
'''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       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  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  ,  )					->       Dict:
														_snake_case       =  parent
														_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

							def 	lowercase							(self  )					->       List[str]:
														_snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size  )

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

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

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

														_snake_case       =  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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Any:
														_snake_case       =  TFRoFormerModel(config=UpperCAmelCase  )
														_snake_case       =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

														_snake_case       =  [input_ids, input_mask]
														_snake_case       =  model(UpperCAmelCase  )

														_snake_case       =  model(UpperCAmelCase  )

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

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[Any]:
														_snake_case       =  True
														_snake_case       =  TFRoFormerForCausalLM(config=UpperCAmelCase  )
														_snake_case       =  {
														    """input_ids""": input_ids,
														    """attention_mask""": input_mask,
														    """token_type_ids""": token_type_ids,
														}
														_snake_case       =  model(UpperCAmelCase  )["""logits"""]
														self.parent.assertListEqual(
														    list(prediction_scores.numpy().shape  )  ,  [self.batch_size, self.seq_length, self.vocab_size]  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[Any]:
														_snake_case       =  TFRoFormerForMaskedLM(config=UpperCAmelCase  )
														_snake_case       =  {
														    """input_ids""": input_ids,
														    """attention_mask""": input_mask,
														    """token_type_ids""": token_type_ids,
														}
														_snake_case       =  model(UpperCAmelCase  )
														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[int]:
														_snake_case       =  self.num_labels
														_snake_case       =  TFRoFormerForSequenceClassification(config=UpperCAmelCase  )
														_snake_case       =  {
														    """input_ids""": input_ids,
														    """attention_mask""": input_mask,
														    """token_type_ids""": token_type_ids,
														}

														_snake_case       =  model(UpperCAmelCase  )
														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[Any]:
														_snake_case       =  self.num_choices
														_snake_case       =  TFRoFormerForMultipleChoice(config=UpperCAmelCase  )
														_snake_case       =  tf.tile(tf.expand_dims(UpperCAmelCase  ,  1  )  ,  (1, self.num_choices, 1)  )
														_snake_case       =  tf.tile(tf.expand_dims(UpperCAmelCase  ,  1  )  ,  (1, self.num_choices, 1)  )
														_snake_case       =  tf.tile(tf.expand_dims(UpperCAmelCase  ,  1  )  ,  (1, self.num_choices, 1)  )
														_snake_case       =  {
														    """input_ids""": multiple_choice_inputs_ids,
														    """attention_mask""": multiple_choice_input_mask,
														    """token_type_ids""": multiple_choice_token_type_ids,
														}
														_snake_case       =  model(UpperCAmelCase  )
														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_choices)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Dict:
														_snake_case       =  self.num_labels
														_snake_case       =  TFRoFormerForTokenClassification(config=UpperCAmelCase  )
														_snake_case       =  {
														    """input_ids""": input_ids,
														    """attention_mask""": input_mask,
														    """token_type_ids""": token_type_ids,
														}
														_snake_case       =  model(UpperCAmelCase  )
														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.num_labels)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Any:
														_snake_case       =  TFRoFormerForQuestionAnswering(config=UpperCAmelCase  )
														_snake_case       =  {
														    """input_ids""": input_ids,
														    """attention_mask""": input_mask,
														    """token_type_ids""": token_type_ids,
														}

														_snake_case       =  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 	lowercase							(self  )					->       int:
														_snake_case       =  self.prepare_config_and_inputs()
														(
														    (
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							
														)       =  config_and_inputs
														_snake_case       =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
														return config, inputs_dict


@require_tf
class       _lowerCAmelCase   (   __snake_case					,							__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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Union[str, Any]:
														if pipeline_test_casse_name == "TextGenerationPipelineTests":
																					return True

														return False

							def 	lowercase							(self  )					->       Optional[Any]:
														_snake_case       =  TFRoFormerModelTester(self  )
														_snake_case       =  ConfigTester(self  ,  config_class=UpperCAmelCase  ,  hidden_size=37  )

							def 	lowercase							(self  )					->       Tuple:
														self.config_tester.run_common_tests()

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       int:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_lm_head(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       Optional[Any]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       Optional[Any]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase  )





							@slow
							def 	lowercase							(self  )					->       Dict:
														_snake_case       =  TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""  )
														self.assertIsNotNone(UpperCAmelCase  )


@require_tf
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							@slow
							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""  )
														_snake_case       =  tf.constant([[0, 1, 2, 3, 4, 5]]  )
														_snake_case       =  model(UpperCAmelCase  )[0]

														# TODO Replace vocab size
														_snake_case       =  50000

														_snake_case       =  [1, 6, vocab_size]
														self.assertEqual(output.shape  ,  UpperCAmelCase  )

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

														# TODO Replace values below with what was printed above.
														_snake_case       =  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]  ,  UpperCAmelCase  ,  atol=1e-4  )


@require_tf
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						1E-4

							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  tf.constant([[4, 10]]  )
														_snake_case       =  TFRoFormerSinusoidalPositionalEmbedding(num_positions=6  ,  embedding_dim=6  )

														_snake_case       =  emba(input_ids.shape  )
														_snake_case       =  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(UpperCAmelCase  ,  UpperCAmelCase  ,  atol=self.tolerance  )





							def 	lowercase							(self  )					->       int:
														_snake_case       =  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],
														    ]  )
														_snake_case       =  TFRoFormerSinusoidalPositionalEmbedding(num_positions=512  ,  embedding_dim=512  )
														emba([2, 16, 512]  )
														_snake_case       =  emba.weight[:3, :5]

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


@require_tf
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						1E-4

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

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

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

														_snake_case,							_snake_case       =  TFRoFormerSelfAttention.apply_rotary_position_embeddings(
														    UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )

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

'''simple docstring'''
from random import randint, random






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE = False							, _SCREAMING_SNAKE_CASE = False							, _SCREAMING_SNAKE_CASE = 5							, ):
							_snake_case       =  [[-1] * number_of_cells]  # Create a highway without any car
							_snake_case       =  0
							_snake_case       =  max(_SCREAMING_SNAKE_CASE							, 0				)
							while i < number_of_cells:
														_snake_case       =  (
														    randint(0							, _SCREAMING_SNAKE_CASE				) if random_speed else initial_speed
														)  # Place the cars
														i += (
														    randint(1							, max_speed * 2				) if random_frequency else frequency
														)  # Arbitrary number, may need tuning
							return highway






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  0
							_snake_case       =  highway_now[car_index + 1 :]
							for cell in range(len(_SCREAMING_SNAKE_CASE				)				):  # May need a better name for this
														if cells[cell] != -1:  # If the cell is not empty then
																					return distance  # we have the distance we wanted
														distance += 1
							# Here if the car is near the end of the highway
							return distance + get_distance(_SCREAMING_SNAKE_CASE							, -1				)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							# Beforce calculations, the highway is empty
							_snake_case       =  [-1] * number_of_cells

							for car_index in range(_SCREAMING_SNAKE_CASE				):
														if highway_now[car_index] != -1:
																					# Add 1 to the current speed of the car and cap the speed
																					_snake_case       =  min(highway_now[car_index] + 1							, _SCREAMING_SNAKE_CASE				)
																					# Number of empty cell before the next car
																					_snake_case       =  get_distance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				) - 1
																					# We can't have the car causing an accident
																					_snake_case       =  min(next_highway[car_index]							, _SCREAMING_SNAKE_CASE				)
																					if random() < probability:
																												# Randomly, a driver will slow down
																												_snake_case       =  max(next_highway[car_index] - 1							, 0				)
							return next_highway






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(highway[0]				)

							for i in range(_SCREAMING_SNAKE_CASE				):
														_snake_case       =  update(highway[i]							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
														_snake_case       =  [-1] * number_of_cells

														for car_index in range(_SCREAMING_SNAKE_CASE				):
																					_snake_case       =  next_speeds_calculated[car_index]
																					if speed != -1:
																												# Change the position based on the speed (with % to create the loop)
																												_snake_case       =  (car_index + speed) % number_of_cells
																												# Commit the change of position
																												_snake_case       =  speed
														highway.append(_SCREAMING_SNAKE_CASE				)

							return highway


if __name__ == "__main__":
				import doctest

				doctest.testmod()