from math import sqrt




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	) and (
		    number >= 0
		), "'number' must been an int and positive"

		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    True

		# 0 and 1 are none primes.
		if number <= 1:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    False

		for divisor in range(2   , int(round(sqrt(lowerCAmelCase	)	)	) + 1	):
				# if 'number' divisible by 'divisor' then sets 'status'
				# of false and break up the loop.
				if number % divisor == 0:
						SCREAMING_SNAKE_CASE_  :		int				      =    False
						break

    # precondition
		assert isinstance(lowerCAmelCase   , lowerCAmelCase	), "'status' must been from type bool"

		return status




def       _snake_case	(   lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	) and (n > 2), "'N' must been an int and > 2"

		# beginList: contains all natural numbers from 2 up to N
		SCREAMING_SNAKE_CASE_  :		str				      =    list(range(2   , n + 1	)	)

		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    []  # this list will be returns.

		# actual sieve of erathostenes
		for i in range(len(lowerCAmelCase	)	):
				for j in range(i + 1   , len(lowerCAmelCase	)	):
						if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
								SCREAMING_SNAKE_CASE_  :		List[str]				      =    0

    # filters actual prime numbers.
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [x for x in begin_list if x != 0]

		# precondition
		assert isinstance(lowerCAmelCase   , lowerCAmelCase	), "'ans' must been from type list"

		return ans




def       _snake_case	(   lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	) and (n > 2), "'N' must been an int and > 2"

		SCREAMING_SNAKE_CASE_  :		Any				      =    []

		# iterates over all numbers between 2 up to N+1
		# if a number is prime then appends to list 'ans'
		for number in range(2   , n + 1	):
				if is_prime(lowerCAmelCase	):
						ans.append(lowerCAmelCase	)

    # precondition
		assert isinstance(lowerCAmelCase   , lowerCAmelCase	), "'ans' must been from type list"

		return ans




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	) and number >= 0, "'number' must been an int and >= 0"

		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    []  # this list will be returns of the function.

		# potential prime number factors.

		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    2

		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    number

		if number == 0 or number == 1:
				ans.append(lowerCAmelCase	)

		# if 'number' not prime then builds the prime factorization of 'number'
		elif not is_prime(lowerCAmelCase	):
				while quotient != 1:
						if is_prime(lowerCAmelCase	) and (quotient % factor == 0):
								ans.append(lowerCAmelCase	)
								quotient /= factor
						else:
								factor += 1

		else:
				ans.append(lowerCAmelCase	)

		# precondition
		assert isinstance(lowerCAmelCase   , lowerCAmelCase	), "'ans' must been from type list"

		return ans




def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	) and (
		    number >= 0
		), "'number' bust been an int and >= 0"

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    0

		# prime factorization of 'number'
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    prime_factorization(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    max(lowerCAmelCase	)

		# precondition
		assert isinstance(lowerCAmelCase   , lowerCAmelCase	), "'ans' must been from type int"

		return ans




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	) and (
		    number >= 0
		), "'number' bust been an int and >= 0"

		SCREAMING_SNAKE_CASE_  :		Dict				      =    0

		# prime factorization of 'number'
		SCREAMING_SNAKE_CASE_  :		Any				      =    prime_factorization(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    min(lowerCAmelCase	)

		# precondition
		assert isinstance(lowerCAmelCase   , lowerCAmelCase	), "'ans' must been from type int"

		return ans




def       _snake_case	(   lowerCAmelCase							:   Optional[Any]	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	), "'number' must been an int"
		assert isinstance(number % 2 == 0   , lowerCAmelCase	), "compare bust been from type bool"

		return number % 2 == 0




def       _snake_case	(   lowerCAmelCase							:   Any	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	), "'number' must been an int"
		assert isinstance(number % 2 != 0   , lowerCAmelCase	), "compare bust been from type bool"

		return number % 2 != 0




def       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		assert (
		    isinstance(lowerCAmelCase   , lowerCAmelCase	) and (number > 2) and is_even(lowerCAmelCase	)
		), "'number' must been an int, even and > 2"

		SCREAMING_SNAKE_CASE_  :		int				      =    []  # this list will returned

		# creates a list of prime numbers between 2 up to 'number'
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    get_prime_numbers(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    len(lowerCAmelCase	)

		# run variable for while-loops.
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    0
		SCREAMING_SNAKE_CASE_  :		int				      =    None

		# exit variable. for break up the loops
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    True

		while i < len_pn and loop:
				SCREAMING_SNAKE_CASE_  :		int				      =    i + 1

				while j < len_pn and loop:
						if prime_numbers[i] + prime_numbers[j] == number:
								SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    False
								ans.append(prime_numbers[i]	)
								ans.append(prime_numbers[j]	)

						j += 1

				i += 1

		# precondition
		assert (
		    isinstance(lowerCAmelCase   , lowerCAmelCase	)
		    and (len(lowerCAmelCase	) == 2)
		    and (ans[0] + ans[1] == number)
		    and is_prime(ans[0]	)
		    and is_prime(ans[1]	)
		), "'ans' must contains two primes. And sum of elements must been eq 'number'"

		return ans




def       _snake_case	(   lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   Any	):
		"""simple docstring"""







		assert (
		    isinstance(lowerCAmelCase   , lowerCAmelCase	)
		    and isinstance(lowerCAmelCase   , lowerCAmelCase	)
		    and (numbera >= 0)
		    and (numbera >= 0)
		), "'number1' and 'number2' must been positive integer."

		SCREAMING_SNAKE_CASE_  :		str				      =    0

		while numbera != 0:
				SCREAMING_SNAKE_CASE_  :		str				      =    numbera % numbera
				SCREAMING_SNAKE_CASE_  :		Any				      =    numbera
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    rest

		# precondition
		assert isinstance(lowerCAmelCase   , lowerCAmelCase	) and (
		    numbera >= 0
		), "'number' must been from type int and positive"

		return numbera




def       _snake_case	(   lowerCAmelCase							:   Any   , lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		assert (
		    isinstance(lowerCAmelCase   , lowerCAmelCase	)
		    and isinstance(lowerCAmelCase   , lowerCAmelCase	)
		    and (numbera >= 1)
		    and (numbera >= 1)
		), "'number1' and 'number2' must been positive integer."

		SCREAMING_SNAKE_CASE_  :		Any				      =    1  # actual answer that will be return.

		# for kgV (x,1)
		if numbera > 1 and numbera > 1:
				# builds the prime factorization of 'number1' and 'number2'
				SCREAMING_SNAKE_CASE_  :		str				      =    prime_factorization(lowerCAmelCase	)
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    prime_factorization(lowerCAmelCase	)

		elif numbera == 1 or numbera == 1:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    []
				SCREAMING_SNAKE_CASE_  :		Any				      =    []
				SCREAMING_SNAKE_CASE_  :		int				      =    max(lowerCAmelCase   , lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Any				      =    0
		SCREAMING_SNAKE_CASE_  :		str				      =    0

		SCREAMING_SNAKE_CASE_  :		Any				      =    []  # captured numbers int both 'primeFac1' and 'primeFac2'

		# iterates through primeFac1
		for n in prime_fac_a:
				if n not in done:
						if n in prime_fac_a:
								SCREAMING_SNAKE_CASE_  :		List[str]				      =    prime_fac_a.count(lowerCAmelCase	)
								SCREAMING_SNAKE_CASE_  :		int				      =    prime_fac_a.count(lowerCAmelCase	)

								for _ in range(max(lowerCAmelCase   , lowerCAmelCase	)	):
										ans *= n

						else:
								SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    prime_fac_a.count(lowerCAmelCase	)

								for _ in range(lowerCAmelCase	):
										ans *= n

						done.append(lowerCAmelCase	)

    # iterates through primeFac2
		for n in prime_fac_a:
				if n not in done:
						SCREAMING_SNAKE_CASE_  :		List[str]				      =    prime_fac_a.count(lowerCAmelCase	)

						for _ in range(lowerCAmelCase	):
								ans *= n

						done.append(lowerCAmelCase	)

    # precondition
		assert isinstance(lowerCAmelCase   , lowerCAmelCase	) and (
		    ans >= 0
		), "'ans' must been from type int and positive"

		return ans




def       _snake_case	(   lowerCAmelCase							:   Optional[Any]	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	) and (n >= 0), "'number' must been a positive int"

		SCREAMING_SNAKE_CASE_  :		str				      =    0
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    2  # this variable holds the answer

		while index < n:
				index += 1

				ans += 1  # counts to the next number

				# if ans not prime then
				# runs to the next prime number.
				while not is_prime(lowerCAmelCase	):
						ans += 1

    # precondition
		assert isinstance(lowerCAmelCase   , lowerCAmelCase	) and is_prime(
		    lowerCAmelCase	), "'ans' must been a prime number and from type int"

		return ans




def       _snake_case	(   lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   str	):
		"""simple docstring"""







		assert (
		    is_prime(lowerCAmelCase	) and is_prime(lowerCAmelCase	) and (p_number_a < p_number_a)
		), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    p_number_a + 1  # jump to the next number

		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    []  # this list will be returns.

		# if number is not prime then
		# fetch the next prime number.
		while not is_prime(lowerCAmelCase	):
				number += 1

		while number < p_number_a:
				ans.append(lowerCAmelCase	)

				number += 1

				# fetch the next prime number.
				while not is_prime(lowerCAmelCase	):
						number += 1

    # precondition
		assert (
		    isinstance(lowerCAmelCase   , lowerCAmelCase	)
		    and ans[0] != p_number_a
		    and ans[len(lowerCAmelCase	) - 1] != p_number_a
		), "'ans' must been a list without the arguments"

		# 'ans' contains not 'pNumber1' and 'pNumber2' !
		return ans




def       _snake_case	(   lowerCAmelCase							:   Optional[Any]	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	) and (n >= 1), "'n' must been int and >= 1"

		SCREAMING_SNAKE_CASE_  :		List[str]				      =    []  # will be returned.

		for divisor in range(1   , n + 1	):
				if n % divisor == 0:
						ans.append(lowerCAmelCase	)

    # precondition
		assert ans[0] == 1 and ans[len(lowerCAmelCase	) - 1] == n, "Error in function getDivisiors(...)"

		return ans




def       _snake_case	(   lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	) and (
		    number > 1
		), "'number' must been an int and >= 1"

		SCREAMING_SNAKE_CASE_  :		int				      =    get_divisors(lowerCAmelCase	)

		# precondition
		assert (
		    isinstance(lowerCAmelCase   , lowerCAmelCase	)
		    and (divisors[0] == 1)
		    and (divisors[len(lowerCAmelCase	) - 1] == number)
		), "Error in help-function getDivisiors(...)"

		# summed all divisors up to 'number' (exclusive), hence [:-1]
		return sum(divisors[:-1]	) == number




def       _snake_case	(   lowerCAmelCase							:   List[Any]   , lowerCAmelCase							:   Any	):
		"""simple docstring"""







		assert (
		    isinstance(lowerCAmelCase   , lowerCAmelCase	)
		    and isinstance(lowerCAmelCase   , lowerCAmelCase	)
		    and (denominator != 0)
		), "The arguments must been from type int and 'denominator' != 0"

		# build the greatest common divisor of numerator and denominator.
		SCREAMING_SNAKE_CASE_  :		Dict				      =    gcd(abs(lowerCAmelCase	)   , abs(lowerCAmelCase	)	)

		# precondition
		assert (
		    isinstance(lowerCAmelCase   , lowerCAmelCase	)
		    and (numerator % gcd_of_fraction == 0)
		    and (denominator % gcd_of_fraction == 0)
		), "Error in function gcd(...,...)"

		return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)




def       _snake_case	(   lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	) and (n >= 0), "'n' must been a int and >= 0"

		SCREAMING_SNAKE_CASE_  :		str				      =    1  # this will be return.

		for factor in range(1   , n + 1	):
				ans *= factor

		return ans







def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	) and (n >= 0), "'n' must been an int and >= 0"

		SCREAMING_SNAKE_CASE_  :		Tuple				      =    0
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    1
		SCREAMING_SNAKE_CASE_  :		Any				      =    1  # this will be return

		for _ in range(n - 1	):
				SCREAMING_SNAKE_CASE_  :		str				      =    ans
				ans += fiba
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tmp

		return ans

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__						(					A__					):

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    parent
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    batch_size
							SCREAMING_SNAKE_CASE_  :		Dict				      =    seq_length
							SCREAMING_SNAKE_CASE_  :		Dict				      =    is_training
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    use_input_mask
							SCREAMING_SNAKE_CASE_  :		int				      =    use_token_type_ids
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    use_labels
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    vocab_size
							SCREAMING_SNAKE_CASE_  :		Any				      =    hidden_size
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    num_hidden_layers
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    num_attention_heads
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    intermediate_size
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    hidden_act
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    hidden_dropout_prob
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_  :		str				      =    max_position_embeddings
							SCREAMING_SNAKE_CASE_  :		str				      =    type_vocab_size
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    type_sequence_label_size
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    initializer_range
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    num_labels
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    num_choices
							SCREAMING_SNAKE_CASE_  :		Dict				      =    scope
							SCREAMING_SNAKE_CASE_  :		int				      =    q_groups
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    k_groups
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    v_groups
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    post_attention_groups
							SCREAMING_SNAKE_CASE_  :		int				      =    intermediate_groups
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    output_groups

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    ids_tensor([self.batch_size, self.seq_length],self.vocab_size    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    None
							if self.use_input_mask:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    random_attention_mask([self.batch_size, self.seq_length]    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    None
							SCREAMING_SNAKE_CASE_  :		Any				      =    None
							SCREAMING_SNAKE_CASE_  :		str				      =    None
							if self.use_labels:
									SCREAMING_SNAKE_CASE_  :		str				      =    ids_tensor([self.batch_size],self.type_sequence_label_size    )
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    ids_tensor([self.batch_size, self.seq_length],self.num_labels    )
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    ids_tensor([self.batch_size],self.num_choices    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.get_config()

							return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

					def 				__UpperCamelCase   (    self      :       str    ):







							"""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 				__UpperCamelCase   (    self      :       Tuple,_A      :       Union[str, Any],_A      :       Union[str, Any],_A      :       int,_A      :       Optional[int],_A      :       Union[str, Any],_A      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    SqueezeBertModel(config=_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Any				      =    model(_A,_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)    )

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Any,_A      :       Tuple,_A      :       str,_A      :       Any,_A      :       Union[str, Any],_A      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    SqueezeBertForMaskedLM(config=_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    model(_A,attention_mask=_A,labels=_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)    )

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       Union[str, Any],_A      :       Union[str, Any],_A      :       Any,_A      :       Tuple,_A      :       int,_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    SqueezeBertForQuestionAnswering(config=_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    model(
							    _A,attention_mask=_A,start_positions=_A,end_positions=_A    )
							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      :       List[Any],_A      :       List[str],_A      :       Tuple,_A      :       List[Any],_A      :       List[str],_A      :       List[str],_A      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    self.num_labels
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    SqueezeBertForSequenceClassification(_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    model(_A,attention_mask=_A,labels=_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       Optional[int],_A      :       str,_A      :       List[Any],_A      :       List[str],_A      :       str,_A      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.num_labels
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    SqueezeBertForTokenClassification(config=_A    )
							model.to(_A    )
							model.eval()

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    model(_A,attention_mask=_A,labels=_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)    )

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       Tuple,_A      :       str,_A      :       Optional[Any],_A      :       int,_A      :       str,_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.num_choices
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    SqueezeBertForMultipleChoice(config=_A    )
							model.to(_A    )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Dict				      =    input_ids.unsqueeze(1    ).expand(-1,self.num_choices,-1    ).contiguous()
							SCREAMING_SNAKE_CASE_  :		str				      =    input_mask.unsqueeze(1    ).expand(-1,self.num_choices,-1    ).contiguous()
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    model(
							    _A,attention_mask=_A,labels=_A,)
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)    )






					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.prepare_config_and_inputs()
							((SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_)      ,    (SCREAMING_SNAKE_CASE_))  :		Dict				      =    config_and_inputs
							SCREAMING_SNAKE_CASE_  :		Dict				      =    {"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict







@require_torch
class 		a__						(					A__    ,		A__    ,		unittest.TestCase					):
					A	       =							(
					    (
					        SqueezeBertModel,
					        SqueezeBertForMaskedLM,
					        SqueezeBertForMultipleChoice,
					        SqueezeBertForQuestionAnswering,
					        SqueezeBertForSequenceClassification,
					        SqueezeBertForTokenClassification,
					    )
					    if is_torch_available()
					    else None
					)
					A	       =							(
					    {
					        'feature-extraction': SqueezeBertModel,
					        'fill-mask': SqueezeBertForMaskedLM,
					        'question-answering': SqueezeBertForQuestionAnswering,
					        'text-classification': SqueezeBertForSequenceClassification,
					        'token-classification': SqueezeBertForTokenClassification,
					        'zero-shot': SqueezeBertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					A	       =							False
					A	       =							True
					A	       =							False

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    SqueezeBertModelTester(self    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    ConfigTester(self,config_class=_A,dim=37    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							self.config_tester.run_common_tests()

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_model(*_A    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_masked_lm(*_A    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_question_answering(*_A    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_sequence_classification(*_A    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_token_classification(*_A    )

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_multiple_choice(*_A    )






					@slow
					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    SqueezeBertModel.from_pretrained(_A    )
									self.assertIsNotNone(_A    )







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

					@slow
					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    model(_A    )[0]
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    torch.Size((1, 3)    )
							self.assertEqual(output.shape,_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    torch.tensor([[0.6401, -0.0349, -0.6041]]    )
							self.assertTrue(torch.allclose(_A,_A,atol=1E-4    )    )
