"""simple docstring"""





import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    int   =  (IPNDMScheduler,)
      lowerCamelCase__		:    str   =  (("num_inference_steps", 5_0),)


      def 		_UpperCAmelCase       (   self		,			**a							)       ->					List[str]:
             lowercase__			:		int       =	{'num_train_timesteps': 1_0_0_0}
             config.update(**a							)
             return config


      def 		_UpperCAmelCase       (   self		,			a=0		,			**a							)       ->					Union[str, Any]:
             lowercase__			:		Tuple       =	dict(self.forward_default_kwargs							)
             lowercase__			:		List[Any]       =	kwargs.pop('num_inference_steps'		,			a							)
             lowercase__			:		Dict       =	self.dummy_sample
             lowercase__			:		Union[str, Any]       =	0.1 * sample
             lowercase__			:		Optional[Any]       =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                    lowercase__			:		List[str]       =	self.get_scheduler_config(**a							)
                    lowercase__			:		Union[str, Any]       =	scheduler_class(**a							)
                    scheduler.set_timesteps(a							)
                    # copy over dummy past residuals
                    lowercase__			:		Optional[int]       =	dummy_past_residuals[:]

                    if time_step is None:
                           lowercase__			:		Optional[int]       =	scheduler.timesteps[len(scheduler.timesteps							) // 2]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(a							)
                           lowercase__			:		int       =	scheduler_class.from_pretrained(a							)
                           new_scheduler.set_timesteps(a							)
                           # copy over dummy past residuals
                           lowercase__			:		Tuple       =	dummy_past_residuals[:]

                    lowercase__			:		Optional[Any]       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample
                    lowercase__			:		List[Any]       =	new_scheduler.step(a		,			a		,			a		,			**a							).prev_sample

                    assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

                    lowercase__			:		Tuple       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample
                    lowercase__			:		List[str]       =	new_scheduler.step(a		,			a		,			a		,			**a							).prev_sample

                    assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             pass


      def 		_UpperCAmelCase       (   self		,			a=0		,			**a							)       ->					List[Any]:
             lowercase__			:		List[str]       =	dict(self.forward_default_kwargs							)
             lowercase__			:		List[str]       =	kwargs.pop('num_inference_steps'		,			a							)
             lowercase__			:		Union[str, Any]       =	self.dummy_sample
             lowercase__			:		Tuple       =	0.1 * sample
             lowercase__			:		Optional[Any]       =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                    lowercase__			:		str       =	self.get_scheduler_config()
                    lowercase__			:		Union[str, Any]       =	scheduler_class(**a							)
                    scheduler.set_timesteps(a							)

                    # copy over dummy past residuals (must be after setting timesteps)
                    lowercase__			:		Optional[Any]       =	dummy_past_residuals[:]

                    if time_step is None:
                           lowercase__			:		List[Any]       =	scheduler.timesteps[len(scheduler.timesteps							) // 2]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(a							)
                           lowercase__			:		Optional[Any]       =	scheduler_class.from_pretrained(a							)
                           # copy over dummy past residuals
                           new_scheduler.set_timesteps(a							)

                           # copy over dummy past residual (must be after setting timesteps)
                           lowercase__			:		Union[str, Any]       =	dummy_past_residuals[:]

                    lowercase__			:		Optional[Any]       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample
                    lowercase__			:		int       =	new_scheduler.step(a		,			a		,			a		,			**a							).prev_sample

                    assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

                    lowercase__			:		List[str]       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample
                    lowercase__			:		int       =	new_scheduler.step(a		,			a		,			a		,			**a							).prev_sample

                    assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"


      def 		_UpperCAmelCase       (   self		,			**a							)       ->					Tuple:
             lowercase__			:		Union[str, Any]       =	self.scheduler_classes[0]
             lowercase__			:		Tuple       =	self.get_scheduler_config(**a							)
             lowercase__			:		str       =	scheduler_class(**a							)

             lowercase__			:		Dict       =	1_0
             lowercase__			:		str       =	self.dummy_model()
             lowercase__			:		Optional[Any]       =	self.dummy_sample_deter
             scheduler.set_timesteps(a							)

             for i, t in enumerate(scheduler.timesteps							):
                    lowercase__			:		Any       =	model(a		,			a							)
                    lowercase__			:		Union[str, Any]       =	scheduler.step(a		,			a		,			a							).prev_sample

             for i, t in enumerate(scheduler.timesteps							):
                    lowercase__			:		str       =	model(a		,			a							)
                    lowercase__			:		Tuple       =	scheduler.step(a		,			a		,			a							).prev_sample

             return sample


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		List[Any]       =	dict(self.forward_default_kwargs							)

             lowercase__			:		Tuple       =	kwargs.pop('num_inference_steps'		,			a							)

             for scheduler_class in self.scheduler_classes:
                    lowercase__			:		List[Any]       =	self.get_scheduler_config()
                    lowercase__			:		Optional[int]       =	scheduler_class(**a							)

                    lowercase__			:		List[Any]       =	self.dummy_sample
                    lowercase__			:		int       =	0.1 * sample

                    if num_inference_steps is not None and hasattr(a		,			'set_timesteps'							):
                           scheduler.set_timesteps(a							)
                    elif num_inference_steps is not None and not hasattr(a		,			'set_timesteps'							):
                           lowercase__			:		Any       =	num_inference_steps

                    # copy over dummy past residuals (must be done after set_timesteps)
                    lowercase__			:		Any       =	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                    lowercase__			:		Union[str, Any]       =	dummy_past_residuals[:]

                    lowercase__			:		Any       =	scheduler.timesteps[5]
                    lowercase__			:		str       =	scheduler.timesteps[6]

                    lowercase__			:		Optional[int]       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample
                    lowercase__			:		Union[str, Any]       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample

                    self.assertEqual(output_a.shape		,			sample.shape							)
                    self.assertEqual(output_a.shape		,			output_a.shape							)

                    lowercase__			:		Union[str, Any]       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample
                    lowercase__			:		Optional[Any]       =	scheduler.step(a		,			a		,			a		,			**a							).prev_sample

                    self.assertEqual(output_a.shape		,			sample.shape							)
                    self.assertEqual(output_a.shape		,			output_a.shape							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             for timesteps in [1_0_0, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=a		,			time_step=a							)


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             for t, num_inference_steps in zip([1, 5, 1_0]		,			[1_0, 5_0, 1_0_0]							):
                    self.check_over_forward(num_inference_steps=a		,			time_step=a							)


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             lowercase__			:		List[str]       =	self.full_loop()
             lowercase__			:		Tuple       =	torch.mean(torch.abs(a							)							)

             assert abs(result_mean.item() - 2_5_4_0_5_2_9							) < 1_0





import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
lowerCAmelCase__      		= object()

# For specifying empty leaf dict `{}`
lowerCAmelCase__      		= object()
def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  Union[str, Any]						):
       _A      :       str				    = tuple((re.compile(x + "$"						) for x in qs)						)
       for i in range(len(UpperCamelCase__						) - len(UpperCamelCase__						) + 1						):
              _A      :       Tuple				    = [x.match(UpperCamelCase__						) for x, y in zip(UpperCamelCase__				,							ks[i:]						)]
              if matches and all(UpperCamelCase__						):
                     return True
       return False
def 							_UpperCAmelCase				(UpperCamelCase__     :  str						):
       def replace(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Optional[int]						):
              for rule, replacement in rules:
                     if _match(UpperCamelCase__				,							UpperCamelCase__						):
                            return replacement
              return val

       return replace
def 							_UpperCAmelCase				():
       return [
           # embeddings
           (("transformer", "wpe", "embedding"), P("mp"				,							UpperCamelCase__						)),
           (("transformer", "wte", "embedding"), P("mp"				,							UpperCamelCase__						)),
           # atention
           (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__				,							"mp"						)),
           (("attention", "out_proj", "kernel"), P("mp"				,							UpperCamelCase__						)),
           (("attention", "out_proj", "bias"), None),
           # mlp
           (("mlp", "c_fc", "kernel"), P(UpperCamelCase__				,							"mp"						)),
           (("mlp", "c_fc", "bias"), P("mp"						)),
           (("mlp", "c_proj", "kernel"), P("mp"				,							UpperCamelCase__						)),
           (("mlp", "c_proj", "bias"), None),
           # layer norms
           ((r"ln_\d+", "bias"), None),
           ((r"\d+", r"ln_\d+", "scale"), None),
           (("ln_f", "bias"), None),
           (("ln_f", "scale"), None),
       ]






def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]						):
       _A      :       int				    = _get_partition_rules()
       _A      :       Optional[int]				    = _replacement_rules(UpperCamelCase__						)
       _A      :       Optional[int]				    = {k: _unmatched for k in flatten_dict(UpperCamelCase__						)}
       _A      :       List[str]				    = {k: replace(UpperCamelCase__				,							UpperCamelCase__						) for k, v in initd.items()}
       assert _unmatched not in result.values(), "Incomplete partition spec."
       return freeze(unflatten_dict(UpperCamelCase__						)						)
