

import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE						,      unittest.TestCase ):
    __UpperCamelCase    =AudioLDMPipeline
    __UpperCamelCase    =TEXT_TO_AUDIO_PARAMS
    __UpperCamelCase    =TEXT_TO_AUDIO_BATCH_PARAMS
    __UpperCamelCase    =frozenset(
        [
            "num_inference_steps",
            "num_waveforms_per_prompt",
            "generator",
            "latents",
            "output_type",
            "return_dict",
            "callback",
            "callback_steps",
        ] )

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      UNetaDConditionModel(
         block_out_channels=(3_2, 6_4)				,     layers_per_block=2				,     sample_size=3_2				,     in_channels=4				,     out_channels=4				,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,     cross_attention_dim=(3_2, 6_4)				,     class_embed_type='simple_projection'				,     projection_class_embeddings_input_dim=3_2				,     class_embeddings_concat=snake_case__				,     )
     SCREAMING_SNAKE_CASE		    =      DDIMScheduler(
         beta_start=0.00_085				,     beta_end=0.012				,     beta_schedule='scaled_linear'				,     clip_sample=snake_case__				,     set_alpha_to_one=snake_case__				,     )
     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      AutoencoderKL(
         block_out_channels=[3_2, 6_4]				,     in_channels=1				,     out_channels=1				,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,     latent_channels=4				,     )
     torch.manual_seed(0     )
     SCREAMING_SNAKE_CASE		    =      ClapTextConfig(
         bos_token_id=0				,     eos_token_id=2				,     hidden_size=3_2				,     intermediate_size=3_7				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_0_0_0				,     projection_dim=3_2				,     )
     SCREAMING_SNAKE_CASE		    =      ClapTextModelWithProjection(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'				,     model_max_length=7_7     )

     SCREAMING_SNAKE_CASE		    =      SpeechTaHifiGanConfig(
         model_in_dim=8				,     sampling_rate=1_6_0_0_0				,     upsample_initial_channel=1_6				,     upsample_rates=[2, 2]				,     upsample_kernel_sizes=[4, 4]				,     resblock_kernel_sizes=[3, 7]				,     resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]				,     normalize_before=snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      SpeechTaHifiGan(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      {
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'vocoder': vocoder,
     }
     return components

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   int				,     snake_case__			:   Dict=0     ):

     """simple docstring"""






     if str(snake_case__     ).startswith('mps'     ):
      SCREAMING_SNAKE_CASE		    =      torch.manual_seed(snake_case__     )
     else:
      SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      {
         'prompt': 'A hammer hitting a wooden surface',
         'generator': generator,
         'num_inference_steps': 2,
         'guidance_scale': 6.0,
     }
     return inputs

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'  # ensure determinism for the device-dependent torch.Generator

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_components()
     SCREAMING_SNAKE_CASE		    =      AudioLDMPipeline(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.to(snake_case__     )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      output.audios[0]

     assert audio.ndim == 1
     assert len(snake_case__     ) == 2_5_6

     SCREAMING_SNAKE_CASE		    =      audio[:1_0]
     SCREAMING_SNAKE_CASE		    =      np.array(
         [-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033]     )

     assert np.abs(audio_slice - expected_slice     ).max() < 1E-2

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.get_dummy_components()
     SCREAMING_SNAKE_CASE		    =      AudioLDMPipeline(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.to(snake_case__     )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      3 * [inputs['prompt']]

     # forward
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      output.audios[0]

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      3 * [inputs.pop('prompt'     )]

     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.tokenizer(
         snake_case__				,     padding='max_length'				,     max_length=audioldm_pipe.tokenizer.model_max_length				,     truncation=snake_case__				,     return_tensors='pt'				,     )
     SCREAMING_SNAKE_CASE		    =      text_inputs['input_ids'].to(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.text_encoder(
         snake_case__				,     )
     SCREAMING_SNAKE_CASE		    =      prompt_embeds.text_embeds
     # additional L_2 normalization over each hidden-state
     SCREAMING_SNAKE_CASE		    =      F.normalize(snake_case__				,     dim=-1     )

     SCREAMING_SNAKE_CASE		    =      prompt_embeds

     # forward
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      output.audios[0]

     assert np.abs(audio_a - audio_a     ).max() < 1E-2

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.get_dummy_components()
     SCREAMING_SNAKE_CASE		    =      AudioLDMPipeline(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.to(snake_case__     )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      3 * ['this is a negative prompt']
     SCREAMING_SNAKE_CASE		    =      negative_prompt
     SCREAMING_SNAKE_CASE		    =      3 * [inputs['prompt']]

     # forward
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      output.audios[0]

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      3 * [inputs.pop('prompt'     )]

     SCREAMING_SNAKE_CASE		    =      []
     for p in [prompt, negative_prompt]:
      SCREAMING_SNAKE_CASE		    =      audioldm_pipe.tokenizer(
          snake_case__				,     padding='max_length'				,     max_length=audioldm_pipe.tokenizer.model_max_length				,     truncation=snake_case__				,     return_tensors='pt'				,     )
      SCREAMING_SNAKE_CASE		    =      text_inputs['input_ids'].to(snake_case__     )

      SCREAMING_SNAKE_CASE		    =      audioldm_pipe.text_encoder(
          snake_case__				,     )
      SCREAMING_SNAKE_CASE		    =      text_embeds.text_embeds
      # additional L_2 normalization over each hidden-state
      SCREAMING_SNAKE_CASE		    =      F.normalize(snake_case__				,     dim=-1     )

      embeds.append(snake_case__     )

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      embeds

     # forward
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      output.audios[0]

     assert np.abs(audio_a - audio_a     ).max() < 1E-2

    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE		    =      self.get_dummy_components()
     SCREAMING_SNAKE_CASE		    =      PNDMScheduler(skip_prk_steps=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      AudioLDMPipeline(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.to(snake_case__     )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      'egg cracking'
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(**snake_case__				,     negative_prompt=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      output.audios[0]

     assert audio.ndim == 1
     assert len(snake_case__     ) == 2_5_6

     SCREAMING_SNAKE_CASE		    =      audio[:1_0]
     SCREAMING_SNAKE_CASE		    =      np.array(
         [-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032]     )

     assert np.abs(audio_slice - expected_slice     ).max() < 1E-2

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE		    =      self.get_dummy_components()
     SCREAMING_SNAKE_CASE		    =      PNDMScheduler(skip_prk_steps=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      AudioLDMPipeline(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.to(snake_case__     )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      'A hammer hitting a wooden surface'

     # test num_waveforms_per_prompt=1 (default)
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(snake_case__				,     num_inference_steps=2     ).audios

     assert audios.shape == (1, 2_5_6)

     # test num_waveforms_per_prompt=1 (default) for batch of prompts
     SCREAMING_SNAKE_CASE		    =      2
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe([prompt] * batch_size				,     num_inference_steps=2     ).audios

     assert audios.shape == (batch_size, 2_5_6)

     # test num_waveforms_per_prompt for single prompt
     SCREAMING_SNAKE_CASE		    =      2
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(snake_case__				,     num_inference_steps=2				,     num_waveforms_per_prompt=snake_case__     ).audios

     assert audios.shape == (num_waveforms_per_prompt, 2_5_6)

     # test num_waveforms_per_prompt for batch of prompts
     SCREAMING_SNAKE_CASE		    =      2
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(
         [prompt] * batch_size				,     num_inference_steps=2				,     num_waveforms_per_prompt=snake_case__     ).audios

     assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_5_6)

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE		    =      self.get_dummy_components()
     SCREAMING_SNAKE_CASE		    =      AudioLDMPipeline(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.to(snake_case__     )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.vocoder.config.sampling_rate

     SCREAMING_SNAKE_CASE		    =      self.get_dummy_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(audio_length_in_s=0.016				,     **snake_case__     )
     SCREAMING_SNAKE_CASE		    =      output.audios[0]

     assert audio.ndim == 1
     assert len(snake_case__     ) / vocoder_sampling_rate == 0.016

     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(audio_length_in_s=0.032				,     **snake_case__     )
     SCREAMING_SNAKE_CASE		    =      output.audios[0]

     assert audio.ndim == 1
     assert len(snake_case__     ) / vocoder_sampling_rate == 0.032

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.get_dummy_components()
     SCREAMING_SNAKE_CASE		    =      AudioLDMPipeline(**snake_case__     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.to(snake_case__     )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      ['hey']

     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(snake_case__				,     num_inference_steps=1     )
     SCREAMING_SNAKE_CASE		    =      output.audios.shape
     assert audio_shape == (1, 2_5_6)

     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.vocoder.config
     config.model_in_dim *= 2
     SCREAMING_SNAKE_CASE		    =      SpeechTaHifiGan(snake_case__     ).to(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(snake_case__				,     num_inference_steps=1     )
     SCREAMING_SNAKE_CASE		    =      output.audios.shape
     # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
     assert audio_shape == (1, 2_5_6)

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     self._test_attention_slicing_forward_pass(test_mean_pixel_difference=snake_case__     )

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     self._test_inference_batch_single_identical(test_mean_pixel_difference=snake_case__     )




    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()				,     reason='XFormers attention is only available with CUDA and `xformers` installed'				,     )
    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=snake_case__     )




@slow
class   UpperCamelCase			(  unittest.TestCase ):

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

    def UpperCamelCase      (      self			:   Union[str, Any]				,     snake_case__			:   Tuple				,     snake_case__			:   Union[str, Any]="cpu"				,     snake_case__			:   Tuple=torch.floataa				,     snake_case__			:   Dict=0     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      np.random.RandomState(snake_case__     ).standard_normal((1, 8, 1_2_8, 1_6)     )
     SCREAMING_SNAKE_CASE		    =      torch.from_numpy(snake_case__     ).to(device=snake_case__				,     dtype=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      {
         'prompt': 'A hammer hitting a wooden surface',
         'latents': latents,
         'generator': generator,
         'num_inference_steps': 3,
         'guidance_scale': 2.5,
     }
     return inputs

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      AudioLDMPipeline.from_pretrained('cvssp/audioldm'     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.to(snake_case__     )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      self.get_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      2_5
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(**snake_case__     ).audios[0]

     assert audio.ndim == 1
     assert len(snake_case__     ) == 8_1_9_2_0

     SCREAMING_SNAKE_CASE		    =      audio[7_7_2_3_0:7_7_2_4_0]
     SCREAMING_SNAKE_CASE		    =      np.array(
         [-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315]     )
     SCREAMING_SNAKE_CASE		    =      np.abs(expected_slice - audio_slice     ).max()
     assert max_diff < 1E-2




    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      AudioLDMPipeline.from_pretrained('cvssp/audioldm'     )
     SCREAMING_SNAKE_CASE		    =      LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe.to(snake_case__     )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      self.get_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      audioldm_pipe(**snake_case__     ).audios[0]

     assert audio.ndim == 1
     assert len(snake_case__     ) == 8_1_9_2_0

     SCREAMING_SNAKE_CASE		    =      audio[2_7_7_8_0:2_7_7_9_0]
     SCREAMING_SNAKE_CASE		    =      np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212]     )
     SCREAMING_SNAKE_CASE		    =      np.abs(expected_slice - audio_slice     ).max()
     assert max_diff < 3E-2




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

import pytest

from .sola import PokerHand

a_     :    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)
)

a_     :    int     		=   (
    ("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"),
)

a_     :    Tuple     		=   (
    ("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),
)

a_     :    Tuple     		=   (
    ("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),
)

a_     :    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]),
)

a_     :    List[str]     		=   (
    ("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),
)

a_     :    Optional[Any]     		=   (
    ("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     (							)		->				Dict:
 '''simple docstring'''


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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int = 1_00						)		->				int:
 '''simple docstring'''


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


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


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


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


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


@pytest.mark.parametrize('hand, expected, card_values'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	List[Any]						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      PokerHand(_UpperCamelCase						)
 assert player._is_five_high_straight() == expected
 assert player._card_values == card_values


@pytest.mark.parametrize('hand, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	str						)		->				int:
 '''simple docstring'''


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


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


 assert PokerHand(_UpperCamelCase						)._hand_type == expected


@pytest.mark.parametrize('hand, other, expected'		,				_UpperCamelCase						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Dict						)		->				Tuple:
 '''simple docstring'''


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


@pytest.mark.parametrize('hand, other, expected'		,				generate_random_hands()						)
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Tuple						)		->				List[Any]:
 '''simple docstring'''


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


def 							__lowerCAmelCase     (							)		->				Tuple:
 '''simple docstring'''


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


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


 SCREAMING_SNAKE_CASE		    =      [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     (							)		->				List[str]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      PokerHand('2C 4S AS 3D 5C'						)
 SCREAMING_SNAKE_CASE		    =      True
 SCREAMING_SNAKE_CASE		    =      [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     (							)		->				Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      0
 SCREAMING_SNAKE_CASE		    =      os.path.abspath(os.path.dirname(_UpperCamelCase						)						)
 SCREAMING_SNAKE_CASE		    =      os.path.join(_UpperCamelCase		,				'poker_hands.txt'						)
 with open(_UpperCamelCase						) as file_hand:
  for line in file_hand:
   SCREAMING_SNAKE_CASE		    =      line[:14].strip()
   SCREAMING_SNAKE_CASE		    =      line[15:].strip()
   SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      PokerHand(_UpperCamelCase						), PokerHand(_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      player.compare_with(_UpperCamelCase						)
   if output == "Win":
    answer += 1
 assert answer == 3_76

