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

import pytest

from .sola import PokerHand

__lowerCAmelCase					   =       (
    '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)
)

__lowerCAmelCase					   =       (
    ('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'),
)

__lowerCAmelCase					   =       (
    ('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),
)

__lowerCAmelCase					   =       (
    ('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),
)

__lowerCAmelCase					   =       (
    ('2H 4D 3C AS 5S', True, [5, 4, 3, 2, 1_4]),
    ('2H 5D 3C AS 5S', False, [1_4, 5, 5, 3, 2]),
    ('JH QD KC AS TS', False, [1_4, 1_3, 1_2, 1_1, 1_0]),
    ('9D 3S 2C 7S 7C', False, [9, 7, 7, 3, 2]),
)

__lowerCAmelCase					   =       (
    ('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),
)

__lowerCAmelCase					   =       (
    ('JH AH TH KH QH', 2_3),
    ('JH 9H TH KH QH', 2_2),
    ('JC KH JS JD JH', 2_1),
    ('KH KC 3S 3H 3D', 2_0),
    ('8C 9C 5C 3C TC', 1_9),
    ('JS QS 9H TS KH', 1_8),
    ('7C 7S KH 2H 7H', 1_7),
    ('3C KH 5D 5S KH', 1_6),
    ('QH 8H KD JH 8S', 1_5),
    ('2D 6D 9D TH 7D', 1_4),
)


def    a      ( )     ->Optional[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= randrange(len(a	)	), randrange(len(a	)	)
			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    a      ( a = 100	)     ->List[Any]:




			'''simple docstring'''


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


@pytest.mark.parametrize('''hand, expected'''		,						a	)
def    a      ( a		,						a	)     ->Optional[int]:




			'''simple docstring'''


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


@pytest.mark.parametrize('''hand, expected'''		,						a	)
def    a      ( a		,						a	)     ->List[str]:




			'''simple docstring'''


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


@pytest.mark.parametrize('''hand, expected, card_values'''		,						a	)
def    a      ( a		,						a		,						a	)     ->int:




			'''simple docstring'''


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


@pytest.mark.parametrize('''hand, expected'''		,						a	)
def    a      ( a		,						a	)     ->str:




			'''simple docstring'''


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


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




			'''simple docstring'''


			assert PokerHand(a	)._hand_type == expected


@pytest.mark.parametrize('''hand, other, expected'''		,						a	)
def    a      ( a		,						a		,						a	)     ->Optional[Any]:




			'''simple docstring'''


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


@pytest.mark.parametrize('''hand, other, expected'''		,						generate_random_hands()	)
def    a      ( a		,						a		,						a	)     ->List[Any]:




			'''simple docstring'''


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


def    a      ( )     ->Any:




			'''simple docstring'''


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


def    a      ( )     ->Optional[int]:




			'''simple docstring'''


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


def    a      ( )     ->Optional[Any]:




			'''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    a      ( )     ->str:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= 0
			SCREAMING_SNAKE_CASE     						= os.path.abspath(os.path.dirname(a	)	)
			SCREAMING_SNAKE_CASE     						= os.path.join(a		,						'''poker_hands.txt'''	)
			with open(a	) 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(a	), PokerHand(a	)
									SCREAMING_SNAKE_CASE     						= player.compare_with(a	)
									if output == "Win":
												answer += 1
			assert answer == 376

import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def    a      ( a		,						a	)     ->Dict:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= old_name

			if "patch_embed" in old_name:
						SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= old_name.split('''.'''	)

						if layer == "0":
									SCREAMING_SNAKE_CASE     						= old_name.replace('''0'''		,						'''convolution1'''	)
						elif layer == "1":
									SCREAMING_SNAKE_CASE     						= old_name.replace('''1'''		,						'''batchnorm_before'''	)
						elif layer == "3":
									SCREAMING_SNAKE_CASE     						= old_name.replace('''3'''		,						'''convolution2'''	)
						else:
									SCREAMING_SNAKE_CASE     						= old_name.replace('''4'''		,						'''batchnorm_after'''	)

			if "network" in old_name and re.search(r'''\d\.\d'''		,						a	):
						SCREAMING_SNAKE_CASE     						= r'''\b\d{2}\b'''
						if bool(re.search(a		,						a	)	):
									SCREAMING_SNAKE_CASE     						= re.search(r'''\d\.\d\d.'''		,						a	).group()
						else:
									SCREAMING_SNAKE_CASE     						= re.search(r'''\d\.\d.'''		,						a	).group()
						if int(match[0]	) < 6:
									SCREAMING_SNAKE_CASE     						= old_name.replace(a		,						''''''	)
									SCREAMING_SNAKE_CASE     						= trimmed_name.replace('''network'''		,						match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]	)
									SCREAMING_SNAKE_CASE     						= '''intermediate_stages.''' + trimmed_name
						else:
									SCREAMING_SNAKE_CASE     						= old_name.replace(a		,						''''''	)
									if int(match[2]	) < num_meta4D_last_stage:
												SCREAMING_SNAKE_CASE     						= trimmed_name.replace('''network'''		,						'''meta4D_layers.blocks.''' + match[2]	)
									else:
												SCREAMING_SNAKE_CASE     						= str(int(match[2]	) - num_meta4D_last_stage	)
												SCREAMING_SNAKE_CASE     						= trimmed_name.replace('''network'''		,						'''meta3D_layers.blocks.''' + layer_index	)
												if "norm1" in old_name:
															SCREAMING_SNAKE_CASE     						= trimmed_name.replace('''norm1'''		,						'''layernorm1'''	)
												elif "norm2" in old_name:
															SCREAMING_SNAKE_CASE     						= trimmed_name.replace('''norm2'''		,						'''layernorm2'''	)
												elif "fc1" in old_name:
															SCREAMING_SNAKE_CASE     						= trimmed_name.replace('''fc1'''		,						'''linear_in'''	)
												elif "fc2" in old_name:
															SCREAMING_SNAKE_CASE     						= trimmed_name.replace('''fc2'''		,						'''linear_out'''	)

									SCREAMING_SNAKE_CASE     						= '''last_stage.''' + trimmed_name

			elif "network" in old_name and re.search(r'''.\d.'''		,						a	):
						SCREAMING_SNAKE_CASE     						= old_name.replace('''network'''		,						'''intermediate_stages'''	)

			if "fc" in new_name:
						SCREAMING_SNAKE_CASE     						= new_name.replace('''fc'''		,						'''convolution'''	)
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						SCREAMING_SNAKE_CASE     						= new_name.replace('''norm1'''		,						'''batchnorm_before'''	)
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						SCREAMING_SNAKE_CASE     						= new_name.replace('''norm2'''		,						'''batchnorm_after'''	)
			if "proj" in new_name:
						SCREAMING_SNAKE_CASE     						= new_name.replace('''proj'''		,						'''projection'''	)
			if "dist_head" in new_name:
						SCREAMING_SNAKE_CASE     						= new_name.replace('''dist_head'''		,						'''distillation_classifier'''	)
			elif "head" in new_name:
						SCREAMING_SNAKE_CASE     						= new_name.replace('''head'''		,						'''classifier'''	)
			elif "patch_embed" in new_name:
						SCREAMING_SNAKE_CASE     						= '''efficientformer.''' + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						SCREAMING_SNAKE_CASE     						= new_name.replace('''norm'''		,						'''layernorm'''	)
						SCREAMING_SNAKE_CASE     						= '''efficientformer.''' + new_name
			else:
						SCREAMING_SNAKE_CASE     						= '''efficientformer.encoder.''' + new_name

			return new_name


def    a      ( a		,						a	)     ->Any:




			'''simple docstring'''


			for key in checkpoint.copy().keys():
						SCREAMING_SNAKE_CASE     						= checkpoint.pop(a	)
						SCREAMING_SNAKE_CASE     						= val

			return checkpoint


def    a      ( )     ->List[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			SCREAMING_SNAKE_CASE     						= Image.open(requests.get(a		,						stream=a	).raw	)

			return image


def    a      ( a		,						a		,						a		,						a	)     ->Tuple:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= torch.load(a		,						map_location='''cpu'''	)['''model''']
			SCREAMING_SNAKE_CASE     						= EfficientFormerConfig.from_json_file(a	)
			SCREAMING_SNAKE_CASE     						= EfficientFormerForImageClassificationWithTeacher(a	)
			SCREAMING_SNAKE_CASE     						= '''_'''.join(checkpoint_path.split('''/'''	)[-1].split('''.'''	)[0].split('''_'''	)[:-1]	)

			SCREAMING_SNAKE_CASE     						= config.depths[-1] - config.num_metaad_blocks + 1
			SCREAMING_SNAKE_CASE     						= convert_torch_checkpoint(a		,						a	)

			model.load_state_dict(a	)
			model.eval()

			SCREAMING_SNAKE_CASE     						= {
			    '''bilinear''': PILImageResampling.BILINEAR,
			    '''bicubic''': PILImageResampling.BICUBIC,
			    '''nearest''': PILImageResampling.NEAREST,
			}

			# prepare image
			SCREAMING_SNAKE_CASE     						= prepare_img()
			SCREAMING_SNAKE_CASE     						= 256
			SCREAMING_SNAKE_CASE     						= 224
			SCREAMING_SNAKE_CASE     						= EfficientFormerImageProcessor(
			    size={'''shortest_edge''': image_size}		,						crop_size={'''height''': crop_size, '''width''': crop_size}		,						resample=pillow_resamplings['''bicubic''']		,						)
			SCREAMING_SNAKE_CASE     						= processor(images=a		,						return_tensors='''pt'''	).pixel_values

			# original processing pipeline
			SCREAMING_SNAKE_CASE     						= Compose(
			    [
			        Resize(a		,						interpolation=pillow_resamplings['''bicubic''']	),
			        CenterCrop(a	),
			        ToTensor(),
			        Normalize(a		,						a	),
			    ]	)
			SCREAMING_SNAKE_CASE     						= image_transforms(a	).unsqueeze(0	)

			assert torch.allclose(a		,						a	)

			SCREAMING_SNAKE_CASE     						= model(a	)
			SCREAMING_SNAKE_CASE     						= outputs.logits

			SCREAMING_SNAKE_CASE     						= (1, 1000)

			if "l1" in model_name:
						SCREAMING_SNAKE_CASE     						= torch.Tensor(
						    [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]	)
						assert torch.allclose(logits[0, :10]		,						a		,						atol=1E-3	)
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						SCREAMING_SNAKE_CASE     						= torch.Tensor(
						    [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]	)
						assert torch.allclose(logits[0, :10]		,						a		,						atol=1E-3	)
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						SCREAMING_SNAKE_CASE     						= torch.Tensor(
						    [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]	)
						assert logits.shape == expected_shape
			else:
						raise ValueError(
						    F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""	)

			# Save Checkpoints
			Path(a	).mkdir(exist_ok=a	)
			model.save_pretrained(a	)
			print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)
			processor.save_pretrained(a	)
			print(F"""Processor successfuly saved at {pytorch_dump_path}"""	)

			if push_to_hub:
						print('''Pushing model to the hub...'''	)

						model.push_to_hub(
						    repo_id=F"""Bearnardd/{pytorch_dump_path}"""		,						commit_message='''Add model'''		,						use_temp_dir=a		,						)
						processor.push_to_hub(
						    repo_id=F"""Bearnardd/{pytorch_dump_path}"""		,						commit_message='''Add image processor'''		,						use_temp_dir=a		,						)


if __name__ == "__main__":
							__lowerCAmelCase					   =       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--pytorch_model_path',
							    default=None,
							    type=str,
							    required=True,
							    help='Path to EfficientFormer pytorch checkpoint.',
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The json file for EfficientFormer model config.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)

							parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
							parser.add_argument(
							    '--no-push_to_hub',
							    dest='push_to_hub',
							    action='store_false',
							    help='Do not push model and image processor to the hub',
							)
							parser.set_defaults(push_to_hub=True)

							__lowerCAmelCase					   =       parser.parse_args()
							convert_efficientformer_checkpoint(
							    checkpoint_path=args.pytorch_model_path,
							    efficientformer_config_file=args.config_file,
							    pytorch_dump_path=args.pytorch_dump_path,
							    push_to_hub=args.push_to_hub,
							)