
"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFRoFormerForCausalLM,
		    TFRoFormerForMaskedLM,
		    TFRoFormerForMultipleChoice,
		    TFRoFormerForQuestionAnswering,
		    TFRoFormerForSequenceClassification,
		    TFRoFormerForTokenClassification,
		    TFRoFormerModel,
		)
		from transformers.models.roformer.modeling_tf_roformer import (
		    TFRoFormerSelfAttention,
		    TFRoFormerSinusoidalPositionalEmbedding,
		)







class 							__magic_name__     :





					def __init__(				self   :							Any							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Any=13							,						snake_case_   :							Union[str, Any]=7							,						snake_case_   :							Optional[Any]=True							,						snake_case_   :							List[Any]=True							,						snake_case_   :							Optional[int]=True							,						snake_case_   :							Any=True							,						snake_case_   :							Optional[Any]=99							,						snake_case_   :							Any=32							,						snake_case_   :							Tuple=2							,						snake_case_   :							List[str]=4							,						snake_case_   :							Union[str, Any]=37							,						snake_case_   :							Dict="gelu"							,						snake_case_   :							Any=0.1							,						snake_case_   :							List[str]=0.1							,						snake_case_   :							Optional[Any]=512							,						snake_case_   :							Tuple=16							,						snake_case_   :							Dict=2							,						snake_case_   :							Tuple=0.02							,						snake_case_   :							List[str]=3							,						snake_case_   :							List[Any]=4							,						snake_case_   :							Tuple=None							,						):
												__snake_case     			=       parent
												__snake_case     			=       13
												__snake_case     			=       7
												__snake_case     			=       True
												__snake_case     			=       True
												__snake_case     			=       True
												__snake_case     			=       True
												__snake_case     			=       99
												__snake_case     			=       32
												__snake_case     			=       2
												__snake_case     			=       4
												__snake_case     			=       37
												__snake_case     			=       "gelu"
												__snake_case     			=       0.1
												__snake_case     			=       0.1
												__snake_case     			=       512
												__snake_case     			=       16
												__snake_case     			=       2
												__snake_case     			=       0.02
												__snake_case     			=       3
												__snake_case     			=       4
												__snake_case     			=       None





					def 							lowerCAmelCase      (				self   :							Dict    ):
												__snake_case     			=       ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size    )

												__snake_case     			=       None
												if self.use_input_mask:
																			__snake_case     			=       random_attention_mask([self.batch_size, self.seq_length]    )

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

												__snake_case     			=       None
												__snake_case     			=       None
												__snake_case     			=       None
												if self.use_labels:
																			__snake_case     			=       ids_tensor([self.batch_size]							,						self.type_sequence_label_size    )
																			__snake_case     			=       ids_tensor([self.batch_size, self.seq_length]							,						self.num_labels    )
																			__snake_case     			=       ids_tensor([self.batch_size]							,						self.num_choices    )

												__snake_case     			=       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							,						initializer_range=self.initializer_range							,						return_dict=snake_case_							,						)

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





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Optional[int]							,						snake_case_   :							Optional[int]							,						snake_case_   :							Optional[Any]							,						snake_case_   :							int							,						snake_case_   :							Any							,						snake_case_   :							List[Any]							,						snake_case_   :							List[Any]    ):
												__snake_case     			=       TFRoFormerModel(config=snake_case_    )
												__snake_case     			=       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

												__snake_case     			=       [input_ids, input_mask]
												__snake_case     			=       model(snake_case_    )

												__snake_case     			=       model(snake_case_    )

												self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size)    )





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Optional[Any]							,						snake_case_   :							str							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Tuple							,						snake_case_   :							List[str]							,						snake_case_   :							Dict    ):
												__snake_case     			=       True
												__snake_case     			=       TFRoFormerForCausalLM(config=snake_case_    )
												__snake_case     			=       {
												    "input_ids": input_ids,
												    "attention_mask": input_mask,
												    "token_type_ids": token_type_ids,
												}
												__snake_case     			=       model(snake_case_    )["logits"]
												self.parent.assertListEqual(
												    list(prediction_scores.numpy().shape    )							,						[self.batch_size, self.seq_length, self.vocab_size]    )





					def 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							Optional[Any]							,						snake_case_   :							List[Any]							,						snake_case_   :							Tuple							,						snake_case_   :							str							,						snake_case_   :							int							,						snake_case_   :							int							,						snake_case_   :							Optional[int]    ):
												__snake_case     			=       TFRoFormerForMaskedLM(config=snake_case_    )
												__snake_case     			=       {
												    "input_ids": input_ids,
												    "attention_mask": input_mask,
												    "token_type_ids": token_type_ids,
												}
												__snake_case     			=       model(snake_case_    )
												self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size)    )





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							Tuple							,						snake_case_   :							List[str]							,						snake_case_   :							Dict							,						snake_case_   :							Dict							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Dict							,						snake_case_   :							Tuple    ):
												__snake_case     			=       self.num_labels
												__snake_case     			=       TFRoFormerForSequenceClassification(config=snake_case_    )
												__snake_case     			=       {
												    "input_ids": input_ids,
												    "attention_mask": input_mask,
												    "token_type_ids": token_type_ids,
												}

												__snake_case     			=       model(snake_case_    )
												self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels)    )





					def 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							Any							,						snake_case_   :							int							,						snake_case_   :							List[Any]							,						snake_case_   :							Dict							,						snake_case_   :							Optional[int]							,						snake_case_   :							Tuple							,						snake_case_   :							List[str]    ):
												__snake_case     			=       self.num_choices
												__snake_case     			=       TFRoFormerForMultipleChoice(config=snake_case_    )
												__snake_case     			=       tf.tile(tf.expand_dims(snake_case_							,						1    )							,						(1, self.num_choices, 1)    )
												__snake_case     			=       tf.tile(tf.expand_dims(snake_case_							,						1    )							,						(1, self.num_choices, 1)    )
												__snake_case     			=       tf.tile(tf.expand_dims(snake_case_							,						1    )							,						(1, self.num_choices, 1)    )
												__snake_case     			=       {
												    "input_ids": multiple_choice_inputs_ids,
												    "attention_mask": multiple_choice_input_mask,
												    "token_type_ids": multiple_choice_token_type_ids,
												}
												__snake_case     			=       model(snake_case_    )
												self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_choices)    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Dict							,						snake_case_   :							List[Any]							,						snake_case_   :							Optional[int]							,						snake_case_   :							Tuple							,						snake_case_   :							int							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Optional[int]    ):
												__snake_case     			=       self.num_labels
												__snake_case     			=       TFRoFormerForTokenClassification(config=snake_case_    )
												__snake_case     			=       {
												    "input_ids": input_ids,
												    "attention_mask": input_mask,
												    "token_type_ids": token_type_ids,
												}
												__snake_case     			=       model(snake_case_    )
												self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.num_labels)    )





					def 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							List[Any]							,						snake_case_   :							List[str]							,						snake_case_   :							List[Any]							,						snake_case_   :							Any							,						snake_case_   :							str							,						snake_case_   :							List[Any]							,						snake_case_   :							Optional[int]    ):
												__snake_case     			=       TFRoFormerForQuestionAnswering(config=snake_case_    )
												__snake_case     			=       {
												    "input_ids": input_ids,
												    "attention_mask": input_mask,
												    "token_type_ids": token_type_ids,
												}

												__snake_case     			=       model(snake_case_    )
												self.parent.assertEqual(result.start_logits.shape							,						(self.batch_size, self.seq_length)    )
												self.parent.assertEqual(result.end_logits.shape							,						(self.batch_size, self.seq_length)    )





					def 							lowerCAmelCase      (				self   :							List[str]    ):
												__snake_case     			=       self.prepare_config_and_inputs()
												(
												    (
												    __snake_case
												)			,   (
												    __snake_case
												)			,   (
												    __snake_case
												)			,   (
												    __snake_case
												)			,   (
												    __snake_case
												)			,   (
												    __snake_case
												)			,   (
												    __snake_case
												)			,   
												)     			=       config_and_inputs
												__snake_case     			=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
												return config, inputs_dict







@require_tf
class 							__magic_name__     (		lowercase__      ,		lowercase__      ,		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					List[Any]     							=   (
					    (
					        TFRoFormerModel,
					        TFRoFormerForCausalLM,
					        TFRoFormerForMaskedLM,
					        TFRoFormerForQuestionAnswering,
					        TFRoFormerForSequenceClassification,
					        TFRoFormerForTokenClassification,
					        TFRoFormerForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   (
					    {
					        'feature-extraction': TFRoFormerModel,
					        'fill-mask': TFRoFormerForMaskedLM,
					        'question-answering': TFRoFormerForQuestionAnswering,
					        'text-classification': TFRoFormerForSequenceClassification,
					        'text-generation': TFRoFormerForCausalLM,
					        'token-classification': TFRoFormerForTokenClassification,
					        'zero-shot': TFRoFormerForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					_SCREAMING_SNAKE_CASE       :					Tuple     							=   False
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   False





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Dict							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Union[str, Any]    ):
												if pipeline_test_casse_name == "TextGenerationPipelineTests":
																			return True

												return False





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       TFRoFormerModelTester(self    )
												__snake_case     			=       ConfigTester(self							,						config_class=snake_case_							,						hidden_size=37    )





					def 							lowerCAmelCase      (				self   :							Dict    ):
												self.config_tester.run_common_tests()





					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_lm_head(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							List[str]    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_multiple_choice(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*snake_case_    )





					def 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*snake_case_    )





					@slow
					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__snake_case     			=       TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"    )
												self.assertIsNotNone(snake_case_    )







@require_tf
class 							__magic_name__     (		unittest.TestCase   ):





					@slow
					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"    )
												__snake_case     			=       tf.constant([[0, 1, 2, 3, 4, 5]]    )
												__snake_case     			=       model(snake_case_    )[0]

												# TODO Replace vocab size
												__snake_case     			=       50000

												__snake_case     			=       [1, 6, vocab_size]
												self.assertEqual(output.shape							,						snake_case_    )

												print(output[:, :3, :3]    )

												# TODO Replace values below with what was printed above.
												__snake_case     			=       tf.constant(
												    [
												        [
												            [-0.12053341, -1.0264901, 0.29221946],
												            [-1.5133783, 0.197433, 0.15190607],
												            [-5.0135403, -3.900256, -0.84038764],
												        ]
												    ]    )
												tf.debugging.assert_near(output[:, :3, :3]							,						snake_case_							,						atol=1e-4    )







@require_tf
class 							__magic_name__     (		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   1E-4





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       tf.constant([[4, 10]]    )
												__snake_case     			=       TFRoFormerSinusoidalPositionalEmbedding(num_positions=6							,						embedding_dim=6    )

												__snake_case     			=       emba(input_ids.shape    )
												__snake_case     			=       tf.constant(
												    [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]    )

												tf.debugging.assert_near(snake_case_							,						snake_case_							,						atol=self.tolerance    )





					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       tf.constant(
												    [
												        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
												        [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
												        [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
												    ]    )
												__snake_case     			=       TFRoFormerSinusoidalPositionalEmbedding(num_positions=512							,						embedding_dim=512    )
												emba([2, 16, 512]    )
												__snake_case     			=       emba.weight[:3, :5]

												tf.debugging.assert_near(snake_case_							,						snake_case_							,						atol=self.tolerance    )







@require_tf
class 							__magic_name__     (		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					Any     							=   1E-4





					def 							lowerCAmelCase      (				self   :							Any    ):
												# 2,12,16,64
												__snake_case     			=       tf.reshape(tf.range(2 * 12 * 16 * 64							,						dtype=tf.floataa    )							,						shape=(2, 12, 16, 64)    ) / 100

												__snake_case     			=       -tf.reshape(tf.range(2 * 12 * 16 * 64							,						dtype=tf.floataa    )							,						shape=(2, 12, 16, 64)    ) / 100

												__snake_case     			=       TFRoFormerSinusoidalPositionalEmbedding(num_positions=32							,						embedding_dim=64    )
												__snake_case     			=       embed_positions([2, 16, 768]    )[None, None, :, :]

												__snake_case			,   __snake_case     			=       TFRoFormerSelfAttention.apply_rotary_position_embeddings(
												    snake_case_							,						snake_case_							,						snake_case_    )

												__snake_case     			=       tf.constant(
												    [
												        [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
												        [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
												        [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
												        [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
												        [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
												        [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
												    ]    )
												__snake_case     			=       tf.constant(
												    [
												        [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
												        [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
												        [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
												        [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
												        [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
												        [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
												    ]    )

												tf.debugging.assert_near(query_layer[0, 0, :6, :8]							,						snake_case_							,						atol=self.tolerance    )
												tf.debugging.assert_near(key_layer[0, 0, :6, :8]							,						snake_case_							,						atol=self.tolerance    )


"""simple docstring"""







from __future__ import annotations

from collections.abc import Callable
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = 1_00							,    )  ->     float:







							"""simple docstring"""


							__snake_case     			=       x_start
							__snake_case     			=       fnc(SCREAMING_SNAKE_CASE )
							__snake_case     			=       0.0
							for _ in range(SCREAMING_SNAKE_CASE ):
														# Approximates small segments of curve as linear and solve
														# for trapezoidal area
														__snake_case     			=       (x_end - x_start) / steps + xa
														__snake_case     			=       fnc(SCREAMING_SNAKE_CASE )
														area += abs(fxa + fxa ) * (xa - xa) / 2
														# Increment step
														__snake_case     			=       xa
														__snake_case     			=       fxa
							return area


if __name__ == "__main__":
		def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     List[Any]:







									"""simple docstring"""


									return x**3 + x**2

		print("""f(x) = x^3 + x^2""")
		print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
		_SCREAMING_SNAKE_CASE	       =      10
		while i <= 100_000:
				print(F"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
				i *= 10
