






import bisect
import copy
from collections import defaultdict

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

from utils import logger
def    a__    (	A		,					A       ):
   lowercase__    :						Union[str, Any]       =			copy.deepcopy(_UpperCAmelCase       )
   lowercase__    :						Optional[Any]       =			sorted(_UpperCAmelCase       )
   lowercase__    :						Optional[int]       =			[bisect.bisect_right(_UpperCAmelCase		,					_UpperCAmelCase       ) for y in x]
   return quantized
def    a__    (	A		,					A=0       ):
   lowercase__    :						Dict       =			np.arange(start=3		,					stop=_UpperCAmelCase		,					step=4       ).tolist() if k > 0 else [10]
   lowercase__    :						Tuple       =			_quantize(_UpperCAmelCase		,					_UpperCAmelCase       )
   # count number of elements per group
   lowercase__    :						Union[str, Any]       =			np.unique(_UpperCAmelCase		,					return_counts=_UpperCAmelCase       )[1]
   lowercase__    :						str       =			[0] + bins + [np.inf]
   logger.info("""Using {} as bins for aspect lengths quantization""".format(_UpperCAmelCase       )       )
   logger.info("""Count of instances per bin: {}""".format(_UpperCAmelCase       )       )
   return groups






class 							__snake_case(       _snake_case   ):



 def __init__(						self : List[Any]			,		UpperCAmelCase__ : List[str]			,		UpperCAmelCase__ : str			,		UpperCAmelCase__ : List[Any]						):

    """simple docstring"""






    if not isinstance(lowercase__			,		lowercase__						):
       raise ValueError(
           """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowercase__						)						)
    lowercase__    :						Tuple       =			sampler
    lowercase__    :						Union[str, Any]       =			group_ids
    lowercase__    :						Union[str, Any]       =			batch_size



 def __iter__(						self : Optional[int]						):

    """simple docstring"""






    lowercase__    :						int       =			defaultdict(lowercase__						)
    lowercase__    :						Any       =			defaultdict(lowercase__						)

    lowercase__    :						List[str]       =			0
    for idx in self.sampler:
       lowercase__    :						Any       =			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
    lowercase__    :						List[Any]       =			len(self						)
    lowercase__    :						Union[str, Any]       =			expected_num_batches - num_batches
    if num_remaining > 0:
       # for the remaining batches, group the batches by similar lengths
       lowercase__    :						Tuple       =			[]
       for group_id, idxs in sorted(buffer_per_group.items()			,		key=lambda UpperCAmelCase__						: x[0]						):
          batch_idx.extend(lowercase__						)
          if len(lowercase__						) >= self.batch_size:
             yield batch_idx[: self.batch_size]
             lowercase__    :						Tuple       =			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 : List[Any]						):

    """simple docstring"""






    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

__magic_name__       =     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 		__snake_case						(    cls				):
							"""simple docstring"""






							return cls





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			_UpperCAmelCase = None       ,			):
					"""simple docstring"""






					lowercase						   =       device
					lowercase						   =       shape[0]

					lowercase						   =       layout or torch.strided
					lowercase						   =       device or torch.device('cpu'				)

					if generator is not None:
										lowercase						   =       generator.device.type if not isinstance(_UpperCAmelCase       ,			_UpperCAmelCase				) else generator[0].device.type
										if gen_device_type != device.type and gen_device_type == "cpu":
															lowercase						   =       '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(_UpperCAmelCase       ,			_UpperCAmelCase				):
										lowercase						   =       (1,) + shape[1:]
										lowercase						   =       [
										    torch.randn(_UpperCAmelCase       ,			generator=generator[i]       ,			device=_UpperCAmelCase       ,			dtype=_UpperCAmelCase       ,			layout=_UpperCAmelCase				)
										    for i in range(_UpperCAmelCase				)
										]
										lowercase						   =       torch.cat(_UpperCAmelCase       ,			dim=0				).to(_UpperCAmelCase				)
					else:
										lowercase						   =       torch.randn(_UpperCAmelCase       ,			generator=_UpperCAmelCase       ,			device=_UpperCAmelCase       ,			dtype=_UpperCAmelCase       ,			layout=_UpperCAmelCase				).to(_UpperCAmelCase				)

					return latents





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					if is_torch_version('<'       ,			'2.0.0'				) or not hasattr(_UpperCAmelCase       ,			'_dynamo'				):
										return False
					return isinstance(_UpperCAmelCase       ,			torch._dynamo.eval_frame.OptimizedModule				)






