
"""simple docstring"""


from __future__ import annotations

import os







class 						lowerCamelCase__   :





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

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



			def __init__(				self	,	__UpperCAmelCase    )					->	None:
				if not isinstance(__UpperCAmelCase	,	__UpperCAmelCase    ):
					_lowerCAmelCase								=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:
					_lowerCAmelCase								=f'''Hand should contain only 5 cards: {hand!r}'''
					raise ValueError(__UpperCAmelCase    )
				_lowerCAmelCase								=hand
				_lowerCAmelCase								=0
				_lowerCAmelCase								=0
				_lowerCAmelCase		,				_lowerCAmelCase								=self._internal_state()
				_lowerCAmelCase								=self._get_hand_type()
				_lowerCAmelCase								=self._card_values[0]



			@property
			def    _lowerCAmelCase						(				self    )					->	Any:
				return self._hand



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase    )					->	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    _lowerCAmelCase						(				self    )					->	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    _lowerCAmelCase						(				self	,	__UpperCAmelCase    )					->	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    _lowerCAmelCase						(				self    )					->	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    _lowerCAmelCase						(				self    )					->	bool:
				return len(self._card_suit    ) == 1



			def    _lowerCAmelCase						(				self    )					->	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(__UpperCAmelCase    )
					return True
				return False



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



			def    _lowerCAmelCase						(				self    )					->	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(__UpperCAmelCase	,	__UpperCAmelCase    )
				_lowerCAmelCase								=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:
					_lowerCAmelCase		,				_lowerCAmelCase								=second, first
				_lowerCAmelCase								=first
				_lowerCAmelCase								=second
				return kind



			def    _lowerCAmelCase						(				self    )					->	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(__UpperCAmelCase    )    ).split()
				_lowerCAmelCase								=[int(card[:-1]    ) for card in new_hand]
				_lowerCAmelCase								={card[-1] for card in new_hand}
				return sorted(__UpperCAmelCase	,	reverse=__UpperCAmelCase    ), card_suit



			def __repr__(				self    )					->	List[Any]:
				return f'''{self.__class__}("{self._hand}")'''



			def __str__(				self    )					->	int:
				return self._hand



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



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



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



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



			def __ge__(				self	,	__UpperCAmelCase    )					->	Dict:
				if isinstance(__UpperCAmelCase	,	__UpperCAmelCase    ):
					return not self < other
				return NotImplemented



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







def        _lowerCamelCase()							->					int:
	# Solution for problem number 54 from Project Euler
	# Input from poker_hands.txt file
	_lowerCAmelCase								=0
	_lowerCAmelCase								=os.path.abspath(os.path.dirname(__UpperCamelCase			)			)
	_lowerCAmelCase								=os.path.join(__UpperCamelCase       ,					"""poker_hands.txt"""			)
	with open(__UpperCamelCase			) as file_hand:
		for line in file_hand:
			_lowerCAmelCase								=line[:14].strip()
			_lowerCAmelCase								=line[15:].strip()
			_lowerCAmelCase		,				_lowerCAmelCase								=PokerHand(__UpperCamelCase			), PokerHand(__UpperCamelCase			)
			_lowerCAmelCase								=player.compare_with(__UpperCamelCase			)
			if output == "Win":
				answer += 1
	return answer


if __name__ == "__main__":
			solution()





"""simple docstring"""


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
			raise Exception('requires fairseq >= 0.9.0')


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

__A					=			'Hello world! cécé herlolip'



def        _lowerCamelCase(__UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase			)							->					List[Any]:
	_lowerCAmelCase		,				_lowerCAmelCase								=os.path.split(__UpperCamelCase			)
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase       ,					checkpoint_file=__UpperCamelCase			)
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=DataaVecTextConfig(
	    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings       ,					hidden_size=dataavec_model.args.encoder_embed_dim       ,					num_hidden_layers=dataavec_model.args.encoder_layers       ,					num_attention_heads=dataavec_model.args.encoder_attention_heads       ,					intermediate_size=dataavec_model.args.encoder_ffn_embed_dim       ,					max_position_embeddings=514       ,					type_vocab_size=1       ,					layer_norm_eps=1E-5       ,					)
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""       ,					__UpperCamelCase			)

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase			) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase			)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	_lowerCAmelCase								=dataavec_sent_encoder.embed_tokens.weight
	_lowerCAmelCase								=dataavec_sent_encoder.embed_positions.weight
	_lowerCAmelCase								=torch.zeros_like(
	    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.weight
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers			):
		# Encoder: start of layer
		_lowerCAmelCase								=model.dataavec_text.encoder.layer[i]
		_lowerCAmelCase								=dataavec_sent_encoder.layers[i]

		# self attention
		_lowerCAmelCase								=layer.attention.self
		assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)			), (
		    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
		)
		assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)			), (
		    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
		)
		assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)			), (
		    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
		)

		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		_lowerCAmelCase								=layer.attention.output
		assert (
		    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
		), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=layer.intermediate
		assert (
		    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=layer.output
		assert (
		    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.bias
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.bias
	else:
		# LM Head
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.bias

	# Let's check that we get the same results.
	_lowerCAmelCase								=dataavec.encode(__UpperCamelCase			).unsqueeze(0			)  # batch of size 1

	_lowerCAmelCase								=model(__UpperCamelCase			)[0]
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__UpperCamelCase			)			)
	else:
		_lowerCAmelCase								=dataavec_model(__UpperCamelCase			)[0]
	print(our_output.shape       ,					their_output.shape			)
	_lowerCAmelCase								=torch.max(torch.abs(our_output - their_output			)			).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
	_lowerCAmelCase								=torch.allclose(__UpperCamelCase       ,					__UpperCamelCase       ,					atol=1E-3			)
	print("""Do both models output the same tensors?"""       ,					"""🔥""" if success else """💩"""			)
	if not success:
		raise Exception("""Something went wRoNg"""			)

	pathlib.Path(__UpperCamelCase			).mkdir(parents=__UpperCamelCase       ,					exist_ok=__UpperCamelCase			)
	print(F'''Saving model to {pytorch_dump_folder_path}'''			)
	model.save_pretrained(__UpperCamelCase			)


if __name__ == "__main__":
			__A					=			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
			)
			__A					=			parser.parse_args()
			convert_dataavec_checkpoint_to_pytorch(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



