

from math import sqrt




def 					_lowercase     (				a_				:					int			)				->	bool:




					'''simple docstring'''







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

					__magic_name__							=    True

					# 0 and 1 are none primes.
					if number <= 1:
										__magic_name__							=    False

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

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

					return status




def 					_lowercase     (				a_				:					Any			)				->	str:




					'''simple docstring'''







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

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

					__magic_name__							=    []  # this list will be returns.

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

    # filters actual prime numbers.
					__magic_name__							=    [x for x in begin_list if x != 0]

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

					return ans




def 					_lowercase     (				a_				:					Union[str, Any]			)				->	Optional[int]:




					'''simple docstring'''







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

					__magic_name__							=    []

					# 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(a_			):
															ans.append(a_			)

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

					return ans




def 					_lowercase     (				a_				:					Union[str, Any]			)				->	Any:




					'''simple docstring'''







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

					__magic_name__							=    []  # this list will be returns of the function.

					# potential prime number factors.

					__magic_name__							=    2

					__magic_name__							=    number

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

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

					else:
										ans.append(a_			)

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

					return ans




def 					_lowercase     (				a_				:					Union[str, Any]			)				->	str:




					'''simple docstring'''







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

					__magic_name__							=    0

					# prime factorization of 'number'
					__magic_name__							=    prime_factorization(a_			)

					__magic_name__							=    max(a_			)

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

					return ans




def 					_lowercase     (				a_				:					str			)				->	Tuple:




					'''simple docstring'''







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

					__magic_name__							=    0

					# prime factorization of 'number'
					__magic_name__							=    prime_factorization(a_			)

					__magic_name__							=    min(a_			)

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

					return ans




def 					_lowercase     (				a_				:					Tuple			)				->	Tuple:




					'''simple docstring'''







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

					return number % 2 == 0




def 					_lowercase     (				a_				:					Optional[int]			)				->	str:




					'''simple docstring'''







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

					return number % 2 != 0




def 					_lowercase     (				a_				:					int			)				->	Any:




					'''simple docstring'''







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

					__magic_name__							=    []  # this list will returned

					# creates a list of prime numbers between 2 up to 'number'
					__magic_name__							=    get_prime_numbers(a_			)
					__magic_name__							=    len(a_			)

					# run variable for while-loops.
					__magic_name__							=    0
					__magic_name__							=    None

					# exit variable. for break up the loops
					__magic_name__							=    True

					while i < len_pn and loop:
										__magic_name__							=    i + 1

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

															j += 1

										i += 1

					# precondition
					assert (
					    isinstance(a_					,a_			)
					    and (len(a_			) == 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 					_lowercase     (				a_				:					Union[str, Any]					,a_				:					List[str]			)				->	int:




					'''simple docstring'''







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

					__magic_name__							=    0

					while numbera != 0:
										__magic_name__							=    numbera % numbera
										__magic_name__							=    numbera
										__magic_name__							=    rest

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

					return numbera




def 					_lowercase     (				a_				:					Optional[Any]					,a_				:					List[Any]			)				->	str:




					'''simple docstring'''







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

					__magic_name__							=    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'
										__magic_name__							=    prime_factorization(a_			)
										__magic_name__							=    prime_factorization(a_			)

					elif numbera == 1 or numbera == 1:
										__magic_name__							=    []
										__magic_name__							=    []
										__magic_name__							=    max(a_					,a_			)

					__magic_name__							=    0
					__magic_name__							=    0

					__magic_name__							=    []  # 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:
																				__magic_name__							=    prime_fac_a.count(a_			)
																				__magic_name__							=    prime_fac_a.count(a_			)

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

															else:
																				__magic_name__							=    prime_fac_a.count(a_			)

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

															done.append(a_			)

    # iterates through primeFac2
					for n in prime_fac_a:
										if n not in done:
															__magic_name__							=    prime_fac_a.count(a_			)

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

															done.append(a_			)

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

					return ans




def 					_lowercase     (				a_				:					Union[str, Any]			)				->	List[str]:




					'''simple docstring'''







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

					__magic_name__							=    0
					__magic_name__							=    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(a_			):
															ans += 1

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

					return ans




def 					_lowercase     (				a_				:					List[Any]					,a_				:					List[str]			)				->	Dict:




					'''simple docstring'''







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

					__magic_name__							=    p_number_a + 1  # jump to the next number

					__magic_name__							=    []  # this list will be returns.

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

					while number < p_number_a:
										ans.append(a_			)

										number += 1

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

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

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




def 					_lowercase     (				a_				:					Union[str, Any]			)				->	int:




					'''simple docstring'''







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

					__magic_name__							=    []  # will be returned.

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

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

					return ans




def 					_lowercase     (				a_				:					Union[str, Any]			)				->	Union[str, Any]:




					'''simple docstring'''







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

					__magic_name__							=    get_divisors(a_			)

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

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




def 					_lowercase     (				a_				:					Any					,a_				:					Union[str, Any]			)				->	List[Any]:




					'''simple docstring'''







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

					# build the greatest common divisor of numerator and denominator.
					__magic_name__							=    gcd(abs(a_			)					,abs(a_			)			)

					# precondition
					assert (
					    isinstance(a_					,a_			)
					    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 					_lowercase     (				a_				:					List[Any]			)				->	Tuple:




					'''simple docstring'''







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

					__magic_name__							=    1  # this will be return.

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

					return ans




def 					_lowercase     (				a_				:					Optional[int]			)				->	List[str]:




					'''simple docstring'''







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

					__magic_name__							=    0
					__magic_name__							=    1
					__magic_name__							=    1  # this will be return

					for _ in range(n - 1			):
										__magic_name__							=    ans
										ans += fiba
										__magic_name__							=    tmp

					return ans







import copy
import tempfile
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
						from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder




def 					_lowercase     (				a_				:					Optional[Any]					,a_				:					Dict					,a_				:					Any					,a_				:					Any=None					,a_				:					Any=None					,a_				:					List[str]=None					,a_				:					Union[str, Any]=None					,a_				:					Dict=None					,)				->	Optional[Any]:




					'''simple docstring'''







					if attention_mask is None:
										__magic_name__							=    input_ids.ne(config.pad_token_id				)
					if decoder_attention_mask is None:
										__magic_name__							=    decoder_input_ids.ne(config.pad_token_id				)
					if head_mask is None:
										__magic_name__							=    torch.ones(config.encoder_layers					,config.encoder_attention_heads					,device=a_				)
					if decoder_head_mask is None:
										__magic_name__							=    torch.ones(config.decoder_layers					,config.decoder_attention_heads					,device=a_				)
					if cross_attn_head_mask is None:
										__magic_name__							=    torch.ones(config.decoder_layers					,config.decoder_attention_heads					,device=a_				)
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					    "cross_attn_head_mask": cross_attn_head_mask,
					}

class 				__UpperCamelCase				:







	def __init__(   self: Union[str, Any]  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: List[Any]=13  , __UpperCamelCase: Tuple=7  , __UpperCamelCase: Dict=True  , __UpperCamelCase: Optional[int]=False  , __UpperCamelCase: str=99  , __UpperCamelCase: Optional[Any]=16  , __UpperCamelCase: List[Any]=2  , __UpperCamelCase: Optional[int]=4  , __UpperCamelCase: Tuple=4  , __UpperCamelCase: Optional[int]="relu"  , __UpperCamelCase: Optional[Any]=0.1  , __UpperCamelCase: Dict=0.1  , __UpperCamelCase: int=0.0  , __UpperCamelCase: int=0.0  , __UpperCamelCase: List[Any]=20  , __UpperCamelCase: Union[str, Any]=2  , __UpperCamelCase: List[Any]=1  , __UpperCamelCase: Tuple=0  , ):

						'''simple docstring'''





						__magic_name__							=    parent
						__magic_name__							=    batch_size
						__magic_name__							=    seq_length
						__magic_name__							=    is_training
						__magic_name__							=    use_labels
						__magic_name__							=    vocab_size
						__magic_name__							=    hidden_size
						__magic_name__							=    num_hidden_layers
						__magic_name__							=    num_attention_heads
						__magic_name__							=    intermediate_size
						__magic_name__							=    hidden_act
						__magic_name__							=    hidden_dropout_prob
						__magic_name__							=    attention_probs_dropout_prob
						__magic_name__							=    encoder_layerdrop
						__magic_name__							=    decoder_layerdrop
						__magic_name__							=    max_position_embeddings
						__magic_name__							=    eos_token_id
						__magic_name__							=    pad_token_id
						__magic_name__							=    bos_token_id







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    ids_tensor([self.batch_size, self.seq_length]  , self.vocab_size      )
						__magic_name__							=    self.eos_token_id  # Eos Token
						__magic_name__							=    ids_tensor([self.batch_size, self.seq_length]  , self.vocab_size      )

						# we need to clamp the input ids here to avoid having pad token in between
						# this is because for M2M100 the position_ids are prepared such that
						# all pad tokens have pos id = 2 and rest are between 2..seq_length
						# and the seq_length here is seq_length - num_pad_tokens
						# but when using past, there is no way of knowing if the past input ids had
						# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
						# position_ids being off by num_pad_tokens in past input
						__magic_name__							=    input_ids.clamp(self.pad_token_id + 1      )
						__magic_name__							=    decoder_input_ids.clamp(self.pad_token_id + 1      )

						__magic_name__							=    self.get_config()
						__magic_name__							=    prepare_mam_aaa_inputs_dict(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )
						return config, inputs_dict







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						return MaMaaaConfig(
						    vocab_size=self.vocab_size  , d_model=self.hidden_size  , encoder_layers=self.num_hidden_layers  , decoder_layers=self.num_hidden_layers  , encoder_attention_heads=self.num_attention_heads  , decoder_attention_heads=self.num_attention_heads  , encoder_ffn_dim=self.intermediate_size  , decoder_ffn_dim=self.intermediate_size  , dropout=self.hidden_dropout_prob  , attention_dropout=self.attention_probs_dropout_prob  , encoder_layerdrop=self.encoder_layerdrop  , decoder_layerdrop=self.decoder_layerdrop  , max_position_embeddings=self.max_position_embeddings  , eos_token_id=self.eos_token_id  , bos_token_id=self.bos_token_id  , pad_token_id=self.pad_token_id  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__							=    self.prepare_config_and_inputs()
						return config, inputs_dict







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

						'''simple docstring'''





						__magic_name__							=    MaMaaaModel(config=__UpperCamelCase      ).get_decoder().to(__UpperCamelCase      ).eval()
						__magic_name__							=    inputs_dict['input_ids']
						__magic_name__							=    inputs_dict['attention_mask']
						__magic_name__							=    inputs_dict['head_mask']

						# first forward pass
						__magic_name__							=    model(__UpperCamelCase  , attention_mask=__UpperCamelCase  , head_mask=__UpperCamelCase  , use_cache=__UpperCamelCase      )

						__magic_name__,	__magic_name__							=    outputs.to_tuple()

						# create hypothetical multiple next token and extent to next_input_ids
						__magic_name__							=    ids_tensor((self.batch_size, 3)  , config.vocab_size      )
						__magic_name__							=    ids_tensor((self.batch_size, 3)  , 2      )

						# append to next input_ids and
						__magic_name__							=    torch.cat([input_ids, next_tokens]  , dim=-1      )
						__magic_name__							=    torch.cat([attention_mask, next_attn_mask]  , dim=-1      )

						__magic_name__							=    model(__UpperCamelCase  , attention_mask=__UpperCamelCase      )['last_hidden_state']
						__magic_name__							=    model(__UpperCamelCase  , attention_mask=__UpperCamelCase  , past_key_values=__UpperCamelCase      )[
						    'last_hidden_state'
						]

						# select random slice
						__magic_name__							=    ids_tensor((1,)  , output_from_past.shape[-1]      ).item()
						__magic_name__							=    output_from_no_past[:, -3:, random_slice_idx].detach()
						__magic_name__							=    output_from_past[:, :, random_slice_idx].detach()

						self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(__UpperCamelCase  , __UpperCamelCase  , atol=1E-2      )      )







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

						'''simple docstring'''





						__magic_name__							=    MaMaaaModel(config=__UpperCamelCase      ).to(__UpperCamelCase      ).eval()
						__magic_name__							=    model(**__UpperCamelCase      )

						__magic_name__							=    outputs.encoder_last_hidden_state
						__magic_name__							=    outputs.last_hidden_state

						with tempfile.TemporaryDirectory() as tmpdirname:
											__magic_name__							=    model.get_encoder()
											encoder.save_pretrained(__UpperCamelCase      )
											__magic_name__							=    MaMaaaEncoder.from_pretrained(__UpperCamelCase      ).to(__UpperCamelCase      )

						__magic_name__							=    encoder(inputs_dict['input_ids']  , attention_mask=inputs_dict['attention_mask']      )[
						    0
						]

						self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3      )

						with tempfile.TemporaryDirectory() as tmpdirname:
											__magic_name__							=    model.get_decoder()
											decoder.save_pretrained(__UpperCamelCase      )
											__magic_name__							=    MaMaaaDecoder.from_pretrained(__UpperCamelCase      ).to(__UpperCamelCase      )

						__magic_name__							=    decoder(
						    input_ids=inputs_dict['decoder_input_ids']  , attention_mask=inputs_dict['decoder_attention_mask']  , encoder_hidden_states=__UpperCamelCase  , encoder_attention_mask=inputs_dict['attention_mask']  , )[0]

						self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3      )




@require_torch
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Tuple       				=  (
	    (
	        MaMaaaModel,
	        MaMaaaForConditionalGeneration,
	    )
	    if is_torch_available()
	    else ()
	)
	_lowercase  :   Dict       				=  (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
	_lowercase  :   Tuple       				=  (
	    {
	        "conversational": MaMaaaForConditionalGeneration,
	        "feature-extraction": MaMaaaModel,
	        "summarization": MaMaaaForConditionalGeneration,
	        "text2text-generation": MaMaaaForConditionalGeneration,
	        "translation": MaMaaaForConditionalGeneration,
	    }
	    if is_torch_available()
	    else {}
	)
	_lowercase  :   List[str]       				=  True
	_lowercase  :   Dict       				=  True
	_lowercase  :   Union[str, Any]       				=  False
	_lowercase  :   List[Any]       				=  False







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , __UpperCamelCase: Any  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: List[str]  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Tuple      ):

						'''simple docstring'''





						if pipeline_test_casse_name == "TranslationPipelineTests":
											# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
											# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
											return True

						return False







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    MaMaaaModelTester(self      )
						__magic_name__							=    ConfigTester(self  , config_class=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						self.config_tester.run_common_tests()







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__							=    self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
											__magic_name__							=    model_class(__UpperCamelCase      )

											with tempfile.TemporaryDirectory() as tmpdirname:
																model.save_pretrained(__UpperCamelCase      )
																__magic_name__,	__magic_name__							=    model_class.from_pretrained(__UpperCamelCase  , output_loading_info=__UpperCamelCase      )
											self.assertEqual(info['missing_keys']  , []      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(*__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_encoder_decoder_model_standalone(*__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__							=    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
											__magic_name__							=    model_class(__UpperCamelCase      )
											model.to(__UpperCamelCase      )
											model.eval()

											__magic_name__							=    copy.deepcopy(self._prepare_for_class(__UpperCamelCase  , __UpperCamelCase      )      )

											if not self.is_encoder_decoder:
																__magic_name__							=    inputs['input_ids']
																del inputs["input_ids"]
											else:
																__magic_name__							=    inputs['input_ids']
																__magic_name__							=    inputs.get('decoder_input_ids'  , __UpperCamelCase      )
																del inputs["input_ids"]
																inputs.pop('decoder_input_ids'  , __UpperCamelCase      )

											__magic_name__							=    model.get_input_embeddings()
											if not self.is_encoder_decoder:
																__magic_name__							=    wte(__UpperCamelCase      )
											else:
																__magic_name__							=    wte(__UpperCamelCase      )
																__magic_name__							=    wte(__UpperCamelCase      )

											with torch.no_grad():
																model(**__UpperCamelCase      )[0]







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

						'''simple docstring'''





						__magic_name__,	__magic_name__							=    self.model_tester.prepare_config_and_inputs()
						__magic_name__							=    input_dict['input_ids']
						__magic_name__							=    input_ids.ne(1      ).to(__UpperCamelCase      )
						__magic_name__							=    MaMaaaForConditionalGeneration(__UpperCamelCase      ).eval().to(__UpperCamelCase      )
						if torch_device == "cuda":
											model.half()
						model.generate(__UpperCamelCase  , attention_mask=__UpperCamelCase      )
						model.generate(num_beams=4  , do_sample=__UpperCamelCase  , early_stopping=__UpperCamelCase  , num_return_sequences=3      )




def 					_lowercase     (				a_				:					List[Any]				)				->	Union[str, Any]:




					'''simple docstring'''







					return torch.tensor(a_					,dtype=torch.long					,device=a_				)


A__						=							1e-4


@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 				__UpperCamelCase				(					unittest.TestCase			):







	@cached_property
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						__magic_name__							=    MaMaaaModel.from_pretrained('facebook/m2m100_418M'      ).to(__UpperCamelCase      )
						__magic_name__							=    _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]      )
						__magic_name__							=    _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]      )
						__magic_name__							=    prepare_mam_aaa_inputs_dict(model.config  , __UpperCamelCase  , __UpperCamelCase      )
						with torch.no_grad():
											__magic_name__							=    model(**__UpperCamelCase      )[0]
						__magic_name__							=    torch.Size((1, 11, 10_24)      )
						self.assertEqual(output.shape  , __UpperCamelCase      )
						# change to expected output here
						__magic_name__							=    torch.tensor(
						    [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]  , device=__UpperCamelCase      )
						self.assertTrue(torch.allclose(output[:, :3, :3]  , __UpperCamelCase  , atol=__UpperCamelCase      )      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'      ).to(__UpperCamelCase      )

						# change to intended input
						__magic_name__							=    _long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]      )
						__magic_name__							=    _long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]      )
						__magic_name__							=    prepare_mam_aaa_inputs_dict(model.config  , __UpperCamelCase  , __UpperCamelCase      )
						with torch.no_grad():
											__magic_name__							=    model(**__UpperCamelCase      )[0]
						__magic_name__							=    torch.Size((1, 11, model.config.vocab_size)      )
						self.assertEqual(output.shape  , __UpperCamelCase      )
						# change to expected output here
						__magic_name__							=    torch.tensor(
						    [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]  , device=__UpperCamelCase      )
						self.assertTrue(torch.allclose(output[:, :3, :3]  , __UpperCamelCase  , atol=__UpperCamelCase      )      )







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

						'''simple docstring'''





						__magic_name__							=    MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'      ).to(__UpperCamelCase      )
						__magic_name__							=    MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'  , src_lang='fr'  , tgt_lang='en'      )

						__magic_name__							=    [
						    'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
						    'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
						    'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
						    ' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
						    ' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
						]

						# The below article tests that we don't add any hypotheses outside of the top n_beams
						__magic_name__							=    tokenizer(__UpperCamelCase  , padding=__UpperCamelCase  , return_tensors='pt'      )

						__magic_name__							=    model.generate(
						    input_ids=dct['input_ids'].to(__UpperCamelCase      )  , attention_mask=dct['attention_mask'].to(__UpperCamelCase      )  , num_beams=5  , forced_bos_token_id=tokenizer.get_lang_id('en'      )  , )

						__magic_name__							=    [
						    'The NSA case highlights the total absence of intelligence debate',
						    'I think there are two levels of response from the French government.',
						    'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
						    ' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
						    ' communications in France.',
						]

						__magic_name__							=    tokenizer.batch_decode(
						    hypotheses_batch.tolist()  , clean_up_tokenization_spaces=__UpperCamelCase  , skip_special_tokens=__UpperCamelCase      )
						assert generated == expected_en




