

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


_UpperCAmelCase		:       Tuple						    =	logging.get_logger(__name__)





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''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_          =   flatten_dict(jax.tree_util.tree_map(lambda UpperCamelCase__				: x.dtype == jnp.bfloataa       ,							UpperCamelCase__				)				).values()
							if any(UpperCamelCase__				):
														# 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_          =   jax.tree_util.tree_map(
														    lambda UpperCamelCase__				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params       ,							UpperCamelCase__				)

							snake_case_          =   ''

							snake_case_          =   flatten_dict(UpperCamelCase__       ,							sep='.'				)
							snake_case_          =   pt_model.state_dict()

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

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

														if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
																					snake_case_          =   flax_key_tuple_array[:-1] + ['weight']
																					snake_case_          =   jnp.transpose(UpperCamelCase__       ,							(3, 2, 0, 1)				)
														elif flax_key_tuple_array[-1] == "kernel":
																					snake_case_          =   flax_key_tuple_array[:-1] + ['weight']
																					snake_case_          =   flax_tensor.T
														elif flax_key_tuple_array[-1] == "scale":
																					snake_case_          =   flax_key_tuple_array[:-1] + ['weight']

														if "time_embedding" not in flax_key_tuple_array:
																					for i, flax_key_tuple_string in enumerate(UpperCamelCase__				):
																												snake_case_          =   (
																												    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_          =   '.'.join(UpperCamelCase__				)

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

							pt_model.load_state_dict(UpperCamelCase__				)

							# re-transform missing_keys to list
							snake_case_          =   list(UpperCamelCase__				)

							if len(UpperCamelCase__				) > 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(UpperCamelCase__				) > 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 diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
					import jax
					import jax.numpy as jnp
					from flax.jax_utils import replicate
					from flax.training.common_utils import shard







@slow
@require_flax
class 			lowercase    (	unittest.TestCase      ):




				def    a							( self	):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()




				def    a							( self	):
											snake_case_    ,   snake_case_          =   FlaxControlNetModel.from_pretrained(
											    'lllyasviel/sd-controlnet-canny'				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_    ,   snake_case_          =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    'runwayml/stable-diffusion-v1-5'				,       controlnet=snake_case				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_          =   controlnet_params

											snake_case_          =   'bird'
											snake_case_          =   jax.device_count()
											snake_case_          =   pipe.prepare_text_inputs([prompts] * num_samples	)

											snake_case_          =   load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'	)
											snake_case_          =   pipe.prepare_image_inputs([canny_image] * num_samples	)

											snake_case_          =   jax.random.PRNGKey(0	)
											snake_case_          =   jax.random.split(snake_case				,       jax.device_count()	)

											snake_case_          =   replicate(snake_case	)
											snake_case_          =   shard(snake_case	)
											snake_case_          =   shard(snake_case	)

											snake_case_          =   pipe(
											    prompt_ids=snake_case				,       image=snake_case				,       params=snake_case				,       prng_seed=snake_case				,       num_inference_steps=50				,       jit=snake_case				,       ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

											snake_case_          =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
											snake_case_          =   images[0, 253:256, 253:256, -1]

											snake_case_          =   jnp.asarray(jax.device_get(image_slice.flatten()	)	)
											snake_case_          =   jnp.array(
											    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]	)
											print(F'''output_slice: {output_slice}'''	)
											assert jnp.abs(output_slice - expected_slice	).max() < 1e-2




				def    a							( self	):
											snake_case_    ,   snake_case_          =   FlaxControlNetModel.from_pretrained(
											    'lllyasviel/sd-controlnet-openpose'				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_    ,   snake_case_          =   FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    'runwayml/stable-diffusion-v1-5'				,       controlnet=snake_case				,       from_pt=snake_case				,       dtype=jnp.bfloataa	)
											snake_case_          =   controlnet_params

											snake_case_          =   'Chef in the kitchen'
											snake_case_          =   jax.device_count()
											snake_case_          =   pipe.prepare_text_inputs([prompts] * num_samples	)

											snake_case_          =   load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'	)
											snake_case_          =   pipe.prepare_image_inputs([pose_image] * num_samples	)

											snake_case_          =   jax.random.PRNGKey(0	)
											snake_case_          =   jax.random.split(snake_case				,       jax.device_count()	)

											snake_case_          =   replicate(snake_case	)
											snake_case_          =   shard(snake_case	)
											snake_case_          =   shard(snake_case	)

											snake_case_          =   pipe(
											    prompt_ids=snake_case				,       image=snake_case				,       params=snake_case				,       prng_seed=snake_case				,       num_inference_steps=50				,       jit=snake_case				,       ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

											snake_case_          =   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
											snake_case_          =   images[0, 253:256, 253:256, -1]

											snake_case_          =   jnp.asarray(jax.device_get(image_slice.flatten()	)	)
											snake_case_          =   jnp.array(
											    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]	)
											print(F'''output_slice: {output_slice}'''	)
											assert jnp.abs(output_slice - expected_slice	).max() < 1e-2




