
from __future__ import annotations

import os





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

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



							def __init__(     self	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								if not isinstance(__lowercase	,				__lowercase							):
									UpperCAmelCase:					int 						=     F'Hand should be of type \'str\': {hand!r}'
									raise TypeError(__lowercase							)
								# split removes duplicate whitespaces so no need of strip
								if len(hand.split(" "							)							) != 5:
									UpperCAmelCase:					List[Any] 						=     F'Hand should contain only 5 cards: {hand!r}'
									raise ValueError(__lowercase							)
								UpperCAmelCase:					Union[str, Any] 						=     hand
								UpperCAmelCase:					int 						=     0
								UpperCAmelCase:					Tuple 						=     0
								UpperCAmelCase						,   UpperCAmelCase:					Union[str, Any] 						=     self._internal_state()
								UpperCAmelCase:					Union[str, Any] 						=     self._get_hand_type()
								UpperCAmelCase:					Tuple 						=     self._card_values[0]



							@property
							def     A__ (     self							)						->     Union[str, Any]:






								"""simple docstring"""



								return self._hand



							def     A__ (     self	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								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(__lowercase							)
									else:
										return "Win" if self._second_pair > other._second_pair else "Loss"
								return "Win" if self._first_pair > other._first_pair else "Loss"



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     PokerHand._HAND_NAME[self._hand_type - 1_4]
								UpperCAmelCase:					Any 						=     PokerHand._CARD_NAME[self._high_card]
								UpperCAmelCase:					List[str] 						=     PokerHand._CARD_NAME[self._first_pair]
								UpperCAmelCase:					str 						=     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]:
									UpperCAmelCase:					int 						=     "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     A__ (     self	,				__snake_case							)						->     Optional[int]:






								"""simple docstring"""



								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     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



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






								"""simple docstring"""



								return len(self._card_suit							) == 1



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






								"""simple docstring"""



								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
										UpperCAmelCase:					Optional[Any] 						=     self._card_values.pop(0							)
										self._card_values.append(__lowercase							)
									return True
								return False



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






								"""simple docstring"""



								for i in range(4							):
									if self._card_values[i] - self._card_values[i + 1] != 1:
										return False
								return True



							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     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:
											UpperCAmelCase:					str 						=     self._card_values[i]
											kind += 1
										elif vala == self._card_values[i]:
											kind += 2
										elif not vala:
											UpperCAmelCase:					Any 						=     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)
								UpperCAmelCase:					Dict 						=     kind + 2 if kind in [4, 5] else kind
								# first meaning first pair to compare in 'compare_with'
								UpperCAmelCase:					Union[str, Any] 						=     max(__lowercase	,				__lowercase							)
								UpperCAmelCase:					Tuple 						=     min(__lowercase	,				__lowercase							)
								# 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(__lowercase							) != 3:
									UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     second, first
								UpperCAmelCase:					str 						=     first
								UpperCAmelCase:					Union[str, Any] 						=     second
								return kind



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






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     {"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
								UpperCAmelCase:					List[Any] 						=     self._hand.translate(str.maketrans(__lowercase							)							).split()
								UpperCAmelCase:					int 						=     [int(card[:-1]							) for card in new_hand]
								UpperCAmelCase:					List[Any] 						=     {card[-1] for card in new_hand}
								return sorted(__lowercase	,				reverse=__lowercase							), card_suit



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






								"""simple docstring"""



								return F'{self.__class__}(\"{self._hand}\")'



							def __str__(     self							)						->     str:






								"""simple docstring"""



								return self._hand



							def __eq__(     self	,				__snake_case							)						->     Optional[int]:






								"""simple docstring"""



								if isinstance(__lowercase	,				__lowercase							):
									return self.compare_with(__lowercase							) == "Tie"
								return NotImplemented



							def __lt__(     self	,				__snake_case							)						->     str:






								"""simple docstring"""



								if isinstance(__lowercase	,				__lowercase							):
									return self.compare_with(__lowercase							) == "Loss"
								return NotImplemented



							def __le__(     self	,				__snake_case							)						->     List[Any]:






								"""simple docstring"""



								if isinstance(__lowercase	,				__lowercase							):
									return self < other or self == other
								return NotImplemented



							def __gt__(     self	,				__snake_case							)						->     int:






								"""simple docstring"""



								if isinstance(__lowercase	,				__lowercase							):
									return not self < other and self != other
								return NotImplemented



							def __ge__(     self	,				__snake_case							)						->     List[str]:






								"""simple docstring"""



								if isinstance(__lowercase	,				__lowercase							):
									return not self < other
								return NotImplemented




							def __hash__(     self							)						->     Any:






								"""simple docstring"""



								return object.__hash__(self							)



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					List[Any] 						=     0
	UpperCAmelCase:					str 						=     os.path.abspath(os.path.dirname(snake_case_				)				)
	UpperCAmelCase:					Any 						=     os.path.join(snake_case_				,					"poker_hands.txt"				)
	with open(snake_case_				) as file_hand:
		for line in file_hand:
			UpperCAmelCase:					str 						=     line[:1_4].strip()
			UpperCAmelCase:					int 						=     line[1_5:].strip()
			UpperCAmelCase						,   UpperCAmelCase:					str 						=     PokerHand(snake_case_				), PokerHand(snake_case_				)
			UpperCAmelCase:					Union[str, Any] 						=     player.compare_with(snake_case_				)
			if output == "Win":
				answer += 1
	return answer


if __name__ == "__main__":
	solution()




'''simple docstring'''

import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


_UpperCAmelCase					:			str				     =					[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-generation""",
        """text-classification""",
        """token-classification""",
        """language-modeling""",
        """multiple-choice""",
        """question-answering""",
        """summarization""",
        """translation""",
        """image-classification""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
	import run_clm
	import run_image_classification
	import run_mlm
	import run_ner
	import run_qa as run_squad
	import run_summarization
	import run_swag
	import run_text_classification
	import run_translation


logging.basicConfig(level=logging.DEBUG)

_UpperCAmelCase					:			Optional[Any]				     =					logging.getLogger()

def 				__magic_name__(    ):
						__lowerCAmelCase											=					argparse.ArgumentParser()
						parser.add_argument('''-f''')
						__lowerCAmelCase											=					parser.parse_args()
						return args.f

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase											=					{}
						__lowerCAmelCase											=					os.path.join(lowerCamelCase,					'''all_results.json''')
						if os.path.exists(lowerCamelCase):
												with open(lowerCamelCase,					'''r''') as f:
																		__lowerCAmelCase											=					json.load(lowerCamelCase)
						else:
												raise ValueError(F"""can't find {path}""")
						return results

def 				__magic_name__(    ):
						return bool(tf.config.list_physical_devices('''GPU'''))


_UpperCAmelCase					:			str				     =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class    a__					(			__A   ):







				"""simple docstring"""



				@skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''		)
				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

										if is_cuda_available():
																testargs.append('''--fp16'''		)

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_text_classification.main()
																# Reset the mixed precision policy so we don't break other tests
																tf.keras.mixed_precision.set_global_policy('''float32'''		)
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''eval_accuracy''']							,      0.7_5		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_clm.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

										if len(tf.config.list_physical_devices('''GPU'''		)		) > 1:
																# Skipping because there are not enough batches to train the model + would need a drop_last to work.
																return

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_clm.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertLess(result['''eval_perplexity''']							,      1_00		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_mlm.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertLess(result['''eval_perplexity''']							,      42		)




				def        _snake_case	(self		):
										# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
										__lowerCAmelCase											=					7 if get_gpu_count() > 1 else 2

										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_ner.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''accuracy''']							,      0.7_5		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_squad.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''f1''']							,      30		)
																self.assertGreaterEqual(result['''exact''']							,      30		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_swag.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''val_accuracy''']							,      0.8		)




				@slow
				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_summarization.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''rouge1''']							,      10		)
																self.assertGreaterEqual(result['''rouge2''']							,      2		)
																self.assertGreaterEqual(result['''rougeL''']							,      7		)
																self.assertGreaterEqual(result['''rougeLsum''']							,      7		)




				@slow
				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_translation.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''bleu''']							,      30		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_image_classification.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''accuracy''']							,      0.7		)

