


import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	Union[str, Any]:
				lowerCAmelCase_	: List[str]      			=			copy.deepcopy(lowerCAmelCase_	)
				lowerCAmelCase_	: Optional[int]      			=			sorted(lowerCAmelCase_	)
				lowerCAmelCase_	: Dict      			=			[bisect.bisect_right(lowerCAmelCase_	,				lowerCAmelCase_	) for y in x]
				return quantized



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_=0	)->	List[str]:
				lowerCAmelCase_	: Optional[int]      			=			np.arange(start=3	,				stop=lowerCAmelCase_	,				step=4	).tolist() if k > 0 else [10]
				lowerCAmelCase_	: Union[str, Any]      			=			_quantize(lowerCAmelCase_	,				lowerCAmelCase_	)
				# count number of elements per group
				lowerCAmelCase_	: int      			=			np.unique(lowerCAmelCase_	,				return_counts=lowerCAmelCase_	)[1]
				lowerCAmelCase_	: List[str]      			=			[0] + bins + [np.inf]
				logger.info('''Using {} as bins for aspect lengths quantization'''.format(lowerCAmelCase_	)	)
				logger.info('''Count of instances per bin: {}'''.format(lowerCAmelCase_	)	)
				return groups







class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						def __init__(			self				,  __lowercase				,  __lowercase				,  __lowercase							)   ->      Tuple:
										if not isinstance(__lowercase				,  __lowercase							):
														raise ValueError(
														    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(__lowercase							)							)
										lowerCAmelCase_	: Optional[Any]      			=			sampler
										lowerCAmelCase_	: List[Any]      			=			group_ids
										lowerCAmelCase_	: List[Any]      			=			batch_size




						def __iter__(			self							)   ->      str:
										lowerCAmelCase_	: Dict      			=			defaultdict(__lowercase							)
										lowerCAmelCase_	: Dict      			=			defaultdict(__lowercase							)

										lowerCAmelCase_	: Dict      			=			0
										for idx in self.sampler:
														lowerCAmelCase_	: Dict      			=			self.group_ids[idx]
														buffer_per_group[group_id].append(__lowercase							)
														samples_per_group[group_id].append(__lowercase							)
														if len(buffer_per_group[group_id]							) == self.batch_size:
																		yield buffer_per_group[group_id]  # TODO
																		num_batches += 1
																		del buffer_per_group[group_id]
														assert len(buffer_per_group[group_id]							) < self.batch_size

										# now we have run out of elements that satisfy
										# the group criteria, let's return the remaining
										# elements so that the size of the sampler is
										# deterministic
										lowerCAmelCase_	: List[str]      			=			len(self							)
										lowerCAmelCase_	: str      			=			expected_num_batches - num_batches
										if num_remaining > 0:
														# for the remaining batches, group the batches by similar lengths
														lowerCAmelCase_	: str      			=			[]
														for group_id, idxs in sorted(buffer_per_group.items()				,  key=lambda __lowercase							: x[0]							):
																		batch_idx.extend(__lowercase							)
																		if len(__lowercase							) >= self.batch_size:
																						yield batch_idx[: self.batch_size]
																						lowerCAmelCase_	: Dict      			=			batch_idx[self.batch_size :]
																						num_remaining -= 1
														if len(__lowercase							) > 0:
																		yield batch_idx
																		num_remaining -= 1
										assert num_remaining == 0




						def __len__(			self							)   ->      int:
										return (len(self.sampler							) + self.batch_size - 1) // self.batch_size



from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
						import torch

_UpperCAmelCase     :					List[Any]       =logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
						from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):



						def      lowerCAmelCase	(						cls	)->	int:
										return cls



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_ = None	,				lowerCAmelCase_ = None	,				lowerCAmelCase_ = None	,				lowerCAmelCase_ = None	,				)->	int:
				lowerCAmelCase_	: Tuple      			=			device
				lowerCAmelCase_	: List[str]      			=			shape[0]

				lowerCAmelCase_	: Union[str, Any]      			=			layout or torch.strided
				lowerCAmelCase_	: Union[str, Any]      			=			device or torch.device('''cpu'''	)

				if generator is not None:
								lowerCAmelCase_	: List[str]      			=			generator.device.type if not isinstance(lowerCAmelCase_	,				lowerCAmelCase_	) else generator[0].device.type
								if gen_device_type != device.type and gen_device_type == "cpu":
												lowerCAmelCase_	: Optional[Any]      			=			'''cpu'''
												if device != "mps":
																logger.info(
																    f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																    f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																    f""" slighly speed up this function by passing a generator that was created on the {device} device."""	)
								elif gen_device_type != device.type and gen_device_type == "cuda":
												raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""	)

				if isinstance(lowerCAmelCase_	,				lowerCAmelCase_	):
								lowerCAmelCase_	: Dict      			=			(1,) + shape[1:]
								lowerCAmelCase_	: Union[str, Any]      			=			[
								    torch.randn(lowerCAmelCase_	,				generator=generator[i]	,				device=lowerCAmelCase_	,				dtype=lowerCAmelCase_	,				layout=lowerCAmelCase_	)
								    for i in range(lowerCAmelCase_	)
								]
								lowerCAmelCase_	: Tuple      			=			torch.cat(lowerCAmelCase_	,				dim=0	).to(lowerCAmelCase_	)
				else:
								lowerCAmelCase_	: int      			=			torch.randn(lowerCAmelCase_	,				generator=lowerCAmelCase_	,				device=lowerCAmelCase_	,				dtype=lowerCAmelCase_	,				layout=lowerCAmelCase_	).to(lowerCAmelCase_	)

				return latents




def      lowerCAmelCase	(						lowerCAmelCase_	)->	Optional[Any]:
				if is_torch_version('''<'''	,				'''2.0.0'''	) or not hasattr(lowerCAmelCase_	,				'''_dynamo'''	):
								return False
				return isinstance(lowerCAmelCase_	,				torch._dynamo.eval_frame.OptimizedModule	)