


import argparse
import json
from pathlib import Path

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

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase		=						logging.get_logger(__name__)






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		lowerCAmelCase__  :     List[Any]       	=   ConvNextConfig()

		if "tiny" in checkpoint_url:
				lowerCAmelCase__  :     List[Any]       	=   [3, 3, 9, 3]
				lowerCAmelCase__  :     str       	=   [96, 192, 384, 768]
		if "small" in checkpoint_url:
				lowerCAmelCase__  :     List[Any]       	=   [3, 3, 27, 3]
				lowerCAmelCase__  :     Any       	=   [96, 192, 384, 768]
		if "base" in checkpoint_url:
				lowerCAmelCase__  :     Any       	=   [3, 3, 27, 3]
				lowerCAmelCase__  :     str       	=   [128, 256, 512, 1_024]
		if "large" in checkpoint_url:
				lowerCAmelCase__  :     int       	=   [3, 3, 27, 3]
				lowerCAmelCase__  :     int       	=   [192, 384, 768, 1_536]
		if "xlarge" in checkpoint_url:
				lowerCAmelCase__  :     Union[str, Any]       	=   [3, 3, 27, 3]
				lowerCAmelCase__  :     List[Any]       	=   [256, 512, 1_024, 2_048]

		if "1k" in checkpoint_url:
				lowerCAmelCase__  :     int       	=   1_000
				lowerCAmelCase__  :     Tuple       	=   '''imagenet-1k-id2label.json'''
				lowerCAmelCase__  :     int       	=   (1, 1_000)
		else:
				lowerCAmelCase__  :     List[Any]       	=   21_841
				lowerCAmelCase__  :     Union[str, Any]       	=   '''imagenet-22k-id2label.json'''
				lowerCAmelCase__  :     Tuple       	=   (1, 21_841)

		lowerCAmelCase__  :     Tuple       	=   '''huggingface/label-files'''
		lowerCAmelCase__  :     List[str]       	=   num_labels
		lowerCAmelCase__  :     Optional[Any]       	=   json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  repo_type='''dataset'''			)      ,  '''r'''			)			)
		lowerCAmelCase__  :     List[str]       	=   {int(__SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
		if "1k" not in checkpoint_url:
				# this dataset contains 21843 labels but the model only has 21841
				# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
				del idalabel[9_205]
				del idalabel[15_027]
		lowerCAmelCase__  :     str       	=   idalabel
		lowerCAmelCase__  :     Dict       	=   {v: k for k, v in idalabel.items()}
		lowerCAmelCase__  :     Optional[int]       	=   hidden_sizes
		lowerCAmelCase__  :     str       	=   depths

		return config, expected_shape






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		if "downsample_layers.0.0" in name:
				lowerCAmelCase__  :     int       	=   name.replace('''downsample_layers.0.0'''      ,  '''embeddings.patch_embeddings'''			)
		if "downsample_layers.0.1" in name:
				lowerCAmelCase__  :     Any       	=   name.replace('''downsample_layers.0.1'''      ,  '''embeddings.norm'''			)  # we rename to layernorm later on
		if "downsample_layers.1.0" in name:
				lowerCAmelCase__  :     List[str]       	=   name.replace('''downsample_layers.1.0'''      ,  '''stages.1.downsampling_layer.0'''			)
		if "downsample_layers.1.1" in name:
				lowerCAmelCase__  :     Optional[int]       	=   name.replace('''downsample_layers.1.1'''      ,  '''stages.1.downsampling_layer.1'''			)
		if "downsample_layers.2.0" in name:
				lowerCAmelCase__  :     int       	=   name.replace('''downsample_layers.2.0'''      ,  '''stages.2.downsampling_layer.0'''			)
		if "downsample_layers.2.1" in name:
				lowerCAmelCase__  :     List[str]       	=   name.replace('''downsample_layers.2.1'''      ,  '''stages.2.downsampling_layer.1'''			)
		if "downsample_layers.3.0" in name:
				lowerCAmelCase__  :     Dict       	=   name.replace('''downsample_layers.3.0'''      ,  '''stages.3.downsampling_layer.0'''			)
		if "downsample_layers.3.1" in name:
				lowerCAmelCase__  :     Union[str, Any]       	=   name.replace('''downsample_layers.3.1'''      ,  '''stages.3.downsampling_layer.1'''			)
		if "stages" in name and "downsampling_layer" not in name:
				# stages.0.0. for instance should be renamed to stages.0.layers.0.
				lowerCAmelCase__  :     Union[str, Any]       	=   name[: len('''stages.0'''			)] + '''.layers''' + name[len('''stages.0'''			) :]
		if "stages" in name:
				lowerCAmelCase__  :     str       	=   name.replace('''stages'''      ,  '''encoder.stages'''			)
		if "norm" in name:
				lowerCAmelCase__  :     List[Any]       	=   name.replace('''norm'''      ,  '''layernorm'''			)
		if "gamma" in name:
				lowerCAmelCase__  :     Optional[Any]       	=   name.replace('''gamma'''      ,  '''layer_scale_parameter'''			)
		if "head" in name:
				lowerCAmelCase__  :     Union[str, Any]       	=   name.replace('''head'''      ,  '''classifier'''			)

		return name






def 	lowerCamelCase_       (			):




		"""simple docstring"""






		lowerCAmelCase__  :     int       	=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase__  :     List[str]       	=   Image.open(requests.get(__SCREAMING_SNAKE_CASE      ,  stream=__SCREAMING_SNAKE_CASE			).raw			)
		return im






@torch.no_grad()
def 	lowerCamelCase_       (			_a      ,  _a			):




		"""simple docstring"""






		lowerCAmelCase__	,      lowerCAmelCase__  :     Tuple       	=   get_convnext_config(__SCREAMING_SNAKE_CASE			)
		# load original state_dict from URL
		lowerCAmelCase__  :     Any       	=   torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE			)['''model''']
		# rename keys
		for key in state_dict.copy().keys():
				lowerCAmelCase__  :     Optional[Any]       	=   state_dict.pop(__SCREAMING_SNAKE_CASE			)
				lowerCAmelCase__  :     List[str]       	=   val
		# add prefix to all keys expect classifier head
		for key in state_dict.copy().keys():
				lowerCAmelCase__  :     Tuple       	=   state_dict.pop(__SCREAMING_SNAKE_CASE			)
				if not key.startswith('''classifier'''			):
						lowerCAmelCase__  :     List[str]       	=   '''convnext.''' + key
				lowerCAmelCase__  :     List[str]       	=   val

		# load HuggingFace model
		lowerCAmelCase__  :     Dict       	=   ConvNextForImageClassification(__SCREAMING_SNAKE_CASE			)
		model.load_state_dict(__SCREAMING_SNAKE_CASE			)
		model.eval()

		# Check outputs on an image, prepared by ConvNextImageProcessor
		lowerCAmelCase__  :     Union[str, Any]       	=   224 if '''224''' in checkpoint_url else 384
		lowerCAmelCase__  :     List[str]       	=   ConvNextImageProcessor(size=__SCREAMING_SNAKE_CASE			)
		lowerCAmelCase__  :     Tuple       	=   image_processor(images=prepare_img()      ,  return_tensors='''pt'''			).pixel_values

		lowerCAmelCase__  :     Dict       	=   model(__SCREAMING_SNAKE_CASE			).logits

		# note: the logits below were obtained without center cropping
		if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
				lowerCAmelCase__  :     Union[str, Any]       	=   torch.tensor([-0.12_10, -0.66_05, 0.19_18]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
				lowerCAmelCase__  :     Tuple       	=   torch.tensor([-0.44_73, -0.18_47, -0.63_65]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
				lowerCAmelCase__  :     Dict       	=   torch.tensor([0.45_25, 0.75_39, 0.03_08]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
				lowerCAmelCase__  :     Optional[Any]       	=   torch.tensor([0.35_61, 0.63_50, -0.03_84]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
				lowerCAmelCase__  :     List[Any]       	=   torch.tensor([0.41_74, -0.09_89, 0.14_89]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
				lowerCAmelCase__  :     Optional[Any]       	=   torch.tensor([0.25_13, -0.13_49, -0.16_13]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
				lowerCAmelCase__  :     Tuple       	=   torch.tensor([1.29_80, 0.36_31, -0.11_98]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
				lowerCAmelCase__  :     Union[str, Any]       	=   torch.tensor([1.29_63, 0.12_27, 0.17_23]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
				lowerCAmelCase__  :     Any       	=   torch.tensor([1.79_56, 0.83_90, 0.28_20]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
				lowerCAmelCase__  :     List[str]       	=   torch.tensor([-0.28_22, -0.05_02, -0.08_78]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
				lowerCAmelCase__  :     Optional[Any]       	=   torch.tensor([-0.56_72, -0.07_30, -0.43_48]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
				lowerCAmelCase__  :     Union[str, Any]       	=   torch.tensor([0.26_81, 0.23_65, 0.62_46]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
				lowerCAmelCase__  :     Optional[Any]       	=   torch.tensor([-0.26_42, 0.39_31, 0.51_16]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
				lowerCAmelCase__  :     List[str]       	=   torch.tensor([-0.66_77, -0.18_73, -0.83_79]			)
		elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
				lowerCAmelCase__  :     List[str]       	=   torch.tensor([-0.77_49, -0.29_67, -0.64_44]			)
		else:
				raise ValueError(f'Unknown URL: {checkpoint_url}'			)

		assert torch.allclose(logits[0, :3]      ,  __SCREAMING_SNAKE_CASE      ,  atol=1e-3			)
		assert logits.shape == expected_shape

		Path(__SCREAMING_SNAKE_CASE			).mkdir(exist_ok=__SCREAMING_SNAKE_CASE			)
		print(f'Saving model to {pytorch_dump_folder_path}'			)
		model.save_pretrained(__SCREAMING_SNAKE_CASE			)
		print(f'Saving image processor to {pytorch_dump_folder_path}'			)
		image_processor.save_pretrained(__SCREAMING_SNAKE_CASE			)

		print('''Pushing model to the hub...'''			)
		lowerCAmelCase__  :     List[Any]       	=   '''convnext'''
		if "tiny" in checkpoint_url:
				model_name += "-tiny"
		elif "small" in checkpoint_url:
				model_name += "-small"
		elif "base" in checkpoint_url:
				model_name += "-base"
		elif "xlarge" in checkpoint_url:
				model_name += "-xlarge"
		elif "large" in checkpoint_url:
				model_name += "-large"
		if "224" in checkpoint_url:
				model_name += "-224"
		elif "384" in checkpoint_url:
				model_name += "-384"
		if "22k" in checkpoint_url and "1k" not in checkpoint_url:
				model_name += "-22k"
		if "22k" in checkpoint_url and "1k" in checkpoint_url:
				model_name += "-22k-1k"

		model.push_to_hub(
		    repo_path_or_name=Path(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)      ,  organization='''nielsr'''      ,  commit_message='''Add model'''      ,  )


if __name__ == "__main__":
		lowerCamelCase		=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth''',
		    type=str,
		    help='''URL of the original ConvNeXT checkpoint you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to the output PyTorch model directory.''',
		)

		lowerCamelCase		=						parser.parse_args()
		convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






from __future__ import annotations

import os





class 						__lowercase:


       """simple docstring"""







       UpperCamelCase_				 =							(
           '''High card''',
           '''One pair''',
           '''Two pairs''',
           '''Three of a kind''',
           '''Straight''',
           '''Flush''',
           '''Full house''',
           '''Four of a kind''',
           '''Straight flush''',
           '''Royal flush''',
       )

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



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



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   List[Any]:
         return self._hand



       def SCREAMING_SNAKE_CASE_				(     self					:			int  ,      _lowerCAmelCase					:			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(_lowerCAmelCase       )
           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					:			str       )					->   str:
         _lowerCAmelCase						=		PokerHand._HAND_NAME[self._hand_type - 14]
         _lowerCAmelCase						=		PokerHand._CARD_NAME[self._high_card]
         _lowerCAmelCase						=		PokerHand._CARD_NAME[self._first_pair]
         _lowerCAmelCase						=		PokerHand._CARD_NAME[self._second_pair]
         if self._hand_type in [22, 19, 18]:
           return name + F''', {high}-high'''
         elif self._hand_type in [21, 17, 15]:
           return name + F''', {paira}s'''
         elif self._hand_type in [20, 16]:
           _lowerCAmelCase						=		'over' if self._hand_type == 20 else 'and'
           return name + F''', {paira}s {join} {paira}s'''
         elif self._hand_type == 23:
           return name
         else:
           return name + F''', {high}'''



       def SCREAMING_SNAKE_CASE_				(     self					:			Any  ,      _lowerCAmelCase					:			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					:			Optional[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 23 if sum(self._card_values       ) == 60 else 22
           return 19
         elif self._is_five_high_straight() or self._is_straight():
           return 18
         return 14 + self._is_same_kind()



       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   bool:
         return len(self._card_suit       ) == 1



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   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, 14]:
           if self._card_values[0] == 14:
             # Remember, our list is sorted in reverse order
             _lowerCAmelCase						=		self._card_values.pop(0       )
             self._card_values.append(_lowerCAmelCase       )
           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					:			Dict       )					->   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
         _lowerCAmelCase						=		_lowerCAmelCase						=		_lowerCAmelCase						=		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:
               _lowerCAmelCase						=		self._card_values[i]
               kind += 1
             elif vala == self._card_values[i]:
               kind += 2
             elif not vala:
               _lowerCAmelCase						=		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)
         _lowerCAmelCase						=		kind + 2 if kind in [4, 5] else kind
         # first meaning first pair to compare in 'compare_with'
         _lowerCAmelCase						=		max(_lowerCAmelCase  ,      _lowerCAmelCase       )
         _lowerCAmelCase						=		min(_lowerCAmelCase  ,      _lowerCAmelCase       )
         # 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(_lowerCAmelCase       ) != 3:
           _lowerCAmelCase	,       _lowerCAmelCase						=		second, first
         _lowerCAmelCase						=		first
         _lowerCAmelCase						=		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
         _lowerCAmelCase						=		{"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
         _lowerCAmelCase						=		self._hand.translate(str.maketrans(_lowerCAmelCase       )       ).split()
         _lowerCAmelCase						=		[int(card[:-1]       ) for card in new_hand]
         _lowerCAmelCase						=		{card[-1] for card in new_hand}
         return sorted(_lowerCAmelCase  ,      reverse=_lowerCAmelCase       ), card_suit



       def __repr__(     self					:			Tuple       )					->   Optional[Any]:
         return F'''{self.__class__}("{self._hand}")'''



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



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



       def __lt__(     self					:			List[Any]  ,      _lowerCAmelCase					:			int       )					->   List[str]:
         if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           return self.compare_with(_lowerCAmelCase       ) == "Loss"
         return NotImplemented



       def __le__(     self					:			str  ,      _lowerCAmelCase					:			Dict       )					->   str:
         if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           return self < other or self == other
         return NotImplemented



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



       def __ge__(     self					:			Dict  ,      _lowerCAmelCase					:			Dict       )					->   Optional[Any]:
         if isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           return not self < other
         return NotImplemented






       def __hash__(     self					:			Optional[int]       )					->   List[Any]:
         return object.__hash__(self       )



def 	_a  (					):






  """simple docstring"""




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


if __name__ == "__main__":
       solution()
