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

def        lowerCamelCase_      (			UpperCAmelCase_							:				Union[str, Any]							,			UpperCAmelCase_							:				Any=0       ):
						lowercase       :    Optional[Any]											=  np.arange(start=3							,			stop=UpperCAmelCase_							,			step=4       ).tolist() if k > 0 else [10]
						lowercase       :    str											=  _quantize(UpperCAmelCase_							,			UpperCAmelCase_       )
						# count number of elements per group
						lowercase       :    str											=  np.unique(UpperCAmelCase_							,			return_counts=UpperCAmelCase_       )[1]
						lowercase       :    Dict											=  [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 							UpperCAmelCase	(   a_					):

					def __init__(			self      :					Tuple ,      lowerCAmelCase      :					Any ,      lowerCAmelCase      :					int ,      lowerCAmelCase      :					Optional[int]					):
											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					)					)
											lowercase       :    int											=  sampler
											lowercase       :    Dict											=  group_ids
											lowercase       :    Optional[int]											=  batch_size

					def __iter__(			self      :					Optional[int]					):
											lowercase       :    Optional[int]											=  defaultdict(_snake_case					)
											lowercase       :    Optional[Any]											=  defaultdict(_snake_case					)

											lowercase       :    Any											=  0
											for idx in self.sampler:
																	lowercase       :    str											=  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
											lowercase       :    str											=  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       :    int											=  []
																	for group_id, idxs in sorted(buffer_per_group.items() ,      key=lambda lowerCAmelCase					: x[0]					):
																							batch_idx.extend(_snake_case					)
																							if len(_snake_case					) >= self.batch_size:
																													yield batch_idx[: self.batch_size]
																													lowercase       :    Union[str, Any]											=  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      :					Optional[Any]					):
											return (len(self.sampler					) + self.batch_size - 1) // self.batch_size




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__  							=							{"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

				snake_case__  							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


