




'''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   UpperCamelCase_(			snake_case							: Union[str, Any]							,       snake_case							: int  ):

							'''simple docstring'''






							snake_case_			      =   copy.deepcopy(snake_case  )
							snake_case_			      =   sorted(snake_case  )
							snake_case_			      =   [bisect.bisect_right(snake_case							,       snake_case  ) for y in x]
							return quantized


def   UpperCamelCase_(			snake_case							: Dict							,       snake_case							: List[str]=0  ):

							'''simple docstring'''






							snake_case_			      =   np.arange(start=3							,       stop=snake_case							,       step=4  ).tolist() if k > 0 else [1_0]
							snake_case_			      =   _quantize(snake_case							,       snake_case  )
							# count number of elements per group
							snake_case_			      =   np.unique(snake_case							,       return_counts=snake_case  )[1]
							snake_case_			      =   [0] + bins + [np.inf]
							logger.info("Using {} as bins for aspect lengths quantization".format(snake_case  )  )
							logger.info("Count of instances per bin: {}".format(snake_case  )  )
							return groups




class 				_snake_case	(			lowercase_							):




			def __init__(      self       ,							a__       ,							a__       ,							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_			      =   sampler
										snake_case_			      =   group_ids
										snake_case_			      =   batch_size




			def __iter__(      self							)       ->       Tuple:

										'''simple docstring'''




										snake_case_			      =   defaultdict(a__							)
										snake_case_			      =   defaultdict(a__							)

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

										'''simple docstring'''




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










'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 				_snake_case	(			unittest.TestCase							):




			@slow
			def      lowerCAmelCase__       (      self							)       ->       List[Any]:

										'''simple docstring'''




										snake_case_			      =   CamembertModel.from_pretrained("camembert-base"							)
										model.to(a__							)

										snake_case_			      =   torch.tensor(
										    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]       ,							device=a__       ,							dtype=torch.long       ,							)  # J'aime le camembert !
										with torch.no_grad():
																	snake_case_			      =   model(a__							)["last_hidden_state"]
										snake_case_			      =   torch.Size((1, 10, 768)							)
										self.assertEqual(output.shape       ,							a__							)
										# compare the actual values for a slice.
										snake_case_			      =   torch.tensor(
										    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]       ,							device=a__       ,							dtype=torch.float       ,							)
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]       ,							a__       ,							atol=1e-4							)							)




