


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					:       Dict			    =     logging.get_logger(__name__)



def UpperCamelCase_				(				__a      )	->   Union[str, Any]:
      a__	:  Tuple     =   R"\w+[.]\d+"
      a__	:  List[Any]     =   re.findall(__a			,				__a      )
      for pat in pats:
            a__	:  Union[str, Any]     =   key.replace(__a			,				"_".join(pat.split("."      )      )      )
      return key



def UpperCamelCase_				(				__a			,				__a			,				__a      )	->   List[str]:
      a__	:  List[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)
      ):
            a__	:  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:
            a__	:  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:
            a__	:  Union[str, Any]     =   pt_tuple_key[:-1] + ("embedding",)
            return renamed_pt_tuple_key, pt_tensor

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

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

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

      # old PyTorch layer norm bias
      a__	:  Union[str, Any]     =   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      )	->   str:
      # Step 1: Convert pytorch tensor to numpy
      a__	:  Optional[int]     =   {k: v.numpy() for k, v in pt_state_dict.items()}

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

      a__	:  Optional[Any]     =   flatten_dict(__a      )
      a__	:  Union[str, Any]     =   {}

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

            # Correctly rename weight parameters
            a__,	a__	:  Union[str, 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
            a__	:  str     =   jnp.asarray(__a      )

      return unflatten_dict(__a      )



"""simple docstring"""






import math

import flax.linen as nn
import jax.numpy as jnp




def       A_  (				__lowercase						,	__lowercase						,	__lowercase = 1						,	__lowercase = 1						,	__lowercase = 1.0e4						,	__lowercase = False						,	__lowercase = 1.0						,	):
 assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
 assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
 UpperCamelCase_     :					Optional[int]   	=float(embedding_dim // 2	)
 UpperCamelCase_     :					Optional[Any]   	=math.log(max_timescale / min_timescale	) / (num_timescales - freq_shift)
 UpperCamelCase_     :					List[Any]   	=min_timescale * jnp.exp(jnp.arange(__lowercase						,	dtype=jnp.floataa	) * -log_timescale_increment	)
 UpperCamelCase_     :					int   	=jnp.expand_dims(__lowercase						,	1	) * jnp.expand_dims(__lowercase						,	0	)

 # scale embeddings
 UpperCamelCase_     :					List[str]   	=scale * emb

 if flip_sin_to_cos:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.cos(__lowercase	), jnp.sin(__lowercase	)]						,	axis=1	)
 else:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.sin(__lowercase	), jnp.cos(__lowercase	)]						,	axis=1	)
 UpperCamelCase_     :					List[Any]   	=jnp.reshape(__lowercase						,	[jnp.shape(__lowercase	)[0], embedding_dim]	)
 return signal





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      jnp.floataa



     @nn.compact
     def __call__(							self :Optional[Any]      ,  _lowerCamelCase :List[str]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_1'   )(_lowerCamelCase   )
      UpperCamelCase_     :					Any   	=nn.silu(_lowerCamelCase   )
      UpperCamelCase_     :					Tuple   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_2'   )(_lowerCamelCase   )
      return temb





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      1



     @nn.compact
     def __call__(							self :Union[str, Any]      ,  _lowerCamelCase :Dict   ):







      '''simple docstring'''



      return get_sinusoidal_embeddings(
          _lowerCamelCase      ,  embedding_dim=self.dim      ,  flip_sin_to_cos=self.flip_sin_to_cos      ,  freq_shift=self.freq_shift   )
