



import unittest

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

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


if is_torch_available():
 import torch

 from transformers import (
     SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     SqueezeBertForMaskedLM,
     SqueezeBertForMultipleChoice,
     SqueezeBertForQuestionAnswering,
     SqueezeBertForSequenceClassification,
     SqueezeBertForTokenClassification,
     SqueezeBertModel,
 )

class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					List[str]=13					,		__SCREAMING_SNAKE_CASE							:					str=7					,		__SCREAMING_SNAKE_CASE							:					List[str]=True					,		__SCREAMING_SNAKE_CASE							:					List[str]=True					,		__SCREAMING_SNAKE_CASE							:					Any=False					,		__SCREAMING_SNAKE_CASE							:					List[str]=True					,		__SCREAMING_SNAKE_CASE							:					List[str]=99					,		__SCREAMING_SNAKE_CASE							:					List[Any]=32					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=5					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=4					,		__SCREAMING_SNAKE_CASE							:					Dict=64					,		__SCREAMING_SNAKE_CASE							:					Dict="gelu"					,		__SCREAMING_SNAKE_CASE							:					Any=0.1					,		__SCREAMING_SNAKE_CASE							:					Tuple=0.1					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=512					,		__SCREAMING_SNAKE_CASE							:					List[Any]=16					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=2					,		__SCREAMING_SNAKE_CASE							:					Dict=0.02					,		__SCREAMING_SNAKE_CASE							:					Any=3					,		__SCREAMING_SNAKE_CASE							:					Dict=4					,		__SCREAMING_SNAKE_CASE							:					Any=None					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=2					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]=2					,		__SCREAMING_SNAKE_CASE							:					List[Any]=2					,		__SCREAMING_SNAKE_CASE							:					Any=2					,		__SCREAMING_SNAKE_CASE							:					Dict=4					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=1					,		):


           '''simple docstring'''

           __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
           __a							=    q_groups
           __a							=    k_groups
           __a							=    v_groups
           __a							=    post_attention_groups
           __a							=    intermediate_groups
           __a							=    output_groups



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __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
           __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, input_mask, sequence_labels, token_labels, choice_labels



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           return SqueezeBertConfig(
               embedding_size=self.hidden_size					,		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					,		attention_probs_dropout_prob=self.hidden_dropout_prob					,		attention_dropout=self.attention_probs_dropout_prob					,		max_position_embeddings=self.max_position_embeddings					,		initializer_range=self.initializer_range					,		q_groups=self.q_groups					,		k_groups=self.k_groups					,		v_groups=self.v_groups					,		post_attention_groups=self.post_attention_groups					,		intermediate_groups=self.intermediate_groups					,		output_groups=self.output_groups					,		)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __a							=    SqueezeBertModel(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           __a							=    model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.last_hidden_state.shape					,		(self.batch_size, self.seq_length, self.hidden_size))



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Dict):


           '''simple docstring'''

           __a							=    SqueezeBertForMaskedLM(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.seq_length, self.vocab_size))



       def 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           __a							=    SqueezeBertForQuestionAnswering(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(
               __SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		start_positions=__SCREAMING_SNAKE_CASE					,		end_positions=__SCREAMING_SNAKE_CASE)
           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 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           __a							=    self.num_labels
           __a							=    SqueezeBertForSequenceClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.num_labels))



       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    self.num_labels
           __a							=    SqueezeBertForTokenClassification(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           __a							=    model(__SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.seq_length, self.num_labels))



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Any					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    self.num_choices
           __a							=    SqueezeBertForMultipleChoice(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    input_ids.unsqueeze(1).expand(-1					,		self.num_choices					,		-1).contiguous()
           __a							=    input_mask.unsqueeze(1).expand(-1					,		self.num_choices					,		-1).contiguous()
           __a							=    model(
               __SCREAMING_SNAKE_CASE					,		attention_mask=__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE					,		)
           self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.num_choices))





       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    self.prepare_config_and_inputs()
           ((__a)	,       (__a)	,       (__a)	,       (__a)	,       (__a)	,       (__a))							=    config_and_inputs
           __a							=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
           return config, inputs_dict


@require_torch
class    _A	(     __UpperCAmelCase				,__UpperCAmelCase				,unittest.TestCase   ):
       UpperCamelCase__  :   Union[str, Any]						 =						(
           (
               SqueezeBertModel,
               SqueezeBertForMaskedLM,
               SqueezeBertForMultipleChoice,
               SqueezeBertForQuestionAnswering,
               SqueezeBertForSequenceClassification,
               SqueezeBertForTokenClassification,
           )
           if is_torch_available()
           else None
       )
       UpperCamelCase__  :   List[str]						 =						(
           {
               '''feature-extraction''': SqueezeBertModel,
               '''fill-mask''': SqueezeBertForMaskedLM,
               '''question-answering''': SqueezeBertForQuestionAnswering,
               '''text-classification''': SqueezeBertForSequenceClassification,
               '''token-classification''': SqueezeBertForTokenClassification,
               '''zero-shot''': SqueezeBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       UpperCamelCase__  :   List[str]						 =						False
       UpperCamelCase__  :   Dict						 =						True
       UpperCamelCase__  :   str						 =						False



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    SqueezeBertModelTester(self)
           __a							=    ConfigTester(self					,		config_class=__SCREAMING_SNAKE_CASE					,		dim=37)



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           self.config_tester.run_common_tests()



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_model(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_masked_lm(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_question_answering(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_token_classification(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__SCREAMING_SNAKE_CASE)





       @slow
       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a							=    SqueezeBertModel.from_pretrained(__SCREAMING_SNAKE_CASE)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE)


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



       @slow
       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    SqueezeBertForSequenceClassification.from_pretrained('''squeezebert/squeezebert-mnli''')

           __a							=    torch.tensor([[1, 29_414, 232, 328, 740, 1_140, 12_695, 69, 13, 1_588, 2]])
           __a							=    model(__SCREAMING_SNAKE_CASE)[0]
           __a							=    torch.Size((1, 3))
           self.assertEqual(output.shape					,		__SCREAMING_SNAKE_CASE)
           __a							=    torch.tensor([[0.64_01, -0.03_49, -0.60_41]])
           self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		atol=1E-4))










def 					__snake_case     (      _UpperCAmelCase = 1000000    ):
    __a							=    limit + 1
    __a							=    [0] * limit
    for first_term in range(1       ,			_UpperCAmelCase    ):
        for n in range(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
            __a							=    first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    __a							=    sum(1 for x in frequency[1:limit] if x == 10    )

    return count


if __name__ == "__main__":
 print(f'{solution() = }')





