
'''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 	snake_case__       :







		"""simple docstring"""

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






				"""simple docstring"""







				snake_case  :       Any     			=						parent
				snake_case  :       int     			=						13
				snake_case  :       Optional[Any]     			=						7
				snake_case  :       Union[str, Any]     			=						True
				snake_case  :       Union[str, Any]     			=						True
				snake_case  :       int     			=						True
				snake_case  :       Optional[Any]     			=						True
				snake_case  :       List[Any]     			=						99
				snake_case  :       List[Any]     			=						32
				snake_case  :       Any     			=						2
				snake_case  :       int     			=						4
				snake_case  :       int     			=						37
				snake_case  :       Dict     			=						'''gelu'''
				snake_case  :       Dict     			=						0.1
				snake_case  :       List[str]     			=						0.1
				snake_case  :       Union[str, Any]     			=						512
				snake_case  :       Any     			=						16
				snake_case  :       Optional[int]     			=						2
				snake_case  :       Any     			=						0.02
				snake_case  :       Optional[int]     			=						3
				snake_case  :       List[Any]     			=						4
				snake_case  :       Any     			=						None



		def       lowerCAmelCase				(      self				:			str      )						->			Tuple:






				"""simple docstring"""







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

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

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

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

				snake_case  :       Union[str, Any]     			=						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=UpperCamelCase__  ,	)

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



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			Dict      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						TFRoFormerModel(config=UpperCamelCase__      )
				snake_case  :       Optional[int]     			=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

				snake_case  :       Optional[Any]     			=						[input_ids, input_mask]
				snake_case  :       Tuple     			=						model(UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						model(UpperCamelCase__      )

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



		def       lowerCAmelCase				(      self				:			Any  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			Any      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						True
				snake_case  :       Dict     			=						TFRoFormerForCausalLM(config=UpperCamelCase__      )
				snake_case  :       int     			=						{
				    '''input_ids''': input_ids,
				    '''attention_mask''': input_mask,
				    '''token_type_ids''': token_type_ids,
				}
				snake_case  :       str     			=						model(UpperCamelCase__      )['''logits''']
				self.parent.assertListEqual(
				    list(prediction_scores.numpy().shape      )  ,	[self.batch_size, self.seq_length, self.vocab_size]      )



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			int      )						->			str:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						TFRoFormerForMaskedLM(config=UpperCamelCase__      )
				snake_case  :       Dict     			=						{
				    '''input_ids''': input_ids,
				    '''attention_mask''': input_mask,
				    '''token_type_ids''': token_type_ids,
				}
				snake_case  :       Optional[int]     			=						model(UpperCamelCase__      )
				self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.vocab_size)      )



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			List[Any]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			List[Any]      )						->			int:






				"""simple docstring"""







				snake_case  :       int     			=						self.num_labels
				snake_case  :       Optional[Any]     			=						TFRoFormerForSequenceClassification(config=UpperCamelCase__      )
				snake_case  :       str     			=						{
				    '''input_ids''': input_ids,
				    '''attention_mask''': input_mask,
				    '''token_type_ids''': token_type_ids,
				}

				snake_case  :       Tuple     			=						model(UpperCamelCase__      )
				self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.num_labels)      )



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			Dict  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Dict      )						->			Any:






				"""simple docstring"""







				snake_case  :       int     			=						self.num_choices
				snake_case  :       str     			=						TFRoFormerForMultipleChoice(config=UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						tf.tile(tf.expand_dims(UpperCamelCase__  ,	1      )  ,	(1, self.num_choices, 1)      )
				snake_case  :       List[Any]     			=						tf.tile(tf.expand_dims(UpperCamelCase__  ,	1      )  ,	(1, self.num_choices, 1)      )
				snake_case  :       Union[str, Any]     			=						tf.tile(tf.expand_dims(UpperCamelCase__  ,	1      )  ,	(1, self.num_choices, 1)      )
				snake_case  :       Any     			=						{
				    '''input_ids''': multiple_choice_inputs_ids,
				    '''attention_mask''': multiple_choice_input_mask,
				    '''token_type_ids''': multiple_choice_token_type_ids,
				}
				snake_case  :       Any     			=						model(UpperCamelCase__      )
				self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.num_choices)      )



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			List[Any]  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Optional[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						self.num_labels
				snake_case  :       Dict     			=						TFRoFormerForTokenClassification(config=UpperCamelCase__      )
				snake_case  :       Any     			=						{
				    '''input_ids''': input_ids,
				    '''attention_mask''': input_mask,
				    '''token_type_ids''': token_type_ids,
				}
				snake_case  :       Optional[Any]     			=						model(UpperCamelCase__      )
				self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.num_labels)      )



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Union[str, Any]  ,	UpperCamelCase__				:			List[Any]  ,	UpperCamelCase__				:			str      )						->			str:






				"""simple docstring"""







				snake_case  :       int     			=						TFRoFormerForQuestionAnswering(config=UpperCamelCase__      )
				snake_case  :       Tuple     			=						{
				    '''input_ids''': input_ids,
				    '''attention_mask''': input_mask,
				    '''token_type_ids''': token_type_ids,
				}

				snake_case  :       Optional[int]     			=						model(UpperCamelCase__      )
				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				:			Optional[int]      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       int     			=						self.prepare_config_and_inputs()
				(
				    (
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,(
				    snake_case
				)			,
				)  :       Optional[Any]     			=						config_and_inputs
				snake_case  :       List[Any]     			=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
				return config, inputs_dict







@require_tf
class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						(
		    (
		        TFRoFormerModel,
		        TFRoFormerForCausalLM,
		        TFRoFormerForMaskedLM,
		        TFRoFormerForQuestionAnswering,
		        TFRoFormerForSequenceClassification,
		        TFRoFormerForTokenClassification,
		        TFRoFormerForMultipleChoice,
		    )
		    if is_tf_available()
		    else ()
		)
		lowerCamelCase     							=						(
		    {
		        """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 {}
		)

		lowerCamelCase     							=						False
		lowerCamelCase     							=						False



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			List[Any]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			int      )						->			Dict:






				"""simple docstring"""







				if pipeline_test_casse_name == "TextGenerationPipelineTests":
						return True

				return False



		def       lowerCAmelCase				(      self				:			Tuple      )						->			str:






				"""simple docstring"""







				snake_case  :       List[str]     			=						TFRoFormerModelTester(self      )
				snake_case  :       Optional[int]     			=						ConfigTester(self  ,	config_class=UpperCamelCase__  ,	hidden_size=37      )



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Dict:






				"""simple docstring"""







				self.config_tester.run_common_tests()



		def       lowerCAmelCase				(      self				:			int      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       int     			=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Tuple      )						->			int:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Any      )						->			Any:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_lm_head(*UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Any      )						->			Dict:






				"""simple docstring"""







				snake_case  :       Tuple     			=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			int      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Tuple      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       Dict     			=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Any     			=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__      )



		@slow
		def       lowerCAmelCase				(      self				:			Any      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       List[str]     			=						TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''      )
				self.assertIsNotNone(UpperCamelCase__      )







@require_tf
class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		@slow
		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''      )
				snake_case  :       Dict     			=						tf.constant([[0, 1, 2, 3, 4, 5]]      )
				snake_case  :       int     			=						model(UpperCamelCase__      )[0]

				# TODO Replace vocab size
				snake_case  :       int     			=						5_0000

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

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

				# TODO Replace values below with what was printed above.
				snake_case  :       List[str]     			=						tf.constant(
				    [
				        [
				            [-0.12_053_341, -1.0_264_901, 0.29_221_946],
				            [-1.5_133_783, 0.197_433, 0.15_190_607],
				            [-5.0_135_403, -3.900_256, -0.84_038_764],
				        ]
				    ]      )
				tf.debugging.assert_near(output[:, :3, :3]  ,	UpperCamelCase__  ,	atol=1e-4      )







@require_tf
class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						1E-4



		def       lowerCAmelCase				(      self				:			str      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						tf.constant([[4, 10]]      )
				snake_case  :       List[Any]     			=						TFRoFormerSinusoidalPositionalEmbedding(num_positions=6  ,	embedding_dim=6      )

				snake_case  :       Optional[int]     			=						emba(input_ids.shape      )
				snake_case  :       List[str]     			=						tf.constant(
				    [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]      )

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



		def       lowerCAmelCase				(      self				:			str      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						tf.constant(
				    [
				        [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
				        [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
				        [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
				    ]      )
				snake_case  :       List[Any]     			=						TFRoFormerSinusoidalPositionalEmbedding(num_positions=512  ,	embedding_dim=512      )
				emba([2, 16, 512]      )
				snake_case  :       List[str]     			=						emba.weight[:3, :5]

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







@require_tf
class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						1E-4



		def       lowerCAmelCase				(      self				:			Any      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       str     			=						tf.reshape(tf.range(2 * 12 * 16 * 64  ,	dtype=tf.floataa      )  ,	shape=(2, 12, 16, 64)      ) / 100

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

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

				snake_case			,snake_case  :       Union[str, Any]     			=						TFRoFormerSelfAttention.apply_rotary_position_embeddings(
				    UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       List[Any]     			=						tf.constant(
				    [
				        [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
				        [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
				        [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
				        [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
				        [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
				        [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
				    ]      )
				snake_case  :       List[Any]     			=						tf.constant(
				    [
				        [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
				        [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
				        [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
				        [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
				        [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
				        [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
				    ]      )

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




'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


lowercase__						       =      logging.get_logger(__name__)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def __init__(      self				:			Optional[Any]  ,	*UpperCamelCase__				:			Optional[Any]  ,	**UpperCamelCase__				:			str      )						->			None:






				"""simple docstring"""







				warnings.warn(
				    '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use GLPNImageProcessor instead.'''  ,	UpperCamelCase__  ,	)
				super().__init__(*UpperCamelCase__  ,	**UpperCamelCase__      )


