"""simple docstring"""
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    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     List[str]										=	FlaxControlNetModel.from_pretrained(
            'lllyasviel/sd-controlnet-canny'    ,			from_pt=lowerCamelCase__    ,			dtype=jnp.bfloataa)
        a__:     Optional[Any]										=	FlaxStableDiffusionControlNetPipeline.from_pretrained(
            'runwayml/stable-diffusion-v1-5'    ,			controlnet=lowerCamelCase__    ,			from_pt=lowerCamelCase__    ,			dtype=jnp.bfloataa)
        a__:     Any										=	controlnet_params

        a__:     Dict										=	"""bird"""
        a__:     List[Any]										=	jax.device_count()
        a__:     Tuple										=	pipe.prepare_text_inputs([prompts] * num_samples)

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

        a__:     Optional[int]										=	jax.random.PRNGKey(0)
        a__:     Optional[Any]										=	jax.random.split(lowerCamelCase__    ,			jax.device_count())

        a__:     Dict										=	replicate(lowerCamelCase__)
        a__:     List[Any]										=	shard(lowerCamelCase__)
        a__:     Dict										=	shard(lowerCamelCase__)

        a__:     int										=	pipe(
            prompt_ids=lowerCamelCase__    ,			image=lowerCamelCase__    ,			params=lowerCamelCase__    ,			prng_seed=lowerCamelCase__    ,			num_inference_steps=50    ,			jit=lowerCamelCase__    ,			).images
        assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

        a__:     Union[str, Any]										=	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
        a__:     Union[str, Any]										=	images[0, 2_53:2_56, 2_53:2_56, -1]

        a__:     Union[str, Any]										=	jnp.asarray(jax.device_get(image_slice.flatten()))
        a__:     int										=	jnp.array(
            [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078])
        print(f'output_slice: {output_slice}')
        assert jnp.abs(output_slice - expected_slice).max() < 1e-2



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     Optional[int]										=	FlaxControlNetModel.from_pretrained(
            'lllyasviel/sd-controlnet-openpose'    ,			from_pt=lowerCamelCase__    ,			dtype=jnp.bfloataa)
        a__:     Any										=	FlaxStableDiffusionControlNetPipeline.from_pretrained(
            'runwayml/stable-diffusion-v1-5'    ,			controlnet=lowerCamelCase__    ,			from_pt=lowerCamelCase__    ,			dtype=jnp.bfloataa)
        a__:     str										=	controlnet_params

        a__:     List[Any]										=	"""Chef in the kitchen"""
        a__:     int										=	jax.device_count()
        a__:     str										=	pipe.prepare_text_inputs([prompts] * num_samples)

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

        a__:     int										=	jax.random.PRNGKey(0)
        a__:     Tuple										=	jax.random.split(lowerCamelCase__    ,			jax.device_count())

        a__:     Tuple										=	replicate(lowerCamelCase__)
        a__:     Optional[Any]										=	shard(lowerCamelCase__)
        a__:     int										=	shard(lowerCamelCase__)

        a__:     Union[str, Any]										=	pipe(
            prompt_ids=lowerCamelCase__    ,			image=lowerCamelCase__    ,			params=lowerCamelCase__    ,			prng_seed=lowerCamelCase__    ,			num_inference_steps=50    ,			jit=lowerCamelCase__    ,			).images
        assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

        a__:     Union[str, Any]										=	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
        a__:     str										=	images[0, 2_53:2_56, 2_53:2_56, -1]

        a__:     Union[str, Any]										=	jnp.asarray(jax.device_get(image_slice.flatten()))
        a__:     Any										=	jnp.array(
            [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]])
        print(f'output_slice: {output_slice}')
        assert jnp.abs(output_slice - expected_slice).max() < 1e-2







import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
     from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
     from tensorflow.keras.optimizers import Adam






class SCREAMING_SNAKE_CASE__     (		tf.keras.optimizers.schedules.LearningRateSchedule    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = 1.0,		lowerCamelCase__ = None,		):
        super().__init__()
        A :       Union[str, Any]          =   initial_learning_rate
        A :       List[Any]          =   warmup_steps
        A :       int          =   power
        A :       Optional[int]          =   decay_schedule_fn
        A :       int          =   name






    def __call__(       self,		lowerCamelCase__		):
        with tf.name_scope(self.name or """WarmUp"""		) as name:
            # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
            # learning rate will be `global_step/num_warmup_steps * init_lr`.
            A :       str          =   tf.cast(lowerCamelCase__,		tf.floataa		)
            A :       List[Any]          =   tf.cast(self.warmup_steps,		tf.floataa		)
            A :       Dict          =   global_step_float / warmup_steps_float
            A :       Union[str, Any]          =   self.initial_learning_rate * tf.math.pow(lowerCamelCase__,		self.power		)
            return tf.cond(
                global_step_float < warmup_steps_float,		lambda: warmup_learning_rate,		lambda: self.decay_schedule_fn(step - self.warmup_steps		),		name=lowerCamelCase__,		)






    def 			_lowerCAmelCase					(       self		):
        return {
            "initial_learning_rate": self.initial_learning_rate,
            "decay_schedule_fn": self.decay_schedule_fn,
            "warmup_steps": self.warmup_steps,
            "power": self.power,
            "name": self.name,
        }




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = 0.0      ,     _lowerCAmelCase = 0.9      ,     _lowerCAmelCase = 0.999      ,     _lowerCAmelCase = 1e-8      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = 0.0      ,     _lowerCAmelCase = 1.0      ,     _lowerCAmelCase = None      ,     )       ->  Union[str, Any]:



    """simple docstring"""






    A :       Optional[int]          =   tf.keras.optimizers.schedules.PolynomialDecay(
        initial_learning_rate=_lowerCAmelCase      ,     decay_steps=num_train_steps - num_warmup_steps      ,     end_learning_rate=init_lr * min_lr_ratio      ,     power=_lowerCAmelCase      ,     )
    if num_warmup_steps:
        A :       Dict          =   WarmUp(
            initial_learning_rate=_lowerCAmelCase      ,     decay_schedule_fn=_lowerCAmelCase      ,     warmup_steps=_lowerCAmelCase      ,     )
    if weight_decay_rate > 0.0:
        A :       str          =   AdamWeightDecay(
            learning_rate=_lowerCAmelCase      ,     weight_decay_rate=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     epsilon=_lowerCAmelCase      ,     clipnorm=_lowerCAmelCase      ,     global_clipnorm=_lowerCAmelCase      ,     exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]      ,     include_in_weight_decay=_lowerCAmelCase      ,     )
    else:
        A :       Optional[int]          =   tf.keras.optimizers.Adam(
            learning_rate=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     epsilon=_lowerCAmelCase      ,     clipnorm=_lowerCAmelCase      ,     global_clipnorm=_lowerCAmelCase      ,     )
    # We return the optimizer and the LR scheduler in order to better track the
    # evolution of the LR independently of the optimizer.
    return optimizer, lr_schedule






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__ = 0.001,		lowerCamelCase__ = 0.9,		lowerCamelCase__ = 0.999,		lowerCamelCase__ = 1e-7,		lowerCamelCase__ = False,		lowerCamelCase__ = 0.0,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = "AdamWeightDecay",		**lowerCamelCase__,		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)
        A :       int          =   weight_decay_rate
        A :       Any          =   include_in_weight_decay
        A :       Dict          =   exclude_from_weight_decay






    @classmethod
    def 			_lowerCAmelCase					(       cls,		lowerCamelCase__		):
        A :       Tuple          =   {"""WarmUp""": WarmUp}
        return super(lowerCamelCase__,		cls		).from_config(lowerCamelCase__,		custom_objects=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        super(lowerCamelCase__,		self		)._prepare_local(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        A :       List[str]          =   tf.constant(
            self.weight_decay_rate,		name="""adam_weight_decay_rate"""		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Any          =   self._do_use_weight_decay(var.name		)
        if do_decay:
            return var.assign_sub(
                learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["""weight_decay_rate"""],		use_locking=self._use_locking,		)
        return tf.no_op()






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=None,		**lowerCamelCase__		):
        A     ,							A :       Dict          =   list(zip(*lowerCamelCase__		)		)
        return super(lowerCamelCase__,		self		).apply_gradients(zip(lowerCamelCase__,		lowerCamelCase__		),		name=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        if apply_state is None:
            return self._decayed_lr_t[var_dtype], {}

        A :       Union[str, Any]          =   apply_state or {}
        A :       Optional[int]          =   apply_state.get((var_device, var_dtype)		)
        if coefficients is None:
            A :       Dict          =   self._fallback_apply_state(lowerCamelCase__,		lowerCamelCase__		)
            A :       List[Any]          =   coefficients

        return coefficients["lr_t"], {"apply_state": apply_state}






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A     ,							A :       str          =   self._get_lr(var.device,		var.dtype.base_dtype,		lowerCamelCase__		)
        A :       Any          =   self._decay_weights_op(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        with tf.control_dependencies([decay]		):
            return super(lowerCamelCase__,		self		)._resource_apply_dense(lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A     ,							A :       Tuple          =   self._get_lr(var.device,		var.dtype.base_dtype,		lowerCamelCase__		)
        A :       Optional[Any]          =   self._decay_weights_op(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        with tf.control_dependencies([decay]		):
            return super(lowerCamelCase__,		self		)._resource_apply_sparse(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   super().get_config()
        config.update({"""weight_decay_rate""": self.weight_decay_rate}		)
        return config






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if self.weight_decay_rate == 0:
            return False

        if self._include_in_weight_decay:
            for r in self._include_in_weight_decay:
                if re.search(lowerCamelCase__,		lowerCamelCase__		) is not None:
                    return True

        if self._exclude_from_weight_decay:
            for r in self._exclude_from_weight_decay:
                if re.search(lowerCamelCase__,		lowerCamelCase__		) is not None:
                    return False
        return True






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def __init__(       self		):
        A :       List[str]          =   []
        A :       List[str]          =   None






    @property
    def 			_lowerCAmelCase					(       self		):
        if self._accum_steps is None:
            A :       str          =   tf.Variable(
                tf.constant(0,		dtype=tf.intaa		),		trainable=lowerCamelCase__,		synchronization=tf.VariableSynchronization.ON_READ,		aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,		)

        return self._accum_steps.value()






    @property
    def 			_lowerCAmelCase					(       self		):
        if not self._gradients:
            raise ValueError("""The accumulator should be called first to initialize the gradients"""		)
        return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]






    def __call__(       self,		lowerCamelCase__		):
        if not self._gradients:
            A :       int          =   self.step  # Create the step variable.
            self._gradients.extend(
                [
                    tf.Variable(
                        tf.zeros_like(lowerCamelCase__		),		trainable=lowerCamelCase__,		synchronization=tf.VariableSynchronization.ON_READ,		aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,		)
                    if gradient is not None
                    else gradient
                    for gradient in gradients
                ]		)
        if len(lowerCamelCase__		) != len(self._gradients		):
            raise ValueError(f'''Expected {len(self._gradients		)} gradients, but got {len(lowerCamelCase__		)}'''		)

        for accum_gradient, gradient in zip(self._gradients,		lowerCamelCase__		):
            if accum_gradient is not None and gradient is not None:
                accum_gradient.assign_add(lowerCamelCase__		)

        self._accum_steps.assign_add(1		)






    def 			_lowerCAmelCase					(       self		):
        if not self._gradients:
            return
        self._accum_steps.assign(0		)
        for gradient in self._gradients:
            if gradient is not None:
                gradient.assign(tf.zeros_like(lowerCamelCase__		)		)


