"""simple docstring"""






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					: int        =logging.get_logger(__name__)

__lowerCAmelCase					: Tuple        ="""The Nymphenburg Palace is a beautiful palace in Munich!"""


def     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :str	)				->					int:
      '''simple docstring'''


      lowercase														=			{
          """attention_cell""": """multi_head""",
          """num_layers""": 4,
          """units""": 1_0_2_4,
          """hidden_size""": 7_6_8,
          """max_length""": 5_1_2,
          """num_heads""": 8,
          """scaled""": True,
          """dropout""": 0.1,
          """use_residual""": True,
          """embed_size""": 1_0_2_4,
          """embed_dropout""": 0.1,
          """word_embed""": None,
          """layer_norm_eps""": 1e-5,
          """token_type_vocab_size""": 2,
      }

      lowercase														=			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
      lowercase														=			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=lowerCAmelCase__      ,	output_all_encodings=lowerCAmelCase__      ,	use_residual=predefined_args["""use_residual"""]      ,	activation=predefined_args.get("""activation"""      ,	"""gelu"""	)      ,	layer_norm_eps=predefined_args.get("""layer_norm_eps"""      ,	lowerCAmelCase__	)      ,	)

      # Vocab information needs to be fetched first
      # It's the same as RoBERTa, so RobertaTokenizer can be used later
      lowercase														=			"""openwebtext_ccnews_stories_books_cased"""

      # Specify download folder to Gluonnlp's vocab
      lowercase														=			os.path.join(get_home_dir()      ,	"""models"""	)
      lowercase														=			_load_vocab(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	cls=lowerCAmelCase__	)

      lowercase														=			nlp.model.BERTModel(
          lowerCAmelCase__      ,	len(lowerCAmelCase__	)      ,	units=predefined_args["""units"""]      ,	embed_size=predefined_args["""embed_size"""]      ,	embed_dropout=predefined_args["""embed_dropout"""]      ,	word_embed=predefined_args["""word_embed"""]      ,	use_pooler=lowerCAmelCase__      ,	use_token_type_embed=lowerCAmelCase__      ,	token_type_vocab_size=predefined_args["""token_type_vocab_size"""]      ,	use_classifier=lowerCAmelCase__      ,	use_decoder=lowerCAmelCase__      ,	)

      original_bort.load_parameters(lowerCAmelCase__      ,	cast_dtype=lowerCAmelCase__      ,	ignore_extra=lowerCAmelCase__	)
      lowercase														=			original_bort._collect_params_with_prefix()

      # Build our config 🤗
      lowercase														=			{
          """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(lowerCAmelCase__	),
      }

      lowercase														=			BertConfig.from_dict(lowerCAmelCase__	)
      lowercase														=			BertForMaskedLM(lowerCAmelCase__	)
      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(lowerCAmelCase__  :Optional[Any]	) -> 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(lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :List[Any]	):
            lowercase														=			hf_param.shape

            lowercase														=			to_torch(params[gluon_param]	)
            lowercase														=			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

      lowercase														=			check_and_map_params(
          hf_bort_model.bert.embeddings.word_embeddings.weight      ,	"""word_embed.0.weight"""	)
      lowercase														=			check_and_map_params(
          hf_bort_model.bert.embeddings.position_embeddings.weight      ,	"""encoder.position_weight"""	)
      lowercase														=			check_and_map_params(
          hf_bort_model.bert.embeddings.LayerNorm.bias      ,	"""encoder.layer_norm.beta"""	)
      lowercase														=			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)
      lowercase														=			torch.zeros_like(
          hf_bort_model.bert.embeddings.token_type_embeddings.weight.data	)

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

            # self attention
            lowercase														=			layer.attention.self

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

            lowercase														=			check_and_map_params(
                self_attn.key.weight.data      ,	f'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'	)
            lowercase														=			check_and_map_params(
                self_attn.query.bias.data      ,	f'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'	)
            lowercase														=			check_and_map_params(
                self_attn.query.weight.data      ,	f'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'	)
            lowercase														=			check_and_map_params(
                self_attn.value.bias.data      ,	f'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'	)
            lowercase														=			check_and_map_params(
                self_attn.value.weight.data      ,	f'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'	)

            # self attention output
            lowercase														=			layer.attention.output

            lowercase														=			check_and_map_params(
                self_output.dense.bias      ,	f'encoder.transformer_cells.{i}.proj.bias'	)
            lowercase														=			check_and_map_params(
                self_output.dense.weight      ,	f'encoder.transformer_cells.{i}.proj.weight'	)
            lowercase														=			check_and_map_params(
                self_output.LayerNorm.bias      ,	f'encoder.transformer_cells.{i}.layer_norm.beta'	)
            lowercase														=			check_and_map_params(
                self_output.LayerNorm.weight      ,	f'encoder.transformer_cells.{i}.layer_norm.gamma'	)

            # intermediate
            lowercase														=			layer.intermediate

            lowercase														=			check_and_map_params(
                intermediate.dense.bias      ,	f'encoder.transformer_cells.{i}.ffn.ffn_1.bias'	)
            lowercase														=			check_and_map_params(
                intermediate.dense.weight      ,	f'encoder.transformer_cells.{i}.ffn.ffn_1.weight'	)

            # output
            lowercase														=			layer.output

            lowercase														=			check_and_map_params(
                bert_output.dense.bias      ,	f'encoder.transformer_cells.{i}.ffn.ffn_2.bias'	)
            lowercase														=			check_and_map_params(
                bert_output.dense.weight      ,	f'encoder.transformer_cells.{i}.ffn.ffn_2.weight'	)
            lowercase														=			check_and_map_params(
                bert_output.LayerNorm.bias      ,	f'encoder.transformer_cells.{i}.ffn.layer_norm.beta'	)
            lowercase														=			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
      lowercase														=			RobertaTokenizer.from_pretrained("""roberta-base"""	)

      lowercase														=			tokenizer.encode_plus(lowerCAmelCase__	)["""input_ids"""]

      # Get gluon output
      lowercase														=			mx.nd.array([input_ids]	)
      lowercase														=			original_bort(inputs=lowerCAmelCase__      ,	token_types=[]	)

      # Get Transformer output (save and reload model again)
      hf_bort_model.save_pretrained(lowerCAmelCase__	)
      lowercase														=			BertModel.from_pretrained(lowerCAmelCase__	)
      hf_bort_model.eval()

      lowercase														=			tokenizer.encode_plus(lowerCAmelCase__      ,	return_tensors="""pt"""	)
      lowercase														=			hf_bort_model(**lowerCAmelCase__	)[0]

      lowercase														=			output_gluon[0].asnumpy()
      lowercase														=			output_hf[0].detach().numpy()

      lowercase														=			np.max(np.abs(hf_layer - gluon_layer	)	).item()
      lowercase														=			np.allclose(lowerCAmelCase__      ,	lowerCAmelCase__      ,	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:"""      ,	lowerCAmelCase__	)


if __name__ == "__main__":
       __lowerCAmelCase					: Union[str, Any]        =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					: Optional[int]        =parser.parse_args()
       convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




"""simple docstring"""






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

import pytest

from .sola import PokerHand

__lowerCAmelCase					: Optional[Any]        =(
    """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					: Union[str, Any]        =(
    ("""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					: Optional[int]        =(
    ("""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					: Union[str, Any]        =(
    ("""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					: Optional[int]        =(
    ("""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					: int        =(
    ("""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					: List[str]        =(
    ("""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     UpperCAmelCase__ (       )				->					Optional[int]:
      '''simple docstring'''


      lowercase							,							lowercase														=			randrange(len(lowerCAmelCase__	)	), randrange(len(lowerCAmelCase__	)	)
      lowercase														=			["""Loss""", """Tie""", """Win"""][(play >= oppo) + (play > oppo)]
      lowercase							,							lowercase														=			SORTED_HANDS[play], SORTED_HANDS[oppo]
      return hand, other, expected


def     UpperCAmelCase__ (       lowerCAmelCase__  :int = 1_0_0	)				->					Optional[Any]:
      '''simple docstring'''


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


@pytest.mark.parametrize("""hand, expected"""      ,	lowerCAmelCase__	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :int      ,	lowerCAmelCase__  :Union[str, Any]	)				->					Tuple:
      '''simple docstring'''


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


@pytest.mark.parametrize("""hand, expected"""      ,	lowerCAmelCase__	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Tuple	)				->					int:
      '''simple docstring'''


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


@pytest.mark.parametrize("""hand, expected, card_values"""      ,	lowerCAmelCase__	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :List[str]	)				->					Optional[int]:
      '''simple docstring'''


      lowercase														=			PokerHand(lowerCAmelCase__	)
      assert player._is_five_high_straight() == expected
      assert player._card_values == card_values


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


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


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


      assert PokerHand(lowerCAmelCase__	)._hand_type == expected


@pytest.mark.parametrize("""hand, other, expected"""      ,	lowerCAmelCase__	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Tuple	)				->					Any:
      '''simple docstring'''


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


@pytest.mark.parametrize("""hand, other, expected"""      ,	generate_random_hands()	)
def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :Union[str, Any]	)				->					Optional[Any]:
      '''simple docstring'''


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


def     UpperCAmelCase__ (       )				->					Optional[Any]:
      '''simple docstring'''


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


def     UpperCAmelCase__ (       )				->					Optional[Any]:
      '''simple docstring'''


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


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


      lowercase														=			PokerHand("""2C 4S AS 3D 5C"""	)
      lowercase														=			True
      lowercase														=			[5, 4, 3, 2, 1_4]
      for _ in range(1_0	):
            assert pokerhand._is_five_high_straight() == expected
            assert pokerhand._card_values == expected_card_values






def     UpperCAmelCase__ (       )				->					Any:
      '''simple docstring'''


      lowercase														=			0
      lowercase														=			os.path.abspath(os.path.dirname(lowerCAmelCase__	)	)
      lowercase														=			os.path.join(lowerCAmelCase__      ,	"""poker_hands.txt"""	)
      with open(lowerCAmelCase__	) as file_hand:
            for line in file_hand:
                  lowercase														=			line[:1_4].strip()
                  lowercase														=			line[1_5:].strip()
                  lowercase							,							lowercase														=			PokerHand(lowerCAmelCase__	), PokerHand(lowerCAmelCase__	)
                  lowercase														=			player.compare_with(lowerCAmelCase__	)
                  if output == "Win":
                        answer += 1
      assert answer == 3_7_6



