'''simple docstring'''






import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


_UpperCAmelCase    :     Tuple         =       logging.get_logger(__name__)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Tuple   ,		__snake_case							:			Union[str, Any]   ,		__snake_case							:			List[str]   ,		__snake_case							:			Tuple=False				):
     try:
          import torch  # noqa: F401
     except ImportError:
          logger.error(
              'Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'
              ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
              ' instructions.'				)
          raise

     if not is_sharded:
          _A    		=  os.path.abspath(__lowercase				)
          logger.info(F'Loading PyTorch weights from {pt_path}'				)

          _A    		=  torch.load(__lowercase   ,		map_location='cpu'				)
          logger.info(F'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()				):,} parameters.'				)

          _A    		=  convert_pytorch_state_dict_to_flax(__lowercase   ,		__lowercase				)
     else:
          # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
          _A    		=  convert_pytorch_sharded_state_dict_to_flax(__lowercase   ,		__lowercase				)
     return flax_state_dict





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Dict   ,		__snake_case							:			Tuple   ,		__snake_case							:			List[str]   ,		__snake_case							:			Tuple   ,		):

     def is_key_or_prefix_key_in_dict(__snake_case							:			str				) -> bool:
          return len(set(__lowercase				) & {key, (model_prefix,) + key}				) > 0

     # layer norm
     _A    		=  pt_tuple_key[:-1] + ('scale',)
     if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__lowercase				):
          return renamed_pt_tuple_key, pt_tensor

     # batch norm layer mean
     _A    		=  pt_tuple_key[:-1] + ('mean',)
     if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__lowercase				):
          return renamed_pt_tuple_key, pt_tensor

     # batch norm layer var
     _A    		=  pt_tuple_key[:-1] + ('var',)
     if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__lowercase				):
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     _A    		=  pt_tuple_key[:-1] + ('embedding',)
     if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__lowercase				):
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     _A    		=  pt_tuple_key[:-1] + ('kernel',)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__lowercase				):
          _A    		=  pt_tensor.transpose(2   ,		3   ,		1   ,		0				)
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     _A    		=  pt_tuple_key[:-1] + ('kernel',)
     if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__lowercase				):
          _A    		=  pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

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

     # old PyTorch layer norm bias
     _A    		=  pt_tuple_key[:-1] + ('bias',)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
     _A    		=  None
     if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
          _A    		=  pt_tuple_key[-2] + '_g'
     elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
          _A    		=  pt_tuple_key[-2] + '_v'
     if name is not None:
          _A    		=  pt_tuple_key[:-3] + (name,)
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[Any]   ,		__snake_case							:			int				):
     # convert pytorch tensor to numpy
     _A    		=  {k: v.numpy() for k, v in pt_state_dict.items()}

     _A    		=  flax_model.base_model_prefix

     # use params dict if the model contains batch norm layers
     if "params" in flax_model.params:
          _A    		=  flax_model.params['params']
     else:
          _A    		=  flax_model.params
     _A    		=  flatten_dict(__lowercase				)

     # add batch_stats keys,values to dict
     if "batch_stats" in flax_model.params:
          _A    		=  flatten_dict(flax_model.params['batch_stats']				)
          random_flax_state_dict.update(__lowercase				)

     _A    		=  {}

     _A    		=  (model_prefix not in flax_model_params) and (
         model_prefix in {k.split('.'				)[0] for k in pt_state_dict.keys()}
     )
     _A    		=  (model_prefix in flax_model_params) and (
         model_prefix not in {k.split('.'				)[0] for k in pt_state_dict.keys()}
     )

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          _A    		=  tuple(pt_key.split('.'				)				)

          # remove base model prefix if necessary
          _A    		=  pt_tuple_key[0] == model_prefix
          if load_model_with_head_into_base_model and has_base_model_prefix:
               _A    		=  pt_tuple_key[1:]

          # Correctly rename weight parameters
          _A    		=  rename_key_and_reshape_tensor(
              __lowercase   ,		__lowercase   ,		__lowercase   ,		__lowercase				)

          # add model prefix if necessary
          _A    		=  (model_prefix,) + flax_key in random_flax_state_dict
          if load_base_model_into_model_with_head and require_base_model_prefix:
               _A    		=  (model_prefix,) + flax_key

          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}.'				)

                        # add batch stats if the model contains batchnorm layers
          if "batch_stats" in flax_model.params:
               if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                    _A    		=  jnp.asarray(__lowercase				)
                    continue
               # remove num_batches_tracked key
               if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(__lowercase   ,		__lowercase				)
                    continue

               # also add unexpected weight so that warning is thrown
               _A    		=  jnp.asarray(__lowercase				)

          else:
               # also add unexpected weight so that warning is thrown
               _A    		=  jnp.asarray(__lowercase				)

     return unflatten_dict(__lowercase				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[Any]   ,		__snake_case							:			Tuple				):
     import torch

     # Load the index
     _A    		=  {}
     for shard_file in shard_filenames:
          # load using msgpack utils
          _A    		=  torch.load(__lowercase				)
          _A    		=  {k: v.numpy() for k, v in pt_state_dict.items()}

          _A    		=  flax_model.base_model_prefix

          # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
          if "batch_stats" in flax_model.params:
               _A    		=  flax_model.params['params']

               _A    		=  flatten_dict(__lowercase				)
               random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']				)				)
          else:
               _A    		=  flax_model.params
               _A    		=  flatten_dict(__lowercase				)

          _A    		=  (model_prefix not in flax_model_params) and (
              model_prefix in {k.split('.'				)[0] for k in pt_state_dict.keys()}
          )
          _A    		=  (model_prefix in flax_model_params) and (
              model_prefix not in {k.split('.'				)[0] for k in pt_state_dict.keys()}
          )
          # Need to change some parameters name to match Flax names
          for pt_key, pt_tensor in pt_state_dict.items():
               _A    		=  tuple(pt_key.split('.'				)				)

               # remove base model prefix if necessary
               _A    		=  pt_tuple_key[0] == model_prefix
               if load_model_with_head_into_base_model and has_base_model_prefix:
                    _A    		=  pt_tuple_key[1:]

               # Correctly rename weight parameters
               _A    		=  rename_key_and_reshape_tensor(
                   __lowercase   ,		__lowercase   ,		__lowercase   ,		__lowercase				)
               # add model prefix if necessary
               _A    		=  (model_prefix,) + flax_key in random_flax_state_dict
               if load_base_model_into_model_with_head and require_base_model_prefix:
                    _A    		=  (model_prefix,) + flax_key

               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}.'				)

                                # add batch stats if the model contains batchnorm layers
               if "batch_stats" in flax_model.params:
                    if "mean" in flax_key[-1]:
                         _A    		=  jnp.asarray(__lowercase				)
                         continue
                    if "var" in flax_key[-1]:
                         _A    		=  jnp.asarray(__lowercase				)
                         continue
                    # remove num_batches_tracked key
                    if "num_batches_tracked" in flax_key[-1]:
                         flax_state_dict.pop(__lowercase   ,		__lowercase				)
                         continue

                    # also add unexpected weight so that warning is thrown
                    _A    		=  jnp.asarray(__lowercase				)

               else:
                    # also add unexpected weight so that warning is thrown
                    _A    		=  jnp.asarray(__lowercase				)
     return unflatten_dict(__lowercase				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			List[str]				):
     _A    		=  os.path.abspath(__lowercase				)
     logger.info(F'Loading Flax weights from {flax_checkpoint_path}'				)

     # import correct flax class
     _A    		=  getattr(__lowercase   ,		'Flax' + model.__class__.__name__				)

     # load flax weight dict
     with open(__lowercase   ,		'rb'				) as state_f:
          try:
               _A    		=  from_bytes(__lowercase   ,		state_f.read()				)
          except UnpicklingError:
               raise EnvironmentError(F'Unable to convert {flax_checkpoint_path} to Flax deserializable object. '				)

     return load_flax_weights_in_pytorch_model(__lowercase   ,		__lowercase				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Dict   ,		__snake_case							:			Optional[int]				):

     try:
          import torch  # noqa: F401
     except ImportError:
          logger.error(
              'Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'
              ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
              ' instructions.'				)
          raise

     # check if we have bf16 weights
     _A    		=  flatten_dict(jax.tree_util.tree_map(lambda __snake_case				: x.dtype == jnp.bfloataa   ,		__lowercase				)				).values()
     if any(__lowercase				):
          # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
          # and bf16 is not fully supported in PT yet.
          logger.warning(
              'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
              'before loading those in PyTorch model.'				)
          _A    		=  jax.tree_util.tree_map(
              lambda __snake_case				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params   ,		__lowercase				)

     _A    		=  flatten_dict(__lowercase				)
     _A    		=  pt_model.state_dict()

     _A    		=  (pt_model.base_model_prefix in flax_state) and (
         pt_model.base_model_prefix not in {k.split('.'				)[0] for k in pt_model_dict.keys()}
     )
     _A    		=  (pt_model.base_model_prefix not in flax_state) and (
         pt_model.base_model_prefix in {k.split('.'				)[0] for k in pt_model_dict.keys()}
     )

     # keep track of unexpected & missing keys
     _A    		=  []
     _A    		=  set(pt_model_dict.keys()				)

     for flax_key_tuple, flax_tensor in flax_state_dict.items():
          _A    		=  flax_key_tuple[0] == pt_model.base_model_prefix
          _A    		=  '.'.join((pt_model.base_model_prefix,) + flax_key_tuple				) in pt_model_dict

          # adapt flax_key to prepare for loading from/to base model only
          if load_model_with_head_into_base_model and has_base_model_prefix:
               _A    		=  flax_key_tuple[1:]
          elif load_base_model_into_model_with_head and require_base_model_prefix:
               _A    		=  (pt_model.base_model_prefix,) + flax_key_tuple

          # rename flax weights to PyTorch format
          if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__lowercase				) not in pt_model_dict:
               # conv layer
               _A    		=  flax_key_tuple[:-1] + ('weight',)
               _A    		=  jnp.transpose(__lowercase   ,		(3, 2, 0, 1)				)
          elif flax_key_tuple[-1] == "kernel" and ".".join(__lowercase				) not in pt_model_dict:
               # linear layer
               _A    		=  flax_key_tuple[:-1] + ('weight',)
               _A    		=  flax_tensor.T
          elif flax_key_tuple[-1] in ["scale", "embedding"]:
               _A    		=  flax_key_tuple[:-1] + ('weight',)

          # adding batch stats from flax batch norm to pt
          elif "mean" in flax_key_tuple[-1]:
               _A    		=  flax_key_tuple[:-1] + ('running_mean',)
          elif "var" in flax_key_tuple[-1]:
               _A    		=  flax_key_tuple[:-1] + ('running_var',)

          if "batch_stats" in flax_state:
               _A    		=  '.'.join(flax_key_tuple[1:]				)  # Remove the params/batch_stats header
          else:
               _A    		=  '.'.join(__lowercase				)

          # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
          _A    		=  {}
          # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
          for key in pt_model_dict:
               _A    		=  key.split('.'				)
               _A    		=  None
               if key_components[-3::2] == ["parametrizations", "original0"]:
                    _A    		=  key_components[-2] + '_g'
               elif key_components[-3::2] == ["parametrizations", "original1"]:
                    _A    		=  key_components[-2] + '_v'
               if name is not None:
                    _A    		=  key_components[:-3] + [name]
                    _A    		=  '.'.join(__lowercase				)
                    _A    		=  key

          if flax_key in special_pt_names:
               _A    		=  special_pt_names[flax_key]

          if flax_key in pt_model_dict:
               if flax_tensor.shape != pt_model_dict[flax_key].shape:
                    raise ValueError(
                        F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
                        F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'				)
               else:
                    # add weight to pytorch dict
                    _A    		=  np.asarray(__lowercase				) if not isinstance(__lowercase   ,		np.ndarray				) else flax_tensor
                    _A    		=  torch.from_numpy(__lowercase				)
                    # remove from missing keys
                    missing_keys.remove(__lowercase				)
          else:
               # weight is not expected by PyTorch model
               unexpected_keys.append(__lowercase				)

     pt_model.load_state_dict(__lowercase				)

     # re-transform missing_keys to list
     _A    		=  list(__lowercase				)

     if len(__lowercase				) > 0:
          logger.warning(
              'Some weights of the Flax model were not used when initializing the PyTorch model'
              F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
              F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
              ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
              F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
              ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
              ' FlaxBertForSequenceClassification model).'				)
     else:
          logger.warning(F'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'				)
     if len(__lowercase				) > 0:
          logger.warning(
              F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
              F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
              ' use it for predictions and inference.'				)
     else:
          logger.warning(
              F'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
              'If your task is similar to the task the model of the checkpoint was trained on, '
              F'you can already use {pt_model.__class__.__name__} for predictions without further training.'				)

     return pt_model





"""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   )
