


"""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    ,   __a    ,   __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=5_12    ,   __a=16    ,   __a=2    ,   __a=0.0_2    ,   __a=3    ,   __a=4    ,   __a=None    ,   ):
										__lowerCAmelCase				    =			parent
										__lowerCAmelCase				    =			13
										__lowerCAmelCase				    =			7
										__lowerCAmelCase				    =			True
										__lowerCAmelCase				    =			True
										__lowerCAmelCase				    =			True
										__lowerCAmelCase				    =			True
										__lowerCAmelCase				    =			99
										__lowerCAmelCase				    =			32
										__lowerCAmelCase				    =			2
										__lowerCAmelCase				    =			4
										__lowerCAmelCase				    =			37
										__lowerCAmelCase				    =			"gelu"
										__lowerCAmelCase				    =			0.1
										__lowerCAmelCase				    =			0.1
										__lowerCAmelCase				    =			5_12
										__lowerCAmelCase				    =			16
										__lowerCAmelCase				    =			2
										__lowerCAmelCase				    =			0.0_2
										__lowerCAmelCase				    =			3
										__lowerCAmelCase				    =			4
										__lowerCAmelCase				    =			None

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size	)

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

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

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

										__lowerCAmelCase				    =			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=__a    ,   )

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

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			TFRoFormerModel(config=__a	)
										__lowerCAmelCase				    =			{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

										__lowerCAmelCase				    =			[input_ids, input_mask]
										__lowerCAmelCase				    =			model(__a	)

										__lowerCAmelCase				    =			model(__a	)

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

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			True
										__lowerCAmelCase				    =			TFRoFormerForCausalLM(config=__a	)
										__lowerCAmelCase				    =			{
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										__lowerCAmelCase				    =			model(__a	)["logits"]
										self.parent.assertListEqual(
										    list(prediction_scores.numpy().shape	)    ,   [self.batch_size, self.seq_length, self.vocab_size]	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			TFRoFormerForMaskedLM(config=__a	)
										__lowerCAmelCase				    =			{
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										__lowerCAmelCase				    =			model(__a	)
										self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.vocab_size)	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			self.num_labels
										__lowerCAmelCase				    =			TFRoFormerForSequenceClassification(config=__a	)
										__lowerCAmelCase				    =			{
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										__lowerCAmelCase				    =			model(__a	)
										self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_labels)	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			self.num_choices
										__lowerCAmelCase				    =			TFRoFormerForMultipleChoice(config=__a	)
										__lowerCAmelCase				    =			tf.tile(tf.expand_dims(__a    ,   1	)    ,   (1, self.num_choices, 1)	)
										__lowerCAmelCase				    =			tf.tile(tf.expand_dims(__a    ,   1	)    ,   (1, self.num_choices, 1)	)
										__lowerCAmelCase				    =			tf.tile(tf.expand_dims(__a    ,   1	)    ,   (1, self.num_choices, 1)	)
										__lowerCAmelCase				    =			{
										    "input_ids": multiple_choice_inputs_ids,
										    "attention_mask": multiple_choice_input_mask,
										    "token_type_ids": multiple_choice_token_type_ids,
										}
										__lowerCAmelCase				    =			model(__a	)
										self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_choices)	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			self.num_labels
										__lowerCAmelCase				    =			TFRoFormerForTokenClassification(config=__a	)
										__lowerCAmelCase				    =			{
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										__lowerCAmelCase				    =			model(__a	)
										self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.num_labels)	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			TFRoFormerForQuestionAnswering(config=__a	)
										__lowerCAmelCase				    =			{
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										__lowerCAmelCase				    =			model(__a	)
										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	):
										__lowerCAmelCase				    =			self.prepare_config_and_inputs()
										(
										    (
										    __lowerCAmelCase
										)    ,	(
										    __lowerCAmelCase
										)    ,	(
										    __lowerCAmelCase
										)    ,	(
										    __lowerCAmelCase
										)    ,	(
										    __lowerCAmelCase
										)    ,	(
										    __lowerCAmelCase
										)    ,	(
										    __lowerCAmelCase
										)    ,	
										)				    =			config_and_inputs
										__lowerCAmelCase				    =			{"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'''







							__UpperCAmelCase						:    str				      =(
							    (
							        TFRoFormerModel,
							        TFRoFormerForCausalLM,
							        TFRoFormerForMaskedLM,
							        TFRoFormerForQuestionAnswering,
							        TFRoFormerForSequenceClassification,
							        TFRoFormerForTokenClassification,
							        TFRoFormerForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							__UpperCAmelCase						:    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 {}
							)

							__UpperCAmelCase						:    int				      =False
							__UpperCAmelCase						:    List[Any]				      =False

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a    ,   __a	):
										if pipeline_test_casse_name == "TextGenerationPipelineTests":
													return True

										return False

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			TFRoFormerModelTester(self	)
										__lowerCAmelCase				    =			ConfigTester(self    ,   config_class=__a    ,   hidden_size=37	)

							def 			snake_case				(     self	):
										self.config_tester.run_common_tests()

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head(*__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*__a	)



							@slow
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"	)
										self.assertIsNotNone(__a	)
@require_tf
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							@slow
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"	)
										__lowerCAmelCase				    =			tf.constant([[0, 1, 2, 3, 4, 5]]	)
										__lowerCAmelCase				    =			model(__a	)[0]

										# TODO Replace vocab size
										__lowerCAmelCase				    =			5_00_00

										__lowerCAmelCase				    =			[1, 6, vocab_size]
										self.assertEqual(output.shape    ,   __a	)

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

										# TODO Replace values below with what was printed above.
										__lowerCAmelCase				    =			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]    ,   __a    ,   atol=1e-4	)
@require_tf
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Dict				      =1E-4

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			tf.constant([[4, 10]]	)
										__lowerCAmelCase				    =			TFRoFormerSinusoidalPositionalEmbedding(num_positions=6    ,   embedding_dim=6	)

										__lowerCAmelCase				    =			emba(input_ids.shape	)
										__lowerCAmelCase				    =			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(__a    ,   __a    ,   atol=self.tolerance	)



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			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],
										    ]	)
										__lowerCAmelCase				    =			TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12    ,   embedding_dim=5_12	)
										emba([2, 16, 5_12]	)
										__lowerCAmelCase				    =			emba.weight[:3, :5]

										tf.debugging.assert_near(__a    ,   __a    ,   atol=self.tolerance	)
@require_tf
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Any				      =1E-4

							def 			snake_case				(     self	):
										# 2,12,16,64
										__lowerCAmelCase				    =			tf.reshape(tf.range(2 * 12 * 16 * 64    ,   dtype=tf.floataa	)    ,   shape=(2, 12, 16, 64)	) / 1_00

										__lowerCAmelCase				    =			-tf.reshape(tf.range(2 * 12 * 16 * 64    ,   dtype=tf.floataa	)    ,   shape=(2, 12, 16, 64)	) / 1_00

										__lowerCAmelCase				    =			TFRoFormerSinusoidalPositionalEmbedding(num_positions=32    ,   embedding_dim=64	)
										__lowerCAmelCase				    =			embed_positions([2, 16, 7_68]	)[None, None, :, :]

										__lowerCAmelCase    ,	__lowerCAmelCase				    =			TFRoFormerSelfAttention.apply_rotary_position_embeddings(
										    __a    ,   __a    ,   __a	)

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








"""simple docstring"""


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if not isinstance(_UpperCamelCase    , _UpperCamelCase      ):
						raise ValueError("iterations must be defined as integers"      )
			if not isinstance(_UpperCamelCase    , _UpperCamelCase      ) or not number >= 1:
						raise ValueError(
						    "starting number must be\n                         and integer and be more than 0"      )
			if not iterations >= 1:
						raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"      )

			__lowerCAmelCase				    =			""
			while number <= iterations:
						if number % 3 == 0:
									out += "Fizz"
						if number % 5 == 0:
									out += "Buzz"
						if 0 not in (number % 3, number % 5):
									out += str(_UpperCamelCase      )

						# print(out)
						number += 1
						out += " "
			return out


if __name__ == "__main__":
	import doctest

	doctest.testmod()




