

"""simple docstring"""




from __future__ import annotations

import os





class 				lowerCamelCase  :
			lowerCamelCase__				:						str   						=							(
			    'High card',
			    'One pair',
			    'Two pairs',
			    'Three of a kind',
			    'Straight',
			    'Flush',
			    'Full house',
			    'Four of a kind',
			    'Straight flush',
			    'Royal flush',
			)

			lowerCamelCase__				:						List[Any]   						=							(
			    '',  # placeholder as tuples are zero-indexed
			    'One',
			    'Two',
			    'Three',
			    'Four',
			    'Five',
			    'Six',
			    'Seven',
			    'Eight',
			    'Nine',
			    'Ten',
			    'Jack',
			    'Queen',
			    'King',
			    'Ace',
			)

			def __init__(   self			:  Optional[Any]      ,			__UpperCAmelCase			:  str    )				->      None:
						if not isinstance(__UpperCAmelCase      ,			__UpperCAmelCase    ):
									SCREAMING_SNAKE_CASE__									= F"""Hand should be of type 'str': {hand!r}"""
									raise TypeError(__UpperCAmelCase    )
						# split removes duplicate whitespaces so no need of strip
						if len(hand.split(""" """    )    ) != 5:
									SCREAMING_SNAKE_CASE__									= F"""Hand should contain only 5 cards: {hand!r}"""
									raise ValueError(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= hand
						SCREAMING_SNAKE_CASE__									= 0
						SCREAMING_SNAKE_CASE__									= 0
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self._internal_state()
						SCREAMING_SNAKE_CASE__									= self._get_hand_type()
						SCREAMING_SNAKE_CASE__									= self._card_values[0]

			@property
			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      List[str]:
						return self._hand

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]      ,			__UpperCAmelCase			:  PokerHand    )				->      str:
						# Breaking the tie works on the following order of precedence:
						# 1. First pair (default 0)
						# 2. Second pair (default 0)
						# 3. Compare all cards in reverse order because they are sorted.

						# First pair and second pair will only be a non-zero value if the card
						# type is either from the following:
						# 21: Four of a kind
						# 20: Full house
						# 17: Three of a kind
						# 16: Two pairs
						# 15: One pair
						if self._hand_type > other._hand_type:
									return "Win"
						elif self._hand_type < other._hand_type:
									return "Loss"
						elif self._first_pair == other._first_pair:
									if self._second_pair == other._second_pair:
												return self._compare_cards(__UpperCAmelCase    )
									else:
												return "Win" if self._second_pair > other._second_pair else "Loss"
						return "Win" if self._first_pair > other._first_pair else "Loss"

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      str:
						SCREAMING_SNAKE_CASE__									= PokerHand._HAND_NAME[self._hand_type - 1_4]
						SCREAMING_SNAKE_CASE__									= PokerHand._CARD_NAME[self._high_card]
						SCREAMING_SNAKE_CASE__									= PokerHand._CARD_NAME[self._first_pair]
						SCREAMING_SNAKE_CASE__									= PokerHand._CARD_NAME[self._second_pair]
						if self._hand_type in [2_2, 1_9, 1_8]:
									return name + F""", {high}-high"""
						elif self._hand_type in [2_1, 1_7, 1_5]:
									return name + F""", {paira}s"""
						elif self._hand_type in [2_0, 1_6]:
									SCREAMING_SNAKE_CASE__									= """over""" if self._hand_type == 2_0 else """and"""
									return name + F""", {paira}s {join} {paira}s"""
						elif self._hand_type == 2_3:
									return name
						else:
									return name + F""", {high}"""

			def    SCREAMING_SNAKE_CASE			(   self			:  int      ,			__UpperCAmelCase			:  PokerHand    )				->      str:
						# Enumerate gives us the index as well as the element of a list
						for index, card_value in enumerate(self._card_values    ):
									if card_value != other._card_values[index]:
												return "Win" if card_value > other._card_values[index] else "Loss"
						return "Tie"

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      int:
						# Number representing the type of hand internally:
						# 23: Royal flush
						# 22: Straight flush
						# 21: Four of a kind
						# 20: Full house
						# 19: Flush
						# 18: Straight
						# 17: Three of a kind
						# 16: Two pairs
						# 15: One pair
						# 14: High card
						if self._is_flush():
									if self._is_five_high_straight() or self._is_straight():
												return 2_3 if sum(self._card_values    ) == 6_0 else 2_2
									return 1_9
						elif self._is_five_high_straight() or self._is_straight():
									return 1_8
						return 1_4 + self._is_same_kind()

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      bool:
						return len(self._card_suit    ) == 1

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      bool:
						# If a card is a five high straight (low ace) change the location of
						# ace from the start of the list to the end. Check whether the first
						# element is ace or not. (Don't want to change again)
						# Five high straight (low ace): AH 2H 3S 4C 5D
						# Why use sorted here? One call to this function will mutate the list to
						# [5, 4, 3, 2, 14] and so for subsequent calls (which will be rare) we
						# need to compare the sorted version.
						# Refer test_multiple_calls_five_high_straight in test_poker_hand.py
						if sorted(self._card_values    ) == [2, 3, 4, 5, 1_4]:
									if self._card_values[0] == 1_4:
												# Remember, our list is sorted in reverse order
												SCREAMING_SNAKE_CASE__									= self._card_values.pop(0    )
												self._card_values.append(__UpperCAmelCase    )
									return True
						return False

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      bool:
						for i in range(4    ):
									if self._card_values[i] - self._card_values[i + 1] != 1:
												return False
						return True

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      int:
						# Kind Values for internal use:
						# 7: Four of a kind
						# 6: Full house
						# 3: Three of a kind
						# 2: Two pairs
						# 1: One pair
						# 0: False
						SCREAMING_SNAKE_CASE__									= SCREAMING_SNAKE_CASE__									= SCREAMING_SNAKE_CASE__									= 0
						for i in range(4    ):
									# Compare two cards at a time, if they are same increase 'kind',
									# add the value of the card to val1, if it is repeating again we
									# will add 2 to 'kind' as there are now 3 cards with same value.
									# If we get card of different value than val1, we will do the same
									# thing with val2
									if self._card_values[i] == self._card_values[i + 1]:
												if not vala:
															SCREAMING_SNAKE_CASE__									= self._card_values[i]
															kind += 1
												elif vala == self._card_values[i]:
															kind += 2
												elif not vala:
															SCREAMING_SNAKE_CASE__									= self._card_values[i]
															kind += 1
												elif vala == self._card_values[i]:
															kind += 2
        # For consistency in hand type (look at note in _get_hand_type function)
						SCREAMING_SNAKE_CASE__									= kind + 2 if kind in [4, 5] else kind
						# first meaning first pair to compare in 'compare_with'
						SCREAMING_SNAKE_CASE__									= max(__UpperCAmelCase      ,			__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= min(__UpperCAmelCase      ,			__UpperCAmelCase    )
						# If it's full house (three count pair + two count pair), make sure
						# first pair is three count and if not then switch them both.
						if kind == 6 and self._card_values.count(__UpperCAmelCase    ) != 3:
									SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= second, first
						SCREAMING_SNAKE_CASE__									= first
						SCREAMING_SNAKE_CASE__									= second
						return kind

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      tuple[list[int], set[str]]:
						# Internal representation of hand as a list of card values and
						# a set of card suit
						SCREAMING_SNAKE_CASE__									= {"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
						SCREAMING_SNAKE_CASE__									= self._hand.translate(str.maketrans(__UpperCAmelCase    )    ).split()
						SCREAMING_SNAKE_CASE__									= [int(card[:-1]    ) for card in new_hand]
						SCREAMING_SNAKE_CASE__									= {card[-1] for card in new_hand}
						return sorted(__UpperCAmelCase      ,			reverse=__UpperCAmelCase    ), card_suit

			def __repr__(   self			:  str    )				->      int:
						return F"""{self.__class__}(\"{self._hand}\")"""

			def __str__(   self			:  Union[str, Any]    )				->      List[Any]:
						return self._hand

			def __eq__(   self			:  Optional[int]      ,			__UpperCAmelCase			:  str    )				->      List[Any]:
						if isinstance(__UpperCAmelCase      ,			__UpperCAmelCase    ):
									return self.compare_with(__UpperCAmelCase    ) == "Tie"
						return NotImplemented

			def __lt__(   self			:  int      ,			__UpperCAmelCase			:  Optional[Any]    )				->      Tuple:
						if isinstance(__UpperCAmelCase      ,			__UpperCAmelCase    ):
									return self.compare_with(__UpperCAmelCase    ) == "Loss"
						return NotImplemented

			def __le__(   self			:  List[Any]      ,			__UpperCAmelCase			:  List[str]    )				->      List[Any]:
						if isinstance(__UpperCAmelCase      ,			__UpperCAmelCase    ):
									return self < other or self == other
						return NotImplemented

			def __gt__(   self			:  List[Any]      ,			__UpperCAmelCase			:  str    )				->      Union[str, Any]:
						if isinstance(__UpperCAmelCase      ,			__UpperCAmelCase    ):
									return not self < other and self != other
						return NotImplemented

			def __ge__(   self			:  List[str]      ,			__UpperCAmelCase			:  int    )				->      int:
						if isinstance(__UpperCAmelCase      ,			__UpperCAmelCase    ):
									return not self < other
						return NotImplemented





			def __hash__(   self			:  Tuple    )				->      int:
						return object.__hash__(self    )

def 		A    (       ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= 0
			SCREAMING_SNAKE_CASE__									= os.path.abspath(os.path.dirname(snake_case__      )      )
			SCREAMING_SNAKE_CASE__									= os.path.join(snake_case__						,			"""poker_hands.txt"""      )
			with open(snake_case__      ) as file_hand:
						for line in file_hand:
									SCREAMING_SNAKE_CASE__									= line[:14].strip()
									SCREAMING_SNAKE_CASE__									= line[15:].strip()
									SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= PokerHand(snake_case__      ), PokerHand(snake_case__      )
									SCREAMING_SNAKE_CASE__									= player.compare_with(snake_case__      )
									if output == "Win":
												answer += 1
			return answer


if __name__ == "__main__":
						solution()





"""simple docstring"""




import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
						from tokenizers import Tokenizer
						from tokenizers.models import WordPiece





class 				lowerCamelCase  (unittest.TestCase     ):

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  str    )				->      int:
						SCREAMING_SNAKE_CASE__									= list(tokenizer_class.max_model_input_sizes.keys()    )
						for model_name in sa_models[:1]:
									SCREAMING_SNAKE_CASE__									= tokenizer_class.from_pretrained(__UpperCAmelCase    )
									self.assertIsNotNone(__UpperCAmelCase    )
									self.assertIsInstance(__UpperCAmelCase      ,			__UpperCAmelCase    )
									self.assertIsInstance(__UpperCAmelCase      ,			__UpperCAmelCase    )

									for special_tok in tokenizer.all_special_tokens:
												self.assertIsInstance(__UpperCAmelCase      ,			__UpperCAmelCase    )
												SCREAMING_SNAKE_CASE__									= tokenizer.convert_tokens_to_ids(__UpperCAmelCase    )
												self.assertIsInstance(__UpperCAmelCase      ,			__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict      ,			__UpperCAmelCase			:  BatchEncoding      ,			__UpperCAmelCase			:  Optional[Callable] = None    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= pickle.dumps(__UpperCAmelCase    )
						self.assertIsNotNone(__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= pickle.loads(__UpperCAmelCase    )

						# Ensure is_fast is correctly restored
						self.assertEqual(be_restored.is_fast      ,			be_original.is_fast    )

						# Ensure encodings are potentially correctly restored
						if be_original.is_fast:
									self.assertIsNotNone(be_restored.encodings    )
						else:
									self.assertIsNone(be_restored.encodings    )

						# Ensure the keys are the same
						for original_v, restored_v in zip(be_original.values()      ,			be_restored.values()    ):
									if equal_op:
												self.assertTrue(equal_op(__UpperCAmelCase      ,			__UpperCAmelCase    )    )
									else:
												self.assertEqual(__UpperCAmelCase      ,			__UpperCAmelCase    )

			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      Optional[Any]:
						self.check_tokenizer_from_pretrained(__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Optional[int]:
						self.assertEqual(TensorType("""tf"""    )      ,			TensorType.TENSORFLOW    )
						self.assertEqual(TensorType("""pt"""    )      ,			TensorType.PYTORCH    )
						self.assertEqual(TensorType("""np"""    )      ,			TensorType.NUMPY    )

			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      List[str]:
						import numpy as np

						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= BertTokenizerFast.from_pretrained("""bert-base-cased"""    )

						# Python no tensor
						with self.subTest("""BatchEncoding (Python, return_tensors=None)"""    ):
									self.assert_dump_and_restore(tokenizer_p("""Small example to encode"""    )    )

						with self.subTest("""BatchEncoding (Python, return_tensors=NUMPY)"""    ):
									self.assert_dump_and_restore(
									    tokenizer_p("""Small example to encode"""      ,			return_tensors=TensorType.NUMPY    )      ,			np.array_equal    )

						with self.subTest("""BatchEncoding (Rust, return_tensors=None)"""    ):
									self.assert_dump_and_restore(tokenizer_r("""Small example to encode"""    )    )

						with self.subTest("""BatchEncoding (Rust, return_tensors=NUMPY)"""    ):
									self.assert_dump_and_restore(
									    tokenizer_r("""Small example to encode"""      ,			return_tensors=TensorType.NUMPY    )      ,			np.array_equal    )

			@require_tf
			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      int:
						import tensorflow as tf

						def tf_array_equals(__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  str    ):
									return tf.reduce_all(tf.equal(__UpperCAmelCase      ,			__UpperCAmelCase    )    )

						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= BertTokenizerFast.from_pretrained("""bert-base-cased"""    )

						with self.subTest("""BatchEncoding (Python, return_tensors=TENSORFLOW)"""    ):
									self.assert_dump_and_restore(
									    tokenizer_p("""Small example to encode"""      ,			return_tensors=TensorType.TENSORFLOW    )      ,			__UpperCAmelCase    )

						with self.subTest("""BatchEncoding (Rust, return_tensors=TENSORFLOW)"""    ):
									self.assert_dump_and_restore(
									    tokenizer_r("""Small example to encode"""      ,			return_tensors=TensorType.TENSORFLOW    )      ,			__UpperCAmelCase    )

			@require_torch
			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      Optional[int]:
						import torch

						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= BertTokenizerFast.from_pretrained("""bert-base-cased"""    )

						with self.subTest("""BatchEncoding (Python, return_tensors=PYTORCH)"""    ):
									self.assert_dump_and_restore(
									    tokenizer_p("""Small example to encode"""      ,			return_tensors=TensorType.PYTORCH    )      ,			torch.equal    )

						with self.subTest("""BatchEncoding (Rust, return_tensors=PYTORCH)"""    ):
									self.assert_dump_and_restore(
									    tokenizer_r("""Small example to encode"""      ,			return_tensors=TensorType.PYTORCH    )      ,			torch.equal    )

			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      int:
						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= BertTokenizerFast.from_pretrained("""bert-base-cased"""    )

						with self.subTest("""Python Tokenizer"""    ):
									self.assertFalse(tokenizer_p("""Small example to_encode"""    ).is_fast    )

						with self.subTest("""Rust Tokenizer"""    ):
									self.assertTrue(tokenizer_r("""Small example to_encode"""    ).is_fast    )

			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= BertTokenizerFast.from_pretrained("""bert-base-cased"""    )
						SCREAMING_SNAKE_CASE__									= tokenizer_r(["""Test""", """\xad""", """test"""]      ,			is_split_into_words=__UpperCAmelCase    )

						self.assertEqual(encoded.word_to_tokens(0    )      ,			TokenSpan(start=1      ,			end=2    )    )
						self.assertEqual(encoded.word_to_tokens(1    )      ,			__UpperCAmelCase    )
						self.assertEqual(encoded.word_to_tokens(2    )      ,			TokenSpan(start=2      ,			end=3    )    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""np"""    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(2, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(2,)    )
						# test converting the converted
						with CaptureStderr() as cs:
									SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""np"""    )
						self.assertFalse(len(cs.err    )      ,			msg=F"""should have no warning, but got {cs.err}"""    )

						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""np"""      ,			prepend_batch_axis=__UpperCAmelCase    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(1, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(1,)    )

			@require_torch
			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      Optional[Any]:
						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""pt"""    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(2, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(2,)    )
						# test converting the converted
						with CaptureStderr() as cs:
									SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""pt"""    )
						self.assertFalse(len(cs.err    )      ,			msg=F"""should have no warning, but got {cs.err}"""    )

						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""pt"""      ,			prepend_batch_axis=__UpperCAmelCase    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(1, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(1,)    )

			@require_tf
			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      int:
						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""tf"""    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(2, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(2,)    )
						# test converting the converted
						with CaptureStderr() as cs:
									SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""tf"""    )
						self.assertFalse(len(cs.err    )      ,			msg=F"""should have no warning, but got {cs.err}"""    )

						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""tf"""      ,			prepend_batch_axis=__UpperCAmelCase    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(1, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(1,)    )

			@require_flax
			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""jax"""    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(2, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(2,)    )
						# test converting the converted
						with CaptureStderr() as cs:
									SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""jax"""    )
						self.assertFalse(len(cs.err    )      ,			msg=F"""should have no warning, but got {cs.err}"""    )

						SCREAMING_SNAKE_CASE__									= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}    )
						SCREAMING_SNAKE_CASE__									= batch.convert_to_tensors(tensor_type="""jax"""      ,			prepend_batch_axis=__UpperCAmelCase    )
						self.assertEqual(tensor_batch["""inputs"""].shape      ,			(1, 3)    )
						self.assertEqual(tensor_batch["""labels"""].shape      ,			(1,)    )

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= [{"""input_ids""": np.array([0, 1, 2]    )}, {"""input_ids""": np.array([0, 1, 2, 3]    )}]
						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )

						SCREAMING_SNAKE_CASE__									= tokenizer.pad(__UpperCAmelCase      ,			padding=__UpperCAmelCase    )
						self.assertTrue(isinstance(batch["""input_ids"""]      ,			np.ndarray    )    )
						self.assertEqual(batch["""input_ids"""].tolist()      ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
						SCREAMING_SNAKE_CASE__									= tokenizer.pad(__UpperCAmelCase      ,			padding=__UpperCAmelCase      ,			return_tensors="""np"""    )
						self.assertTrue(isinstance(batch["""input_ids"""]      ,			np.ndarray    )    )
						self.assertEqual(batch["""input_ids"""].tolist()      ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

			@require_torch
			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      Optional[int]:
						import torch

						SCREAMING_SNAKE_CASE__									= [{"""input_ids""": torch.tensor([0, 1, 2]    )}, {"""input_ids""": torch.tensor([0, 1, 2, 3]    )}]
						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )

						SCREAMING_SNAKE_CASE__									= tokenizer.pad(__UpperCAmelCase      ,			padding=__UpperCAmelCase    )
						self.assertTrue(isinstance(batch["""input_ids"""]      ,			torch.Tensor    )    )
						self.assertEqual(batch["""input_ids"""].tolist()      ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
						SCREAMING_SNAKE_CASE__									= tokenizer.pad(__UpperCAmelCase      ,			padding=__UpperCAmelCase      ,			return_tensors="""pt"""    )
						self.assertTrue(isinstance(batch["""input_ids"""]      ,			torch.Tensor    )    )
						self.assertEqual(batch["""input_ids"""].tolist()      ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

			@require_tf
			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Dict:
						import tensorflow as tf

						SCREAMING_SNAKE_CASE__									= [{"""input_ids""": tf.constant([0, 1, 2]    )}, {"""input_ids""": tf.constant([0, 1, 2, 3]    )}]
						SCREAMING_SNAKE_CASE__									= BertTokenizer.from_pretrained("""bert-base-cased"""    )

						SCREAMING_SNAKE_CASE__									= tokenizer.pad(__UpperCAmelCase      ,			padding=__UpperCAmelCase    )
						self.assertTrue(isinstance(batch["""input_ids"""]      ,			tf.Tensor    )    )
						self.assertEqual(batch["""input_ids"""].numpy().tolist()      ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
						SCREAMING_SNAKE_CASE__									= tokenizer.pad(__UpperCAmelCase      ,			padding=__UpperCAmelCase      ,			return_tensors="""tf"""    )
						self.assertTrue(isinstance(batch["""input_ids"""]      ,			tf.Tensor    )    )
						self.assertEqual(batch["""input_ids"""].numpy().tolist()      ,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= Tokenizer(WordPiece(unk_token="""[UNK]"""    )    )
						PreTrainedTokenizerFast(tokenizer_object=__UpperCAmelCase    )





			@require_tokenizers
			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      int:
						SCREAMING_SNAKE_CASE__									= Tokenizer(WordPiece(unk_token="""[UNK]"""    )    )
						with tempfile.TemporaryDirectory() as tmpdirname:
									bert_tokenizer.save(os.path.join(__UpperCAmelCase      ,			"""tokenizer.json"""    )    )
									PreTrainedTokenizerFast(tokenizer_file=os.path.join(__UpperCAmelCase      ,			"""tokenizer.json"""    )    )


