

import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_a		       =   random.Random()







def _a (       SCREAMING_SNAKE_CASE		:			int    ,					SCREAMING_SNAKE_CASE		:			Any=1.0    ,					SCREAMING_SNAKE_CASE		:			Optional[int]=None    ,					SCREAMING_SNAKE_CASE		:			List[Any]=None		)    ->    Union[str, Any]:

 """simple docstring"""






 if rng is None:
  __lowerCAmelCase:   List[Any]   	=      global_rng

 __lowerCAmelCase:   List[str]   	=      []
 for batch_idx in range(shape[0]		):
  values.append([]		)
  for _ in range(shape[1]		):
   values[-1].append(rng.random() * scale		)

 return values






class 							A_ (		unittest.TestCase					):


     def __init__(							self			:		Any			,  UpperCAmelCase			:		Any			,  UpperCAmelCase			:		Optional[Any]=7			,  UpperCAmelCase			:		List[str]=4_0_0			,  UpperCAmelCase			:		str=2_0_0_0			,  UpperCAmelCase			:		Any=1			,  UpperCAmelCase			:		Optional[Any]=0.0			,  UpperCAmelCase			:		Tuple=1_6_0_0_0			,  UpperCAmelCase			:		List[str]=True			,  UpperCAmelCase			:		Dict=True			,  )							->	str:
      __lowerCAmelCase:   Optional[Any]   	=      parent
      __lowerCAmelCase:   Dict   	=      batch_size
      __lowerCAmelCase:   Optional[int]   	=      min_seq_length
      __lowerCAmelCase:   int   	=      max_seq_length
      __lowerCAmelCase:   Dict   	=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      __lowerCAmelCase:   List[Any]   	=      feature_size
      __lowerCAmelCase:   Tuple   	=      padding_value
      __lowerCAmelCase:   int   	=      sampling_rate
      __lowerCAmelCase:   Union[str, Any]   	=      return_attention_mask
      __lowerCAmelCase:   int   	=      do_normalize


     def   UpperCAmelCase  (							self			:		Optional[int]					)							->	Any:
      return {
          "feature_size": self.feature_size,
          "padding_value": self.padding_value,
          "sampling_rate": self.sampling_rate,
          "return_attention_mask": self.return_attention_mask,
          "do_normalize": self.do_normalize,
      }


     def   UpperCAmelCase  (							self			:		Union[str, Any]			,  UpperCAmelCase			:		Optional[int]=False			,  UpperCAmelCase			:		Union[str, Any]=False					)							->	List[str]:







      def _flatten(UpperCAmelCase			:		Optional[Any]					):
       return list(itertools.chain(*UpperCamelCase__					)					)

      if equal_length:
       __lowerCAmelCase:   Tuple   	=      floats_list((self.batch_size, self.max_seq_length)					)
      else:
       # make sure that inputs increase in size
       __lowerCAmelCase:   Optional[int]   	=      [
           _flatten(floats_list((x, self.feature_size)					)					)
           for x in range(self.min_seq_length			,  self.max_seq_length			,  self.seq_length_diff					)
       ]

      if numpify:
       __lowerCAmelCase:   List[Any]   	=      [np.asarray(UpperCamelCase__					) for x in speech_inputs]

      return speech_inputs







class 							A_ (		_a							,	unittest.TestCase					):
     _lowercase			:     Dict								=	WavaVecaFeatureExtractor


     def   UpperCAmelCase  (							self			:		Union[str, Any]					)							->	Optional[Any]:
      __lowerCAmelCase:   Any   	=      WavaVecaFeatureExtractionTester(self					)


     def   UpperCAmelCase  (							self			:		Optional[Any]			,  UpperCAmelCase			:		List[str]					)							->	Optional[int]:
      self.assertTrue(np.all(np.mean(UpperCamelCase__			,  axis=0					) < 1E-3					)					)
      self.assertTrue(np.all(np.abs(np.var(UpperCamelCase__			,  axis=0					) - 1					) < 1E-3					)					)


     def   UpperCAmelCase  (							self			:		Optional[int]					)							->	List[Any]:
      __lowerCAmelCase:   int   	=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
      # create three inputs of length 800, 1000, and 1200
      __lowerCAmelCase:   List[Any]   	=      [floats_list((1, x)					)[0] for x in range(8_0_0			,  1_4_0_0			,  2_0_0					)]
      __lowerCAmelCase:   Dict   	=      [np.asarray(UpperCamelCase__					) for speech_input in speech_inputs]

      # Test not batched input
      __lowerCAmelCase:   Optional[int]   	=      feat_extract(speech_inputs[0]			,  return_tensors='np'					).input_values
      __lowerCAmelCase:   Tuple   	=      feat_extract(np_speech_inputs[0]			,  return_tensors='np'					).input_values
      self.assertTrue(np.allclose(UpperCamelCase__			,  UpperCamelCase__			,  atol=1E-3					)					)

      # Test batched
      __lowerCAmelCase:   Union[str, Any]   	=      feat_extract(UpperCamelCase__			,  return_tensors='np'					).input_values
      __lowerCAmelCase:   int   	=      feat_extract(UpperCamelCase__			,  return_tensors='np'					).input_values
      for enc_seq_a, enc_seq_a in zip(UpperCamelCase__			,  UpperCamelCase__					):
       self.assertTrue(np.allclose(UpperCamelCase__			,  UpperCamelCase__			,  atol=1E-3					)					)

      # Test 2-D numpy arrays are batched.
      __lowerCAmelCase:   str   	=      [floats_list((1, x)					)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
      __lowerCAmelCase:   List[Any]   	=      np.asarray(UpperCamelCase__					)
      __lowerCAmelCase:   Optional[Any]   	=      feat_extract(UpperCamelCase__			,  return_tensors='np'					).input_values
      __lowerCAmelCase:   Optional[Any]   	=      feat_extract(UpperCamelCase__			,  return_tensors='np'					).input_values
      for enc_seq_a, enc_seq_a in zip(UpperCamelCase__			,  UpperCamelCase__					):
       self.assertTrue(np.allclose(UpperCamelCase__			,  UpperCamelCase__			,  atol=1E-3					)					)


     def   UpperCAmelCase  (							self			:		Dict					)							->	Optional[int]:
      __lowerCAmelCase:   Optional[int]   	=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
      __lowerCAmelCase:   int   	=      [floats_list((1, x)					)[0] for x in range(8_0_0			,  1_4_0_0			,  2_0_0					)]

      __lowerCAmelCase:   Optional[int]   	=      ['longest', 'max_length', 'do_not_pad']
      __lowerCAmelCase:   Optional[Any]   	=      [None, 1_6_0_0, None]
      for max_length, padding in zip(UpperCamelCase__			,  UpperCamelCase__					):
       __lowerCAmelCase:   int   	=      feat_extract(UpperCamelCase__			,  padding=UpperCamelCase__			,  max_length=UpperCamelCase__			,  return_tensors='np'					)
       __lowerCAmelCase:   List[str]   	=      processed.input_values

       self._check_zero_mean_unit_variance(input_values[0][:8_0_0]					)
       self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6					)
       self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]					)
       self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6					)
       self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]					)


     def   UpperCAmelCase  (							self			:		Optional[Any]					)							->	int:
      __lowerCAmelCase:   int   	=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
      __lowerCAmelCase:   Tuple   	=      range(8_0_0			,  1_4_0_0			,  2_0_0					)
      __lowerCAmelCase:   List[Any]   	=      [floats_list((1, x)					)[0] for x in lengths]

      __lowerCAmelCase:   Union[str, Any]   	=      ['longest', 'max_length', 'do_not_pad']
      __lowerCAmelCase:   List[str]   	=      [None, 1_6_0_0, None]

      for max_length, padding in zip(UpperCamelCase__			,  UpperCamelCase__					):
       __lowerCAmelCase:   Optional[int]   	=      feat_extract(UpperCamelCase__			,  max_length=UpperCamelCase__			,  padding=UpperCamelCase__					)
       __lowerCAmelCase:   Union[str, Any]   	=      processed.input_values

       self._check_zero_mean_unit_variance(input_values[0][:8_0_0]					)
       self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]					)
       self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]					)


     def   UpperCAmelCase  (							self			:		str					)							->	Tuple:
      __lowerCAmelCase:   List[str]   	=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
      __lowerCAmelCase:   Dict   	=      [floats_list((1, x)					)[0] for x in range(8_0_0			,  1_4_0_0			,  2_0_0					)]
      __lowerCAmelCase:   Tuple   	=      feat_extract(
          UpperCamelCase__			,  truncation=UpperCamelCase__			,  max_length=1_0_0_0			,  padding='max_length'			,  return_tensors='np'					)
      __lowerCAmelCase:   int   	=      processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
      self._check_zero_mean_unit_variance(input_values[1]					)
      self._check_zero_mean_unit_variance(input_values[2]					)


     def   UpperCAmelCase  (							self			:		Tuple					)							->	Dict:
      __lowerCAmelCase:   Optional[int]   	=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
      __lowerCAmelCase:   Optional[int]   	=      [floats_list((1, x)					)[0] for x in range(8_0_0			,  1_4_0_0			,  2_0_0					)]
      __lowerCAmelCase:   Optional[int]   	=      feat_extract(
          UpperCamelCase__			,  truncation=UpperCamelCase__			,  max_length=1_0_0_0			,  padding='longest'			,  return_tensors='np'					)
      __lowerCAmelCase:   List[Any]   	=      processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
      self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]					)
      self._check_zero_mean_unit_variance(input_values[2]					)

      # make sure that if max_length < longest -> then pad to max_length
      self.assertTrue(input_values.shape == (3, 1_0_0_0)					)

      __lowerCAmelCase:   str   	=      [floats_list((1, x)					)[0] for x in range(8_0_0			,  1_4_0_0			,  2_0_0					)]
      __lowerCAmelCase:   Optional[Any]   	=      feat_extract(
          UpperCamelCase__			,  truncation=UpperCamelCase__			,  max_length=2_0_0_0			,  padding='longest'			,  return_tensors='np'					)
      __lowerCAmelCase:   Optional[int]   	=      processed.input_values

      self._check_zero_mean_unit_variance(input_values[0, :8_0_0]					)
      self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]					)
      self._check_zero_mean_unit_variance(input_values[2]					)

      # make sure that if max_length > longest -> then pad to longest
      self.assertTrue(input_values.shape == (3, 1_2_0_0)					)


     @require_torch
     def   UpperCAmelCase  (							self			:		Optional[Any]					)							->	Union[str, Any]:
      import torch

      __lowerCAmelCase:   Union[str, Any]   	=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
      __lowerCAmelCase:   List[str]   	=      np.random.rand(1_0_0					).astype(np.floataa					)
      __lowerCAmelCase:   Union[str, Any]   	=      np_speech_inputs.tolist()

      for inputs in [py_speech_inputs, np_speech_inputs]:
       __lowerCAmelCase:   int   	=      feature_extractor.pad([{'input_values': inputs}]			,  return_tensors='np'					)
       self.assertTrue(np_processed.input_values.dtype == np.floataa					)
       __lowerCAmelCase:   str   	=      feature_extractor.pad([{'input_values': inputs}]			,  return_tensors='pt'					)
       self.assertTrue(pt_processed.input_values.dtype == torch.floataa					)


     @slow
     @require_torch
     def   UpperCAmelCase  (							self			:		Any					)							->	Optional[int]:







      for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
       __lowerCAmelCase:   Optional[int]   	=      WavaVecaConfig.from_pretrained(UpperCamelCase__					)
       __lowerCAmelCase:   Any   	=      WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase__					)

       # only "layer" feature extraction norm should make use of
       # attention_mask
       self.assertEqual(feat_extract.return_attention_mask			,  config.feat_extract_norm == 'layer'					)










'''simple docstring'''


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

import pytest

from .sola import PokerHand

_lowerCamelCase	:       Optional[int]      	=	(
    "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	:       Dict      	=	(
    ("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	:       Dict      	=	(
    ("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[Any]      	=	(
    ("2H 4D 3C AS 5S", True, [5, 4, 3, 2, 14]),
    ("2H 5D 3C AS 5S", False, [14, 5, 5, 3, 2]),
    ("JH QD KC AS TS", False, [14, 13, 12, 11, 10]),
    ("9D 3S 2C 7S 7C", False, [9, 7, 7, 3, 2]),
)

_lowerCamelCase	:       List[Any]      	=	(
    ("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", 23),
    ("JH 9H TH KH QH", 22),
    ("JC KH JS JD JH", 21),
    ("KH KC 3S 3H 3D", 20),
    ("8C 9C 5C 3C TC", 19),
    ("JS QS 9H TS KH", 18),
    ("7C 7S KH 2H 7H", 17),
    ("3C KH 5D 5S KH", 16),
    ("QH 8H KD JH 8S", 15),
    ("2D 6D 9D TH 7D", 14),
)







def 				__lowerCamelCase				(   )       ->	Optional[Any]:






   """simple docstring"""
   UpperCamelCase				,      UpperCamelCase					=  randrange(len(A__    )    ), randrange(len(A__    )    )
   UpperCamelCase					=  ['Loss', 'Tie', 'Win'][(play >= oppo) + (play > oppo)]
   UpperCamelCase				,      UpperCamelCase					=  SORTED_HANDS[play], SORTED_HANDS[oppo]
   return hand, other, expected







def 				__lowerCamelCase				(   A__ = 100    )       ->	Optional[Any]:






   """simple docstring"""
   return (generate_random_hand() for _ in range(A__    ))







@pytest.mark.parametrize('hand, expected'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   assert PokerHand(A__    )._is_flush() == expected







@pytest.mark.parametrize('hand, expected'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Any:






   """simple docstring"""
   assert PokerHand(A__    )._is_straight() == expected







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






   """simple docstring"""
   UpperCamelCase					=  PokerHand(A__    )
   assert player._is_five_high_straight() == expected
   assert player._card_values == card_values







@pytest.mark.parametrize('hand, expected'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Dict:






   """simple docstring"""
   assert PokerHand(A__    )._is_same_kind() == expected







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






   """simple docstring"""
   assert PokerHand(A__    )._hand_type == expected







@pytest.mark.parametrize('hand, other, expected'    ,						A__    )
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	Tuple:






   """simple docstring"""
   assert PokerHand(A__    ).compare_with(PokerHand(A__    )    ) == expected







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






   """simple docstring"""
   assert PokerHand(A__    ).compare_with(PokerHand(A__    )    ) == expected







def 				__lowerCamelCase				(   )       ->	str:






   """simple docstring"""
   UpperCamelCase					=  [PokerHand(A__    ) for hand in SORTED_HANDS]
   UpperCamelCase					=  poker_hands.copy()
   shuffle(A__    )
   UpperCamelCase					=  chain(sorted(A__    )    )
   for index, hand in enumerate(A__    ):
      assert hand == poker_hands[index]







def 				__lowerCamelCase				(   )       ->	Optional[int]:






   """simple docstring"""
   # Test that five high straights are compared correctly.
   UpperCamelCase					=  [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 				__lowerCamelCase				(   )       ->	str:






   """simple docstring"""
   # Multiple calls to five_high_straight function should still return True
   # and shouldn't mutate the list in every call other than the first.
   UpperCamelCase					=  PokerHand('2C 4S AS 3D 5C'    )
   UpperCamelCase					=  True
   UpperCamelCase					=  [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"""
   # Problem number 54 from Project Euler
   # Testing from poker_hands.txt file
   UpperCamelCase					=  0
   UpperCamelCase					=  os.path.abspath(os.path.dirname(A__    )    )
   UpperCamelCase					=  os.path.join(A__    ,						'poker_hands.txt'    )
   with open(A__    ) as file_hand:
      for line in file_hand:
         UpperCamelCase					=  line[:14].strip()
         UpperCamelCase					=  line[15:].strip()
         UpperCamelCase				,      UpperCamelCase					=  PokerHand(A__    ), PokerHand(A__    )
         UpperCamelCase					=  player.compare_with(A__    )
         if output == "Win":
            answer += 1
   assert answer == 376

