


'''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











import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    FlaubertForMultipleChoice,
	    FlaubertForQuestionAnswering,
	    FlaubertForQuestionAnsweringSimple,
	    FlaubertForSequenceClassification,
	    FlaubertForTokenClassification,
	    FlaubertModel,
	    FlaubertWithLMHeadModel,
	)
	from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST




class 	UpperCamelCase_							(  UpperCamelCase__     ):


							def __init__(				self     :Union[str, Any]				,					__A     :Optional[int]				,					__A     :Tuple=13				,					__A     :Dict=7				,					__A     :Dict=True				,					__A     :str=True				,					__A     :Optional[Any]=True				,					__A     :Optional[Any]=True				,					__A     :Optional[Any]=True				,					__A     :Any=False				,					__A     :Dict=False				,					__A     :Any=False				,					__A     :Tuple=2				,					__A     :Dict=99				,					__A     :Optional[Any]=0				,					__A     :List[str]=32				,					__A     :Optional[int]=5				,					__A     :Dict=4				,					__A     :List[str]=0.1				,					__A     :Union[str, Any]=0.1				,					__A     :Tuple=512				,					__A     :Any=12				,					__A     :Optional[int]=2				,					__A     :Union[str, Any]=0.0_2				,					__A     :Dict=3				,					__A     :Optional[int]=4				,					__A     :Any="last"				,					__A     :List[Any]=None				,					__A     :Any=None				,					) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   parent
									SCREAMING_SNAKE_CASE__								=   batch_size
									SCREAMING_SNAKE_CASE__								=   seq_length
									SCREAMING_SNAKE_CASE__								=   is_training
									SCREAMING_SNAKE_CASE__								=   use_input_lengths
									SCREAMING_SNAKE_CASE__								=   use_token_type_ids
									SCREAMING_SNAKE_CASE__								=   use_labels
									SCREAMING_SNAKE_CASE__								=   gelu_activation
									SCREAMING_SNAKE_CASE__								=   sinusoidal_embeddings
									SCREAMING_SNAKE_CASE__								=   causal
									SCREAMING_SNAKE_CASE__								=   asm
									SCREAMING_SNAKE_CASE__								=   n_langs
									SCREAMING_SNAKE_CASE__								=   vocab_size
									SCREAMING_SNAKE_CASE__								=   n_special
									SCREAMING_SNAKE_CASE__								=   hidden_size
									SCREAMING_SNAKE_CASE__								=   num_hidden_layers
									SCREAMING_SNAKE_CASE__								=   num_attention_heads
									SCREAMING_SNAKE_CASE__								=   hidden_dropout_prob
									SCREAMING_SNAKE_CASE__								=   attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__								=   max_position_embeddings
									SCREAMING_SNAKE_CASE__								=   type_vocab_size
									SCREAMING_SNAKE_CASE__								=   type_sequence_label_size
									SCREAMING_SNAKE_CASE__								=   initializer_range
									SCREAMING_SNAKE_CASE__								=   num_labels
									SCREAMING_SNAKE_CASE__								=   num_choices
									SCREAMING_SNAKE_CASE__								=   summary_type
									SCREAMING_SNAKE_CASE__								=   use_proj
									SCREAMING_SNAKE_CASE__								=   scope


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   ids_tensor([self.batch_size, self.seq_length]				,					self.vocab_size		)
									SCREAMING_SNAKE_CASE__								=   random_attention_mask([self.batch_size, self.seq_length]		)

									SCREAMING_SNAKE_CASE__								=   None
									if self.use_input_lengths:
											SCREAMING_SNAKE_CASE__								=   (
											    ids_tensor([self.batch_size]				,					vocab_size=2		) + self.seq_length - 2
											)  # small variation of seq_length

									SCREAMING_SNAKE_CASE__								=   None
									if self.use_token_type_ids:
											SCREAMING_SNAKE_CASE__								=   ids_tensor([self.batch_size, self.seq_length]				,					self.n_langs		)

									SCREAMING_SNAKE_CASE__								=   None
									SCREAMING_SNAKE_CASE__								=   None
									SCREAMING_SNAKE_CASE__								=   None
									if self.use_labels:
											SCREAMING_SNAKE_CASE__								=   ids_tensor([self.batch_size]				,					self.type_sequence_label_size		)
											SCREAMING_SNAKE_CASE__								=   ids_tensor([self.batch_size, self.seq_length]				,					self.num_labels		)
											SCREAMING_SNAKE_CASE__								=   ids_tensor([self.batch_size]				,					2		).float()
											SCREAMING_SNAKE_CASE__								=   ids_tensor([self.batch_size]				,					self.num_choices		)

									SCREAMING_SNAKE_CASE__								=   self.get_config()

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_lengths,
									    sequence_labels,
									    token_labels,
									    is_impossible_labels,
									    choice_labels,
									    input_mask,
									)


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



									"""simple docstring"""



									return FlaubertConfig(
									    vocab_size=self.vocab_size				,					n_special=self.n_special				,					emb_dim=self.hidden_size				,					n_layers=self.num_hidden_layers				,					n_heads=self.num_attention_heads				,					dropout=self.hidden_dropout_prob				,					attention_dropout=self.attention_probs_dropout_prob				,					gelu_activation=self.gelu_activation				,					sinusoidal_embeddings=self.sinusoidal_embeddings				,					asm=self.asm				,					causal=self.causal				,					n_langs=self.n_langs				,					max_position_embeddings=self.max_position_embeddings				,					initializer_range=self.initializer_range				,					summary_type=self.summary_type				,					use_proj=self.use_proj				,					)


							def   _snake_case	(				self     :Tuple				,					__A     :str				,					__A     :int				,					__A     :Optional[int]				,					__A     :Any				,					__A     :Union[str, Any]				,					__A     :Optional[int]				,					__A     :Union[str, Any]				,					__A     :Union[str, Any]				,					__A     :str				,					) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   FlaubertModel(config=__A		)
									model.to(__A		)
									model.eval()
									SCREAMING_SNAKE_CASE__								=   model(__A				,					lengths=__A				,					langs=__A		)
									SCREAMING_SNAKE_CASE__								=   model(__A				,					langs=__A		)
									SCREAMING_SNAKE_CASE__								=   model(__A		)
									self.parent.assertEqual(result.last_hidden_state.shape				,					(self.batch_size, self.seq_length, self.hidden_size)		)


							def   _snake_case	(				self     :str				,					__A     :Any				,					__A     :str				,					__A     :Union[str, Any]				,					__A     :Optional[Any]				,					__A     :Optional[int]				,					__A     :Any				,					__A     :Union[str, Any]				,					__A     :Optional[Any]				,					__A     :Union[str, Any]				,					) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   FlaubertWithLMHeadModel(__A		)
									model.to(__A		)
									model.eval()

									SCREAMING_SNAKE_CASE__								=   model(__A				,					token_type_ids=__A				,					labels=__A		)
									self.parent.assertEqual(result.loss.shape				,					()		)
									self.parent.assertEqual(result.logits.shape				,					(self.batch_size, self.seq_length, self.vocab_size)		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   FlaubertForQuestionAnsweringSimple(__A		)
									model.to(__A		)
									model.eval()

									SCREAMING_SNAKE_CASE__								=   model(__A		)

									SCREAMING_SNAKE_CASE__								=   model(__A				,					start_positions=__A				,					end_positions=__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   _snake_case	(				self     :List[str]				,					__A     :Any				,					__A     :int				,					__A     :Tuple				,					__A     :Optional[Any]				,					__A     :Tuple				,					__A     :Optional[int]				,					__A     :str				,					__A     :int				,					__A     :str				,					) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   FlaubertForQuestionAnswering(__A		)
									model.to(__A		)
									model.eval()

									SCREAMING_SNAKE_CASE__								=   model(__A		)

									SCREAMING_SNAKE_CASE__								=   model(
									    __A				,					start_positions=__A				,					end_positions=__A				,					cls_index=__A				,					is_impossible=__A				,					p_mask=__A				,					)

									SCREAMING_SNAKE_CASE__								=   model(
									    __A				,					start_positions=__A				,					end_positions=__A				,					cls_index=__A				,					is_impossible=__A				,					)

									((SCREAMING_SNAKE_CASE__)						,    )								=   result_with_labels.to_tuple()

									SCREAMING_SNAKE_CASE__								=   model(__A				,					start_positions=__A				,					end_positions=__A		)

									((SCREAMING_SNAKE_CASE__)						,    )								=   result_with_labels.to_tuple()

									self.parent.assertEqual(result_with_labels.loss.shape				,					()		)
									self.parent.assertEqual(result.start_top_log_probs.shape				,					(self.batch_size, model.config.start_n_top)		)
									self.parent.assertEqual(result.start_top_index.shape				,					(self.batch_size, model.config.start_n_top)		)
									self.parent.assertEqual(
									    result.end_top_log_probs.shape				,					(self.batch_size, model.config.start_n_top * model.config.end_n_top)		)
									self.parent.assertEqual(
									    result.end_top_index.shape				,					(self.batch_size, model.config.start_n_top * model.config.end_n_top)		)
									self.parent.assertEqual(result.cls_logits.shape				,					(self.batch_size,)		)


							def   _snake_case	(				self     :Optional[int]				,					__A     :str				,					__A     :Optional[int]				,					__A     :Tuple				,					__A     :Dict				,					__A     :List[str]				,					__A     :Tuple				,					__A     :List[str]				,					__A     :Dict				,					__A     :List[str]				,					) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   FlaubertForSequenceClassification(__A		)
									model.to(__A		)
									model.eval()

									SCREAMING_SNAKE_CASE__								=   model(__A		)
									SCREAMING_SNAKE_CASE__								=   model(__A				,					labels=__A		)

									self.parent.assertEqual(result.loss.shape				,					()		)
									self.parent.assertEqual(result.logits.shape				,					(self.batch_size, self.type_sequence_label_size)		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.num_labels
									SCREAMING_SNAKE_CASE__								=   FlaubertForTokenClassification(__A		)
									model.to(__A		)
									model.eval()

									SCREAMING_SNAKE_CASE__								=   model(__A				,					attention_mask=__A				,					labels=__A		)
									self.parent.assertEqual(result.logits.shape				,					(self.batch_size, self.seq_length, self.num_labels)		)


							def   _snake_case	(				self     :str				,					__A     :Any				,					__A     :Tuple				,					__A     :List[str]				,					__A     :Tuple				,					__A     :Any				,					__A     :int				,					__A     :Dict				,					__A     :List[str]				,					__A     :Tuple				,					) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.num_choices
									SCREAMING_SNAKE_CASE__								=   FlaubertForMultipleChoice(config=__A		)
									model.to(__A		)
									model.eval()
									SCREAMING_SNAKE_CASE__								=   input_ids.unsqueeze(1		).expand(-1				,					self.num_choices				,					-1		).contiguous()
									SCREAMING_SNAKE_CASE__								=   token_type_ids.unsqueeze(1		).expand(-1				,					self.num_choices				,					-1		).contiguous()
									SCREAMING_SNAKE_CASE__								=   input_mask.unsqueeze(1		).expand(-1				,					self.num_choices				,					-1		).contiguous()
									SCREAMING_SNAKE_CASE__								=   model(
									    __A				,					attention_mask=__A				,					token_type_ids=__A				,					labels=__A				,					)
									self.parent.assertEqual(result.logits.shape				,					(self.batch_size, self.num_choices)		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.prepare_config_and_inputs()
									(
									    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    
									)								=   config_and_inputs
									SCREAMING_SNAKE_CASE__								=   {
									    """input_ids""": input_ids,
									    """token_type_ids""": token_type_ids,
									    """lengths""": input_lengths,
									    """attention_mask""": input_mask,
									}
									return config, inputs_dict




@require_torch
class 	UpperCamelCase_							(  UpperCamelCase__					,	UpperCamelCase__					,	unittest.TestCase     ):
							lowerCamelCase_							     =       (
							    (
							        FlaubertModel,
							        FlaubertWithLMHeadModel,
							        FlaubertForQuestionAnswering,
							        FlaubertForQuestionAnsweringSimple,
							        FlaubertForSequenceClassification,
							        FlaubertForTokenClassification,
							        FlaubertForMultipleChoice,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCamelCase_							     =       (
							    {
							        "feature-extraction": FlaubertModel,
							        "fill-mask": FlaubertWithLMHeadModel,
							        "question-answering": FlaubertForQuestionAnsweringSimple,
							        "text-classification": FlaubertForSequenceClassification,
							        "token-classification": FlaubertForTokenClassification,
							        "zero-shot": FlaubertForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)


							def   _snake_case	(				self     :Any				,					__A     :Optional[int]				,					__A     :Optional[int]				,					__A     :Dict				,					__A     :List[Any]				,					__A     :Tuple		) -> str:



									"""simple docstring"""



									if (
									    pipeline_test_casse_name == "QAPipelineTests"
									    and tokenizer_name is not None
									    and not tokenizer_name.endswith("""Fast"""		)
									):
											# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
											# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
											# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
											return True

									return False


							def   _snake_case	(				self     :Tuple				,					__A     :List[str]				,					__A     :Optional[int]				,					__A     :Dict=False		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   super()._prepare_for_class(__A				,					__A				,					return_labels=__A		)

									if return_labels:
											if model_class.__name__ == "FlaubertForQuestionAnswering":
													SCREAMING_SNAKE_CASE__								=   torch.zeros(
													    self.model_tester.batch_size				,					dtype=torch.long				,					device=__A		)
													SCREAMING_SNAKE_CASE__								=   torch.zeros(
													    self.model_tester.batch_size				,					dtype=torch.long				,					device=__A		)

									return inputs_dict


							def   _snake_case	(				self     :str		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   FlaubertModelTester(self		)
									SCREAMING_SNAKE_CASE__								=   ConfigTester(self				,					config_class=__A				,					emb_dim=37		)


							def   _snake_case	(				self     :int		) -> int:



									"""simple docstring"""



									self.config_tester.run_common_tests()


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_model(*__A		)


							def   _snake_case	(				self     :Tuple		) -> List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_lm_head(*__A		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_simple_qa(*__A		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_qa(*__A		)


							def   _snake_case	(				self     :str		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_sequence_classif(*__A		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_token_classif(*__A		)


							def   _snake_case	(				self     :Any		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_multiple_choice(*__A		)


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



									"""simple docstring"""



									for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											SCREAMING_SNAKE_CASE__								=   FlaubertModel.from_pretrained(__A		)
											self.assertIsNotNone(__A		)


							@slow
							@require_torch_gpu
							def   _snake_case	(				self     :Tuple		) -> List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
											# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
											if model_class == FlaubertForMultipleChoice:
													return

											SCREAMING_SNAKE_CASE__								=   True
											SCREAMING_SNAKE_CASE__								=   model_class(config=__A		)

											SCREAMING_SNAKE_CASE__								=   self._prepare_for_class(__A				,					__A		)
											SCREAMING_SNAKE_CASE__								=   torch.jit.trace(
											    __A				,					(inputs_dict["""input_ids"""].to("""cpu"""		), inputs_dict["""attention_mask"""].to("""cpu"""		))		)

											with tempfile.TemporaryDirectory() as tmp:
													torch.jit.save(__A				,					os.path.join(__A				,					"""traced_model.pt"""		)		)
													SCREAMING_SNAKE_CASE__								=   torch.jit.load(os.path.join(__A				,					"""traced_model.pt"""		)				,					map_location=__A		)
													loaded(inputs_dict["""input_ids"""].to(__A		)				,					inputs_dict["""attention_mask"""].to(__A		)		)




@require_torch
class 	UpperCamelCase_							(  unittest.TestCase     ):


							@slow
							def   _snake_case	(				self     :Dict		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased"""		)
									SCREAMING_SNAKE_CASE__								=   torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
									with torch.no_grad():
											SCREAMING_SNAKE_CASE__								=   model(__A		)[0]
									SCREAMING_SNAKE_CASE__								=   torch.Size((1, 11, 768)		)
									self.assertEqual(output.shape				,					__A		)
									SCREAMING_SNAKE_CASE__								=   torch.tensor(
									    [[[-2.6_2_5_1, -1.4_2_9_8, -0.0_2_2_7], [-2.8_5_1_0, -1.6_3_8_7, 0.2_2_5_8], [-2.8_1_1_4, -1.1_8_3_2, -0.3_0_6_6]]]		)

									self.assertTrue(torch.allclose(output[:, :3, :3]				,					__A				,					atol=1E-4		)		)