



"""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 			lowercase__			:
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       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	,       )			->							int:
									_UpperCAmelCase							 =		parent
									_UpperCAmelCase							 =		13
									_UpperCAmelCase							 =		7
									_UpperCAmelCase							 =		True
									_UpperCAmelCase							 =		True
									_UpperCAmelCase							 =		True
									_UpperCAmelCase							 =		True
									_UpperCAmelCase							 =		99
									_UpperCAmelCase							 =		32
									_UpperCAmelCase							 =		2
									_UpperCAmelCase							 =		4
									_UpperCAmelCase							 =		37
									_UpperCAmelCase							 =		'gelu'
									_UpperCAmelCase							 =		0.1
									_UpperCAmelCase							 =		0.1
									_UpperCAmelCase							 =		512
									_UpperCAmelCase							 =		16
									_UpperCAmelCase							 =		2
									_UpperCAmelCase							 =		0.02
									_UpperCAmelCase							 =		3
									_UpperCAmelCase							 =		4
									_UpperCAmelCase							 =		None






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		ids_tensor([self.batch_size, self.seq_length]	,       self.vocab_size						)

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

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

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

									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		TFRoFormerModel(config=A__						)
									_UpperCAmelCase							 =		{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

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

									_UpperCAmelCase							 =		model(A__						)

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






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							Optional[int]:
									_UpperCAmelCase							 =		True
									_UpperCAmelCase							 =		TFRoFormerForCausalLM(config=A__						)
									_UpperCAmelCase							 =		{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									_UpperCAmelCase							 =		model(A__						)['logits']
									self.parent.assertListEqual(
									    list(prediction_scores.numpy().shape						)	,       [self.batch_size, self.seq_length, self.vocab_size]						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		TFRoFormerForMaskedLM(config=A__						)
									_UpperCAmelCase							 =		{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									_UpperCAmelCase							 =		model(A__						)
									self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.seq_length, self.vocab_size)						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		self.num_labels
									_UpperCAmelCase							 =		TFRoFormerForSequenceClassification(config=A__						)
									_UpperCAmelCase							 =		{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

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






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






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		self.num_labels
									_UpperCAmelCase							 =		TFRoFormerForTokenClassification(config=A__						)
									_UpperCAmelCase							 =		{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									_UpperCAmelCase							 =		model(A__						)
									self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.seq_length, self.num_labels)						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		TFRoFormerForQuestionAnswering(config=A__						)
									_UpperCAmelCase							 =		{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		self.prepare_config_and_inputs()
									(
									    (
									    _UpperCAmelCase		
									) ,					(
									    _UpperCAmelCase		
									) ,					(
									    _UpperCAmelCase		
									) ,					(
									    _UpperCAmelCase		
									) ,					(
									    _UpperCAmelCase		
									) ,					(
									    _UpperCAmelCase		
									) ,					(
									    _UpperCAmelCase		
									) ,							
									)							 =		config_and_inputs
									_UpperCAmelCase							 =		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
									return config, inputs_dict







@require_tf
class 			lowercase__			(      __a,					__a,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					(
							    (
							        TFRoFormerModel,
							        TFRoFormerForCausalLM,
							        TFRoFormerForMaskedLM,
							        TFRoFormerForQuestionAnswering,
							        TFRoFormerForSequenceClassification,
							        TFRoFormerForTokenClassification,
							        TFRoFormerForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							_UpperCAmelCase      				=					(
							    {
							        '''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      				=					False
							_UpperCAmelCase      				=					False






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							Union[str, Any]:
									if pipeline_test_casse_name == "TextGenerationPipelineTests":
											return True

									return False






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase							 =		TFRoFormerModelTester(self						)
									_UpperCAmelCase							 =		ConfigTester(self	,       config_class=A__	,       hidden_size=37						)






							def        lowerCamelCase_   (  self						)			->							str:
									self.config_tester.run_common_tests()






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*A__						)






							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*A__						)






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_lm_head(*A__						)






							def        lowerCamelCase_   (  self						)			->							Tuple:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*A__						)






							def        lowerCamelCase_   (  self						)			->							Tuple:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*A__						)






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*A__						)






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*A__						)






							@slow
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'						)
									self.assertIsNotNone(A__						)







@require_tf
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







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

									# TODO Replace vocab size
									_UpperCAmelCase							 =		50000

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

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

									# TODO Replace values below with what was printed above.
									_UpperCAmelCase							 =		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]	,       A__	,       atol=1E-4						)







@require_tf
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					1E-4






							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		tf.constant([[4, 10]]						)
									_UpperCAmelCase							 =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=6	,       embedding_dim=6						)

									_UpperCAmelCase							 =		emba(input_ids.shape						)
									_UpperCAmelCase							 =		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        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		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],
									    ]						)
									_UpperCAmelCase							 =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=512	,       embedding_dim=512						)
									emba([2, 16, 512]						)
									_UpperCAmelCase							 =		emba.weight[:3, :5]

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







@require_tf
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					1E-4






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

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

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

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

									_UpperCAmelCase							 =		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],
									    ]						)
									_UpperCAmelCase							 =		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						)







"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


lowercase  							=					logging.get_logger(__name__)




class 			lowercase__			(      A    ):
							'''simple docstring'''







							def __init__(  self	,       *snake_case	,       **snake_case						)			->							None:
									warnings.warn(
									    'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use YolosImageProcessor instead.'	,       snake_case	,       )
									super().__init__(*snake_case	,       **snake_case						)


