


'''simple docstring'''


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_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: List[str]  	=   copy.deepcopy(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   sorted(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: Dict  	=   [bisect.bisect_right(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ) for y in x]
				return quantized







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__=0  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: List[Any]  	=   np.arange(start=3							,   stop=SCREAMING_SNAKE_CASE__							,   step=4  ).tolist() if k > 0 else [10]
				_SCREAMING_SNAKE_CASE				: Any  	=   _quantize(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
				# count number of elements per group
				_SCREAMING_SNAKE_CASE				: int  	=   np.unique(SCREAMING_SNAKE_CASE__							,   return_counts=SCREAMING_SNAKE_CASE__  )[1]
				_SCREAMING_SNAKE_CASE				: List[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 					lowercase__						(		_snake_case      ):





							'''simple docstring'''


							def __init__(					self					,      __snake_case					,      __snake_case					,      __snake_case						):
											if not isinstance(__snake_case					,      __snake_case						):
															raise ValueError(
															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(__snake_case						)						)
											_SCREAMING_SNAKE_CASE				: int  	=   sampler
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   group_ids
											_SCREAMING_SNAKE_CASE				: int  	=   batch_size






							def __iter__(					self						):
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   defaultdict(__snake_case						)
											_SCREAMING_SNAKE_CASE				: str  	=   defaultdict(__snake_case						)

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






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








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_	:     Any							     =	{
    'configuration_musicgen': [
        'MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MusicgenConfig',
        'MusicgenDecoderConfig',
    ],
    'processing_musicgen': ['MusicgenProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Any							     =	[
				    'MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MusicgenForConditionalGeneration',
				    'MusicgenForCausalLM',
				    'MusicgenModel',
				    'MusicgenPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_musicgen import (
				    MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MusicgenConfig,
				    MusicgenDecoderConfig,
				)
				from .processing_musicgen import MusicgenProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_musicgen import (
								    MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MusicgenForCausalLM,
								    MusicgenForConditionalGeneration,
								    MusicgenModel,
								    MusicgenPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_	:     Tuple							     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




