



"""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


lowerCAmelCase__									=logging.get_logger(__name__)






def     _a (	UpperCAmelCase__     ,	UpperCAmelCase__     ,	UpperCAmelCase__     ,	UpperCAmelCase__=False     )						->    List[Any]:
      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:
            __SCREAMING_SNAKE_CASE       		=   os.path.abspath(__lowerCamelCase     )
            logger.info(f"""Loading PyTorch weights from {pt_path}"""     )

            __SCREAMING_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."""     )

            __SCREAMING_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
            __SCREAMING_SNAKE_CASE       		=   convert_pytorch_sharded_state_dict_to_flax(__lowerCamelCase     ,	__lowerCamelCase     )
      return flax_state_dict






def     _a (	UpperCAmelCase__     ,	UpperCAmelCase__     ,	UpperCAmelCase__     ,	UpperCAmelCase__     ,	)						->    (Tuple[str], np.ndarray):

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

      # layer norm
      __SCREAMING_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
      __SCREAMING_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
      __SCREAMING_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
      __SCREAMING_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
      __SCREAMING_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     ):
            __SCREAMING_SNAKE_CASE       		=   pt_tensor.transpose(2     ,	3     ,	1     ,	0     )
            return renamed_pt_tuple_key, pt_tensor

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

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

      return pt_tuple_key, pt_tensor






def     _a (	UpperCAmelCase__     ,	UpperCAmelCase__     )						->    Optional[Any]:
      # convert pytorch tensor to numpy
      __SCREAMING_SNAKE_CASE       		=   {k: v.numpy() for k, v in pt_state_dict.items()}

      __SCREAMING_SNAKE_CASE       		=   flax_model.base_model_prefix

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

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

      __SCREAMING_SNAKE_CASE       		=   {}

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

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

            # Correctly rename weight parameters
            __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE       		=   rename_key_and_reshape_tensor(
                __lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase     )

            # add model prefix if necessary
            __SCREAMING_SNAKE_CASE       		=   (model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                  __SCREAMING_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]:
                        __SCREAMING_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
                  __SCREAMING_SNAKE_CASE       		=   jnp.asarray(__lowerCamelCase     )

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

      return unflatten_dict(__lowerCamelCase     )






def     _a (	UpperCAmelCase__     ,	UpperCAmelCase__     )						->    Any:
      import torch

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

            __SCREAMING_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:
                  __SCREAMING_SNAKE_CASE       		=   flax_model.params['''params''']

                  __SCREAMING_SNAKE_CASE       		=   flatten_dict(__lowerCamelCase     )
                  random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']     )     )
            else:
                  __SCREAMING_SNAKE_CASE       		=   flax_model.params
                  __SCREAMING_SNAKE_CASE       		=   flatten_dict(__lowerCamelCase     )

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

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

                  # Correctly rename weight parameters
                  __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE       		=   rename_key_and_reshape_tensor(
                      __lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase     )
                  # add model prefix if necessary
                  __SCREAMING_SNAKE_CASE       		=   (model_prefix,) + flax_key in random_flax_state_dict
                  if load_base_model_into_model_with_head and require_base_model_prefix:
                        __SCREAMING_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]:
                              __SCREAMING_SNAKE_CASE       		=   jnp.asarray(__lowerCamelCase     )
                              continue
                        if "var" in flax_key[-1]:
                              __SCREAMING_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
                        __SCREAMING_SNAKE_CASE       		=   jnp.asarray(__lowerCamelCase     )

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






def     _a (	UpperCAmelCase__     ,	UpperCAmelCase__     )						->    Any:
      __SCREAMING_SNAKE_CASE       		=   os.path.abspath(__lowerCamelCase     )
      logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""     )

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

      # load flax weight dict
      with open(__lowerCamelCase     ,	'''rb'''     ) as state_f:
            try:
                  __SCREAMING_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     _a (	UpperCAmelCase__     ,	UpperCAmelCase__     )						->    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
      __SCREAMING_SNAKE_CASE       		=   flatten_dict(jax.tree_util.tree_map(lambda UpperCAmelCase__     : 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.'''     )
            __SCREAMING_SNAKE_CASE       		=   jax.tree_util.tree_map(
                lambda UpperCAmelCase__     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params     ,	__lowerCamelCase     )

      __SCREAMING_SNAKE_CASE       		=   flatten_dict(__lowerCamelCase     )
      __SCREAMING_SNAKE_CASE       		=   pt_model.state_dict()

      __SCREAMING_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()}
      )
      __SCREAMING_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
      __SCREAMING_SNAKE_CASE       		=   []
      __SCREAMING_SNAKE_CASE       		=   set(pt_model_dict.keys()     )

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            __SCREAMING_SNAKE_CASE       		=   flax_key_tuple[0] == pt_model.base_model_prefix
            __SCREAMING_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:
                  __SCREAMING_SNAKE_CASE       		=   flax_key_tuple[1:]
            elif load_base_model_into_model_with_head and require_base_model_prefix:
                  __SCREAMING_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
                  __SCREAMING_SNAKE_CASE       		=   flax_key_tuple[:-1] + ('''weight''',)
                  __SCREAMING_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
                  __SCREAMING_SNAKE_CASE       		=   flax_key_tuple[:-1] + ('''weight''',)
                  __SCREAMING_SNAKE_CASE       		=   flax_tensor.T
            elif flax_key_tuple[-1] in ["scale", "embedding"]:
                  __SCREAMING_SNAKE_CASE       		=   flax_key_tuple[:-1] + ('''weight''',)

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

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

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

            if flax_key in special_pt_names:
                  __SCREAMING_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
                        __SCREAMING_SNAKE_CASE       		=   np.asarray(__lowerCamelCase     ) if not isinstance(__lowerCamelCase     ,	np.ndarray     ) else flax_tensor
                        __SCREAMING_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
      __SCREAMING_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






import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version





def 						__A						(      *__lowerCamelCase				,     __lowerCamelCase = None				,     __lowerCamelCase=True				,     __lowerCamelCase=2       ) ->			Dict:
	from .. import __version__

	a									=				take_from
	a									=				()
	if not isinstance(args[0]				,     __lowerCamelCase       ):
		a									=				(args,)

	for attribute, version_name, message in args:
		if version.parse(version.parse(__lowerCamelCase       ).base_version       ) >= version.parse(__lowerCamelCase       ):
			raise ValueError(
			    f'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
			    f' version {__version__} is >= {version_name}'       )

		a									=				None
		if isinstance(__lowerCamelCase				,     __lowerCamelCase       ) and attribute in deprecated_kwargs:
			values += (deprecated_kwargs.pop(__lowerCamelCase       ),)
			a									=				f'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
		elif hasattr(__lowerCamelCase				,     __lowerCamelCase       ):
			values += (getattr(__lowerCamelCase				,     __lowerCamelCase       ),)
			a									=				f'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
		elif deprecated_kwargs is None:
			a									=				f'`{attribute}` is deprecated and will be removed in version {version_name}.'

		if warning is not None:
			a									=				warning + """ """ if standard_warn else """"""
			warnings.warn(warning + message				,     __lowerCamelCase				,     stacklevel=__lowerCamelCase       )

	if isinstance(__lowerCamelCase				,     __lowerCamelCase       ) and len(__lowerCamelCase       ) > 0:
		a									=				inspect.getouterframes(inspect.currentframe()       )[1]
		a									=				call_frame.filename
		a									=				call_frame.lineno
		a									=				call_frame.function
		a							,					a									=				next(iter(deprecated_kwargs.items()       )       )
		raise TypeError(f'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'       )

	if len(__lowerCamelCase       ) == 0:
		return
	elif len(__lowerCamelCase       ) == 1:
		return values[0]
	return values

