

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__						:       Optional[Any]										=						logging.get_logger(__name__)




def 	_a       (				__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      List[str]     ):

		"""simple docstring"""

		try:
				with open(snake_case__   ,							'''rb'''     ) as flax_state_f:
						snake_case__						:       int       						=  from_bytes(snake_case__   ,							flax_state_f.read()     )
		except UnpicklingError as e:
				try:
						with open(snake_case__     ) 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(snake_case__   ,							snake_case__     )





def 	_a       (				__lowerCAmelCase		:      Dict   ,							__lowerCAmelCase		:      List[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
		snake_case__						:       List[Any]       						=  flatten_dict(jax.tree_util.tree_map(lambda __lowerCAmelCase     : x.dtype == jnp.bfloataa   ,							snake_case__     )     ).values()
		if any(snake_case__     ):
				# 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.'''     )
				snake_case__						:       Tuple       						=  jax.tree_util.tree_map(
				    lambda __lowerCAmelCase     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params   ,							snake_case__     )

		snake_case__						:       Optional[int]       						=  """"""

		snake_case__						:       Dict       						=  flatten_dict(snake_case__   ,							sep='''.'''     )
		snake_case__						:       int       						=  pt_model.state_dict()

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

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				snake_case__						:       Tuple       						=  flax_key_tuple.split('''.'''     )

				if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
						snake_case__						:       str       						=  flax_key_tuple_array[:-1] + ["""weight"""]
						snake_case__						:       str       						=  jnp.transpose(snake_case__   ,							(3, 2, 0, 1)     )
				elif flax_key_tuple_array[-1] == "kernel":
						snake_case__						:       List[str]       						=  flax_key_tuple_array[:-1] + ["""weight"""]
						snake_case__						:       str       						=  flax_tensor.T
				elif flax_key_tuple_array[-1] == "scale":
						snake_case__						:       Union[str, Any]       						=  flax_key_tuple_array[:-1] + ["""weight"""]

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(snake_case__     ):
								snake_case__						:       str       						=  (
								    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'''     )
								)

				snake_case__						:       Dict       						=  """.""".join(snake_case__     )

				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__						:       Optional[Any]       						=  np.asarray(snake_case__     ) if not isinstance(snake_case__   ,							np.ndarray     ) else flax_tensor
								snake_case__						:       Optional[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__						:       Tuple       						=  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).'''     )
		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.'''     )

		return pt_model






'''simple docstring'''


import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
					import jax




@require_flax
class     a			(				SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    FlaxAutoencoderKL







						@property
						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  4
								snake_case__						:       Optional[Any]       						=  3
								snake_case__						:       Optional[int]       						=  (3_2, 3_2)

								snake_case__						:       Optional[int]       						=  jax.random.PRNGKey(0  )
								snake_case__						:       Union[str, Any]       						=  jax.random.uniform(snake_case_			,			((batch_size, num_channels) + sizes)  )

								return {"sample": image, "prng_key": prng_key}







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  {
								    '''block_out_channels''': [3_2, 6_4],
								    '''in_channels''': 3,
								    '''out_channels''': 3,
								    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
								    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
								    '''latent_channels''': 4,
								}
								snake_case__						:       Dict       						=  self.dummy_input
								return init_dict, inputs_dict



