
import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax

						from transformers.models.big_bird.modeling_flax_big_bird import (
						    FlaxBigBirdForCausalLM,
						    FlaxBigBirdForMaskedLM,
						    FlaxBigBirdForMultipleChoice,
						    FlaxBigBirdForPreTraining,
						    FlaxBigBirdForQuestionAnswering,
						    FlaxBigBirdForSequenceClassification,
						    FlaxBigBirdForTokenClassification,
						    FlaxBigBirdModel,
						)
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def __init__(       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			int=2       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=5_6       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=True       ,							SCREAMING_SNAKE_CASE_  :			str=True       ,							SCREAMING_SNAKE_CASE_  :			Dict=True       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=True       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=9_9       ,							SCREAMING_SNAKE_CASE_  :			Tuple=3_2       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=2       ,							SCREAMING_SNAKE_CASE_  :			Tuple=2       ,							SCREAMING_SNAKE_CASE_  :			Dict=7       ,							SCREAMING_SNAKE_CASE_  :			List[Any]="gelu_new"       ,							SCREAMING_SNAKE_CASE_  :			str=0.1       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.1       ,							SCREAMING_SNAKE_CASE_  :			str=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=1_6       ,							SCREAMING_SNAKE_CASE_  :			int=2       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0.02       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=4       ,							SCREAMING_SNAKE_CASE_  :			str="block_sparse"       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=True       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=False       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=2       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=3       ,							)       ->     int:
					lowercase_       =					parent
					lowercase_       =					batch_size
					lowercase_       =					seq_length
					lowercase_       =					is_training
					lowercase_       =					use_attention_mask
					lowercase_       =					use_token_type_ids
					lowercase_       =					use_labels
					lowercase_       =					vocab_size
					lowercase_       =					hidden_size
					lowercase_       =					num_hidden_layers
					lowercase_       =					num_attention_heads
					lowercase_       =					intermediate_size
					lowercase_       =					hidden_act
					lowercase_       =					hidden_dropout_prob
					lowercase_       =					attention_probs_dropout_prob
					lowercase_       =					max_position_embeddings
					lowercase_       =					type_vocab_size
					lowercase_       =					type_sequence_label_size
					lowercase_       =					initializer_range
					lowercase_       =					num_choices

					lowercase_       =					rescale_embeddings
					lowercase_       =					attention_type
					lowercase_       =					use_bias
					lowercase_       =					block_size
					lowercase_       =					num_random_blocks
	def 						_lowercase  (       self  :			Optional[int]       )       ->     int:
					lowercase_       =					ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size       )

					lowercase_       =					None
					if self.use_attention_mask:
									lowercase_       =					random_attention_mask([self.batch_size, self.seq_length]       )

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

					lowercase_       =					BigBirdConfig(
					    vocab_size=self.vocab_size       ,							hidden_size=self.hidden_size       ,							num_hidden_layers=self.num_hidden_layers       ,							num_attention_heads=self.num_attention_heads       ,							intermediate_size=self.intermediate_size       ,							hidden_act=self.hidden_act       ,							hidden_dropout_prob=self.hidden_dropout_prob       ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,							max_position_embeddings=self.max_position_embeddings       ,							type_vocab_size=self.type_vocab_size       ,							is_decoder=SCREAMING_SNAKE_CASE_       ,							initializer_range=self.initializer_range       ,							attention_type=self.attention_type       ,							block_size=self.block_size       ,							num_random_blocks=self.num_random_blocks       ,							use_bias=self.use_bias       ,							rescale_embeddings=self.rescale_embeddings       ,							)

					return config, input_ids, token_type_ids, attention_mask




	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Union[str, Any]:
					lowercase_       =					self.prepare_config_and_inputs()
					lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_       =					config_and_inputs
					lowercase_       =					{
					    '''input_ids''': input_ids,
					    '''token_type_ids''': token_type_ids,
					    '''attention_mask''': attention_mask,
					}
					return config, inputs_dict

@require_flax
class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Optional[Any]         =	(
	    (
	        FlaxBigBirdForCausalLM,
	        FlaxBigBirdModel,
	        FlaxBigBirdForPreTraining,
	        FlaxBigBirdForMaskedLM,
	        FlaxBigBirdForMultipleChoice,
	        FlaxBigBirdForQuestionAnswering,
	        FlaxBigBirdForSequenceClassification,
	        FlaxBigBirdForTokenClassification,
	    )
	    if is_flax_available()
	    else ()
	)

	a			:str         =	False
	a			:Tuple         =	False
	def 						_lowercase  (       self  :			int       )       ->     Optional[int]:
					lowercase_       =					FlaxBigBirdModelTester(self       )
	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 						_lowercase  (       self  :			List[str]       )       ->     Union[str, Any]:
					super().test_from_pretrained_save_pretrained()
	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 						_lowercase  (       self  :			Dict       )       ->     Optional[int]:
					super().test_from_pretrained_with_no_automatic_init()
	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 						_lowercase  (       self  :			List[Any]       )       ->     Optional[int]:
					super().test_no_automatic_init()
	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Dict:
					super().test_hidden_states_output()
	@slow
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Tuple:
					for model_class_name in self.all_model_classes:
									lowercase_       =					model_class_name.from_pretrained('''google/bigbird-roberta-base'''       )
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Union[str, Any]:
					if self.test_attn_probs:
									super().test_attention_outputs()
	@slow
	# copied from `test_modeling_flax_common` because it takes much longer than other models
	def 						_lowercase  (       self  :			Dict       )       ->     Any:
					lowercase_  ,	lowercase_       =					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__       ):
													lowercase_       =					self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
													lowercase_       =					model_class(SCREAMING_SNAKE_CASE_       )

													@jax.jit
													def model_jitted(SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Tuple=None       ,							**SCREAMING_SNAKE_CASE_  :			Tuple       ):
																	return model(input_ids=SCREAMING_SNAKE_CASE_       ,							attention_mask=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

													with self.subTest('''JIT Enabled'''       ):
																	lowercase_       =					model_jitted(**SCREAMING_SNAKE_CASE_       ).to_tuple()

													with self.subTest('''JIT Disabled'''       ):
																	with jax.disable_jit():
																					lowercase_       =					model_jitted(**SCREAMING_SNAKE_CASE_       ).to_tuple()

													self.assertEqual(len(SCREAMING_SNAKE_CASE_       )       ,							len(SCREAMING_SNAKE_CASE_       )       )
													for jitted_output, output in zip(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
																	self.assertEqual(jitted_output.shape       ,							output.shape       )




	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Any=1e-5       ,							SCREAMING_SNAKE_CASE_  :			int="outputs"       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=None       )       ->     List[Any]:
					# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
					# an effort was done to return `attention_probs` (yet to be verified).
					if name.startswith('''outputs.attentions'''       ):
									return
					else:
									super().check_pt_flax_outputs(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )






from __future__ import annotations

from collections.abc import MutableSequence
class  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			MutableSequence[float]       )       ->     None:
					if len(SCREAMING_SNAKE_CASE_       ) != degree + 1:
									raise ValueError(
									    '''The number of coefficients should be equal to the degree + 1.'''       )

					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					degree
	def __add__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:

					if self.degree > polynomial_a.degree:
									lowercase_       =					self.coefficients[:]
									for i in range(polynomial_a.degree + 1       ):
													coefficients[i] += polynomial_a.coefficients[i]
									return Polynomial(self.degree       ,							SCREAMING_SNAKE_CASE_       )
					else:
									lowercase_       =					polynomial_a.coefficients[:]
									for i in range(self.degree + 1       ):
													coefficients[i] += self.coefficients[i]
									return Polynomial(polynomial_a.degree       ,							SCREAMING_SNAKE_CASE_       )
	def __sub__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:
					return self + polynomial_a * Polynomial(0       ,							[-1]       )
	def __neg__(       self  :			int       )       ->     Polynomial:
					return Polynomial(self.degree       ,							[-c for c in self.coefficients]       )
	def __mul__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:
					lowercase_       =					[0] * (self.degree + polynomial_a.degree + 1)
					for i in range(self.degree + 1       ):
									for j in range(polynomial_a.degree + 1       ):
													coefficients[i + j] += (
													    self.coefficients[i] * polynomial_a.coefficients[j]
													)

					return Polynomial(self.degree + polynomial_a.degree       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			int | float       )       ->     int | float:
					lowercase_       =					0
					for i in range(self.degree + 1       ):
									result += self.coefficients[i] * (substitution**i)
					return result
	def __str__(       self  :			Tuple       )       ->     str:
					lowercase_       =					''''''
					for i in range(self.degree       ,							-1       ,							-1       ):
									if self.coefficients[i] == 0:
													continue
									elif self.coefficients[i] > 0:
													if polynomial:
																	polynomial += " + "
									else:
													polynomial += " - "

									if i == 0:
													polynomial += str(abs(self.coefficients[i]       )       )
									elif i == 1:
													polynomial += str(abs(self.coefficients[i]       )       ) + "x"
									else:
													polynomial += str(abs(self.coefficients[i]       )       ) + "x^" + str(SCREAMING_SNAKE_CASE_       )

					return polynomial
	def __repr__(       self  :			Optional[Any]       )       ->     str:
					return self.__str__()
	def 						_lowercase  (       self  :			int       )       ->     Polynomial:
					lowercase_       =					[0] * self.degree
					for i in range(self.degree       ):
									lowercase_       =					self.coefficients[i + 1] * (i + 1)
					return Polynomial(self.degree - 1       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			int | float = 0       )       ->     Polynomial:
					lowercase_       =					[0] * (self.degree + 2)
					lowercase_       =					constant
					for i in range(self.degree + 1       ):
									lowercase_       =					self.coefficients[i] / (i + 1)
					return Polynomial(self.degree + 1       ,							SCREAMING_SNAKE_CASE_       )
	def __eq__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			object       )       ->     bool:
					if not isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									return False

					if self.degree != polynomial_a.degree:
									return False

					for i in range(self.degree + 1       ):
									if self.coefficients[i] != polynomial_a.coefficients[i]:
													return False

					return True




	def __ne__(       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			object       )       ->     bool:
					return not self.__eq__(SCREAMING_SNAKE_CASE_       )




