

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=1_3       ,							_a=7       ,							_a=True       ,							_a=True       ,							_a=True       ,							_a=True       ,							_a=9_9       ,							_a=3_2       ,							_a=2       ,							_a=4       ,							_a=3_7       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=5_1_2       ,							_a=1_6       ,							_a=2       ,							_a=0.02       ,							_a=3       ,							_a=4       ,							_a=None       ,							)    ->    Dict:
												_a	:    int         =		parent
												_a	:    Union[str, Any]         =		1_3
												_a	:    List[Any]         =		7
												_a	:    Optional[Any]         =		True
												_a	:    Optional[int]         =		True
												_a	:    Union[str, Any]         =		True
												_a	:    Optional[Any]         =		True
												_a	:    int         =		9_9
												_a	:    List[Any]         =		3_2
												_a	:    Tuple         =		2
												_a	:    Optional[Any]         =		4
												_a	:    Union[str, Any]         =		3_7
												_a	:    Dict         =		'''gelu'''
												_a	:    List[str]         =		0.1
												_a	:    Any         =		0.1
												_a	:    Tuple         =		5_1_2
												_a	:    Union[str, Any]         =		1_6
												_a	:    int         =		2
												_a	:    Optional[int]         =		0.02
												_a	:    int         =		3
												_a	:    Tuple         =		4
												_a	:    int         =		None


							def 				__lowercase				(			self       )    ->    Tuple:
												_a	:    str         =		ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size       )

												_a	:    str         =		None
												if self.use_input_mask:
																	_a	:    Dict         =		random_attention_mask([self.batch_size, self.seq_length]       )

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

												_a	:    Union[str, Any]         =		None
												_a	:    Dict         =		None
												_a	:    Optional[Any]         =		None
												if self.use_labels:
																	_a	:    Tuple         =		ids_tensor([self.batch_size]       ,							self.type_sequence_label_size       )
																	_a	:    List[str]         =		ids_tensor([self.batch_size, self.seq_length]       ,							self.num_labels       )
																	_a	:    Union[str, Any]         =		ids_tensor([self.batch_size]       ,							self.num_choices       )

												_a	:    Tuple         =		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 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    Any:
												_a	:    Optional[Any]         =		TFRoFormerModel(config=_a       )
												_a	:    Tuple         =		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

												_a	:    str         =		[input_ids, input_mask]
												_a	:    Optional[Any]         =		model(_a       )

												_a	:    int         =		model(_a       )

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


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    List[Any]:
												_a	:    Optional[Any]         =		True
												_a	:    str         =		TFRoFormerForCausalLM(config=_a       )
												_a	:    Union[str, Any]         =		{
												    '''input_ids''': input_ids,
												    '''attention_mask''': input_mask,
												    '''token_type_ids''': token_type_ids,
												}
												_a	:    Union[str, Any]         =		model(_a       )['''logits''']
												self.parent.assertListEqual(
												    list(prediction_scores.numpy().shape       )       ,							[self.batch_size, self.seq_length, self.vocab_size]       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    List[Any]:
												_a	:    Optional[Any]         =		TFRoFormerForMaskedLM(config=_a       )
												_a	:    Dict         =		{
												    '''input_ids''': input_ids,
												    '''attention_mask''': input_mask,
												    '''token_type_ids''': token_type_ids,
												}
												_a	:    Union[str, Any]         =		model(_a       )
												self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.seq_length, self.vocab_size)       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    Tuple:
												_a	:    Dict         =		self.num_labels
												_a	:    int         =		TFRoFormerForSequenceClassification(config=_a       )
												_a	:    Tuple         =		{
												    '''input_ids''': input_ids,
												    '''attention_mask''': input_mask,
												    '''token_type_ids''': token_type_ids,
												}

												_a	:    Optional[int]         =		model(_a       )
												self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    List[str]:
												_a	:    Union[str, Any]         =		self.num_choices
												_a	:    Optional[int]         =		TFRoFormerForMultipleChoice(config=_a       )
												_a	:    List[Any]         =		tf.tile(tf.expand_dims(_a       ,							1       )       ,							(1, self.num_choices, 1)       )
												_a	:    Optional[int]         =		tf.tile(tf.expand_dims(_a       ,							1       )       ,							(1, self.num_choices, 1)       )
												_a	:    Dict         =		tf.tile(tf.expand_dims(_a       ,							1       )       ,							(1, self.num_choices, 1)       )
												_a	:    Union[str, Any]         =		{
												    '''input_ids''': multiple_choice_inputs_ids,
												    '''attention_mask''': multiple_choice_input_mask,
												    '''token_type_ids''': multiple_choice_token_type_ids,
												}
												_a	:    Tuple         =		model(_a       )
												self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_choices)       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    List[str]:
												_a	:    Optional[int]         =		self.num_labels
												_a	:    Dict         =		TFRoFormerForTokenClassification(config=_a       )
												_a	:    List[str]         =		{
												    '''input_ids''': input_ids,
												    '''attention_mask''': input_mask,
												    '''token_type_ids''': token_type_ids,
												}
												_a	:    Any         =		model(_a       )
												self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.seq_length, self.num_labels)       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    Any:
												_a	:    int         =		TFRoFormerForQuestionAnswering(config=_a       )
												_a	:    List[Any]         =		{
												    '''input_ids''': input_ids,
												    '''attention_mask''': input_mask,
												    '''token_type_ids''': token_type_ids,
												}

												_a	:    int         =		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 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    str         =		self.prepare_config_and_inputs()
												(
												    (
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					
												)	:    Dict         =		config_and_inputs
												_a	:    List[Any]         =		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
												return config, inputs_dict



@require_tf
class 				UpperCAmelCase_		(			__lowercase				,       __lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    (
							    (
							        TFRoFormerModel,
							        TFRoFormerForCausalLM,
							        TFRoFormerForMaskedLM,
							        TFRoFormerForQuestionAnswering,
							        TFRoFormerForSequenceClassification,
							        TFRoFormerForTokenClassification,
							        TFRoFormerForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							UpperCAmelCase__       :						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__       :						str	    =    False
							UpperCAmelCase__       :						Optional[Any]	    =    False


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    Any:
												if pipeline_test_casse_name == "TextGenerationPipelineTests":
																	return True

												return False


							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    str         =		TFRoFormerModelTester(self       )
												_a	:    Tuple         =		ConfigTester(self       ,							config_class=_a       ,							hidden_size=3_7       )


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												self.config_tester.run_common_tests()


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    int         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a       )


							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    str         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*_a       )


							def 				__lowercase				(			self       )    ->    Tuple:
												_a	:    Dict         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_lm_head(*_a       )


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    Union[str, Any]         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_multiple_choice(*_a       )


							def 				__lowercase				(			self       )    ->    int:
												_a	:    Optional[int]         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*_a       )


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    Union[str, Any]         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*_a       )


							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Optional[Any]         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*_a       )




							@slow
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    List[Any]         =		TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''       )
												self.assertIsNotNone(_a       )



@require_tf
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							@slow
							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    Optional[Any]         =		TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''       )
												_a	:    Union[str, Any]         =		tf.constant([[0, 1, 2, 3, 4, 5]]       )
												_a	:    int         =		model(_a       )[0]

												# TODO Replace vocab size
												_a	:    Any         =		5_0_0_0_0

												_a	:    int         =		[1, 6, vocab_size]
												self.assertEqual(output.shape       ,							_a       )

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

												# TODO Replace values below with what was printed above.
												_a	:    Optional[Any]         =		tf.constant(
												    [
												        [
												            [-0.1205_3341, -1.026_4901, 0.2922_1946],
												            [-1.513_3783, 0.19_7433, 0.1519_0607],
												            [-5.013_5403, -3.90_0256, -0.8403_8764],
												        ]
												    ]       )
												tf.debugging.assert_near(output[:, :3, :3]       ,							_a       ,							atol=1e-4       )



@require_tf
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[int]	    =    1E-4


							def 				__lowercase				(			self       )    ->    str:
												_a	:    Tuple         =		tf.constant([[4, 1_0]]       )
												_a	:    List[Any]         =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=6       ,							embedding_dim=6       )

												_a	:    Tuple         =		emba(input_ids.shape       )
												_a	:    List[str]         =		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(_a       ,							_a       ,							atol=self.tolerance       )




							def 				__lowercase				(			self       )    ->    Tuple:
												_a	:    Optional[Any]         =		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],
												    ]       )
												_a	:    Dict         =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2       ,							embedding_dim=5_1_2       )
												emba([2, 1_6, 5_1_2]       )
												_a	:    int         =		emba.weight[:3, :5]

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



@require_tf
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[int]	    =    1E-4


							def 				__lowercase				(			self       )    ->    Tuple:
												# 2,12,16,64
												_a	:    str         =		tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4       ,							dtype=tf.floataa       )       ,							shape=(2, 1_2, 1_6, 6_4)       ) / 1_0_0

												_a	:    Any         =		-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4       ,							dtype=tf.floataa       )       ,							shape=(2, 1_2, 1_6, 6_4)       ) / 1_0_0

												_a	:    str         =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2       ,							embedding_dim=6_4       )
												_a	:    Optional[int]         =		embed_positions([2, 1_6, 7_6_8]       )[None, None, :, :]

												_a      ,					_a	:    Optional[int]         =		TFRoFormerSelfAttention.apply_rotary_position_embeddings(
												    _a       ,							_a       ,							_a       )

												_a	:    List[Any]         =		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],
												    ]       )
												_a	:    Any         =		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]       ,							_a       ,							atol=self.tolerance       )
												tf.debugging.assert_near(key_layer[0, 0, :6, :8]       ,							_a       ,							atol=self.tolerance       )






import logging

from transformers import PretrainedConfig


a__				=		logging.getLogger(__name__)


a__				=		{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bertabs"


							def __init__(			self       ,							_a=3_0_5_2_2       ,							_a=5_1_2       ,							_a=6       ,							_a=5_1_2       ,							_a=8       ,							_a=5_1_2       ,							_a=0.2       ,							_a=6       ,							_a=7_6_8       ,							_a=8       ,							_a=2_0_4_8       ,							_a=0.2       ,							**_a       ,							)    ->    Any:
												super().__init__(**_a       )

												_a	:    int         =		vocab_size
												_a	:    List[str]         =		max_pos

												_a	:    Tuple         =		enc_layers
												_a	:    Optional[Any]         =		enc_hidden_size
												_a	:    int         =		enc_heads
												_a	:    Optional[Any]         =		enc_ff_size
												_a	:    List[str]         =		enc_dropout

												_a	:    Tuple         =		dec_layers
												_a	:    Optional[Any]         =		dec_hidden_size
												_a	:    Optional[Any]         =		dec_heads
												_a	:    Optional[Any]         =		dec_ff_size
												_a	:    List[Any]         =		dec_dropout



