a_      )

		return unflatten_dict(a_      )

"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'encoder-decoder'
	lowercase_				=     True




	def __init__(			self		,			**UpperCAmelCase_ )		->							str:
			super().__init__(**UpperCAmelCase_ )
			assert (
			    "encoder" in kwargs and "decoder" in kwargs
			), "Config has to be initialized with encoder and decoder config"
			lowerCamelCase					:      List[Any]          =   kwargs.pop('encoder' )
			lowerCamelCase					:      Optional[int]          =   encoder_config.pop('model_type' )
			lowerCamelCase					:      str          =   kwargs.pop('decoder' )
			lowerCamelCase					:      Dict          =   decoder_config.pop('model_type' )

			from ..auto.configuration_auto import AutoConfig

			lowerCamelCase					:      int          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   True




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )		->							PretrainedConfig:
			logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
			lowerCamelCase					:      str          =   True
			lowerCamelCase					:      Optional[Any]          =   True

			return cls(encoder=encoder_config.to_dict()		,			decoder=decoder_config.to_dict()		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   copy.deepcopy(self.__dict__ )
			lowerCamelCase					:      Union[str, Any]          =   self.encoder.to_dict()
			lowerCamelCase					:      List[Any]          =   self.decoder.to_dict()
			lowerCamelCase					:      Tuple          =   self.__class__.model_type
			return output






