

import unittest

import numpy as np

from transformers import RoFormerConfig, 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.numpy as jnp

			from transformers.models.roformer.modeling_flax_roformer import (
			    FlaxRoFormerForMaskedLM,
			    FlaxRoFormerForMultipleChoice,
			    FlaxRoFormerForQuestionAnswering,
			    FlaxRoFormerForSequenceClassification,
			    FlaxRoFormerForTokenClassification,
			    FlaxRoFormerModel,
			)






class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			def __init__(	self							:       Union[str, Any]					,					a							:       Optional[int]					,					a							:       str=13					,					a							:       str=7					,					a							:       List[Any]=True					,					a							:       List[str]=True					,					a							:       int=True					,					a							:       Any=True					,					a							:       Tuple=99					,					a							:       int=32					,					a							:       Union[str, Any]=5					,					a							:       str=4					,					a							:       Optional[Any]=37					,					a							:       Optional[Any]="gelu"					,					a							:       Any=0.1					,					a							:       Optional[Any]=0.1					,					a							:       Any=512					,					a							:       int=16					,					a							:       Optional[int]=2					,					a							:       Optional[int]=0.02					,					a							:       str=4					,					)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				parent
				SCREAMING_SNAKE_CASE      :					List[str]    =				batch_size
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				seq_length
				SCREAMING_SNAKE_CASE      :					List[Any]    =				is_training
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				use_attention_mask
				SCREAMING_SNAKE_CASE      :					Any    =				use_token_type_ids
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				use_labels
				SCREAMING_SNAKE_CASE      :					List[Any]    =				vocab_size
				SCREAMING_SNAKE_CASE      :					int    =				hidden_size
				SCREAMING_SNAKE_CASE      :					int    =				num_hidden_layers
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				num_attention_heads
				SCREAMING_SNAKE_CASE      :					List[str]    =				intermediate_size
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				hidden_act
				SCREAMING_SNAKE_CASE      :					List[Any]    =				hidden_dropout_prob
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				max_position_embeddings
				SCREAMING_SNAKE_CASE      :					int    =				type_vocab_size
				SCREAMING_SNAKE_CASE      :					List[Any]    =				type_sequence_label_size
				SCREAMING_SNAKE_CASE      :					List[str]    =				initializer_range
				SCREAMING_SNAKE_CASE      :					str    =				num_choices
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size		)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				None
				if self.use_attention_mask:
					SCREAMING_SNAKE_CASE      :					List[str]    =				random_attention_mask([self.batch_size, self.seq_length]		)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				None
				if self.use_token_type_ids:
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				ids_tensor([self.batch_size, self.seq_length]					,					self.type_vocab_size		)

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				RoFormerConfig(
				    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=a					,					initializer_range=self.initializer_range					,					)

				return config, input_ids, token_type_ids, attention_mask






			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				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      :					Optional[Any]    =				config_and_inputs
				SCREAMING_SNAKE_CASE      :					Dict    =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict






@require_flax
class _UpperCamelCase							(							__A							,		unittest.TestCase      ):
			'''simple docstring'''







			lowerCamelCase__							=True

			lowerCamelCase__							=(
			    (
			        FlaxRoFormerModel,
			        FlaxRoFormerForMaskedLM,
			        FlaxRoFormerForSequenceClassification,
			        FlaxRoFormerForTokenClassification,
			        FlaxRoFormerForMultipleChoice,
			        FlaxRoFormerForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				FlaxRoFormerModelTester(self		)






			@slow
			def 							__UpperCamelCase       (	self							:       int		)							->				Optional[Any]:

				"""simple docstring"""

				for model_class_name in self.all_model_classes:
					SCREAMING_SNAKE_CASE      :					int    =				model_class_name.from_pretrained("junnyu/roformer_chinese_small"					,					from_pt=a		)
					SCREAMING_SNAKE_CASE      :					Any    =				model(np.ones((1, 1)		)		)
					self.assertIsNotNone(a		)






@require_flax
class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			@slow
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				jnp.array([[0, 1, 2, 3, 4, 5]]		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				model(a		)[0]

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				5_0000

				SCREAMING_SNAKE_CASE      :					Any    =				(1, 6, vocab_size)
				self.assertEqual(output.shape					,					a		)

				SCREAMING_SNAKE_CASE      :					Any    =				jnp.array(
				    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]		)

				self.assertTrue(jnp.allclose(output[:, :3, :3]					,					a					,					atol=1e-4		)		)
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
   # Create universe of discourse in Python using linspace ()
   __lowerCamelCase			: List[Any]							   =		np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

   # Create two fuzzy sets by defining any membership function
   # (trapmf(), gbellmf(), gaussmf(), etc).
   __lowerCamelCase			: int							   =		[0, 25, 50]
   __lowerCamelCase			: Tuple							   =		[25, 50, 75]
   __lowerCamelCase			: List[str]							   =		fuzz.membership.trimf(X, abca)
   __lowerCamelCase			: Tuple							   =		fuzz.membership.trimf(X, abca)

   # Compute the different operations using inbuilt functions.
   __lowerCamelCase			: List[str]							   =		np.ones(75)
   __lowerCamelCase			: Tuple							   =		np.zeros((75,))
   # 1. Union = max(µA(x), µB(x))
   __lowerCamelCase			: str							   =		fuzz.fuzzy_or(X, young, X, middle_aged)[1]
   # 2. Intersection = min(µA(x), µB(x))
   __lowerCamelCase			: int							   =		fuzz.fuzzy_and(X, young, X, middle_aged)[1]
   # 3. Complement (A) = (1- min(µA(x))
   __lowerCamelCase			: Union[str, Any]							   =		fuzz.fuzzy_not(young)
   # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
   __lowerCamelCase			: Union[str, Any]							   =		fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
   # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
   __lowerCamelCase			: List[Any]							   =		young + middle_aged - (young * middle_aged)
   # 6. Algebraic Product = (µA(x) * µB(x))
   __lowerCamelCase			: int							   =		young * middle_aged
   # 7. Bounded Sum = min[1,(µA(x), µB(x))]
   __lowerCamelCase			: int							   =		fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
   # 8. Bounded difference = min[0,(µA(x), µB(x))]
   __lowerCamelCase			: str							   =		fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

   # max-min composition
   # max-product composition

   # Plot each set A, set B and each operation result using plot() and subplot().
   from matplotlib import pyplot as plt

   plt.figure()

   plt.subplot(4, 3, 1)
   plt.plot(X, young)
   plt.title('''Young''')
   plt.grid(True)

   plt.subplot(4, 3, 2)
   plt.plot(X, middle_aged)
   plt.title('''Middle aged''')
   plt.grid(True)

   plt.subplot(4, 3, 3)
   plt.plot(X, union)
   plt.title('''union''')
   plt.grid(True)

   plt.subplot(4, 3, 4)
   plt.plot(X, intersection)
   plt.title('''intersection''')
   plt.grid(True)

   plt.subplot(4, 3, 5)
   plt.plot(X, complement_a)
   plt.title('''complement_a''')
   plt.grid(True)

   plt.subplot(4, 3, 6)
   plt.plot(X, difference)
   plt.title('''difference a/b''')
   plt.grid(True)

   plt.subplot(4, 3, 7)
   plt.plot(X, alg_sum)
   plt.title('''alg_sum''')
   plt.grid(True)

   plt.subplot(4, 3, 8)
   plt.plot(X, alg_product)
   plt.title('''alg_product''')
   plt.grid(True)

   plt.subplot(4, 3, 9)
   plt.plot(X, bdd_sum)
   plt.title('''bdd_sum''')
   plt.grid(True)

   plt.subplot(4, 3, 10)
   plt.plot(X, bdd_difference)
   plt.title('''bdd_difference''')
   plt.grid(True)

   plt.subplots_adjust(hspace=0.5)
   plt.show()
