



'''simple docstring'''




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation



def       a     (					lowerCamelCase__       ):
			'''simple docstring'''





			A_      :		Tuple      =	3_84
			if "tiny" in model_name:
						A_      :		Optional[int]      =	[3, 3, 9, 3]
						A_      :		List[str]      =	[96, 1_92, 3_84, 7_68]
			if "small" in model_name:
						A_      :		Union[str, Any]      =	[3, 3, 27, 3]
						A_      :		int      =	[96, 1_92, 3_84, 7_68]
			if "base" in model_name:
						A_      :		Optional[Any]      =	[3, 3, 27, 3]
						A_      :		Tuple      =	[1_28, 2_56, 5_12, 10_24]
						A_      :		Union[str, Any]      =	5_12
			if "large" in model_name:
						A_      :		List[Any]      =	[3, 3, 27, 3]
						A_      :		List[str]      =	[1_92, 3_84, 7_68, 15_36]
						A_      :		List[str]      =	7_68
			if "xlarge" in model_name:
						A_      :		Any      =	[3, 3, 27, 3]
						A_      :		List[str]      =	[2_56, 5_12, 10_24, 20_48]
						A_      :		Optional[int]      =	10_24

			# set label information
			A_      :		Dict      =	1_50
			A_      :		Optional[int]      =	"""huggingface/label-files"""
			A_      :		Any      =	"""ade20k-id2label.json"""
			A_      :		Union[str, Any]      =	json.load(open(hf_hub_download(_UpperCamelCase		, _UpperCamelCase		, repo_type="""dataset"""       )		, """r"""       )       )
			A_      :		Union[str, Any]      =	{int(_UpperCamelCase       ): v for k, v in idalabel.items()}
			A_      :		List[Any]      =	{v: k for k, v in idalabel.items()}

			A_      :		Union[str, Any]      =	ConvNextConfig(
			    depths=_UpperCamelCase		, hidden_sizes=_UpperCamelCase		, out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]       )
			A_      :		Any      =	UperNetConfig(
			    backbone_config=_UpperCamelCase		, auxiliary_in_channels=_UpperCamelCase		, num_labels=_UpperCamelCase		, idalabel=_UpperCamelCase		, labelaid=_UpperCamelCase		, )

			return config



def       a     (					lowerCamelCase__       ):
			'''simple docstring'''





			A_      :		Dict      =	[]

			# fmt: off
			# stem
			rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""")       )
			rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""")       )
			rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""")       )
			rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""")       )
			# stages
			for i in range(len(config.backbone_config.depths       )       ):
						for j in range(config.backbone_config.depths[i]       ):
									rename_keys.append((f'backbone.stages.{i}.{j}.gamma', f'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter')       )
									rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.weight', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight')       )
									rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.bias', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias')       )
									rename_keys.append((f'backbone.stages.{i}.{j}.norm.weight', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight')       )
									rename_keys.append((f'backbone.stages.{i}.{j}.norm.bias', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias')       )
									rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight')       )
									rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias')       )
									rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight')       )
									rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias')       )
						if i > 0:
									rename_keys.append((f'backbone.downsample_layers.{i}.0.weight', f'backbone.encoder.stages.{i}.downsampling_layer.0.weight')       )
									rename_keys.append((f'backbone.downsample_layers.{i}.0.bias', f'backbone.encoder.stages.{i}.downsampling_layer.0.bias')       )
									rename_keys.append((f'backbone.downsample_layers.{i}.1.weight', f'backbone.encoder.stages.{i}.downsampling_layer.1.weight')       )
									rename_keys.append((f'backbone.downsample_layers.{i}.1.bias', f'backbone.encoder.stages.{i}.downsampling_layer.1.bias')       )

						rename_keys.append((f'backbone.norm{i}.weight', f'backbone.hidden_states_norms.stage{i+1}.weight')       )
						rename_keys.append((f'backbone.norm{i}.bias', f'backbone.hidden_states_norms.stage{i+1}.bias')       )

			# decode head
			rename_keys.extend(
			    [
			        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
			        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
			        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
			        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
			    ]       )
			# fmt: on

			return rename_keys



def       a     (					lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__       ):
			'''simple docstring'''





			A_      :		Any      =	dct.pop(_UpperCamelCase       )
			A_      :		int      =	val



def       a     (					lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__       ):
			'''simple docstring'''





			A_      :		List[str]      =	{
			    """upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
			    """upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
			    """upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
			    """upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
			    """upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
			}
			A_      :		List[str]      =	model_name_to_url[model_name]
			A_      :		List[Any]      =	torch.hub.load_state_dict_from_url(_UpperCamelCase		, map_location="""cpu"""       )["""state_dict"""]

			A_      :		List[str]      =	get_upernet_config(_UpperCamelCase       )
			A_      :		List[Any]      =	UperNetForSemanticSegmentation(_UpperCamelCase       )
			model.eval()

			# replace "bn" => "batch_norm"
			for key in state_dict.copy().keys():
						A_      :		str      =	state_dict.pop(_UpperCamelCase       )
						if "bn" in key:
									A_      :		Union[str, Any]      =	key.replace("""bn"""		, """batch_norm"""       )
						A_      :		Dict      =	val

			# rename keys
			A_      :		Tuple      =	create_rename_keys(_UpperCamelCase       )
			for src, dest in rename_keys:
						rename_key(_UpperCamelCase		, _UpperCamelCase		, _UpperCamelCase       )

			model.load_state_dict(_UpperCamelCase       )

			# verify on image
			A_      :		Any      =	"""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
			A_      :		int      =	Image.open(requests.get(_UpperCamelCase		, stream=_UpperCamelCase       ).raw       ).convert("""RGB"""       )

			A_      :		Optional[int]      =	SegformerImageProcessor()
			A_      :		List[Any]      =	processor(_UpperCamelCase		, return_tensors="""pt"""       ).pixel_values

			with torch.no_grad():
						A_      :		Optional[int]      =	model(_UpperCamelCase       )

			if model_name == "upernet-convnext-tiny":
						A_      :		List[Any]      =	torch.tensor(
						    [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]       )
			elif model_name == "upernet-convnext-small":
						A_      :		Any      =	torch.tensor(
						    [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]       )
			elif model_name == "upernet-convnext-base":
						A_      :		int      =	torch.tensor(
						    [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]       )
			elif model_name == "upernet-convnext-large":
						A_      :		List[Any]      =	torch.tensor(
						    [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]       )
			elif model_name == "upernet-convnext-xlarge":
						A_      :		Optional[int]      =	torch.tensor(
						    [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]       )
			print("""Logits:"""		, outputs.logits[0, 0, :3, :3]       )
			assert torch.allclose(outputs.logits[0, 0, :3, :3]		, _UpperCamelCase		, atol=1E-4       )
			print("""Looks ok!"""       )

			if pytorch_dump_folder_path is not None:
						print(f'Saving model {model_name} to {pytorch_dump_folder_path}'       )
						model.save_pretrained(_UpperCamelCase       )
						print(f'Saving processor to {pytorch_dump_folder_path}'       )
						processor.save_pretrained(_UpperCamelCase       )

			if push_to_hub:
						print(f'Pushing model and processor for {model_name} to hub'       )
						model.push_to_hub(f'openmmlab/{model_name}'       )
						processor.push_to_hub(f'openmmlab/{model_name}'       )


if __name__ == "__main__":
						lowerCamelCase				:str							       =   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--model_name''',
						    default='''upernet-convnext-tiny''',
						    type=str,
						    choices=[F"upernet-convnext-{size}" for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
						    help='''Name of the ConvNext UperNet model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
						)
						parser.add_argument(
						    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
						)

						lowerCamelCase				:Optional[int]							       =   parser.parse_args()
						convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


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

import pytest

from .sola import PokerHand

a_     :    int     		=   (
    "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_     :    int     		=   (
    ("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_     :    Tuple     		=   (
    ("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_     :    Tuple     		=   (
    ("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[Any]     		=   (
    ("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_     :    List[str]     		=   (
    ("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_     :    Optional[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 							__lowerCAmelCase     (							)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      randrange(len(_UpperCamelCase						)						), randrange(len(_UpperCamelCase						)						)
 SCREAMING_SNAKE_CASE		    =      ['Loss', 'Tie', 'Win'][(play >= oppo) + (play > oppo)]
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      SORTED_HANDS[play], SORTED_HANDS[oppo]
 return hand, other, expected


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int = 1_00						)		->				int:
 '''simple docstring'''


 return (generate_random_hand() for _ in range(_UpperCamelCase						))


@pytest.mark.parametrize('hand, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	Optional[Any]						)		->				int:
 '''simple docstring'''


 assert PokerHand(_UpperCamelCase						)._is_flush() == expected


@pytest.mark.parametrize('hand, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	List[Any]						)		->				Optional[Any]:
 '''simple docstring'''


 assert PokerHand(_UpperCamelCase						)._is_straight() == expected


@pytest.mark.parametrize('hand, expected, card_values'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	List[Any]						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      PokerHand(_UpperCamelCase						)
 assert player._is_five_high_straight() == expected
 assert player._card_values == card_values


@pytest.mark.parametrize('hand, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	str						)		->				int:
 '''simple docstring'''


 assert PokerHand(_UpperCamelCase						)._is_same_kind() == expected


@pytest.mark.parametrize('hand, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 assert PokerHand(_UpperCamelCase						)._hand_type == expected


@pytest.mark.parametrize('hand, other, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Dict						)		->				Tuple:
 '''simple docstring'''


 assert PokerHand(_UpperCamelCase						).compare_with(PokerHand(_UpperCamelCase						)						) == expected


@pytest.mark.parametrize('hand, other, expected'		,				generate_random_hands()						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Tuple						)		->				List[Any]:
 '''simple docstring'''


 assert PokerHand(_UpperCamelCase						).compare_with(PokerHand(_UpperCamelCase						)						) == expected


def 							__lowerCAmelCase     (							)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      [PokerHand(_UpperCamelCase						) for hand in SORTED_HANDS]
 SCREAMING_SNAKE_CASE		    =      poker_hands.copy()
 shuffle(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      chain(sorted(_UpperCamelCase						)						)
 for index, hand in enumerate(_UpperCamelCase						):
  assert hand == poker_hands[index]


def 							__lowerCAmelCase     (							)		->				Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      [PokerHand('2D AC 3H 4H 5S'						), PokerHand('2S 3H 4H 5S 6C'						)]
 pokerhands.sort(reverse=_UpperCamelCase						)
 assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"


def 							__lowerCAmelCase     (							)		->				List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      PokerHand('2C 4S AS 3D 5C'						)
 SCREAMING_SNAKE_CASE		    =      True
 SCREAMING_SNAKE_CASE		    =      [5, 4, 3, 2, 14]
 for _ in range(10						):
  assert pokerhand._is_five_high_straight() == expected
  assert pokerhand._card_values == expected_card_values




def 							__lowerCAmelCase     (							)		->				Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      0
 SCREAMING_SNAKE_CASE		    =      os.path.abspath(os.path.dirname(_UpperCamelCase						)						)
 SCREAMING_SNAKE_CASE		    =      os.path.join(_UpperCamelCase		,				'poker_hands.txt'						)
 with open(_UpperCamelCase						) 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(_UpperCamelCase						), PokerHand(_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      player.compare_with(_UpperCamelCase						)
   if output == "Win":
    answer += 1
 assert answer == 3_76

