




from pickle import UnpicklingError

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

from ..utils import logging


__lowerCamelCase   :						Union[str, Any]				=			logging.get_logger(__name__)







def 		lowercase__   (		__A:	str							,__A:	Union[str, Any]				):





					'''simple docstring'''






					try:
										with open(__A							,'''rb'''				) as flax_state_f:
															__magic_name__							:	str    							=							from_bytes(__A							,flax_state_f.read()				)
					except UnpicklingError as e:
										try:
															with open(__A				) as f:
																				if f.read().startswith('''version'''				):
																									raise OSError(
																									    '''You seem to have cloned a repository without having git-lfs installed. Please'''
																									    ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
																									    ''' folder you cloned.'''				)
																				else:
																									raise ValueError from e
										except (UnicodeDecodeError, ValueError):
															raise EnvironmentError(F'''Unable to convert {model_file} to Flax deserializable object. '''				)

					return load_flax_weights_in_pytorch_model(__A							,__A				)







def 		lowercase__   (		__A:	Optional[int]							,__A:	Any				):





					'''simple docstring'''






					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    '''Loading 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
					__magic_name__							:	str    							=							flatten_dict(jax.tree_util.tree_map(lambda __A				: x.dtype == jnp.bfloataa							,__A				)				).values()
					if any(__A				):
										# convert all weights to fp32 if they 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.'''				)
										__magic_name__							:	List[str]    							=							jax.tree_util.tree_map(
										    lambda __A				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params							,__A				)

					__magic_name__							:	Optional[Any]    							=							''''''

					__magic_name__							:	Tuple    							=							flatten_dict(__A							,sep='''.'''				)
					__magic_name__							:	Optional[int]    							=							pt_model.state_dict()

					# keep track of unexpected & missing keys
					__magic_name__							:	Optional[Any]    							=							[]
					__magic_name__							:	List[Any]    							=							set(pt_model_dict.keys()				)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										__magic_name__							:	int    							=							flax_key_tuple.split('''.'''				)

										if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
															__magic_name__							:	Any    							=							flax_key_tuple_array[:-1] + ['''weight''']
															__magic_name__							:	Any    							=							jnp.transpose(__A							,(3, 2, 0, 1)				)
										elif flax_key_tuple_array[-1] == "kernel":
															__magic_name__							:	Union[str, Any]    							=							flax_key_tuple_array[:-1] + ['''weight''']
															__magic_name__							:	Dict    							=							flax_tensor.T
										elif flax_key_tuple_array[-1] == "scale":
															__magic_name__							:	Dict    							=							flax_key_tuple_array[:-1] + ['''weight''']

										if "time_embedding" not in flax_key_tuple_array:
															for i, flax_key_tuple_string in enumerate(__A				):
																				__magic_name__							:	Any    							=							(
																				    flax_key_tuple_string.replace('''_0'''							,'''.0'''				)
																				    .replace('''_1'''							,'''.1'''				)
																				    .replace('''_2'''							,'''.2'''				)
																				    .replace('''_3'''							,'''.3'''				)
																				    .replace('''_4'''							,'''.4'''				)
																				    .replace('''_5'''							,'''.5'''				)
																				    .replace('''_6'''							,'''.6'''				)
																				    .replace('''_7'''							,'''.7'''				)
																				    .replace('''_8'''							,'''.8'''				)
																				    .replace('''_9'''							,'''.9'''				)
																				)

										__magic_name__							:	Any    							=							'''.'''.join(__A				)

										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
																				__magic_name__							:	Optional[int]    							=							np.asarray(__A				) if not isinstance(__A							,np.ndarray				) else flax_tensor
																				__magic_name__							:	Any    							=							torch.from_numpy(__A				)
																				# remove from missing keys
																				missing_keys.remove(__A				)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(__A				)

					pt_model.load_state_dict(__A				)

					# re-transform missing_keys to list
					__magic_name__							:	Optional[Any]    							=							list(__A				)

					if len(__A				) > 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).'''				)
					if len(__A				) > 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.'''				)

					return pt_model









# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    '''stable diffusion controlnet''',
    '''0.22.0''',
    '''Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.''',
    standard_warn=False,
    stacklevel=3,
)



