



import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				[]
    if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
        for v in tree.values():
            shapes.extend(_fetch_dims(_UpperCAmelCase				)				)
    elif isinstance(_UpperCAmelCase   , (list, tuple)				):
        for t in tree:
            shapes.extend(_fetch_dims(_UpperCAmelCase				)				)
    elif isinstance(_UpperCAmelCase   , torch.Tensor				):
        shapes.append(tree.shape				)
    else:
        raise ValueError('''Not supported'''				)

    return shapes





@torch.jit.ignore
def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				[]
    for d in reversed(_UpperCAmelCase				):
        idx.append(flat_idx % d				)
        __a						       =				flat_idx // d

    return tuple(reversed(_UpperCAmelCase				)				)





@torch.jit.ignore
def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase = None   , _UpperCAmelCase = None   , ):

    # start_edges and end_edges both indicate whether, starting from any given
    # dimension, the start/end index is at the top/bottom edge of the
    # corresponding tensor, modeled as a tree
    def reduce_edge_list(_UpperCAmelCase				) -> None:
        __a						       =				True
        for i in range(len(_UpperCAmelCase				)				):
            __a						       =				-1 * (i + 1)
            l[reversed_idx] &= tally
            __a						       =				l[reversed_idx]

    if start_edges is None:
        __a						       =				[s == 0 for s in start]
        reduce_edge_list(_UpperCAmelCase				)
    if end_edges is None:
        __a						       =				[e == (d - 1) for e, d in zip(_UpperCAmelCase   , _UpperCAmelCase				)]
        reduce_edge_list(_UpperCAmelCase				)

    # Base cases. Either start/end are empty and we're done, or the final,
    # one-dimensional tensor can be simply sliced
    if len(_UpperCAmelCase				) == 0:
        return [()]
    elif len(_UpperCAmelCase				) == 1:
        return [(slice(start[0]   , end[0] + 1				),)]

    __a						       =				[]
    __a						       =				[]

    # Dimensions common to start and end can be selected directly
    for s, e in zip(_UpperCAmelCase   , _UpperCAmelCase				):
        if s == e:
            path_list.append(slice(_UpperCAmelCase   , s + 1				)				)
        else:
            break

    __a						       =				tuple(_UpperCAmelCase				)
    __a						       =				len(_UpperCAmelCase				)

    # start == end, and we're done
    if divergence_idx == len(_UpperCAmelCase				):
        return [path]

    def upper() -> Tuple[Tuple[slice, ...], ...]:
        assert start_edges is not None
        assert end_edges is not None

        __a						       =				start[divergence_idx]
        return tuple(
            path + (slice(_UpperCAmelCase   , sdi + 1				),) + s
            for s in _get_minimal_slice_set(
                start[divergence_idx + 1 :]   , [d - 1 for d in dims[divergence_idx + 1 :]]   , dims[divergence_idx + 1 :]   , start_edges=start_edges[divergence_idx + 1 :]   , end_edges=[True for _ in end_edges[divergence_idx + 1 :]]   , )				)

    def lower() -> Tuple[Tuple[slice, ...], ...]:
        assert start_edges is not None
        assert end_edges is not None

        __a						       =				end[divergence_idx]
        return tuple(
            path + (slice(_UpperCAmelCase   , edi + 1				),) + s
            for s in _get_minimal_slice_set(
                [0 for _ in start[divergence_idx + 1 :]]   , end[divergence_idx + 1 :]   , dims[divergence_idx + 1 :]   , start_edges=[True for _ in start_edges[divergence_idx + 1 :]]   , end_edges=end_edges[divergence_idx + 1 :]   , )				)

    # If both start and end are at the edges of the subtree rooted at
    # divergence_idx, we can just select the whole subtree at once
    if start_edges[divergence_idx] and end_edges[divergence_idx]:
        slices.append(path + (slice(start[divergence_idx]   , end[divergence_idx] + 1				),)				)
    # If just start is at the edge, we can grab almost all of the subtree,
    # treating only the ragged bottom edge as an edge case
    elif start_edges[divergence_idx]:
        slices.append(path + (slice(start[divergence_idx]   , end[divergence_idx]				),)				)
        slices.extend(lower()				)
    # Analogous to the previous case, but the top is ragged this time
    elif end_edges[divergence_idx]:
        slices.extend(upper()				)
        slices.append(path + (slice(start[divergence_idx] + 1   , end[divergence_idx] + 1				),)				)
    # If both sides of the range are ragged, we need to handle both sides
    # separately. If there's contiguous meat in between them, we can index it
    # in one big chunk
    else:
        slices.extend(upper()				)
        __a						       =				end[divergence_idx] - start[divergence_idx]
        if middle_ground > 1:
            slices.append(path + (slice(start[divergence_idx] + 1   , end[divergence_idx]				),)				)
        slices.extend(lower()				)

    return slices





@torch.jit.ignore
def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				t.shape[:no_batch_dims]
    __a						       =				list(_flat_idx_to_idx(_UpperCAmelCase   , _UpperCAmelCase				)				)
    # _get_minimal_slice_set is inclusive
    __a						       =				list(_flat_idx_to_idx(flat_end - 1   , _UpperCAmelCase				)				)

    # Get an ordered list of slices to perform
    __a						       =				_get_minimal_slice_set(
        _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , )

    __a						       =				[t[s] for s in slices]

    return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]				) for s in sliced_tensors]				)





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase = False   , _UpperCAmelCase = None   , _UpperCAmelCase = False   , ):
    if not (len(_UpperCAmelCase				) > 0):
        raise ValueError('''Must provide at least one input'''				)

    __a						       =				[shape[:no_batch_dims] for shape in _fetch_dims(_UpperCAmelCase				)]
    __a						       =				tuple([max(_UpperCAmelCase				) for s in zip(*_UpperCAmelCase				)]				)

    def _prep_inputs(_UpperCAmelCase				) -> torch.Tensor:
        if not low_mem:
            if not sum(t.shape[:no_batch_dims]				) == no_batch_dims:
                __a						       =				t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
            __a						       =				t.reshape(-1   , *t.shape[no_batch_dims:]				)
        else:
            __a						       =				t.expand(orig_batch_dims + t.shape[no_batch_dims:]				)
        return t

    __a						       =				tensor_tree_map(_prep_inputs   , _UpperCAmelCase				)
    __a						       =				None
    if _out is not None:
        __a						       =				tensor_tree_map(lambda _UpperCAmelCase				: t.view([-1] + list(t.shape[no_batch_dims:]				)				)   , _out				)

    __a						       =				1
    for d in orig_batch_dims:
        flat_batch_dim *= d

    __a						       =				flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

    def _select_chunk(_UpperCAmelCase				) -> torch.Tensor:
        return t[i : i + chunk_size] if t.shape[0] != 1 else t

    __a						       =				0
    __a						       =				prepped_outputs
    for _ in range(_UpperCAmelCase				):
        # Chunk the input
        if not low_mem:
            __a						       =				_select_chunk
        else:
            __a						       =				partial(
                _chunk_slice   , flat_start=_UpperCAmelCase   , flat_end=min(_UpperCAmelCase   , i + chunk_size				)   , no_batch_dims=len(_UpperCAmelCase				)   , )

        __a						       =				tensor_tree_map(_UpperCAmelCase   , _UpperCAmelCase				)

        # Run the layer on the chunk
        __a						       =				layer(**_UpperCAmelCase				)

        # Allocate space for the output
        if out is None:
            __a						       =				tensor_tree_map(lambda _UpperCAmelCase				: t.new_zeros((flat_batch_dim,) + t.shape[1:]				)   , _UpperCAmelCase				)

        # Put the chunk in its pre-allocated space
        if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):

            def assign(_UpperCAmelCase   , _UpperCAmelCase				) -> None:
                for k, v in da.items():
                    if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                        assign(_UpperCAmelCase   , da[k]				)
                    else:
                        if _add_into_out:
                            v[i : i + chunk_size] += da[k]
                        else:
                            __a						       =				da[k]

            assign(_UpperCAmelCase   , _UpperCAmelCase				)
        elif isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
            for xa, xa in zip(_UpperCAmelCase   , _UpperCAmelCase				):
                if _add_into_out:
                    xa[i : i + chunk_size] += xa
                else:
                    __a						       =				xa
        elif isinstance(_UpperCAmelCase   , torch.Tensor				):
            if _add_into_out:
                out[i : i + chunk_size] += output_chunk
            else:
                __a						       =				output_chunk
        else:
            raise ValueError('''Not supported'''				)

        i += chunk_size

    __a						       =				tensor_tree_map(lambda _UpperCAmelCase				: t.view(orig_batch_dims + t.shape[1:]				)   , _UpperCAmelCase				)

    return out

class     _A       :



       def __init__(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     int = 512     ,		):


           '''simple docstring'''

           __a						       =				max_chunk_size
           __a						       =				None
           __a						       =				None



       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Callable     ,		__SCREAMING_SNAKE_CASE							:     tuple     ,		__SCREAMING_SNAKE_CASE							:     int):


           '''simple docstring'''

           logging.info('''Tuning chunk size...''')

           if min_chunk_size >= self.max_chunk_size:
               return min_chunk_size

           __a						       =				[2**l for l in range(int(math.log(self.max_chunk_size     ,		2)) + 1)]
           __a						       =				[c for c in candidates if c > min_chunk_size]
           __a						       =				[min_chunk_size] + candidates
           candidates[-1] += 4

           def test_chunk_size(__SCREAMING_SNAKE_CASE							:     int) -> bool:
               try:
                   with torch.no_grad():
                       fn(*__SCREAMING_SNAKE_CASE     ,		chunk_size=__SCREAMING_SNAKE_CASE)
                   return True
               except RuntimeError:
                   return False

           __a						       =				0
           __a						       =				len(__SCREAMING_SNAKE_CASE) - 1
           while i > min_viable_chunk_size_index:
               __a						       =				test_chunk_size(candidates[i])
               if not viable:
                   __a						       =				(min_viable_chunk_size_index + i) // 2
               else:
                   __a						       =				i
                   __a						       =				(i + len(__SCREAMING_SNAKE_CASE) - 1) // 2

           return candidates[min_viable_chunk_size_index]



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Iterable     ,		__SCREAMING_SNAKE_CASE							:     Iterable):


           '''simple docstring'''

           __a						       =				True
           for aa, aa in zip(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               assert type(__SCREAMING_SNAKE_CASE) == type(__SCREAMING_SNAKE_CASE)
               if isinstance(__SCREAMING_SNAKE_CASE     ,		(list, tuple)):
                   consistent &= self._compare_arg_caches(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
               elif isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
                   __a						       =				[v for _, v in sorted(aa.items()     ,		key=lambda __SCREAMING_SNAKE_CASE: x[0])]
                   __a						       =				[v for _, v in sorted(aa.items()     ,		key=lambda __SCREAMING_SNAKE_CASE: x[0])]
                   consistent &= self._compare_arg_caches(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
               else:
                   consistent &= aa == aa

           return consistent





       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Callable     ,		__SCREAMING_SNAKE_CASE							:     tuple     ,		__SCREAMING_SNAKE_CASE							:     int     ,		):


           '''simple docstring'''

           __a						       =				True
           __a						       =				tree_map(lambda __SCREAMING_SNAKE_CASE: a.shape if isinstance(__SCREAMING_SNAKE_CASE     ,		torch.Tensor) else a     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           if self.cached_arg_data is not None:
               # If args have changed shape/value, we need to re-tune
               assert len(self.cached_arg_data) == len(__SCREAMING_SNAKE_CASE)
               __a						       =				self._compare_arg_caches(self.cached_arg_data     ,		__SCREAMING_SNAKE_CASE)
           else:
               # Otherwise, we can reuse the precomputed value
               __a						       =				False

           if not consistent:
               __a						       =				self._determine_favorable_chunk_size(
                   __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		)
               __a						       =				arg_data

           assert self.cached_chunk_size is not None

           return self.cached_chunk_size










import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Tuple											=						(DDPMParallelScheduler,)



       def 		_lowerCamelCase			(		self							:     int     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				{
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.00_01,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''variance_type''': '''fixed_small''',
               '''clip_sample''': True,
           }

           config.update(**__SCREAMING_SNAKE_CASE)
           return config



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for timesteps in [1, 5, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]     ,		[0.0_02, 0.02, 0.2, 2]):
               self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE     ,		beta_end=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           for variance in ["fixed_small", "fixed_large", "other"]:
               self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE)
           for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=__SCREAMING_SNAKE_CASE     ,		prediction_type=__SCREAMING_SNAKE_CASE     ,		sample_max_value=__SCREAMING_SNAKE_CASE     ,		)



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


           '''simple docstring'''

           for prediction_type in ["epsilon", "sample", "v_prediction"]:
               self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for t in [0, 500, 999]:
               self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_09_79)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1E-5



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				self.dummy_sample_deter + 0.1
           __a						       =				self.dummy_sample_deter - 0.1

           __a						       =				samplea.shape[0]
           __a						       =				torch.stack([samplea, samplea, samplea]     ,		dim=0)
           __a						       =				torch.arange(__SCREAMING_SNAKE_CASE)[0:3, None].repeat(1     ,		__SCREAMING_SNAKE_CASE)

           __a						       =				model(samples.flatten(0     ,		1)     ,		timesteps.flatten(0     ,		1))
           __a						       =				scheduler.batch_step_no_noise(__SCREAMING_SNAKE_CASE     ,		timesteps.flatten(0     ,		1)     ,		samples.flatten(0     ,		1))

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 11_53.18_33) < 1E-2
           assert abs(result_mean.item() - 0.50_05) < 1E-3



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				torch.manual_seed(0)

           for t in reversed(range(__SCREAMING_SNAKE_CASE)):
               # 1. predict noise residual
               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               # 2. predict previous mean of sample x_t-1
               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE).prev_sample

               __a						       =				pred_prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 2_58.96_06) < 1E-2
           assert abs(result_mean.item() - 0.33_72) < 1E-3



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config(prediction_type='''v_prediction''')
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				torch.manual_seed(0)

           for t in reversed(range(__SCREAMING_SNAKE_CASE)):
               # 1. predict noise residual
               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               # 2. predict previous mean of sample x_t-1
               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE).prev_sample

               __a						       =				pred_prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 2_02.02_96) < 1E-2
           assert abs(result_mean.item() - 0.26_31) < 1E-3



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)

           __a						       =				scheduler.timesteps

           for i, timestep in enumerate(__SCREAMING_SNAKE_CASE):
               if i == len(__SCREAMING_SNAKE_CASE) - 1:
                   __a						       =				-1
               else:
                   __a						       =				timesteps[i + 1]

               __a						       =				scheduler.previous_timestep(__SCREAMING_SNAKE_CASE)
               __a						       =				prev_t.item()

               self.assertEqual(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 51, 0]

           with self.assertRaises(__SCREAMING_SNAKE_CASE     ,		msg='''`custom_timesteps` must be in descending order.'''):
               scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 1, 0]
           __a						       =				len(__SCREAMING_SNAKE_CASE)

           with self.assertRaises(__SCREAMING_SNAKE_CASE     ,		msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
               scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE     ,		timesteps=__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[scheduler.config.num_train_timesteps]

           with self.assertRaises(
               __SCREAMING_SNAKE_CASE     ,		msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''     ,		):
               scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)





