

"""simple docstring"""




# Lint as: python3

import hashlib
from typing import Union
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					if isinstance(lowercase__     ,				lowercase__ ):
										# Data already in bytes, returns as it as
										return hash_data
					elif isinstance(lowercase__     ,				lowercase__ ):
										# We keep the data as it as for it ot be later encoded to UTF-8
										# However replace `\\` with `/` for Windows compatibility
										A     =   hash_data.replace("\\"     ,				"/" )
					elif isinstance(lowercase__     ,				lowercase__ ):
										A     =   str(lowercase__ )
					else:
										# If data is not of the required type, raise error
										raise InvalidKeyError(lowercase__ )

					return hash_data.encode("utf-8" )





class      __UpperCamelCase					(  _A		):


		def __init__(self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						int):
							A     =   "\nFAILURE TO GENERATE DATASET: Invalid key type detected"
							A     =   F"""\nFound Key {hash_data} of type {type(__SCREAMING_SNAKE_CASE)}"""
							A     =   "\nKeys should be either str, int or bytes type"
							super().__init__(F"""{self.prefix}{self.err_msg}{self.suffix}""")





class      __UpperCamelCase					(  _A		):


		def __init__(self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						int=""):
							A     =   key
							A     =   duplicate_key_indices
							A     =   fix_msg
							A     =   "Found multiple examples generated with the same key"
							if len(__SCREAMING_SNAKE_CASE) <= 2_0:
												A     =   F"""\nThe examples at index {", ".join(__SCREAMING_SNAKE_CASE)} have the key {key}"""
							else:
												A     =   F"""\nThe examples at index {", ".join(duplicate_key_indices[:2_0])}... ({len(__SCREAMING_SNAKE_CASE) - 2_0} more) have the key {key}"""
							A     =   "\n" + fix_msg if fix_msg else ""
							super().__init__(F"""{self.prefix}{self.err_msg}{self.suffix}""")





class      __UpperCamelCase					:


		def __init__(self					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						str):
							A     =   hashlib.mda(_as_bytes(__SCREAMING_SNAKE_CASE))


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						Union[str, int, bytes]):
							A     =   self._split_mda.copy()
							A     =   _as_bytes(__SCREAMING_SNAKE_CASE)
							mda.update(__SCREAMING_SNAKE_CASE)
							# Convert to integer with hexadecimal conversion
							return int(mda.hexdigest()	,	1_6)







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						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:int 		=    copy.deepcopy(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:List[Any] 		=    sorted(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Dict 		=    [bisect.bisect_right(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							) for y in x]
			return quantized




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=0							):




			"""simple docstring"""


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




class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



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


						def __iter__(  self	)  ->Dict:
									UpperCAmelCase__		:List[str] 		=    defaultdict(A	)
									UpperCAmelCase__		:List[str] 		=    defaultdict(A	)

									UpperCAmelCase__		:Any 		=    0
									for idx in self.sampler:
												UpperCAmelCase__		:List[Any] 		=    self.group_ids[idx]
												buffer_per_group[group_id].append(A	)
												samples_per_group[group_id].append(A	)
												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
									UpperCAmelCase__		:Union[str, Any] 		=    len(self	)
									UpperCAmelCase__		:Tuple 		=    expected_num_batches - num_batches
									if num_remaining > 0:
												# for the remaining batches, group the batches by similar lengths
												UpperCAmelCase__		:str 		=    []
												for group_id, idxs in sorted(buffer_per_group.items()  , key=lambda A	: x[0]	):
															batch_idx.extend(A	)
															if len(A	) >= self.batch_size:
																		yield batch_idx[: self.batch_size]
																		UpperCAmelCase__		:int 		=    batch_idx[self.batch_size :]
																		num_remaining -= 1
												if len(A	) > 0:
															yield batch_idx
															num_remaining -= 1
									assert num_remaining == 0


						def __len__(  self	)  ->Union[str, Any]:
									return (len(self.sampler	) + self.batch_size - 1) // self.batch_size





