





'''simple docstring'''

import unittest

from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      YosoForMaskedLM,
      YosoForMultipleChoice,
      YosoForQuestionAnswering,
      YosoForSequenceClassification,
      YosoForTokenClassification,
      YosoModel,
  )
  from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST






class      UpperCAmelCase			:
 def __init__(       self   :List[Any]		, lowercase_   :Any		, lowercase_   :List[Any]=13		, lowercase_   :List[str]=7		, lowercase_   :Optional[int]=True		, lowercase_   :str=True		, lowercase_   :str=True		, lowercase_   :Tuple=True		, lowercase_   :Optional[Any]=99		, lowercase_   :Any=32		, lowercase_   :List[str]=5		, lowercase_   :Optional[Any]=4		, lowercase_   :List[Any]=37		, lowercase_   :Any="gelu"		, lowercase_   :Optional[Any]=0.1		, lowercase_   :Any=0.1		, lowercase_   :List[Any]=5_12		, lowercase_   :Any=16		, lowercase_   :Tuple=2		, lowercase_   :Dict=0.0_2		, lowercase_   :int=3		, lowercase_   :Any=4		, lowercase_   :Union[str, Any]=None		, )->							Optional[Any]:
     A__       =    parent
     A__       =    batch_size
     A__       =    seq_length
     A__       =    is_training
     A__       =    use_input_mask
     A__       =    use_token_type_ids
     A__       =    use_labels
     A__       =    vocab_size
     A__       =    hidden_size
     A__       =    num_hidden_layers
     A__       =    num_attention_heads
     A__       =    intermediate_size
     A__       =    hidden_act
     A__       =    hidden_dropout_prob
     A__       =    attention_probs_dropout_prob
     A__       =    max_position_embeddings
     A__       =    type_vocab_size
     A__       =    type_sequence_label_size
     A__       =    initializer_range
     A__       =    num_labels
     A__       =    num_choices
     A__       =    scope
 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							List[str]:
     A__       =    ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size							)

     A__       =    None
     if self.use_input_mask:
         A__       =    random_attention_mask([self.batch_size, self.seq_length]							)

     A__       =    None
     if self.use_token_type_ids:
         A__       =    ids_tensor([self.batch_size, self.seq_length]		, self.type_vocab_size							)

     A__       =    None
     A__       =    None
     A__       =    None
     if self.use_labels:
         A__       =    ids_tensor([self.batch_size]		, self.type_sequence_label_size							)
         A__       =    ids_tensor([self.batch_size, self.seq_length]		, self.num_labels							)
         A__       =    ids_tensor([self.batch_size]		, self.num_choices							)

     A__       =    self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def   UpperCAmelCase_							(       self   :int							)->							Dict:
     return YosoConfig(
         vocab_size=self.vocab_size		, hidden_size=self.hidden_size		, num_hidden_layers=self.num_hidden_layers		, num_attention_heads=self.num_attention_heads		, intermediate_size=self.intermediate_size		, hidden_act=self.hidden_act		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, max_position_embeddings=self.max_position_embeddings		, type_vocab_size=self.type_vocab_size		, is_decoder=lowercase_		, initializer_range=self.initializer_range		, )
 def   UpperCAmelCase_							(       self   :List[str]							)->							Optional[int]:
     A__       =    self.get_config()
     A__       =    3_00
     return config
 def   UpperCAmelCase_							(       self   :Dict							)->							str:
     (
         (
         A__			
     ),			(
         A__			
     ),			(
         A__			
     ),			(
         A__			
     ),			(
         A__			
     ),			(
         A__			
     ),			(
         A__			
     ),						
     )       =    self.prepare_config_and_inputs()

     A__       =    True
     A__       =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
     A__       =    ids_tensor([self.batch_size, self.seq_length]		, vocab_size=2							)

     return (
         config,
         input_ids,
         token_type_ids,
         input_mask,
         sequence_labels,
         token_labels,
         choice_labels,
         encoder_hidden_states,
         encoder_attention_mask,
     )
 def   UpperCAmelCase_							(       self   :List[str]		, lowercase_   :List[Any]		, lowercase_   :str		, lowercase_   :Any		, lowercase_   :List[Any]		, lowercase_   :Tuple		, lowercase_   :Dict		, lowercase_   :Optional[int]							)->							Tuple:
     A__       =    YosoModel(config=lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__       =    model(lowercase_		, attention_mask=lowercase_		, token_type_ids=lowercase_							)
     A__       =    model(lowercase_		, token_type_ids=lowercase_							)
     A__       =    model(lowercase_							)
     self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)							)
 def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :Union[str, Any]		, lowercase_   :Optional[Any]		, lowercase_   :Optional[Any]		, lowercase_   :str		, lowercase_   :List[str]		, lowercase_   :str		, lowercase_   :Tuple		, lowercase_   :str		, lowercase_   :Any		, )->							Tuple:
     A__       =    True
     A__       =    YosoModel(lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__       =    model(
         lowercase_		, attention_mask=lowercase_		, token_type_ids=lowercase_		, encoder_hidden_states=lowercase_		, encoder_attention_mask=lowercase_		, )
     A__       =    model(
         lowercase_		, attention_mask=lowercase_		, token_type_ids=lowercase_		, encoder_hidden_states=lowercase_		, )
     A__       =    model(lowercase_		, attention_mask=lowercase_		, token_type_ids=lowercase_							)
     self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)							)
 def   UpperCAmelCase_							(       self   :int		, lowercase_   :str		, lowercase_   :Any		, lowercase_   :Any		, lowercase_   :Optional[int]		, lowercase_   :List[str]		, lowercase_   :int		, lowercase_   :str							)->							Any:
     A__       =    YosoForMaskedLM(config=lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__       =    model(lowercase_		, attention_mask=lowercase_		, token_type_ids=lowercase_		, labels=lowercase_							)
     self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.vocab_size)							)
 def   UpperCAmelCase_							(       self   :List[Any]		, lowercase_   :List[str]		, lowercase_   :Tuple		, lowercase_   :Optional[int]		, lowercase_   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :Any		, lowercase_   :Dict							)->							List[str]:
     A__       =    YosoForQuestionAnswering(config=lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__       =    model(
         lowercase_		, attention_mask=lowercase_		, token_type_ids=lowercase_		, start_positions=lowercase_		, end_positions=lowercase_		, )
     self.parent.assertEqual(result.start_logits.shape		, (self.batch_size, self.seq_length)							)
     self.parent.assertEqual(result.end_logits.shape		, (self.batch_size, self.seq_length)							)
 def   UpperCAmelCase_							(       self   :Dict		, lowercase_   :List[str]		, lowercase_   :int		, lowercase_   :str		, lowercase_   :List[str]		, lowercase_   :List[str]		, lowercase_   :Dict		, lowercase_   :Union[str, Any]							)->							Union[str, Any]:
     A__       =    self.num_labels
     A__       =    YosoForSequenceClassification(lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__       =    model(lowercase_		, attention_mask=lowercase_		, token_type_ids=lowercase_		, labels=lowercase_							)
     self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_labels)							)
 def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :str		, lowercase_   :List[Any]		, lowercase_   :Optional[int]		, lowercase_   :Tuple		, lowercase_   :Dict		, lowercase_   :Optional[Any]		, lowercase_   :Optional[Any]							)->							Tuple:
     A__       =    self.num_labels
     A__       =    YosoForTokenClassification(config=lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__       =    model(lowercase_		, attention_mask=lowercase_		, token_type_ids=lowercase_		, labels=lowercase_							)
     self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.seq_length, self.num_labels)							)
 def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :Optional[Any]		, lowercase_   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :int		, lowercase_   :int		, lowercase_   :Union[str, Any]		, lowercase_   :Union[str, Any]							)->							str:
     A__       =    self.num_choices
     A__       =    YosoForMultipleChoice(config=lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__       =    input_ids.unsqueeze(1							).expand(-1		, self.num_choices		, -1							).contiguous()
     A__       =    token_type_ids.unsqueeze(1							).expand(-1		, self.num_choices		, -1							).contiguous()
     A__       =    input_mask.unsqueeze(1							).expand(-1		, self.num_choices		, -1							).contiguous()
     A__       =    model(
         lowercase_		, attention_mask=lowercase_		, token_type_ids=lowercase_		, labels=lowercase_		, )
     self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_choices)							)



 def   UpperCAmelCase_							(       self   :List[Any]							)->							Optional[int]:
     A__       =    self.prepare_config_and_inputs()
     (
         (
         A__			
     ),			(
         A__			
     ),			(
         A__			
     ),			(
         A__			
     ),			(
         A__			
     ),			(
         A__			
     ),			(
         A__			
     ),						
     )       =    config_and_inputs
     A__       =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_torch
class      UpperCAmelCase			( UpperCamelCase__    , UpperCamelCase__    , unittest.TestCase       ):
 __lowercase     	=       (
     (
         YosoModel,
         YosoForMaskedLM,
         YosoForMultipleChoice,
         YosoForQuestionAnswering,
         YosoForSequenceClassification,
         YosoForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 __lowercase     	=       False
 __lowercase     	=       False
 __lowercase     	=       False

 __lowercase     	=       ()
 __lowercase     	=       (
     {
         """feature-extraction""": YosoModel,
         """fill-mask""": YosoForMaskedLM,
         """question-answering""": YosoForQuestionAnswering,
         """text-classification""": YosoForSequenceClassification,
         """token-classification""": YosoForTokenClassification,
         """zero-shot""": YosoForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 def   UpperCAmelCase_							(       self   :Optional[Any]							)->							Any:
     A__       =    YosoModelTester(self							)
     A__       =    ConfigTester(self		, config_class=lowercase_		, hidden_size=37							)
 def   UpperCAmelCase_							(       self   :Optional[Any]							)->							List[str]:
     self.config_tester.run_common_tests()
 def   UpperCAmelCase_							(       self   :Optional[int]							)->							Optional[int]:
     A__       =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_							)
 def   UpperCAmelCase_							(       self   :List[Any]							)->							List[Any]:
     A__       =    self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
         A__       =    type
         self.model_tester.create_and_check_model(*lowercase_							)
 def   UpperCAmelCase_							(       self   :str							)->							str:
     A__       =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*lowercase_							)
 def   UpperCAmelCase_							(       self   :List[str]							)->							List[Any]:
     A__       =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*lowercase_							)
 def   UpperCAmelCase_							(       self   :str							)->							Tuple:
     A__       =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*lowercase_							)
 def   UpperCAmelCase_							(       self   :Optional[int]							)->							str:
     A__       =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*lowercase_							)
 def   UpperCAmelCase_							(       self   :str							)->							Dict:
     A__       =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*lowercase_							)
 @slow
 def   UpperCAmelCase_							(       self   :Tuple							)->							Union[str, Any]:
     for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         A__       =    YosoModel.from_pretrained(lowercase_							)
         self.assertIsNotNone(lowercase_							)



 def   UpperCAmelCase_							(       self   :Any							)->							int:
     return






@require_torch
class      UpperCAmelCase			( unittest.TestCase       ):
 @slow
 def   UpperCAmelCase_							(       self   :Optional[int]							)->							List[str]:
     A__       =    YosoModel.from_pretrained("uw-madison/yoso-4096"							)
     A__       =    torch.tensor([[0, 1, 2, 3, 4, 5]]							)

     with torch.no_grad():
         A__       =    model(lowercase_							)[0]

     A__       =    torch.Size((1, 6, 7_68)							)
     self.assertEqual(output.shape		, lowercase_							)

     A__       =    torch.tensor(
         [[[-0.0_6_1_1, 0.1_2_4_2, 0.0_8_4_0], [0.0_2_8_0, -0.0_0_4_8, 0.1_1_2_5], [0.0_1_0_6, 0.0_2_2_6, 0.0_7_5_1]]]							)

     self.assertTrue(torch.allclose(output[:, :3, :3]		, lowercase_		, atol=1E-4							)							)
 @slow
 def   UpperCAmelCase_							(       self   :List[str]							)->							Optional[int]:
     A__       =    YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096"							)
     A__       =    torch.tensor([[0, 1, 2, 3, 4, 5]]							)

     with torch.no_grad():
         A__       =    model(lowercase_							)[0]

     A__       =    5_02_65

     A__       =    torch.Size((1, 6, vocab_size)							)
     self.assertEqual(output.shape		, lowercase_							)

     A__       =    torch.tensor(
         [[[-2.1_3_1_3, -3.7_2_8_5, -2.2_4_0_7], [-2.7_0_4_7, -3.3_3_1_4, -2.6_4_0_8], [0.0_6_2_9, -2.5_1_6_6, -0.3_3_5_6]]]							)

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



 @slow
 def   UpperCAmelCase_							(       self   :str							)->							Optional[Any]:
     A__       =    YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096"							)
     A__       =    torch.arange(40_96							).unsqueeze(0							)

     with torch.no_grad():
         A__       =    model(lowercase_							)[0]

     A__       =    5_02_65

     A__       =    torch.Size((1, 40_96, vocab_size)							)
     self.assertEqual(output.shape		, lowercase_							)

     A__       =    torch.tensor(
         [[[-2.3_9_1_4, -4.3_7_4_2, -5.0_9_5_6], [-4.0_9_8_8, -4.2_3_8_4, -7.0_4_0_6], [-3.1_4_2_7, -3.7_1_9_2, -6.6_8_0_0]]]							)

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








'''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       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      Optional[Any] ):
    A__       =    copy.deepcopy(_lowerCamelCase )
    A__       =    sorted(_lowerCamelCase )
    A__       =    [bisect.bisect_right(_lowerCamelCase		, _lowerCamelCase ) for y in x]
    return quantized





def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[int]		, _lowerCamelCase      :      Tuple=0 ):
    A__       =    np.arange(start=3		, stop=_lowerCamelCase		, step=4 ).tolist() if k > 0 else [10]
    A__       =    _quantize(_lowerCamelCase		, _lowerCamelCase )
    # count number of elements per group
    A__       =    np.unique(_lowerCamelCase		, return_counts=_lowerCamelCase )[1]
    A__       =    [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      UpperCAmelCase			( UpperCamelCase__       ):
 def __init__(       self   :Optional[Any]		, lowercase_   :List[Any]		, lowercase_   :Optional[int]		, lowercase_   :str							)->							Dict:
     if not isinstance(lowercase_		, lowercase_							):
         raise ValueError(
             "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(lowercase_							)							)
     A__       =    sampler
     A__       =    group_ids
     A__       =    batch_size
 def __iter__(       self   :int							)->							Tuple:
     A__       =    defaultdict(lowercase_							)
     A__       =    defaultdict(lowercase_							)

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



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



