


'''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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=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:
				_UpperCAmelCase						:				Optional[Any]	    =					os.path.abspath(__lowerCAmelCase      )
				logger.info(F"""Loading PyTorch weights from {pt_path}"""      )

				_UpperCAmelCase						:				List[Any]	    =					torch.load(__lowerCAmelCase					,       map_location="cpu"      )
				logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()      ):,} parameters."""      )

				_UpperCAmelCase						:				List[Any]	    =					convert_pytorch_state_dict_to_flax(__lowerCAmelCase					,       __lowerCAmelCase      )
		else:
				# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
				_UpperCAmelCase						:				Tuple	    =					convert_pytorch_sharded_state_dict_to_flax(__lowerCAmelCase					,       __lowerCAmelCase      )
		return flax_state_dict






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       ):

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

		# layer norm
		_UpperCAmelCase						:				Any	    =					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
		_UpperCAmelCase						:				Optional[Any]	    =					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
		_UpperCAmelCase						:				Optional[int]	    =					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
		_UpperCAmelCase						:				Union[str, Any]	    =					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
		_UpperCAmelCase						:				Tuple	    =					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      ):
				_UpperCAmelCase						:				List[str]	    =					pt_tensor.transpose(2					,       3					,       1					,       0      )
				return renamed_pt_tuple_key, pt_tensor

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

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

		return pt_tuple_key, pt_tensor






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		# convert pytorch tensor to numpy
		_UpperCAmelCase						:				Tuple	    =					{k: v.numpy() for k, v in pt_state_dict.items()}

		_UpperCAmelCase						:				str	    =					flax_model.base_model_prefix

		# use params dict if the model contains batch norm layers
		if "params" in flax_model.params:
				_UpperCAmelCase						:				List[str]	    =					flax_model.params["params"]
		else:
				_UpperCAmelCase						:				List[Any]	    =					flax_model.params
		_UpperCAmelCase						:				List[Any]	    =					flatten_dict(__lowerCAmelCase      )

		# add batch_stats keys,values to dict
		if "batch_stats" in flax_model.params:
				_UpperCAmelCase						:				Optional[Any]	    =					flatten_dict(flax_model.params["batch_stats"]      )
				random_flax_state_dict.update(__lowerCAmelCase      )

		_UpperCAmelCase						:				List[Any]	    =					{}

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

				# remove base model prefix if necessary
				_UpperCAmelCase						:				Optional[int]	    =					pt_tuple_key[0] == model_prefix
				if load_model_with_head_into_base_model and has_base_model_prefix:
						_UpperCAmelCase						:				Tuple	    =					pt_tuple_key[1:]

				# Correctly rename weight parameters
				_UpperCAmelCase    ,			_UpperCAmelCase						:				Tuple	    =					rename_key_and_reshape_tensor(
				    __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      )

				# add model prefix if necessary
				_UpperCAmelCase						:				Optional[Any]	    =					(model_prefix,) + flax_key in random_flax_state_dict
				if load_base_model_into_model_with_head and require_base_model_prefix:
						_UpperCAmelCase						:				Optional[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] or "var" in flax_key[-1]:
								_UpperCAmelCase						:				Dict	    =					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
						_UpperCAmelCase						:				Dict	    =					jnp.asarray(__lowerCAmelCase      )

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

		return unflatten_dict(__lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		import torch

		# Load the index
		_UpperCAmelCase						:				List[Any]	    =					{}
		for shard_file in shard_filenames:
				# load using msgpack utils
				_UpperCAmelCase						:				Tuple	    =					torch.load(__lowerCAmelCase      )
				_UpperCAmelCase						:				List[Any]	    =					{k: v.numpy() for k, v in pt_state_dict.items()}

				_UpperCAmelCase						:				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:
						_UpperCAmelCase						:				Optional[Any]	    =					flax_model.params["params"]

						_UpperCAmelCase						:				List[Any]	    =					flatten_dict(__lowerCAmelCase      )
						random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]      )      )
				else:
						_UpperCAmelCase						:				Optional[int]	    =					flax_model.params
						_UpperCAmelCase						:				int	    =					flatten_dict(__lowerCAmelCase      )

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

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

						# Correctly rename weight parameters
						_UpperCAmelCase    ,			_UpperCAmelCase						:				Union[str, Any]	    =					rename_key_and_reshape_tensor(
						    __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      )
						# add model prefix if necessary
						_UpperCAmelCase						:				int	    =					(model_prefix,) + flax_key in random_flax_state_dict
						if load_base_model_into_model_with_head and require_base_model_prefix:
								_UpperCAmelCase						:				List[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]:
										_UpperCAmelCase						:				List[str]	    =					jnp.asarray(__lowerCAmelCase      )
										continue
								if "var" in flax_key[-1]:
										_UpperCAmelCase						:				Dict	    =					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
								_UpperCAmelCase						:				List[Any]	    =					jnp.asarray(__lowerCAmelCase      )

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






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					os.path.abspath(__lowerCAmelCase      )
		logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""      )

		# import correct flax class
		_UpperCAmelCase						:				Optional[Any]	    =					getattr(__lowerCAmelCase					,       "Flax" + model.__class__.__name__      )

		# load flax weight dict
		with open(__lowerCAmelCase					,       "rb"      ) as state_f:
				try:
						_UpperCAmelCase						:				Optional[int]	    =					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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):

		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
		_UpperCAmelCase						:				Any	    =					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."      )
				_UpperCAmelCase						:				Tuple	    =					jax.tree_util.tree_map(
				    lambda __lowerCAmelCase      : params.astype(np.floataa      ) if params.dtype == jnp.bfloataa else params					,       __lowerCAmelCase      )

		_UpperCAmelCase						:				Optional[Any]	    =					flatten_dict(__lowerCAmelCase      )
		_UpperCAmelCase						:				str	    =					pt_model.state_dict()

		_UpperCAmelCase						:				Dict	    =					(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()}
		)
		_UpperCAmelCase						:				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
		_UpperCAmelCase						:				Any	    =					[]
		_UpperCAmelCase						:				Union[str, Any]	    =					set(pt_model_dict.keys()      )

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

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

				if "batch_stats" in flax_state:
						_UpperCAmelCase						:				str	    =					".".join(flax_key_tuple[1:]      )  # Remove the params/batch_stats header
				else:
						_UpperCAmelCase						:				Dict	    =					".".join(__lowerCAmelCase      )

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

				if flax_key in special_pt_names:
						_UpperCAmelCase						:				int	    =					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
								_UpperCAmelCase						:				List[str]	    =					np.asarray(__lowerCAmelCase      ) if not isinstance(__lowerCAmelCase					,       np.ndarray      ) else flax_tensor
								_UpperCAmelCase						:				Optional[int]	    =					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
		_UpperCAmelCase						:				Tuple	    =					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 warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionImg2ImgPipeline` instead.'
)



