


'''simple docstring'''




import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

_a   :   str												=   (
    "4S 3H 2C 7S 5H",
    "9D 8H 2C 6S 7H",
    "2D 6D 9D TH 7D",
    "TC 8C 2S JH 6C",
    "JH 8S TH AH QH",
    "TS KS 5S 9S AC",
    "KD 6S 9D TH AD",
    "KS 8D 4D 9S 4S",  # pair
    "8C 4S KH JS 4D",  # pair
    "QH 8H KD JH 8S",  # pair
    "KC 4H KS 2H 8D",  # pair
    "KD 4S KC 3H 8S",  # pair
    "AH 8S AS KC JH",  # pair
    "3H 4C 4H 3S 2H",  # 2 pairs
    "5S 5D 2C KH KH",  # 2 pairs
    "3C KH 5D 5S KH",  # 2 pairs
    "AS 3C KH AD KH",  # 2 pairs
    "7C 7S 3S 7H 5S",  # 3 of a kind
    "7C 7S KH 2H 7H",  # 3 of a kind
    "AC KH QH AH AS",  # 3 of a kind
    "2H 4D 3C AS 5S",  # straight (low ace)
    "3C 5C 4C 2C 6H",  # straight
    "6S 8S 7S 5H 9H",  # straight
    "JS QS 9H TS KH",  # straight
    "QC KH TS JS AH",  # straight (high ace)
    "8C 9C 5C 3C TC",  # flush
    "3S 8S 9S 5S KS",  # flush
    "4C 5C 9C 8C KC",  # flush
    "JH 8H AH KH QH",  # flush
    "3D 2H 3H 2C 2D",  # full house
    "2H 2C 3S 3H 3D",  # full house
    "KH KC 3S 3H 3D",  # full house
    "JC 6H JS JD JH",  # 4 of a kind
    "JC 7H JS JD JH",  # 4 of a kind
    "JC KH JS JD JH",  # 4 of a kind
    "2S AS 4S 5S 3S",  # straight flush (low ace)
    "2D 6D 3D 4D 5D",  # straight flush
    "5C 6C 3C 7C 4C",  # straight flush
    "JH 9H TH KH QH",  # straight flush
    "JH AH TH KH QH",  # royal flush (high ace straight flush)
)

_a   :   str												=   (
    ("2H 3H 4H 5H 6H", "KS AS TS QS JS", "Loss"),
    ("2H 3H 4H 5H 6H", "AS AD AC AH JD", "Win"),
    ("AS AH 2H AD AC", "JS JD JC JH 3D", "Win"),
    ("2S AH 2H AS AC", "JS JD JC JH AD", "Loss"),
    ("2S AH 2H AS AC", "2H 3H 5H 6H 7H", "Win"),
    ("AS 3S 4S 8S 2S", "2H 3H 5H 6H 7H", "Win"),
    ("2H 3H 5H 6H 7H", "2S 3H 4H 5S 6C", "Win"),
    ("2S 3H 4H 5S 6C", "3D 4C 5H 6H 2S", "Tie"),
    ("2S 3H 4H 5S 6C", "AH AC 5H 6H AS", "Win"),
    ("2S 2H 4H 5S 4C", "AH AC 5H 6H AS", "Loss"),
    ("2S 2H 4H 5S 4C", "AH AC 5H 6H 7S", "Win"),
    ("6S AD 7H 4S AS", "AH AC 5H 6H 7S", "Loss"),
    ("2S AH 4H 5S KC", "AH AC 5H 6H 7S", "Loss"),
    ("2S 3H 6H 7S 9C", "7H 3C TH 6H 9S", "Loss"),
    ("4S 5H 6H TS AC", "3S 5H 6H TS AC", "Win"),
    ("2S AH 4H 5S 6C", "AD 4C 5H 6H 2C", "Tie"),
    ("AS AH 3H AD AC", "AS AH 2H AD AC", "Win"),
    ("AH AC 5H 5C QS", "AH AC 5H 5C KS", "Loss"),
    ("AH AC 5H 5C QS", "KH KC 5H 5C QS", "Win"),
    ("7C 7S KH 2H 7H", "3C 3S AH 2H 3H", "Win"),
    ("3C 3S AH 2H 3H", "7C 7S KH 2H 7H", "Loss"),
    ("6H 5H 4H 3H 2H", "5H 4H 3H 2H AH", "Win"),
    ("5H 4H 3H 2H AH", "5H 4H 3H 2H AH", "Tie"),
    ("5H 4H 3H 2H AH", "6H 5H 4H 3H 2H", "Loss"),
    ("AH AD KS KC AC", "AH KD KH AC KC", "Win"),
    ("2H 4D 3C AS 5S", "2H 4D 3C 6S 5S", "Loss"),
    ("2H 3S 3C 3H 2S", "3S 3C 2S 2H 2D", "Win"),
    ("4D 6D 5D 2D JH", "3S 8S 3H TC KH", "Loss"),
    ("4S 6C 8S 3S 7S", "AD KS 2D 7D 7C", "Loss"),
    ("6S 4C 7H 8C 3H", "5H JC AH 9D 9C", "Loss"),
    ("9D 9H JH TC QH", "3C 2S JS 5C 7H", "Win"),
    ("2H TC 8S AD 9S", "4H TS 7H 2C 5C", "Win"),
    ("9D 3S 2C 7S 7C", "JC TD 3C TC 9H", "Loss"),
)

_a   :   int												=   (
    ("2H 3H 4H 5H 6H", True),
    ("AS AH 2H AD AC", False),
    ("2H 3H 5H 6H 7H", True),
    ("KS AS TS QS JS", True),
    ("8H 9H QS JS TH", False),
    ("AS 3S 4S 8S 2S", True),
)

_a   :   int												=   (
    ("2H 3H 4H 5H 6H", True),
    ("AS AH 2H AD AC", False),
    ("2H 3H 5H 6H 7H", False),
    ("KS AS TS QS JS", True),
    ("8H 9H QS JS TH", True),
)

_a   :   Optional[int]												=   (
    ("2H 4D 3C AS 5S", True, [5, 4, 3, 2, 14]),
    ("2H 5D 3C AS 5S", False, [14, 5, 5, 3, 2]),
    ("JH QD KC AS TS", False, [14, 13, 12, 11, 10]),
    ("9D 3S 2C 7S 7C", False, [9, 7, 7, 3, 2]),
)

_a   :   Union[str, Any]												=   (
    ("JH AH TH KH QH", 0),
    ("JH 9H TH KH QH", 0),
    ("JC KH JS JD JH", 7),
    ("KH KC 3S 3H 3D", 6),
    ("8C 9C 5C 3C TC", 0),
    ("JS QS 9H TS KH", 0),
    ("7C 7S KH 2H 7H", 3),
    ("3C KH 5D 5S KH", 2),
    ("QH 8H KD JH 8S", 1),
    ("2D 6D 9D TH 7D", 0),
)

_a   :   Any												=   (
    ("JH AH TH KH QH", 23),
    ("JH 9H TH KH QH", 22),
    ("JC KH JS JD JH", 21),
    ("KH KC 3S 3H 3D", 20),
    ("8C 9C 5C 3C TC", 19),
    ("JS QS 9H TS KH", 18),
    ("7C 7S KH 2H 7H", 17),
    ("3C KH 5D 5S KH", 16),
    ("QH 8H KD JH 8S", 15),
    ("2D 6D 9D TH 7D", 14),
)





def   _a		()			->			str:






     """simple docstring"""
     __snake_case      ,       __snake_case           =						randrange(len(lowercase__						)						), randrange(len(lowercase__						)						)
     __snake_case           =						['Loss', 'Tie', 'Win'][(play >= oppo) + (play > oppo)]
     __snake_case      ,       __snake_case           =						SORTED_HANDS[play], SORTED_HANDS[oppo]
     return hand, other, expected





def   _a		(lowercase__			:   int = 1_0_0						)			->			Optional[int]:






     """simple docstring"""
     return (generate_random_hand() for _ in range(lowercase__						))





@pytest.mark.parametrize('hand, expected'							,   lowercase__						)
def   _a		(lowercase__			:   Optional[int]							,   lowercase__			:   Any						)			->			Dict:






     """simple docstring"""
     assert PokerHand(lowercase__						)._is_flush() == expected





@pytest.mark.parametrize('hand, expected'							,   lowercase__						)
def   _a		(lowercase__			:   int							,   lowercase__			:   List[Any]						)			->			List[Any]:






     """simple docstring"""
     assert PokerHand(lowercase__						)._is_straight() == expected





@pytest.mark.parametrize('hand, expected, card_values'							,   lowercase__						)
def   _a		(lowercase__			:   int							,   lowercase__			:   Tuple							,   lowercase__			:   List[str]						)			->			str:






     """simple docstring"""
     __snake_case           =						PokerHand(lowercase__						)
     assert player._is_five_high_straight() == expected
     assert player._card_values == card_values





@pytest.mark.parametrize('hand, expected'							,   lowercase__						)
def   _a		(lowercase__			:   str							,   lowercase__			:   int						)			->			Any:






     """simple docstring"""
     assert PokerHand(lowercase__						)._is_same_kind() == expected





@pytest.mark.parametrize('hand, expected'							,   lowercase__						)
def   _a		(lowercase__			:   List[str]							,   lowercase__			:   str						)			->			Union[str, Any]:






     """simple docstring"""
     assert PokerHand(lowercase__						)._hand_type == expected





@pytest.mark.parametrize('hand, other, expected'							,   lowercase__						)
def   _a		(lowercase__			:   Union[str, Any]							,   lowercase__			:   Tuple							,   lowercase__			:   Union[str, Any]						)			->			str:






     """simple docstring"""
     assert PokerHand(lowercase__						).compare_with(PokerHand(lowercase__						)						) == expected





@pytest.mark.parametrize('hand, other, expected'							,   generate_random_hands()						)
def   _a		(lowercase__			:   int							,   lowercase__			:   int							,   lowercase__			:   List[str]						)			->			str:






     """simple docstring"""
     assert PokerHand(lowercase__						).compare_with(PokerHand(lowercase__						)						) == expected





def   _a		()			->			Any:






     """simple docstring"""
     __snake_case           =						[PokerHand(lowercase__						) for hand in SORTED_HANDS]
     __snake_case           =						poker_hands.copy()
     shuffle(lowercase__						)
     __snake_case           =						chain(sorted(lowercase__						)						)
     for index, hand in enumerate(lowercase__						):
          assert hand == poker_hands[index]





def   _a		()			->			str:






     """simple docstring"""
     # Test that five high straights are compared correctly.
     __snake_case           =						[PokerHand('2D AC 3H 4H 5S'						), PokerHand('2S 3H 4H 5S 6C'						)]
     pokerhands.sort(reverse=lowercase__						)
     assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"





def   _a		()			->			Dict:






     """simple docstring"""
     # Multiple calls to five_high_straight function should still return True
     # and shouldn't mutate the list in every call other than the first.
     __snake_case           =						PokerHand('2C 4S AS 3D 5C'						)
     __snake_case           =						True
     __snake_case           =						[5, 4, 3, 2, 1_4]
     for _ in range(1_0						):
          assert pokerhand._is_five_high_straight() == expected
          assert pokerhand._card_values == expected_card_values





def   _a		()			->			Any:






     """simple docstring"""
     # Problem number 54 from Project Euler
     # Testing from poker_hands.txt file
     __snake_case           =						0
     __snake_case           =						os.path.abspath(os.path.dirname(lowercase__						)						)
     __snake_case           =						os.path.join(lowercase__							,   'poker_hands.txt'						)
     with open(lowercase__						) as file_hand:
          for line in file_hand:
               __snake_case           =						line[:1_4].strip()
               __snake_case           =						line[1_5:].strip()
               __snake_case      ,       __snake_case           =						PokerHand(lowercase__						), PokerHand(lowercase__						)
               __snake_case           =						player.compare_with(lowercase__						)
               if output == "Win":
                    answer += 1
     assert answer == 3_7_6





'''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       ,lowerCamelCase_       ,lowerCamelCase_=13       ,lowerCamelCase_=7       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=99       ,lowerCamelCase_=32       ,lowerCamelCase_=2       ,lowerCamelCase_=4       ,lowerCamelCase_=37       ,lowerCamelCase_="gelu"       ,lowerCamelCase_=0.1       ,lowerCamelCase_=0.1       ,lowerCamelCase_=512       ,lowerCamelCase_=16       ,lowerCamelCase_=2       ,lowerCamelCase_=0.02       ,lowerCamelCase_=3       ,lowerCamelCase_=4       ,lowerCamelCase_=None       ,)      ->      str:



         '''simple docstring'''



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




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



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size				)

         UpperCAmelCase__				:					List[str] 					=	None
         if self.use_input_mask:
            UpperCAmelCase__				:					int 					=	random_attention_mask([self.batch_size, self.seq_length]				)

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

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

         UpperCAmelCase__				:					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=lowerCamelCase_       ,)

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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	TFRoFormerModel(config=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

         UpperCAmelCase__				:					str 					=	[input_ids, input_mask]
         UpperCAmelCase__				:					Any 					=	model(lowerCamelCase_				)

         UpperCAmelCase__				:					List[Any] 					=	model(lowerCamelCase_				)

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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	TFRoFormerForMaskedLM(config=lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         UpperCAmelCase__				:					Dict 					=	model(lowerCamelCase_				)
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length, self.vocab_size)				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.num_labels
         UpperCAmelCase__				:					List[Any] 					=	TFRoFormerForSequenceClassification(config=lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         UpperCAmelCase__				:					Union[str, Any] 					=	model(lowerCamelCase_				)
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_labels)				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Any:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	TFRoFormerForQuestionAnswering(config=lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         UpperCAmelCase__				:					int 					=	model(lowerCamelCase_				)
         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				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase__
         )					,   (
             UpperCAmelCase__
         )					,   (
             UpperCAmelCase__
         )					,   (
             UpperCAmelCase__
         )					,   (
             UpperCAmelCase__
         )					,   (
             UpperCAmelCase__
         )					,   (
             UpperCAmelCase__
         )					,   
         )				:					List[Any] 					=	config_and_inputs
         UpperCAmelCase__				:					Optional[int] 					=	{'''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 ):
      '''simple docstring'''


      UpperCAmelCase_   :   List[str]   				= (
          (
              TFRoFormerModel,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      UpperCAmelCase_   :   int   				= (
          {
              '''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_   :   Optional[int]   				= False
      UpperCAmelCase_   :   Optional[int]   				= False




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Any:



         '''simple docstring'''



         if pipeline_test_casse_name == "TextGenerationPipelineTests":
            return True

         return False




      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	TFRoFormerModelTester(self				)
         UpperCAmelCase__				:					int 					=	ConfigTester(self       ,config_class=lowerCamelCase_       ,hidden_size=37				)




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         self.config_tester.run_common_tests()




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



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_				)




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



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head(*lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_				)




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



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_				)




      @slow
      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



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




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


      @slow
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''				)
         UpperCAmelCase__				:					Dict 					=	tf.constant([[0, 1, 2, 3, 4, 5]]				)
         UpperCAmelCase__				:					Union[str, Any] 					=	model(lowerCamelCase_				)[0]

         # TODO Replace vocab size
         UpperCAmelCase__				:					List[str] 					=	50000

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

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

         # TODO Replace values below with what was printed above.
         UpperCAmelCase__				:					Tuple 					=	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]       ,lowerCamelCase_       ,atol=1e-4				)




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


      UpperCAmelCase_   :   int   				= 1E-4




      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	tf.constant([[4, 10]]				)
         UpperCAmelCase__				:					List[str] 					=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=6       ,embedding_dim=6				)

         UpperCAmelCase__				:					Optional[Any] 					=	emba(input_ids.shape				)
         UpperCAmelCase__				:					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(lowerCamelCase_       ,lowerCamelCase_       ,atol=self.tolerance				)




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



         '''simple docstring'''



         UpperCAmelCase__				:					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],
             ]				)
         UpperCAmelCase__				:					Dict 					=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=512       ,embedding_dim=512				)
         emba([2, 16, 512]				)
         UpperCAmelCase__				:					Optional[int] 					=	emba.weight[:3, :5]

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




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


      UpperCAmelCase_   :   Optional[int]   				= 1E-4




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



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	tf.reshape(tf.range(2 * 12 * 16 * 64       ,dtype=tf.floataa				)       ,shape=(2, 12, 16, 64)				) / 100

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

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

         UpperCAmelCase__					,   UpperCAmelCase__				:					Dict 					=	TFRoFormerSelfAttention.apply_rotary_position_embeddings(
             lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)

         UpperCAmelCase__				:					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],
             ]				)
         UpperCAmelCase__				:					List[str] 					=	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]       ,lowerCamelCase_       ,atol=self.tolerance				)
         tf.debugging.assert_near(key_layer[0, 0, :6, :8]       ,lowerCamelCase_       ,atol=self.tolerance				)




