




"""simple docstring"""


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = 42
 lowercase__			 = None







class  lowercase(					__a	,      __a			):







 '''simple docstring'''





 lowercase__			 = 2


 @register_to_config
 def __init__(    self:		Tuple,				a_:		float = 0.02,				a_:		float = 100,				a_:		float = 1.007,				a_:		float = 80,				a_:		float = 0.05,				a_:		float = 50,				):




   '''simple docstring'''



   _snake_case							:		Tuple								=  sigma_max

   # setable values
   _snake_case							:		int								=  None
   _snake_case							:		np.IntTensor								=  None
   _snake_case							:		torch.FloatTensor								=  None  # sigma(t_i)


 def 			UpperCamelCase_       (    self:		Tuple,				a_:		torch.FloatTensor,				a_:		Optional[int] = None		):




   '''simple docstring'''



   return sample


 def 			UpperCamelCase_       (    self:		int,				a_:		int,				a_:		Union[str, torch.device] = None		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  num_inference_steps
   _snake_case							:		int								=  np.arange(0,				self.num_inference_steps		)[::-1].copy()
   _snake_case							:		Tuple								=  torch.from_numpy(a_		).to(a_		)
   _snake_case							:		int								=  [
       (
           self.config.sigma_max**2
           * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
       )
       for i in self.timesteps
   ]
   _snake_case							:		Dict								=  torch.tensor(a_,				dtype=torch.floataa,				device=a_		)


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		torch.FloatTensor,				a_:		float,				a_:		Optional[torch.Generator] = None		):




   '''simple docstring'''



   if self.config.s_min <= sigma <= self.config.s_max:
     _snake_case							:		int								=  min(self.config.s_churn / self.num_inference_steps,				2**0.5 - 1		)
   else:
     _snake_case							:		List[str]								=  0

   # sample eps ~ N(0, S_noise^2 * I)
   _snake_case							:		Any								=  self.config.s_noise * randn_tensor(sample.shape,				generator=a_		).to(sample.device		)
   _snake_case							:		Any								=  sigma + gamma * sigma
   _snake_case							:		int								=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

   return sample_hat, sigma_hat


 def 			UpperCamelCase_       (    self:		List[Any],				a_:		torch.FloatTensor,				a_:		float,				a_:		float,				a_:		torch.FloatTensor,				a_:		bool = True,				):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  sample_hat + sigma_hat * model_output
   _snake_case							:		Union[str, Any]								=  (sample_hat - pred_original_sample) / sigma_hat
   _snake_case							:		Tuple								=  sample_hat + (sigma_prev - sigma_hat) * derivative

   if not return_dict:
     return (sample_prev, derivative)

   return KarrasVeOutput(
       prev_sample=a_,				derivative=a_,				pred_original_sample=a_		)


 def 			UpperCamelCase_       (    self:		List[str],				a_:		torch.FloatTensor,				a_:		float,				a_:		float,				a_:		torch.FloatTensor,				a_:		torch.FloatTensor,				a_:		torch.FloatTensor,				a_:		bool = True,				):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  sample_prev + sigma_prev * model_output
   _snake_case							:		Any								=  (sample_prev - pred_original_sample) / sigma_prev
   _snake_case							:		str								=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

   if not return_dict:
     return (sample_prev, derivative)

   return KarrasVeOutput(
       prev_sample=a_,				derivative=a_,				pred_original_sample=a_		)


 def 			UpperCamelCase_       (    self:		Any,				a_:		Optional[int],				a_:		int,				a_:		List[Any]		):




   '''simple docstring'''



   raise NotImplementedError()








"""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


A_      =    logging.get_logger(__name__)


def  UpperCAmelCase__	(snake_case__							: List[str]		,       snake_case__							: Dict		,       snake_case__							: Union[str, Any]		,       snake_case__							: int=False   ):
  """simple docstring"""

  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:
    _snake_case							:		Dict								=  os.path.abspath(snake_case__   )
    logger.info(F"Loading PyTorch weights from {pt_path}"   )

    _snake_case							:		Tuple								=  torch.load(snake_case__		,       map_location="""cpu"""   )
    logger.info(F"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()   ):,} parameters."   )

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


def  UpperCAmelCase__	(snake_case__							: Tuple[str]		,       snake_case__							: np.ndarray		,       snake_case__							: Dict[str, jnp.ndarray]		,       snake_case__							: str		,       ):
  """simple docstring"""

  def is_key_or_prefix_key_in_dict(snake_case__							: Tuple[str]   ) -> bool:
    return len(set(snake_case__   ) & {key, (model_prefix,) + key}   ) > 0

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

  # batch norm layer mean
  _snake_case							:		Optional[Any]								=  pt_tuple_key[:-1] + ("""mean""",)
  if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(snake_case__   ):
    return renamed_pt_tuple_key, pt_tensor

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

  # embedding
  _snake_case							:		Any								=  pt_tuple_key[:-1] + ("""embedding""",)
  if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(snake_case__   ):
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  _snake_case							:		Optional[int]								=  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(snake_case__   ):
    _snake_case							:		Dict								=  pt_tensor.transpose(2		,       3		,       1		,       0   )
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  _snake_case							:		List[str]								=  pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(snake_case__   ):
    _snake_case							:		List[Any]								=  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

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

  # old PyTorch layer norm bias
  _snake_case							:		Tuple								=  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
  _snake_case							:		Optional[Any]								=  None
  if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
    _snake_case							:		Union[str, Any]								=  pt_tuple_key[-2] + """_g"""
  elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
    _snake_case							:		Dict								=  pt_tuple_key[-2] + """_v"""
  if name is not None:
    _snake_case							:		Union[str, Any]								=  pt_tuple_key[:-3] + (name,)
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor


def  UpperCAmelCase__	(snake_case__							: Union[str, Any]		,       snake_case__							: Optional[int]   ):
  """simple docstring"""

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

  _snake_case							:		int								=  flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    _snake_case							:		Dict								=  flax_model.params["""params"""]
  else:
    _snake_case							:		List[Any]								=  flax_model.params
  _snake_case							:		Tuple								=  flatten_dict(snake_case__   )

  # add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
    _snake_case							:		Union[str, Any]								=  flatten_dict(flax_model.params["""batch_stats"""]   )
    random_flax_state_dict.update(snake_case__   )

  _snake_case							:		Tuple								=  {}

  _snake_case							:		Dict								=  (model_prefix not in flax_model_params) and (
      model_prefix in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
  )
  _snake_case							:		Optional[int]								=  (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():
    _snake_case							:		int								=  tuple(pt_key.split("""."""   )   )

    # remove base model prefix if necessary
    _snake_case							:		Optional[Any]								=  pt_tuple_key[0] == model_prefix
    if load_model_with_head_into_base_model and has_base_model_prefix:
      _snake_case							:		Union[str, Any]								=  pt_tuple_key[1:]

    # Correctly rename weight parameters
    _snake_case		, _snake_case							:		int								=  rename_key_and_reshape_tensor(
        snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )

    # add model prefix if necessary
    _snake_case							:		Dict								=  (model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      _snake_case							:		int								=  (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]:
        _snake_case							:		Union[str, Any]								=  jnp.asarray(snake_case__   )
        continue
      # remove num_batches_tracked key
      if "num_batches_tracked" in flax_key[-1]:
        flax_state_dict.pop(snake_case__		,       snake_case__   )
        continue

      # also add unexpected weight so that warning is thrown
      _snake_case							:		List[Any]								=  jnp.asarray(snake_case__   )

    else:
      # also add unexpected weight so that warning is thrown
      _snake_case							:		Optional[Any]								=  jnp.asarray(snake_case__   )

  return unflatten_dict(snake_case__   )


def  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: Dict   ):
  """simple docstring"""

  import torch

  # Load the index
  _snake_case							:		str								=  {}
  for shard_file in shard_filenames:
    # load using msgpack utils
    _snake_case							:		Union[str, Any]								=  torch.load(snake_case__   )
    _snake_case							:		Optional[Any]								=  {k: v.numpy() for k, v in pt_state_dict.items()}

    _snake_case							:		List[str]								=  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:
      _snake_case							:		str								=  flax_model.params["""params"""]

      _snake_case							:		List[Any]								=  flatten_dict(snake_case__   )
      random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]   )   )
    else:
      _snake_case							:		List[Any]								=  flax_model.params
      _snake_case							:		Tuple								=  flatten_dict(snake_case__   )

    _snake_case							:		Tuple								=  (model_prefix not in flax_model_params) and (
        model_prefix in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
    )
    _snake_case							:		Optional[Any]								=  (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():
      _snake_case							:		List[str]								=  tuple(pt_key.split("""."""   )   )

      # remove base model prefix if necessary
      _snake_case							:		str								=  pt_tuple_key[0] == model_prefix
      if load_model_with_head_into_base_model and has_base_model_prefix:
        _snake_case							:		Optional[Any]								=  pt_tuple_key[1:]

      # Correctly rename weight parameters
      _snake_case		, _snake_case							:		Optional[Any]								=  rename_key_and_reshape_tensor(
          snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )
      # add model prefix if necessary
      _snake_case							:		List[str]								=  (model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
        _snake_case							:		Any								=  (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]:
          _snake_case							:		Optional[int]								=  jnp.asarray(snake_case__   )
          continue
        if "var" in flax_key[-1]:
          _snake_case							:		Any								=  jnp.asarray(snake_case__   )
          continue
        # remove num_batches_tracked key
        if "num_batches_tracked" in flax_key[-1]:
          flax_state_dict.pop(snake_case__		,       snake_case__   )
          continue

        # also add unexpected weight so that warning is thrown
        _snake_case							:		List[str]								=  jnp.asarray(snake_case__   )

      else:
        # also add unexpected weight so that warning is thrown
        _snake_case							:		Optional[Any]								=  jnp.asarray(snake_case__   )
  return unflatten_dict(snake_case__   )


def  UpperCAmelCase__	(snake_case__							: List[str]		,       snake_case__							: Optional[Any]   ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  os.path.abspath(snake_case__   )
  logger.info(F"Loading Flax weights from {flax_checkpoint_path}"   )

  # import correct flax class
  _snake_case							:		Union[str, Any]								=  getattr(snake_case__		,       """Flax""" + model.__class__.__name__   )

  # load flax weight dict
  with open(snake_case__		,       """rb"""   ) as state_f:
    try:
      _snake_case							:		Dict								=  from_bytes(snake_case__		,       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(snake_case__		,       snake_case__   )






def  UpperCAmelCase__	(snake_case__							: Tuple		,       snake_case__							: Optional[int]   ):
  """simple docstring"""

  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
  _snake_case							:		Optional[int]								=  flatten_dict(jax.tree_util.tree_map(lambda snake_case__   : x.dtype == jnp.bfloataa		,       snake_case__   )   ).values()
  if any(snake_case__   ):
    # 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."""   )
    _snake_case							:		Optional[int]								=  jax.tree_util.tree_map(
        lambda snake_case__   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params		,       snake_case__   )

  _snake_case							:		Dict								=  flatten_dict(snake_case__   )
  _snake_case							:		Optional[Any]								=  pt_model.state_dict()

  _snake_case							:		Union[str, Any]								=  (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()}
  )
  _snake_case							:		Optional[int]								=  (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
  _snake_case							:		str								=  []
  _snake_case							:		Tuple								=  set(pt_model_dict.keys()   )

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    _snake_case							:		Tuple								=  flax_key_tuple[0] == pt_model.base_model_prefix
    _snake_case							:		Optional[Any]								=  """.""".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:
      _snake_case							:		List[str]								=  flax_key_tuple[1:]
    elif load_base_model_into_model_with_head and require_base_model_prefix:
      _snake_case							:		Union[str, Any]								=  (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(snake_case__   ) not in pt_model_dict:
      # conv layer
      _snake_case							:		Union[str, Any]								=  flax_key_tuple[:-1] + ("""weight""",)
      _snake_case							:		Optional[int]								=  jnp.transpose(snake_case__		,       (3, 2, 0, 1)   )
    elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case__   ) not in pt_model_dict:
      # linear layer
      _snake_case							:		Optional[int]								=  flax_key_tuple[:-1] + ("""weight""",)
      _snake_case							:		Union[str, Any]								=  flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      _snake_case							:		int								=  flax_key_tuple[:-1] + ("""weight""",)

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

    if "batch_stats" in flax_state:
      _snake_case							:		int								=  """.""".join(flax_key_tuple[1:]   )  # Remove the params/batch_stats header
    else:
      _snake_case							:		int								=  """.""".join(snake_case__   )

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

    if flax_key in special_pt_names:
      _snake_case							:		Union[str, Any]								=  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
        _snake_case							:		List[str]								=  np.asarray(snake_case__   ) if not isinstance(snake_case__		,       np.ndarray   ) else flax_tensor
        _snake_case							:		List[Any]								=  torch.from_numpy(snake_case__   )
        # remove from missing keys
        missing_keys.remove(snake_case__   )
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(snake_case__   )

  pt_model.load_state_dict(snake_case__   )

  # re-transform missing_keys to list
  _snake_case							:		List[str]								=  list(snake_case__   )

  if len(snake_case__   ) > 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(snake_case__   ) > 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


