





from math import sqrt






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						bool:






				"""simple docstring"""


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

				snake_case_						:   Tuple           =  True

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

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

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

				return status






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


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

				# beginList: contains all natural numbers from 2 up to N
				snake_case_						:   Union[str, Any]           =  list(range(2			,    n + 1			)			)

				snake_case_						:   Tuple           =  []  # this list will be returns.

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

    # filters actual prime numbers.
				snake_case_						:   Optional[Any]           =  [x for x in begin_list if x != 0]

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

				return ans






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


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

				snake_case_						:   Union[str, 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(_UpperCAmelCase			):
												ans.append(_UpperCAmelCase			)

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

				return ans






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


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

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

				# potential prime number factors.

				snake_case_						:   Any           =  2

				snake_case_						:   Union[str, Any]           =  number

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

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

				else:
								ans.append(_UpperCAmelCase			)

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

				return ans






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


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

				snake_case_						:   Optional[Any]           =  0

				# prime factorization of 'number'
				snake_case_						:   Dict           =  prime_factorization(_UpperCAmelCase			)

				snake_case_						:   Union[str, Any]           =  max(_UpperCAmelCase			)

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

				return ans






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


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

				snake_case_						:   List[str]           =  0

				# prime factorization of 'number'
				snake_case_						:   int           =  prime_factorization(_UpperCAmelCase			)

				snake_case_						:   int           =  min(_UpperCAmelCase			)

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

				return ans






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


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

				return number % 2 == 0






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


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

				return number % 2 != 0






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


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

				snake_case_						:   Optional[Any]           =  []  # this list will returned

				# creates a list of prime numbers between 2 up to 'number'
				snake_case_						:   List[str]           =  get_prime_numbers(_UpperCAmelCase			)
				snake_case_						:   Optional[Any]           =  len(_UpperCAmelCase			)

				# run variable for while-loops.
				snake_case_						:   Any           =  0
				snake_case_						:   Tuple           =  None

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

				while i < len_pn and loop:
								snake_case_						:   Union[str, Any]           =  i + 1

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

												j += 1

								i += 1

				# precondition
				assert (
				    isinstance(_UpperCAmelCase			,    _UpperCAmelCase			)
				    and (len(_UpperCAmelCase			) == 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 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


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

				snake_case_						:   Optional[Any]           =  0

				while numbera != 0:
								snake_case_						:   Tuple           =  numbera % numbera
								snake_case_						:   Any           =  numbera
								snake_case_						:   Tuple           =  rest

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

				return numbera






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


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

				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'
								snake_case_						:   Optional[int]           =  prime_factorization(_UpperCAmelCase			)
								snake_case_						:   Dict           =  prime_factorization(_UpperCAmelCase			)

				elif numbera == 1 or numbera == 1:
								snake_case_						:   Union[str, Any]           =  []
								snake_case_						:   Union[str, Any]           =  []
								snake_case_						:   List[str]           =  max(_UpperCAmelCase			,    _UpperCAmelCase			)

				snake_case_						:   Union[str, Any]           =  0
				snake_case_						:   str           =  0

				snake_case_						:   Optional[int]           =  []  # 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:
																snake_case_						:   Optional[Any]           =  prime_fac_a.count(_UpperCAmelCase			)
																snake_case_						:   Optional[int]           =  prime_fac_a.count(_UpperCAmelCase			)

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

												else:
																snake_case_						:   List[str]           =  prime_fac_a.count(_UpperCAmelCase			)

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

												done.append(_UpperCAmelCase			)

    # iterates through primeFac2
				for n in prime_fac_a:
								if n not in done:
												snake_case_						:   List[Any]           =  prime_fac_a.count(_UpperCAmelCase			)

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

												done.append(_UpperCAmelCase			)

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

				return ans






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


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

				snake_case_						:   int           =  0
				snake_case_						:   Tuple           =  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(_UpperCAmelCase			):
												ans += 1

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

				return ans






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


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

				snake_case_						:   Any           =  p_number_a + 1  # jump to the next number

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

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

				while number < p_number_a:
								ans.append(_UpperCAmelCase			)

								number += 1

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

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

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






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


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

				snake_case_						:   Union[str, Any]           =  []  # will be returned.

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

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

				return ans






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


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

				snake_case_						:   Tuple           =  get_divisors(_UpperCAmelCase			)

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

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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


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

				# build the greatest common divisor of numerator and denominator.
				snake_case_						:   Optional[int]           =  gcd(abs(_UpperCAmelCase			)			,    abs(_UpperCAmelCase			)			)

				# precondition
				assert (
				    isinstance(_UpperCAmelCase			,    _UpperCAmelCase			)
				    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 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


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

				snake_case_						:   List[str]           =  1  # this will be return.

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

				return ans






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


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

				snake_case_						:   int           =  0
				snake_case_						:   List[str]           =  1
				snake_case_						:   Optional[Any]           =  1  # this will be return

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

				return ans







from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase=None			,    _UpperCamelCase=None			)		->						Optional[Any]:






				"""simple docstring"""


				if attention_mask is None:
								snake_case_						:   Optional[Any]           =  tf.cast(tf.math.not_equal(_UpperCamelCase			,    config.pad_token_id			)			,    tf.inta			)
				return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class  __lowerCAmelCase	:
			lowerCamelCase_					:					Optional[Any]	      =				OPTConfig
			lowerCamelCase_					:					str	      =				{}
			lowerCamelCase_					:					Tuple	      =				'''gelu'''






			def __init__(self ,							__magic_name__ ,							__magic_name__=13 ,							__magic_name__=7 ,							__magic_name__=True ,							__magic_name__=False ,							__magic_name__=99 ,							__magic_name__=16 ,							__magic_name__=2 ,							__magic_name__=4 ,							__magic_name__=4 ,							__magic_name__="gelu" ,							__magic_name__=0.1 ,							__magic_name__=0.1 ,							__magic_name__=20 ,							__magic_name__=2 ,							__magic_name__=1 ,							__magic_name__=0 ,							__magic_name__=16 ,							__magic_name__=16 ,							)					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  parent
							snake_case_						:   int           =  batch_size
							snake_case_						:   Optional[int]           =  seq_length
							snake_case_						:   Dict           =  is_training
							snake_case_						:   str           =  use_labels
							snake_case_						:   List[str]           =  vocab_size
							snake_case_						:   Dict           =  hidden_size
							snake_case_						:   Optional[Any]           =  num_hidden_layers
							snake_case_						:   Union[str, Any]           =  num_attention_heads
							snake_case_						:   Optional[Any]           =  intermediate_size
							snake_case_						:   Optional[Any]           =  hidden_act
							snake_case_						:   Dict           =  hidden_dropout_prob
							snake_case_						:   Optional[int]           =  attention_probs_dropout_prob
							snake_case_						:   Dict           =  max_position_embeddings
							snake_case_						:   Any           =  eos_token_id
							snake_case_						:   Union[str, Any]           =  pad_token_id
							snake_case_						:   List[Any]           =  bos_token_id
							snake_case_						:   List[Any]           =  embed_dim
							snake_case_						:   Optional[int]           =  word_embed_proj_dim
							snake_case_						:   Any           =  False






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  ids_tensor([self.batch_size, self.seq_length - 1] ,							self.vocab_size   )
							snake_case_						:   Tuple           =  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   ) ,							1   )
							snake_case_						:   Union[str, Any]           =  tf.concat([input_ids, eos_tensor] ,							axis=1   )

							snake_case_						:   Optional[int]           =  self.config_cls(
							    vocab_size=self.vocab_size ,							hidden_size=self.hidden_size ,							num_hidden_layers=self.num_hidden_layers ,							num_attention_heads=self.num_attention_heads ,							ffn_dim=self.intermediate_size ,							dropout=self.hidden_dropout_prob ,							attention_dropout=self.attention_probs_dropout_prob ,							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 ,							embed_dim=self.embed_dim ,							word_embed_proj_dim=self.word_embed_proj_dim ,							is_encoder_decoder=__magic_name__ ,							**self.config_updates ,							)
							snake_case_						:   Optional[Any]           =  prepare_opt_inputs_dict(__magic_name__ ,							__magic_name__   )
							return config, inputs_dict






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  TFOPTModel(config=__magic_name__   )
							snake_case_						:   str           =  inputs_dict['''input_ids''']

							snake_case_						:   Tuple           =  input_ids[:1, :]
							snake_case_						:   Union[str, Any]           =  inputs_dict['''attention_mask'''][:1, :]
							snake_case_						:   List[Any]           =  1

							# first forward pass
							snake_case_						:   int           =  model(__magic_name__ ,							attention_mask=__magic_name__ ,							use_cache=__magic_name__   )

							snake_case_      ,							snake_case_						:   Any           =  outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							snake_case_						:   List[Any]           =  ids_tensor((self.batch_size, 3) ,							config.vocab_size   )
							snake_case_						:   Optional[Any]           =  tf.cast(ids_tensor((self.batch_size, 3) ,							2   ) ,							tf.inta   )

							# append to next input_ids and
							snake_case_						:   Any           =  tf.concat([input_ids, next_tokens] ,							axis=-1   )
							snake_case_						:   List[str]           =  tf.concat([attention_mask, next_attn_mask] ,							axis=-1   )

							snake_case_						:   List[str]           =  model(__magic_name__ ,							attention_mask=__magic_name__   )[0]
							snake_case_						:   List[Any]           =  model(__magic_name__ ,							attention_mask=__magic_name__ ,							past_key_values=__magic_name__   )[0]

							self.parent.assertEqual(next_tokens.shape[1] ,							output_from_past.shape[1]   )

							# select random slice
							snake_case_						:   List[str]           =  int(ids_tensor((1,) ,							output_from_past.shape[-1]   )   )
							snake_case_						:   List[Any]           =  output_from_no_past[:, -3:, random_slice_idx]
							snake_case_						:   str           =  output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(__magic_name__ ,							__magic_name__ ,							rtol=1e-3   )




@require_tf
class  __lowerCAmelCase	(      _a,					_a,					unittest.TestCase					):
			lowerCamelCase_					:					Any	      =				(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
			lowerCamelCase_					:					List[str]	      =				(TFOPTForCausalLM,) if is_tf_available() else ()
			lowerCamelCase_					:					Optional[int]	      =				(
			    {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
			)
			lowerCamelCase_					:					List[str]	      =				False
			lowerCamelCase_					:					Tuple	      =				False
			lowerCamelCase_					:					str	      =				False
			lowerCamelCase_					:					List[str]	      =				10






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   str           =  TFOPTModelTester(self   )
							snake_case_						:   Tuple           =  ConfigTester(self ,							config_class=__magic_name__   )






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							self.config_tester.run_common_tests()






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Any           =  self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*__magic_name__   )






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_      ,							snake_case_						:   List[Any]           =  self.model_tester.prepare_config_and_inputs_for_common()

							def _get_word_embedding_weight(__magic_name__ ,							__magic_name__   ):
											if hasattr(__magic_name__ ,							'''weight'''   ):
															return embedding_layer.weight
											else:
															# Here we build the word embeddings weights if not exists.
															# And then we retry to get the attribute once built.
															model.build()
															if hasattr(__magic_name__ ,							'''weight'''   ):
																			return embedding_layer.weight
															else:
																			return None

							for model_class in self.all_model_classes:
											for size in [config.vocab_size - 10, config.vocab_size + 10]:
															# build the embeddings
															snake_case_						:   Optional[Any]           =  model_class(config=__magic_name__   )
															snake_case_						:   str           =  _get_word_embedding_weight(__magic_name__ ,							model.get_input_embeddings()   )
															snake_case_						:   Tuple           =  _get_word_embedding_weight(__magic_name__ ,							model.get_output_embeddings()   )

															# reshape the embeddings
															model.resize_token_embeddings(__magic_name__   )
															snake_case_						:   int           =  _get_word_embedding_weight(__magic_name__ ,							model.get_input_embeddings()   )
															snake_case_						:   Union[str, Any]           =  _get_word_embedding_weight(__magic_name__ ,							model.get_output_embeddings()   )

															# check that the resized embeddings size matches the desired size.
															snake_case_						:   List[str]           =  size if size is not None else config.vocab_size

															self.assertEqual(new_input_embeddings.shape[0] ,							__magic_name__   )

															# check that weights remain the same after resizing
															snake_case_						:   str           =  True
															for pa, pa in zip(old_input_embeddings.value() ,							new_input_embeddings.value()   ):
																			if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
																							snake_case_						:   int           =  False
															self.assertTrue(__magic_name__   )

															if old_output_embeddings is not None and new_output_embeddings is not None:
																			self.assertEqual(new_output_embeddings.shape[0] ,							__magic_name__   )

																			snake_case_						:   Optional[int]           =  True
																			for pa, pa in zip(old_output_embeddings.value() ,							new_output_embeddings.value()   ):
																							if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
																											snake_case_						:   Optional[Any]           =  False
																			self.assertTrue(__magic_name__   )






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return tf.constant(_UpperCamelCase			,    dtype=tf.intaa			)



@require_tf
class  __lowerCAmelCase	(      unittest.TestCase					):
			lowerCamelCase_					:					Dict	      =				99






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  tf.ones((4, 1) ,							dtype=tf.intaa   ) * 2
							snake_case_						:   Dict           =  tf.concat([ids_tensor((4, 6) ,							self.vocab_size - 3   ) + 3, eos_column_vector] ,							axis=1   )
							snake_case_						:   List[str]           =  input_ids.shape[0]
							snake_case_						:   str           =  OPTConfig(
							    vocab_size=self.vocab_size ,							hidden_size=24 ,							num_hidden_layers=2 ,							num_attention_heads=2 ,							ffn_dim=32 ,							max_position_embeddings=48 ,							eos_token_id=2 ,							pad_token_id=1 ,							bos_token_id=0 ,							)
							return config, input_ids, batch_size




@require_sentencepiece
@require_tf
class  __lowerCAmelCase	(      unittest.TestCase					):






			@slow
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  TFOPTModel.from_pretrained('''facebook/opt-350m'''   )
							snake_case_						:   Optional[Any]           =  _long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]   )
							snake_case_						:   Any           =  tf.not_equal(__magic_name__ ,							model.config.pad_token_id   )
							with tf.GradientTape():
											snake_case_						:   int           =  model(input_ids=__magic_name__ ,							attention_mask=__magic_name__   ).last_hidden_state
							snake_case_						:   int           =  (1, 11, 512)
							self.assertEqual(output.shape ,							__magic_name__   )
							snake_case_						:   str           =  tf.constant(
							    [[-0.2_873, -1.9_218, -0.3_033], [-1.2_710, -0.1_338, -0.1_902], [0.4_095, 0.1_214, -1.3_121]]   )
							self.assertTrue(np.allclose(output[:, :3, :3] ,							__magic_name__ ,							atol=4e-3   )   )

							snake_case_						:   Union[str, Any]           =  tf.function(__magic_name__ ,							jit_compile=__magic_name__   )
							snake_case_						:   Any           =  xla_generate(__magic_name__ ,							__magic_name__   )[0]
							self.assertTrue(np.allclose(output[:, :3, :3] ,							__magic_name__ ,							atol=4e-2   )   )




@require_tf
@slow
class  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							super().setUp()
							snake_case_						:   Optional[Any]           =  '''facebook/opt-350m'''






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   int           =  TFOPTForCausalLM.from_pretrained(self.path_model   )
							snake_case_						:   Optional[Any]           =  GPTaTokenizer.from_pretrained(self.path_model   )

							snake_case_						:   List[Any]           =  [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of''',
							    '''Paris is the capital of France and''',
							    '''Computers and mobile phones have taken''',
							]
							# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
							snake_case_						:   List[str]           =  tokenizer(__magic_name__ ,							return_tensors='''tf''' ,							padding=__magic_name__ ,							add_special_tokens=__magic_name__   )
							snake_case_						:   Optional[Any]           =  tf.math.reduce_mean(model(inputs.input_ids ,							attention_mask=inputs.attention_mask   )[0] ,							axis=-1   )
							snake_case_						:   int           =  tf.constant(
							    [
							        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
							        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
							        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
							        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
							    ]   )
							self.assertTrue(np.allclose(__magic_name__ ,							__magic_name__ ,							atol=1e-4   )   )

							snake_case_						:   str           =  tf.function(__magic_name__ ,							jit_compile=__magic_name__   )
							snake_case_						:   Optional[int]           =  tf.math.reduce_mean(xla_generate(inputs.input_ids ,							attention_mask=inputs.attention_mask   )[0] ,							axis=-1   )
							self.assertTrue(np.allclose(__magic_name__ ,							__magic_name__ ,							atol=1e-4   )   )




@require_tf
@slow
class  __lowerCAmelCase	(      unittest.TestCase					):






			@property
			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							return [
							    "Today is a beautiful day and I want",
							    "In the city of",
							    "Paris is the capital of France and",
							    "Computers and mobile phones have taken",
							]






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Any           =  '''facebook/opt-125m'''

							snake_case_						:   List[str]           =  [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of New York, the city''',
							    '''Paris is the capital of France and the capital''',
							    '''Computers and mobile phones have taken over the''',
							]

							snake_case_						:   Optional[Any]           =  []
							snake_case_						:   Any           =  GPTaTokenizer.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[Any]           =  TFOPTForCausalLM.from_pretrained(__magic_name__   )

							for prompt in self.prompts:
											snake_case_						:   List[Any]           =  tokenizer(__magic_name__ ,							return_tensors='''tf'''   ).input_ids

											snake_case_						:   Any           =  model.generate(__magic_name__ ,							max_length=10   )

											snake_case_						:   Union[str, Any]           =  tokenizer.batch_decode(__magic_name__ ,							skip_special_tokens=__magic_name__   )
											predicted_outputs += generated_string

							self.assertListEqual(__magic_name__ ,							__magic_name__   )






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Dict           =  '''facebook/opt-350m'''

							snake_case_						:   int           =  GPTaTokenizer.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[Any]           =  TFOPTForCausalLM.from_pretrained(__magic_name__   )

							snake_case_						:   List[str]           =  '''left'''

							# use different length sentences to test batching
							snake_case_						:   Optional[Any]           =  [
							    '''Hello, my dog is a little''',
							    '''Today, I''',
							]

							snake_case_						:   Tuple           =  tokenizer(__magic_name__ ,							return_tensors='''tf''' ,							padding=__magic_name__   )
							snake_case_						:   int           =  inputs['''input_ids''']

							snake_case_						:   Optional[int]           =  model.generate(input_ids=__magic_name__ ,							attention_mask=inputs['''attention_mask''']   )

							snake_case_						:   Any           =  tokenizer(sentences[0] ,							return_tensors='''tf'''   ).input_ids
							snake_case_						:   int           =  model.generate(input_ids=__magic_name__   )

							snake_case_						:   List[str]           =  inputs_non_padded.shape[-1] - tf.math.reduce_sum(
							    tf.cast(inputs['''attention_mask'''][-1] ,							tf.intaa   )   )
							snake_case_						:   Optional[Any]           =  tokenizer(sentences[1] ,							return_tensors='''tf'''   ).input_ids
							snake_case_						:   int           =  model.generate(input_ids=__magic_name__ ,							max_length=model.config.max_length - num_paddings   )

							snake_case_						:   Union[str, Any]           =  tokenizer.batch_decode(__magic_name__ ,							skip_special_tokens=__magic_name__   )
							snake_case_						:   List[Any]           =  tokenizer.decode(output_non_padded[0] ,							skip_special_tokens=__magic_name__   )
							snake_case_						:   str           =  tokenizer.decode(output_padded[0] ,							skip_special_tokens=__magic_name__   )

							snake_case_						:   Any           =  [
							    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
							    '''Today, I was in the middle of a conversation with a friend about the''',
							]
							self.assertListEqual(__magic_name__ ,							__magic_name__   )
							self.assertListEqual(__magic_name__ ,							[non_padded_sentence, padded_sentence]   )






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''facebook/opt-350m'''

							snake_case_						:   Optional[Any]           =  [
							    '''Today is a beautiful day and I want to''',
							    '''In the city of San Francisco, the city''',
							    '''Paris is the capital of France and the capital''',
							    '''Computers and mobile phones have taken over the''',
							]

							snake_case_						:   Dict           =  []
							snake_case_						:   Tuple           =  GPTaTokenizer.from_pretrained(__magic_name__   )
							snake_case_						:   Tuple           =  TFOPTForCausalLM.from_pretrained(__magic_name__   )

							for prompt in self.prompts:
											snake_case_						:   Tuple           =  tokenizer(__magic_name__ ,							return_tensors='''tf'''   ).input_ids

											snake_case_						:   int           =  model.generate(__magic_name__ ,							max_length=10   )

											snake_case_						:   Optional[int]           =  tokenizer.batch_decode(__magic_name__ ,							skip_special_tokens=__magic_name__   )
											predicted_outputs += generated_string

							self.assertListEqual(__magic_name__ ,							__magic_name__   )
