





import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
	raise Exception('''requires gluonnlp == 0.8.3''')

if version.parse(mx.__version__) != version.parse('''1.5.0'''):
	raise Exception('''requires mxnet == 1.5.0''')

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

lowerCAmelCase_    =				'''The Nymphenburg Palace is a beautiful palace in Munich!'''






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:    str				=  {
				    '''attention_cell''': '''multi_head''',
				    '''num_layers''': 4,
				    '''units''': 1_024,
				    '''hidden_size''': 768,
				    '''max_length''': 512,
				    '''num_heads''': 8,
				    '''scaled''': True,
				    '''dropout''': 0.1,
				    '''use_residual''': True,
				    '''embed_size''': 1_024,
				    '''embed_dropout''': 0.1,
				    '''word_embed''': None,
				    '''layer_norm_eps''': 1E-5,
				    '''token_type_vocab_size''': 2,
				}

				snake_case_						:    Dict				=  bort_4_8_768_1024_hparams

				# Let's construct the original Bort model here
				# Taken from official BERT implementation, see:
				# https://github.com/alexa/bort/blob/master/bort/bort.py
				snake_case_						:    List[Any]				=  BERTEncoder(
				    attention_cell=predefined_args['''attention_cell'''] ,				num_layers=predefined_args['''num_layers'''] ,				units=predefined_args['''units'''] ,				hidden_size=predefined_args['''hidden_size'''] ,				max_length=predefined_args['''max_length'''] ,				num_heads=predefined_args['''num_heads'''] ,				scaled=predefined_args['''scaled'''] ,				dropout=predefined_args['''dropout'''] ,				output_attention=_UpperCamelCase ,				output_all_encodings=_UpperCamelCase ,				use_residual=predefined_args['''use_residual'''] ,				activation=predefined_args.get('''activation''' ,				'''gelu''' ) ,				layer_norm_eps=predefined_args.get('''layer_norm_eps''' ,				_UpperCamelCase ) ,				)

				# Vocab information needs to be fetched first
				# It's the same as RoBERTa, so RobertaTokenizer can be used later
				snake_case_						:    Optional[int]				=  '''openwebtext_ccnews_stories_books_cased'''

				# Specify download folder to Gluonnlp's vocab
				snake_case_						:    Optional[Any]				=  os.path.join(get_home_dir() ,				'''models''' )
				snake_case_						:    Any				=  _load_vocab(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				cls=_UpperCamelCase )

				snake_case_						:    str				=  nlp.model.BERTModel(
				    _UpperCamelCase ,				len(_UpperCamelCase ) ,				units=predefined_args['''units'''] ,				embed_size=predefined_args['''embed_size'''] ,				embed_dropout=predefined_args['''embed_dropout'''] ,				word_embed=predefined_args['''word_embed'''] ,				use_pooler=_UpperCamelCase ,				use_token_type_embed=_UpperCamelCase ,				token_type_vocab_size=predefined_args['''token_type_vocab_size'''] ,				use_classifier=_UpperCamelCase ,				use_decoder=_UpperCamelCase ,				)

				original_bort.load_parameters(_UpperCamelCase ,				cast_dtype=_UpperCamelCase ,				ignore_extra=_UpperCamelCase )
				snake_case_						:    Optional[int]				=  original_bort._collect_params_with_prefix()

				# Build our config 🤗
				snake_case_						:    int				=  {
				    '''architectures''': ['''BertForMaskedLM'''],
				    '''attention_probs_dropout_prob''': predefined_args['''dropout'''],
				    '''hidden_act''': '''gelu''',
				    '''hidden_dropout_prob''': predefined_args['''dropout'''],
				    '''hidden_size''': predefined_args['''embed_size'''],
				    '''initializer_range''': 0.02,
				    '''intermediate_size''': predefined_args['''hidden_size'''],
				    '''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
				    '''max_position_embeddings''': predefined_args['''max_length'''],
				    '''model_type''': '''bort''',
				    '''num_attention_heads''': predefined_args['''num_heads'''],
				    '''num_hidden_layers''': predefined_args['''num_layers'''],
				    '''pad_token_id''': 1,  # 2 = BERT, 1 = RoBERTa
				    '''type_vocab_size''': 1,  # 2 = BERT, 1 = RoBERTa
				    '''vocab_size''': len(_UpperCamelCase ),
				}

				snake_case_						:    Optional[Any]				=  BertConfig.from_dict(_UpperCamelCase )
				snake_case_						:    Union[str, Any]				=  BertForMaskedLM(_UpperCamelCase )
				hf_bort_model.eval()

				# Parameter mapping table (Gluonnlp to Transformers)
				# * denotes layer index
				#
				# | Gluon Parameter                                                | Transformers Parameter
				# | -------------------------------------------------------------- | ----------------------
				# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
				# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
				# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
				# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
				# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
				# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
				# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
				# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
				# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
				# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
				# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
				# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
				# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
				# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
				# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
				# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
				# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
				# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
				# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
				# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

				# Helper function to convert MXNET Arrays to PyTorch
				def to_torch(_UpperCamelCase ) -> nn.Parameter:
								return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )

				# Check param shapes and map new HF param back
				def check_and_map_params(_UpperCamelCase ,				_UpperCamelCase ):
								snake_case_						:    Optional[Any]				=  hf_param.shape

								snake_case_						:    Any				=  to_torch(params[gluon_param] )
								snake_case_						:    Any				=  gluon_param.shape

								assert (
								    shape_hf == shape_gluon
								), f'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''

								return gluon_param

				snake_case_						:    str				=  check_and_map_params(
				    hf_bort_model.bert.embeddings.word_embeddings.weight ,				'''word_embed.0.weight''' )
				snake_case_						:    Any				=  check_and_map_params(
				    hf_bort_model.bert.embeddings.position_embeddings.weight ,				'''encoder.position_weight''' )
				snake_case_						:    List[str]				=  check_and_map_params(
				    hf_bort_model.bert.embeddings.LayerNorm.bias ,				'''encoder.layer_norm.beta''' )
				snake_case_						:    Dict				=  check_and_map_params(
				    hf_bort_model.bert.embeddings.LayerNorm.weight ,				'''encoder.layer_norm.gamma''' )

				# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
				snake_case_						:    Union[str, Any]				=  torch.zeros_like(
				    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )

				for i in range(hf_bort_config.num_hidden_layers ):
								snake_case_						:    BertLayer				=  hf_bort_model.bert.encoder.layer[i]

								# self attention
								snake_case_						:    BertSelfAttention				=  layer.attention.self

								snake_case_						:    Dict				=  check_and_map_params(
								    self_attn.key.bias.data ,				f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias''' )

								snake_case_						:    List[Any]				=  check_and_map_params(
								    self_attn.key.weight.data ,				f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight''' )
								snake_case_						:    Union[str, Any]				=  check_and_map_params(
								    self_attn.query.bias.data ,				f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias''' )
								snake_case_						:    Union[str, Any]				=  check_and_map_params(
								    self_attn.query.weight.data ,				f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight''' )
								snake_case_						:    str				=  check_and_map_params(
								    self_attn.value.bias.data ,				f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias''' )
								snake_case_						:    Optional[Any]				=  check_and_map_params(
								    self_attn.value.weight.data ,				f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight''' )

								# self attention output
								snake_case_						:    BertSelfOutput				=  layer.attention.output

								snake_case_						:    List[str]				=  check_and_map_params(
								    self_output.dense.bias ,				f'''encoder.transformer_cells.{i}.proj.bias''' )
								snake_case_						:    int				=  check_and_map_params(
								    self_output.dense.weight ,				f'''encoder.transformer_cells.{i}.proj.weight''' )
								snake_case_						:    Tuple				=  check_and_map_params(
								    self_output.LayerNorm.bias ,				f'''encoder.transformer_cells.{i}.layer_norm.beta''' )
								snake_case_						:    Any				=  check_and_map_params(
								    self_output.LayerNorm.weight ,				f'''encoder.transformer_cells.{i}.layer_norm.gamma''' )

								# intermediate
								snake_case_						:    BertIntermediate				=  layer.intermediate

								snake_case_						:    List[Any]				=  check_and_map_params(
								    intermediate.dense.bias ,				f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias''' )
								snake_case_						:    List[Any]				=  check_and_map_params(
								    intermediate.dense.weight ,				f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight''' )

								# output
								snake_case_						:    BertOutput				=  layer.output

								snake_case_						:    int				=  check_and_map_params(
								    bert_output.dense.bias ,				f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias''' )
								snake_case_						:    Dict				=  check_and_map_params(
								    bert_output.dense.weight ,				f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight''' )
								snake_case_						:    Dict				=  check_and_map_params(
								    bert_output.LayerNorm.bias ,				f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta''' )
								snake_case_						:    Tuple				=  check_and_map_params(
								    bert_output.LayerNorm.weight ,				f'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma''' )

				# Save space and energy 🎄
				hf_bort_model.half()

				# Compare output of both models
				snake_case_						:    int				=  RobertaTokenizer.from_pretrained('''roberta-base''' )

				snake_case_						:    str				=  tokenizer.encode_plus(_UpperCamelCase )['''input_ids''']

				# Get gluon output
				snake_case_						:    Optional[Any]				=  mx.nd.array([input_ids] )
				snake_case_						:    str				=  original_bort(inputs=_UpperCamelCase ,				token_types=[] )

				# Get Transformer output (save and reload model again)
				hf_bort_model.save_pretrained(_UpperCamelCase )
				snake_case_						:    str				=  BertModel.from_pretrained(_UpperCamelCase )
				hf_bort_model.eval()

				snake_case_						:    Dict				=  tokenizer.encode_plus(_UpperCamelCase ,				return_tensors='''pt''' )
				snake_case_						:    List[Any]				=  hf_bort_model(**_UpperCamelCase )[0]

				snake_case_						:    List[Any]				=  output_gluon[0].asnumpy()
				snake_case_						:    Tuple				=  output_hf[0].detach().numpy()

				snake_case_						:    Tuple				=  np.max(np.abs(hf_layer - gluon_layer ) ).item()
				snake_case_						:    Tuple				=  np.allclose(_UpperCamelCase ,				_UpperCamelCase ,				atol=1E-3 )

				if success:
								print('''✔️ Both model do output the same tensors''' )
				else:
								print('''❌ Both model do **NOT** output the same tensors''' )
								print('''Absolute difference is:''' ,				_UpperCamelCase )


if __name__ == "__main__":
	lowerCAmelCase_    =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	lowerCAmelCase_    =				parser.parse_args()
	convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)







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      lowerCamelCase_						(				)		->						Dict:






				"""simple docstring"""


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






def      lowerCamelCase_						(				_UpperCamelCase = 100 )		->						str:






				"""simple docstring"""


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






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






				"""simple docstring"""


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






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






				"""simple docstring"""


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






@pytest.mark.parametrize('''hand, expected, card_values''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Tuple:






				"""simple docstring"""


				snake_case_						:    str				=  PokerHand(_UpperCamelCase )
				assert player._is_five_high_straight() == expected
				assert player._card_values == card_values






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






				"""simple docstring"""


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






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






				"""simple docstring"""


				assert PokerHand(_UpperCamelCase )._hand_type == expected






@pytest.mark.parametrize('''hand, other, expected''' ,				_UpperCamelCase )
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Any:






				"""simple docstring"""


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






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






				"""simple docstring"""


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






def      lowerCamelCase_						(				)		->						str:






				"""simple docstring"""


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






def      lowerCamelCase_						(				)		->						Dict:






				"""simple docstring"""


				snake_case_						:    Union[str, Any]				=  [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_						(				)		->						str:






				"""simple docstring"""


				snake_case_						:    Dict				=  PokerHand('''2C 4S AS 3D 5C''' )
				snake_case_						:    str				=  True
				snake_case_						:    Tuple				=  [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_						(				)		->						List[str]:






				"""simple docstring"""


				snake_case_						:    List[str]				=  0
				snake_case_						:    Union[str, Any]				=  os.path.abspath(os.path.dirname(_UpperCamelCase ) )
				snake_case_						:    Dict				=  os.path.join(_UpperCamelCase ,				'''poker_hands.txt''' )
				with open(_UpperCamelCase ) as file_hand:
								for line in file_hand:
												snake_case_						:    Dict				=  line[:14].strip()
												snake_case_						:    List[str]				=  line[15:].strip()
												snake_case_			,							snake_case_						:    str				=  PokerHand(_UpperCamelCase ), PokerHand(_UpperCamelCase )
												snake_case_						:    int				=  player.compare_with(_UpperCamelCase )
												if output == "Win":
																answer += 1
				assert answer == 376
