


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


A	:		Dict    =       logging.get_logger(__name__)






def 			UpperCamelCase (     __magic_name__					:							int   ,   __magic_name__					:							List[Any]					)       ->  Tuple:
		"""simple docstring"""




		try:
				with open(__magic_name__   ,   """rb"""					) as flax_state_f:
						lowercase__					      =		from_bytes(__magic_name__   ,   flax_state_f.read()					)
		except UnpicklingError as e:
				try:
						with open(__magic_name__					) 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(__magic_name__   ,   __magic_name__					)







def 			UpperCamelCase (     __magic_name__					:							int   ,   __magic_name__					:							Union[str, Any]					)       ->  int:
		"""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
		lowercase__					      =		flatten_dict(jax.tree_util.tree_map(lambda __magic_name__					: x.dtype == jnp.bfloataa   ,   __magic_name__					)					).values()
		if any(__magic_name__					):
				# 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."""					)
				lowercase__					      =		jax.tree_util.tree_map(
				    lambda __magic_name__					: params.astype(np.floataa					) if params.dtype == jnp.bfloataa else params   ,   __magic_name__					)

		lowercase__					      =		""""""

		lowercase__					      =		flatten_dict(__magic_name__   ,   sep="""."""					)
		lowercase__					      =		pt_model.state_dict()

		# keep track of unexpected & missing keys
		lowercase__					      =		[]
		lowercase__					      =		set(pt_model_dict.keys()					)

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				lowercase__					      =		flax_key_tuple.split("""."""					)

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

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(__magic_name__					):
								lowercase__					      =		(
								    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"""					)
								)

				lowercase__					      =		""".""".join(__magic_name__					)

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

		pt_model.load_state_dict(__magic_name__					)

		# re-transform missing_keys to list
		lowercase__					      =		list(__magic_name__					)

		if len(__magic_name__					) > 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(__magic_name__					) > 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





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
	import jax
	import jax.numpy as jnp



@slow
@require_flax
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					def   lowerCamelCase__					(self   :						Any				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Union[str, Any]		)						->       int:




							"""simple docstring"""


							return f'''gaussian_noise_s={seed}_shape={"_".join([str(_UpperCAmelCase		) for s in shape]		)}.npy'''

					def   lowerCamelCase__					(self   :						Any		)						->       int:




							"""simple docstring"""


							super().tearDown()
							gc.collect()

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						Any=0				, _UpperCAmelCase   :						Optional[Any]=(4, 4, 64, 64)				, _UpperCAmelCase   :						Any=False		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		jnp.bfloataa if fpaa else jnp.floataa
							lowercase__					      =		jnp.array(load_hf_numpy(self.get_file_format(_UpperCAmelCase				, _UpperCAmelCase		)		)				, dtype=_UpperCAmelCase		)
							return image

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						str=False				, _UpperCAmelCase   :						str="CompVis/stable-diffusion-v1-4"		)						->       Dict:




							"""simple docstring"""


							lowercase__					      =		jnp.bfloataa if fpaa else jnp.floataa
							lowercase__					      =		"""bf16""" if fpaa else None

							lowercase__      ,     lowercase__					      =		FlaxUNetaDConditionModel.from_pretrained(
							    _UpperCAmelCase				, subfolder="""unet"""				, dtype=_UpperCAmelCase				, revision=_UpperCAmelCase		)
							return model, params

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						int=0				, _UpperCAmelCase   :						Optional[int]=(4, 77, 768)				, _UpperCAmelCase   :						Any=False		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		jnp.bfloataa if fpaa else jnp.floataa
							lowercase__					      =		jnp.array(load_hf_numpy(self.get_file_format(_UpperCAmelCase				, _UpperCAmelCase		)		)				, dtype=_UpperCAmelCase		)
							return hidden_states

					@parameterized.expand(
					    [
					        # fmt: off
					        [83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
					        [17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
					        [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
					        [3, 1000, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
					        # fmt: on
					    ]		)
					def   lowerCamelCase__					(self   :						int				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						int		)						->       str:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""				, fpaa=_UpperCAmelCase		)
							lowercase__					      =		self.get_latents(_UpperCAmelCase				, fpaa=_UpperCAmelCase		)
							lowercase__					      =		self.get_encoder_hidden_states(_UpperCAmelCase				, fpaa=_UpperCAmelCase		)

							lowercase__					      =		model.apply(
							    {"""params""": params}				, _UpperCAmelCase				, jnp.array(_UpperCAmelCase				, dtype=jnp.intaa		)				, encoder_hidden_states=_UpperCAmelCase				, ).sample

							assert sample.shape == latents.shape

							lowercase__					      =		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)				, dtype=jnp.floataa		)
							lowercase__					      =		jnp.array(_UpperCAmelCase				, dtype=jnp.floataa		)

							# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
							assert jnp.allclose(_UpperCAmelCase				, _UpperCAmelCase				, atol=1E-2		)


					@parameterized.expand(
					    [
					        # fmt: off
					        [83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
					        [17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
					        [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
					        [3, 1000, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
					        # fmt: on
					    ]		)
					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						List[str]		)						->       str:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""				, fpaa=_UpperCAmelCase		)
							lowercase__					      =		self.get_latents(_UpperCAmelCase				, shape=(4, 4, 96, 96)				, fpaa=_UpperCAmelCase		)
							lowercase__					      =		self.get_encoder_hidden_states(_UpperCAmelCase				, shape=(4, 77, 1024)				, fpaa=_UpperCAmelCase		)

							lowercase__					      =		model.apply(
							    {"""params""": params}				, _UpperCAmelCase				, jnp.array(_UpperCAmelCase				, dtype=jnp.intaa		)				, encoder_hidden_states=_UpperCAmelCase				, ).sample

							assert sample.shape == latents.shape

							lowercase__					      =		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)				, dtype=jnp.floataa		)
							lowercase__					      =		jnp.array(_UpperCAmelCase				, dtype=jnp.floataa		)

							# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
							assert jnp.allclose(_UpperCAmelCase				, _UpperCAmelCase				, atol=1E-2		)

