






"""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      __lowercase       (     a			:					Optional[Any]							,		a			:					List[Any]			)       ->       Dict:
      __snake_case   :     Tuple	      =copy.deepcopy(a			)
      __snake_case   :     Optional[int]	      =sorted(a			)
      __snake_case   :     Any	      =[bisect.bisect_right(a							,		a			) for y in x]
      return quantized



def      __lowercase       (     a			:					Tuple							,		a			:					Optional[Any]=0			)       ->       Optional[int]:
      __snake_case   :     List[Any]	      =np.arange(start=3							,		stop=a							,		step=4			).tolist() if k > 0 else [10]
      __snake_case   :     Any	      =_quantize(a							,		a			)
      # count number of elements per group
      __snake_case   :     int	      =np.unique(a							,		return_counts=a			)[1]
      __snake_case   :     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   _lowercase			(  lowerCAmelCase    ):


      def __init__(     self					:						Union[str, Any]				, a					:						Dict				, a					:						Optional[int]				, a					:						str    ):





            """simple docstring"""







            if not isinstance(a				, a    ):
                  raise ValueError(
                      '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(a    )    )
            __snake_case   :     str	      =sampler
            __snake_case   :     Dict	      =group_ids
            __snake_case   :     List[Any]	      =batch_size


      def __iter__(     self					:						Dict    ):





            """simple docstring"""







            __snake_case   :     Optional[Any]	      =defaultdict(a    )
            __snake_case   :     str	      =defaultdict(a    )

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





            """simple docstring"""







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










"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_						:					Dict      				=			{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					List[Any]      				=			["""LayoutLMTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[Any]      				=			[
        """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """LayoutLMForMaskedLM""",
        """LayoutLMForSequenceClassification""",
        """LayoutLMForTokenClassification""",
        """LayoutLMForQuestionAnswering""",
        """LayoutLMModel""",
        """LayoutLMPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[int]      				=			[
        """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFLayoutLMForMaskedLM""",
        """TFLayoutLMForSequenceClassification""",
        """TFLayoutLMForTokenClassification""",
        """TFLayoutLMForQuestionAnswering""",
        """TFLayoutLMMainLayer""",
        """TFLayoutLMModel""",
        """TFLayoutLMPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlm import (
            LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMForMaskedLM,
            LayoutLMForQuestionAnswering,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMModel,
            LayoutLMPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlm import (
            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForQuestionAnswering,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForTokenClassification,
            TFLayoutLMMainLayer,
            TFLayoutLMModel,
            TFLayoutLMPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_						:					Optional[Any]      				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


