




import bisect
import copy
from collections import defaultdict

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

from utils import logger






def 					snake_case					(							A						,   A			)							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		Dict							   =							copy.deepcopy(A			)
	lowerCamelCase_							:		Optional[int]							   =							sorted(A			)
	lowerCamelCase_							:		str							   =							[bisect.bisect_right(A						,   A			) for y in x]
	return quantized






def 					snake_case					(							A						,   A=0			)							->							Optional[Any]:




	'''simple docstring'''


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



class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					def __init__(				self ,       __lowercase ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						if not isinstance(__lowercase ,       __lowercase					):
							raise ValueError(
							    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(__lowercase					)					)
						lowerCamelCase_							:		Any							   =							sampler
						lowerCamelCase_							:		Any							   =							group_ids
						lowerCamelCase_							:		Dict							   =							batch_size

					def __iter__(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							   =							defaultdict(__lowercase					)
						lowerCamelCase_							:		Any							   =							defaultdict(__lowercase					)

						lowerCamelCase_							:		Any							   =							0
						for idx in self.sampler:
							lowerCamelCase_							:		List[str]							   =							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_							:		Optional[int]							   =							len(self					)
						lowerCamelCase_							:		Dict							   =							expected_num_batches - num_batches
						if num_remaining > 0:
							# for the remaining batches, group the batches by similar lengths
							lowerCamelCase_							:		List[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_							:		List[Any]							   =							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					):







						'''simple docstring'''




						return (len(self.sampler					) + self.batch_size - 1) // self.batch_size






from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCamelCase__              =						[
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

UpperCamelCase__              =						get_logger(__name__)


UpperCamelCase__              =						{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}






def 					snake_case					(							A			)							->							Optional[TaskTemplate]:




	'''simple docstring'''


	lowerCamelCase_							:		Any							   =							task_template_dict.get('task'			)
	if task_name is None:
		logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(A			)}"			)
		return None
	lowerCamelCase_							:		Any							   =							NAMEaTEMPLATE.get(A			)
	return template.from_dict(A			)
