




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							:

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

					def       a__			(						self       )		->							Tuple:
											_A							:  List[str]    =		ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size       )

											_A							:  Optional[Any]    =		None
											if self.use_input_mask:
																	_A							:  Dict    =		random_attention_mask([self.batch_size, self.seq_length]       )

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

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

											_A							:  str    =		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       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a       )		->							Union[str, Any]:
											_A							:  str    =		TFRoFormerModel(config=A__       )
											_A							:  Dict    =		{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

											_A							:  Optional[Any]    =		[input_ids, input_mask]
											_A							:  Optional[Any]    =		model(A__       )

											_A							:  Dict    =		model(A__       )

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

					def       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a       )		->							str:
											_A							:  Tuple    =		True
											_A							:  str    =		TFRoFormerForCausalLM(config=A__       )
											_A							:  int    =		{
											    """input_ids""": input_ids,
											    """attention_mask""": input_mask,
											    """token_type_ids""": token_type_ids,
											}
											_A							:  Optional[int]    =		model(A__       )["""logits"""]
											self.parent.assertListEqual(
											    list(prediction_scores.numpy().shape       )	,  [self.batch_size, self.seq_length, self.vocab_size]       )

					def       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a       )		->							Dict:
											_A							:  Any    =		TFRoFormerForMaskedLM(config=A__       )
											_A							:  Dict    =		{
											    """input_ids""": input_ids,
											    """attention_mask""": input_mask,
											    """token_type_ids""": token_type_ids,
											}
											_A							:  List[Any]    =		model(A__       )
											self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.seq_length, self.vocab_size)       )

					def       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a       )		->							List[str]:
											_A							:  Optional[Any]    =		self.num_labels
											_A							:  Tuple    =		TFRoFormerForSequenceClassification(config=A__       )
											_A							:  Optional[int]    =		{
											    """input_ids""": input_ids,
											    """attention_mask""": input_mask,
											    """token_type_ids""": token_type_ids,
											}

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

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

					def       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a       )		->							List[str]:
											_A							:  str    =		self.num_labels
											_A							:  int    =		TFRoFormerForTokenClassification(config=A__       )
											_A							:  Tuple    =		{
											    """input_ids""": input_ids,
											    """attention_mask""": input_mask,
											    """token_type_ids""": token_type_ids,
											}
											_A							:  List[Any]    =		model(A__       )
											self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.seq_length, self.num_labels)       )

					def       a__			(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a       )		->							Tuple:
											_A							:  Optional[Any]    =		TFRoFormerForQuestionAnswering(config=A__       )
											_A							:  Tuple    =		{
											    """input_ids""": input_ids,
											    """attention_mask""": input_mask,
											    """token_type_ids""": token_type_ids,
											}

											_A							:  Tuple    =		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       a__			(						self       )		->							List[str]:
											_A							:  str    =		self.prepare_config_and_inputs()
											(
											    (
											    _A							
											)    ,     (
											    _A							
											)    ,     (
											    _A							
											)    ,     (
											    _A							
											)    ,     (
											    _A							
											)    ,     (
											    _A							
											)    ,     (
											    _A							
											)    ,     							
											)							:  Optional[Any]    =		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 		lowercase							(     _lowerCamelCase,_lowerCamelCase,unittest.TestCase	):
					_a											=		(
					    (
					        TFRoFormerModel,
					        TFRoFormerForCausalLM,
					        TFRoFormerForMaskedLM,
					        TFRoFormerForQuestionAnswering,
					        TFRoFormerForSequenceClassification,
					        TFRoFormerForTokenClassification,
					        TFRoFormerForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					_a											=		(
					    {
					        "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 {}
					)

					_a											=		False
					_a											=		False

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

											return False

					def       a__			(						self       )		->							Union[str, Any]:
											_A							:  Dict    =		TFRoFormerModelTester(self       )
											_A							:  Any    =		ConfigTester(self	,  config_class=A__	,  hidden_size=37       )

					def       a__			(						self       )		->							Optional[Any]:
											self.config_tester.run_common_tests()

					def       a__			(						self       )		->							List[Any]:
											_A							:  Optional[Any]    =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*A__       )

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

					def       a__			(						self       )		->							int:
											_A							:  Tuple    =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_lm_head(*A__       )

					def       a__			(						self       )		->							Dict:
											_A							:  int    =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_multiple_choice(*A__       )

					def       a__			(						self       )		->							Union[str, Any]:
											_A							:  str    =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*A__       )

					def       a__			(						self       )		->							Tuple:
											_A							:  Optional[Any]    =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*A__       )

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



					@slow
					def       a__			(						self       )		->							str:
											_A							:  List[Any]    =		TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""       )
											self.assertIsNotNone(A__       )







@require_tf
class 		lowercase							(     unittest.TestCase	):

					@slow
					def       a__			(						self       )		->							Optional[Any]:
											_A							:  Any    =		TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""       )
											_A							:  Tuple    =		tf.constant([[0, 1, 2, 3, 4, 5]]       )
											_A							:  Optional[Any]    =		model(A__       )[0]

											# TODO Replace vocab size
											_A							:  Any    =		5_0000

											_A							:  Dict    =		[1, 6, vocab_size]
											self.assertEqual(output.shape	,  A__       )

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

											# TODO Replace values below with what was printed above.
											_A							:  Dict    =		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	):
					_a											=		1e-4

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

											_A							:  Any    =		emba(input_ids.shape       )
											_A							:  Union[str, Any]    =		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       a__			(						self       )		->							List[Any]:
											_A							:  int    =		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							:  Union[str, Any]    =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=512	,  embedding_dim=512       )
											emba([2, 16, 512]       )
											_A							:  Optional[Any]    =		emba.weight[:3, :5]

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







@require_tf
class 		lowercase							(     unittest.TestCase	):
					_a											=		1e-4

					def       a__			(						self       )		->							int:
											# 2,12,16,64
											_A							:  int    =		tf.reshape(tf.range(2 * 12 * 16 * 64	,  dtype=tf.floataa       )	,  shape=(2, 12, 16, 64)       ) / 100

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

											_A							:  Optional[Any]    =		TFRoFormerSinusoidalPositionalEmbedding(num_positions=32	,  embedding_dim=64       )
											_A							:  int    =		embed_positions([2, 16, 768]       )[None, None, :, :]

											_A    ,     _A							:  Any    =		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 operator







def        lowerCAmelCase_			(  snake_case_,snake_case_ = False,snake_case_ = None  ):
      _A							:  str    =		operator.lt if reverse else operator.gt
      _A							:  Optional[Any]    =		solution or []

      if not arr:
            return solution

      _A							:  Dict    =		[arr.pop(0  )]
      for i, item in enumerate(snake_case_  ):
            if _operator(snake_case_,sublist[-1]  ):
                  sublist.append(snake_case_  )
                  arr.pop(snake_case_  )

    #  merging sublist into solution list
      if not solution:
            solution.extend(snake_case_  )
      else:
            while sublist:
                  _A							:  Union[str, Any]    =		sublist.pop(0  )
                  for i, xx in enumerate(snake_case_  ):
                        if not _operator(snake_case_,snake_case_  ):
                              solution.insert(snake_case_,snake_case_  )
                              break
                  else:
                        solution.append(snake_case_  )

      strand_sort(snake_case_,snake_case_,snake_case_  )
      return solution


if __name__ == "__main__":
  assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
  assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]

