



"""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


snake_case   					=				logging.get_logger(__name__)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=False	)						->				Optional[int]:
  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   	=      os.path.abspath(lowerCAmelCase_	)
    logger.info(f"""Loading PyTorch weights from {pt_path}"""	)

    _snake_case   	=      torch.load(lowerCAmelCase_  ,						map_location='''cpu'''	)
    logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()	):,} parameters."""	)

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




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)						->				(Tuple[str], np.ndarray):

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

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

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

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

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

  # conv layer
  _snake_case   	=      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(lowerCAmelCase_	):
    _snake_case   	=      pt_tensor.transpose(2  ,						3  ,						1  ,						0	)
    return renamed_pt_tuple_key, pt_tensor

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

  # old PyTorch layer norm weight
  _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
  _snake_case   	=      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   	=      None
  if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
    _snake_case   	=      pt_tuple_key[-2] + '''_g'''
  elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
    _snake_case   	=      pt_tuple_key[-2] + '''_v'''
  if name is not None:
    _snake_case   	=      pt_tuple_key[:-3] + (name,)
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Tuple:
  # convert pytorch tensor to numpy
  _snake_case   	=      {k: v.numpy() for k, v in pt_state_dict.items()}

  _snake_case   	=      flax_model.base_model_prefix

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

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

  _snake_case   	=      {}

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

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

    # Correctly rename weight parameters
    _snake_case	,     _snake_case   	=      rename_key_and_reshape_tensor(
        lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)

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

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

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

  return unflatten_dict(lowerCAmelCase_	)




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				str:
  import torch

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

    _snake_case   	=      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   	=      flax_model.params['''params''']

      _snake_case   	=      flatten_dict(lowerCAmelCase_	)
      random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']	)	)
    else:
      _snake_case   	=      flax_model.params
      _snake_case   	=      flatten_dict(lowerCAmelCase_	)

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

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

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

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

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




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Any:
  _snake_case   	=      os.path.abspath(lowerCAmelCase_	)
  logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""	)

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

  # load flax weight dict
  with open(lowerCAmelCase_  ,						'''rb'''	) as state_f:
    try:
      _snake_case   	=      from_bytes(lowerCAmelCase_  ,						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(lowerCAmelCase_  ,						lowerCAmelCase_	)







def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				str:

  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   	=      flatten_dict(jax.tree_util.tree_map(lambda lowerCAmelCase_	: x.dtype == jnp.bfloataa  ,						lowerCAmelCase_	)	).values()
  if any(lowerCAmelCase_	):
    # 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   	=      jax.tree_util.tree_map(
        lambda lowerCAmelCase_	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params  ,						lowerCAmelCase_	)

  _snake_case   	=      flatten_dict(lowerCAmelCase_	)
  _snake_case   	=      pt_model.state_dict()

  _snake_case   	=      (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   	=      (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   	=      []
  _snake_case   	=      set(pt_model_dict.keys()	)

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

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

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

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

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

  pt_model.load_state_dict(lowerCAmelCase_	)

  # re-transform missing_keys to list
  _snake_case   	=      list(lowerCAmelCase_	)

  if len(lowerCAmelCase_	) > 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(lowerCAmelCase_	) > 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 unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


__UpperCAmelCase						=			False



class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			Optional[Any]=32    )	->	Union[str, Any]:



							'''simple docstring'''



							set_seed(0    )
							a__  :	Any		 =			UNetaDModel(sample_size=a_	, in_channels=3	, out_channels=3    )
							a__  :	Union[str, Any]		 =			torch.optim.SGD(model.parameters()	, lr=0.0001    )
							return model, optimizer






				@slow
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Any:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							a__  :	Dict		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )
							a__  :	Optional[Any]		 =			DDIMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )

							assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

							# shared batches for DDPM and DDIM
							set_seed(0    )
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).clip(-1	, 1    ).to(a_    ) for _ in range(4    )]
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).to(a_    ) for _ in range(4    )]
							a__  :	Optional[Any]		 =			[torch.randint(0	, 10_00	, (4,)    ).long().to(a_    ) for _ in range(4    )]

							# train with a DDPM scheduler
							a__ ,	a__  :	Union[str, Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	Optional[int]		 =			ddpm_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	List[str]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Optional[int]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							a__ ,	a__  :	Optional[Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	List[str]		 =			ddim_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	Optional[int]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Union[str, Any]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )
							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )