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





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





class 							A__	(			__SCREAMING_SNAKE_CASE):
      def __init__(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__   ):
       if not isinstance(__magic_name__					,						__magic_name__   ):
        raise ValueError(
            """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(__magic_name__   )   )
       lowerCamelCase	:			Union[str, Any]           =   sampler
       lowerCamelCase	:			Union[str, Any]           =   group_ids
       lowerCamelCase	:			int           =   batch_size
      def __iter__(   self   ):
       lowerCamelCase	:			List[str]           =   defaultdict(__magic_name__   )
       lowerCamelCase	:			Tuple           =   defaultdict(__magic_name__   )

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






from typing import TYPE_CHECKING

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


_lowerCamelCase           ={
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase           =["""GPT2TokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase           =[
          """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPT2DoubleHeadsModel""",
          """GPT2ForQuestionAnswering""",
          """GPT2ForSequenceClassification""",
          """GPT2ForTokenClassification""",
          """GPT2LMHeadModel""",
          """GPT2Model""",
          """GPT2PreTrainedModel""",
          """load_tf_weights_in_gpt2""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase           =[
          """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFGPT2DoubleHeadsModel""",
          """TFGPT2ForSequenceClassification""",
          """TFGPT2LMHeadModel""",
          """TFGPT2MainLayer""",
          """TFGPT2Model""",
          """TFGPT2PreTrainedModel""",
      ]

try:
      if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase           =["""TFGPT2Tokenizer"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase           =["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpta_fast import GPTaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpta import (
                GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTaDoubleHeadsModel,
                GPTaForQuestionAnswering,
                GPTaForSequenceClassification,
                GPTaForTokenClassification,
                GPTaLMHeadModel,
                GPTaModel,
                GPTaPreTrainedModel,
                load_tf_weights_in_gpta,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_gpta import (
                TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFGPTaDoubleHeadsModel,
                TFGPTaForSequenceClassification,
                TFGPTaLMHeadModel,
                TFGPTaMainLayer,
                TFGPTaModel,
                TFGPTaPreTrainedModel,
            )

      try:
            if not is_keras_nlp_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpta_tf import TFGPTaTokenizer

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
      import sys

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




