



"""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       UpperCamelCase__			:

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=13						,	SCREAMING_SNAKE_CASE__=7						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=99						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=4						,	SCREAMING_SNAKE_CASE__=37						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=4						,	SCREAMING_SNAKE_CASE__=None						,	)      ->	int:
				A__											=					parent
				A__											=					13
				A__											=					7
				A__											=					True
				A__											=					True
				A__											=					True
				A__											=					True
				A__											=					99
				A__											=					32
				A__											=					2
				A__											=					4
				A__											=					37
				A__											=					"gelu"
				A__											=					0.1
				A__											=					0.1
				A__											=					512
				A__											=					16
				A__											=					2
				A__											=					0.0_2
				A__											=					3
				A__											=					4
				A__											=					None


			def   snake_case__	(      self		)      ->	str:
				A__											=					ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size		)

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

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

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

				A__											=					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=SCREAMING_SNAKE_CASE__						,	)

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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	str:
				A__											=					TFRoFormerModel(config=SCREAMING_SNAKE_CASE__		)
				A__											=					{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

				A__											=					[input_ids, input_mask]
				A__											=					model(SCREAMING_SNAKE_CASE__		)

				A__											=					model(SCREAMING_SNAKE_CASE__		)

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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				A__											=					True
				A__											=					TFRoFormerForCausalLM(config=SCREAMING_SNAKE_CASE__		)
				A__											=					{
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}
				A__											=					model(SCREAMING_SNAKE_CASE__		)["logits"]
				self.parent.assertListEqual(
				    list(prediction_scores.numpy().shape		)						,	[self.batch_size, self.seq_length, self.vocab_size]		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Any:
				A__											=					TFRoFormerForMaskedLM(config=SCREAMING_SNAKE_CASE__		)
				A__											=					{
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}
				A__											=					model(SCREAMING_SNAKE_CASE__		)
				self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	int:
				A__											=					self.num_labels
				A__											=					TFRoFormerForSequenceClassification(config=SCREAMING_SNAKE_CASE__		)
				A__											=					{
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}

				A__											=					model(SCREAMING_SNAKE_CASE__		)
				self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				A__											=					self.num_choices
				A__											=					TFRoFormerForMultipleChoice(config=SCREAMING_SNAKE_CASE__		)
				A__											=					tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__						,	1		)						,	(1, self.num_choices, 1)		)
				A__											=					tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__						,	1		)						,	(1, self.num_choices, 1)		)
				A__											=					tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE__						,	1		)						,	(1, self.num_choices, 1)		)
				A__											=					{
				    "input_ids": multiple_choice_inputs_ids,
				    "attention_mask": multiple_choice_input_mask,
				    "token_type_ids": multiple_choice_token_type_ids,
				}
				A__											=					model(SCREAMING_SNAKE_CASE__		)
				self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Tuple:
				A__											=					self.num_labels
				A__											=					TFRoFormerForTokenClassification(config=SCREAMING_SNAKE_CASE__		)
				A__											=					{
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}
				A__											=					model(SCREAMING_SNAKE_CASE__		)
				self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				A__											=					TFRoFormerForQuestionAnswering(config=SCREAMING_SNAKE_CASE__		)
				A__											=					{
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				    "token_type_ids": token_type_ids,
				}

				A__											=					model(SCREAMING_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   snake_case__	(      self		)      ->	Any:
				A__											=					self.prepare_config_and_inputs()
				(
				    (
				    A__
				)							,							(
				    A__
				)							,							(
				    A__
				)							,							(
				    A__
				)							,							(
				    A__
				)							,							(
				    A__
				)							,							(
				    A__
				)							,							
				)											=					config_and_inputs
				A__											=					{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
				return config, inputs_dict






@require_tf
class       UpperCamelCase__			(   _lowerCAmelCase       ,		_lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					Dict							  =     (
			    (
			        TFRoFormerModel,
			        TFRoFormerForCausalLM,
			        TFRoFormerForMaskedLM,
			        TFRoFormerForQuestionAnswering,
			        TFRoFormerForSequenceClassification,
			        TFRoFormerForTokenClassification,
			        TFRoFormerForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			A__	:					List[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 {}
			)

			A__	:					int							  =     False
			A__	:					List[str]							  =     False


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	List[Any]:
				if pipeline_test_casse_name == "TextGenerationPipelineTests":
					return True

				return False


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					TFRoFormerModelTester(self		)
				A__											=					ConfigTester(self						,	config_class=SCREAMING_SNAKE_CASE__						,	hidden_size=37		)


			def   snake_case__	(      self		)      ->	Optional[int]:
				self.config_tester.run_common_tests()


			def   snake_case__	(      self		)      ->	str:
				A__											=					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	str:
				A__											=					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_lm_head(*SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Any:
				A__											=					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__		)







			@slow
			def   snake_case__	(      self		)      ->	Dict:
				A__											=					TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"		)
				self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)






@require_tf
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			@slow
			def   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"		)
				A__											=					tf.constant([[0, 1, 2, 3, 4, 5]]		)
				A__											=					model(SCREAMING_SNAKE_CASE__		)[0]

				# TODO Replace vocab size
				A__											=					50000

				A__											=					[1, 6, vocab_size]
				self.assertEqual(output.shape						,	SCREAMING_SNAKE_CASE__		)

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

				# TODO Replace values below with what was printed above.
				A__											=					tf.constant(
				    [
				        [
				            [-0.1_2_0_5_3_3_4_1, -1.0_2_6_4_9_0_1, 0.2_9_2_2_1_9_4_6],
				            [-1.5_1_3_3_7_8_3, 0.1_9_7_4_3_3, 0.1_5_1_9_0_6_0_7],
				            [-5.0_1_3_5_4_0_3, -3.9_0_0_2_5_6, -0.8_4_0_3_8_7_6_4],
				        ]
				    ]		)
				tf.debugging.assert_near(output[:, :3, :3]						,	SCREAMING_SNAKE_CASE__						,	atol=1e-4		)






@require_tf
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			A__	:					Optional[int]							  =     1e-4


			def   snake_case__	(      self		)      ->	Optional[Any]:
				A__											=					tf.constant([[4, 10]]		)
				A__											=					TFRoFormerSinusoidalPositionalEmbedding(num_positions=6						,	embedding_dim=6		)

				A__											=					emba(input_ids.shape		)
				A__											=					tf.constant(
				    [[0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0], [0.8_4_1_5, 0.0_4_6_4, 0.0_0_2_2, 0.5_4_0_3, 0.9_9_8_9, 1.0_0_0_0]]		)

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







			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					tf.constant(
				    [
				        [0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0],
				        [0.8_4_1_5, 0.8_2_1_9, 0.8_0_2_0, 0.7_8_1_9, 0.7_6_1_7],
				        [0.9_0_9_3, 0.9_3_6_4, 0.9_5_8_1, 0.9_7_4_9, 0.9_8_7_0],
				    ]		)
				A__											=					TFRoFormerSinusoidalPositionalEmbedding(num_positions=512						,	embedding_dim=512		)
				emba([2, 16, 512]		)
				A__											=					emba.weight[:3, :5]

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






@require_tf
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			A__	:					str							  =     1e-4


			def   snake_case__	(      self		)      ->	List[Any]:
				# 2,12,16,64
				A__											=					tf.reshape(tf.range(2 * 12 * 16 * 64						,	dtype=tf.floataa		)						,	shape=(2, 12, 16, 64)		) / 100

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

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

				A__							,							A__											=					TFRoFormerSelfAttention.apply_rotary_position_embeddings(
				    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				A__											=					tf.constant(
				    [
				        [0.0_0_0_0, 0.0_1_0_0, 0.0_2_0_0, 0.0_3_0_0, 0.0_4_0_0, 0.0_5_0_0, 0.0_6_0_0, 0.0_7_0_0],
				        [-0.2_0_1_2, 0.8_8_9_7, 0.0_2_6_3, 0.9_4_0_1, 0.2_0_7_4, 0.9_4_6_3, 0.3_4_8_1, 0.9_3_4_3],
				        [-1.7_0_5_7, 0.6_2_7_1, -1.2_1_4_5, 1.3_8_9_7, -0.6_3_0_3, 1.7_6_4_7, -0.1_1_7_3, 1.8_9_8_5],
				        [-2.1_7_3_1, -1.6_3_9_7, -2.7_3_5_8, 0.2_8_5_4, -2.1_8_4_0, 1.7_1_8_3, -1.3_0_1_8, 2.4_8_7_1],
				        [0.2_7_1_7, -3.6_1_7_3, -2.9_2_0_6, -2.1_9_8_8, -3.6_6_3_8, 0.3_8_5_8, -2.9_1_5_5, 2.2_9_8_0],
				        [3.9_8_5_9, -2.1_5_8_0, -0.7_9_8_4, -4.4_9_0_4, -4.1_1_8_1, -2.0_2_5_2, -4.4_7_8_2, 1.1_2_5_3],
				    ]		)
				A__											=					tf.constant(
				    [
				        [0.0_0_0_0, -0.0_1_0_0, -0.0_2_0_0, -0.0_3_0_0, -0.0_4_0_0, -0.0_5_0_0, -0.0_6_0_0, -0.0_7_0_0],
				        [0.2_0_1_2, -0.8_8_9_7, -0.0_2_6_3, -0.9_4_0_1, -0.2_0_7_4, -0.9_4_6_3, -0.3_4_8_1, -0.9_3_4_3],
				        [1.7_0_5_7, -0.6_2_7_1, 1.2_1_4_5, -1.3_8_9_7, 0.6_3_0_3, -1.7_6_4_7, 0.1_1_7_3, -1.8_9_8_5],
				        [2.1_7_3_1, 1.6_3_9_7, 2.7_3_5_8, -0.2_8_5_4, 2.1_8_4_0, -1.7_1_8_3, 1.3_0_1_8, -2.4_8_7_1],
				        [-0.2_7_1_7, 3.6_1_7_3, 2.9_2_0_6, 2.1_9_8_8, 3.6_6_3_8, -0.3_8_5_8, 2.9_1_5_5, -2.2_9_8_0],
				        [-3.9_8_5_9, 2.1_5_8_0, 0.7_9_8_4, 4.4_9_0_4, 4.1_1_8_1, 2.0_2_5_2, 4.4_7_8_2, -1.1_2_5_3],
				    ]		)

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









"""simple docstring"""







def       _lowerCamelCase				(							UpperCAmelCase_ :       bytes     )	-> str:






	"""simple docstring"""





	return "".join([hex(UpperCAmelCase_     )[2:].zfill(2     ).upper() for byte in list(UpperCAmelCase_     )]     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       str     )	-> bytes:






	"""simple docstring"""





	if (len(UpperCAmelCase_     ) % 2) != 0:
		raise ValueError(
		    "Base16 encoded data is invalid:\nData does not have an even number of hex digits."     )
	# Check the character set - the standard base16 alphabet
	# is uppercase according to RFC3548 section 6
	if not set(UpperCAmelCase_     ) <= set("0123456789ABCDEF"     ):
		raise ValueError(
		    "Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters."     )
	# For every two hexadecimal digits (= a byte), turn it into an integer.
	# Then, string the result together into bytes, and return it.
	return bytes(int(data[i] + data[i + 1],	16     ) for i in range(0,	len(UpperCAmelCase_     ),	2     )     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()




