





import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin



class 				_lowerCAmelCase			(	UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase							):


  '''simple docstring'''
  a_							:				str					    =UNetaDModel
  a_							:				Union[str, Any]					    ="""sample"""







  @property
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    _snake_case      : Union[str, Any]														=		4
    _snake_case      : List[Any]														=		14
    _snake_case      : str														=		16

    _snake_case      : int														=		floats_tensor((batch_size, num_features, seq_len)							).to(UpperCamelCase							)
    _snake_case      : Union[str, Any]														=		torch.tensor([10] * batch_size							).to(UpperCamelCase							)

    return {"sample": noise, "timestep": time_step}







  @property
  def UpperCamelCase_       (					self					:    Optional[int]							):


    '''simple docstring'''





    return (4, 14, 16)







  @property
  def UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    return (4, 14, 16)







  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    pass







  def UpperCamelCase_       (					self					:    Optional[Any]							):


    '''simple docstring'''





    pass







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    Any							):


    '''simple docstring'''





    super().test_determinism()







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    super().test_outputs_equivalence()







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    Optional[int]							):


    '''simple docstring'''





    super().test_from_save_pretrained()







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    super().test_from_save_pretrained_variant()







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    super().test_model_from_pretrained()







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    super().test_output()







  def UpperCamelCase_       (					self					:    Optional[int]							):


    '''simple docstring'''





    _snake_case      : Dict														=		{
        'block_out_channels': (32, 64, 1_28, 2_56),
        'in_channels': 14,
        'out_channels': 14,
        'time_embedding_type': 'positional',
        'use_timestep_embedding': True,
        'flip_sin_to_cos': False,
        'freq_shift': 1.0,
        'out_block_type': 'OutConv1DBlock',
        'mid_block_type': 'MidResTemporalBlock1D',
        'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
        'up_block_types': ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D'),
        'act_fn': 'mish',
    }
    _snake_case      : int														=		self.dummy_input
    return init_dict, inputs_dict







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    Union[str, Any]							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : Any														=		UNetaDModel.from_pretrained(
        'bglick13/hopper-medium-v2-value-function-hor32'					, output_loading_info=UpperCamelCase					, subfolder='unet'							)
    self.assertIsNotNone(UpperCamelCase							)
    self.assertEqual(len(loading_info['missing_keys']							)					, 0							)

    model.to(UpperCamelCase							)
    _snake_case      : Optional[Any]														=		model(**self.dummy_input							)

    assert image is not None, "Make sure output is not None"







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    _snake_case      : int														=		UNetaDModel.from_pretrained('bglick13/hopper-medium-v2-value-function-hor32'					, subfolder='unet'							)
    torch.manual_seed(0							)
    if torch.cuda.is_available():
      torch.cuda.manual_seed_all(0							)

    _snake_case      : Optional[Any]														=		model.config.in_channels
    _snake_case      : List[str]														=		16
    _snake_case      : int														=		torch.randn((1, seq_len, num_features)							).permute(
        0					, 2					, 1							)  # match original, we can update values and remove
    _snake_case      : str														=		torch.full((num_features,)					, 0							)

    with torch.no_grad():
      _snake_case      : List[Any]														=		model(UpperCamelCase					, UpperCamelCase							).sample.permute(0					, 2					, 1							)

    _snake_case      : Tuple														=		output[0, -3:, -3:].flatten()
    # fmt: off
    _snake_case      : Tuple														=		torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]							)
    # fmt: on
    self.assertTrue(torch.allclose(UpperCamelCase					, UpperCamelCase					, rtol=1e-3							)							)







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


    '''simple docstring'''





    pass







  @slow
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    _snake_case      : Tuple														=		'harmonai/maestro-150k'
    _snake_case      : Dict														=		UNetaDModel.from_pretrained(UpperCamelCase					, subfolder='unet'							)
    model.to(UpperCamelCase							)

    _snake_case      : str														=		6_55_36
    _snake_case      : int														=		torch.sin(torch.arange(UpperCamelCase							)[None, None, :].repeat(1					, 2					, 1							)							).to(UpperCamelCase							)
    _snake_case      : str														=		torch.tensor([1]							).to(UpperCamelCase							)

    with torch.no_grad():
      _snake_case      : str														=		model(UpperCamelCase					, UpperCamelCase							).sample

    _snake_case      : str														=		output.abs().sum()
    _snake_case      : Tuple														=		output.abs().max()

    assert (output_sum - 2_24.08_96).abs() < 0.5
    assert (output_max - 0.06_07).abs() < 4e-4




class 				_lowerCAmelCase			(	UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase							):


  '''simple docstring'''
  a_							:				Dict					    =UNetaDModel
  a_							:				str					    ="""sample"""







  @property
  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    _snake_case      : Tuple														=		4
    _snake_case      : Optional[int]														=		14
    _snake_case      : Optional[int]														=		16

    _snake_case      : Union[str, Any]														=		floats_tensor((batch_size, num_features, seq_len)							).to(UpperCamelCase							)
    _snake_case      : str														=		torch.tensor([10] * batch_size							).to(UpperCamelCase							)

    return {"sample": noise, "timestep": time_step}







  @property
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    return (4, 14, 16)







  @property
  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    return (4, 14, 1)







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    Union[str, Any]							):


    '''simple docstring'''





    super().test_determinism()







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    Optional[Any]							):


    '''simple docstring'''





    super().test_outputs_equivalence()







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    Optional[Any]							):


    '''simple docstring'''





    super().test_from_save_pretrained()







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    super().test_from_save_pretrained_variant()







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    Any							):


    '''simple docstring'''





    super().test_model_from_pretrained()







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : Optional[int]														=		self.prepare_init_args_and_inputs_for_common()
    _snake_case      : Tuple														=		self.model_class(**UpperCamelCase							)
    model.to(UpperCamelCase							)
    model.eval()

    with torch.no_grad():
      _snake_case      : List[Any]														=		model(**UpperCamelCase							)

      if isinstance(UpperCamelCase					, UpperCamelCase							):
        _snake_case      : Any														=		output.sample

    self.assertIsNotNone(UpperCamelCase							)
    _snake_case      : Optional[int]														=		torch.Size((inputs_dict['sample'].shape[0], 1)							)
    self.assertEqual(output.shape					, UpperCamelCase					, 'Input and output shapes do not match'							)







  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    pass







  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    pass







  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    _snake_case      : Any														=		{
        'in_channels': 14,
        'out_channels': 14,
        'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
        'up_block_types': [],
        'out_block_type': 'ValueFunction',
        'mid_block_type': 'ValueFunctionMidBlock1D',
        'block_out_channels': [32, 64, 1_28, 2_56],
        'layers_per_block': 1,
        'downsample_each_block': True,
        'use_timestep_embedding': True,
        'freq_shift': 1.0,
        'flip_sin_to_cos': False,
        'time_embedding_type': 'positional',
        'act_fn': 'mish',
    }
    _snake_case      : Any														=		self.dummy_input
    return init_dict, inputs_dict







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : List[str]														=		UNetaDModel.from_pretrained(
        'bglick13/hopper-medium-v2-value-function-hor32'					, output_loading_info=UpperCamelCase					, subfolder='value_function'							)
    self.assertIsNotNone(UpperCamelCase							)
    self.assertEqual(len(vf_loading_info['missing_keys']							)					, 0							)

    value_function.to(UpperCamelCase							)
    _snake_case      : Any														=		value_function(**self.dummy_input							)

    assert image is not None, "Make sure output is not None"







  @unittest.skipIf(torch_device == 'mps'					, 'mish op not supported in MPS'							)
  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : int														=		UNetaDModel.from_pretrained(
        'bglick13/hopper-medium-v2-value-function-hor32'					, output_loading_info=UpperCamelCase					, subfolder='value_function'							)
    torch.manual_seed(0							)
    if torch.cuda.is_available():
      torch.cuda.manual_seed_all(0							)

    _snake_case      : str														=		value_function.config.in_channels
    _snake_case      : Any														=		14
    _snake_case      : Any														=		torch.randn((1, seq_len, num_features)							).permute(
        0					, 2					, 1							)  # match original, we can update values and remove
    _snake_case      : List[Any]														=		torch.full((num_features,)					, 0							)

    with torch.no_grad():
      _snake_case      : List[str]														=		value_function(UpperCamelCase					, UpperCamelCase							).sample

    # fmt: off
    _snake_case      : Union[str, Any]														=		torch.tensor([1_65.25] * seq_len							)
    # fmt: on
    self.assertTrue(torch.allclose(UpperCamelCase					, UpperCamelCase					, rtol=1e-3							)							)







  def UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    pass







from __future__ import annotations

import os



class SCREAMING_SNAKE_CASE	:
  """simple docstring"""






  lowerCamelCase    :			Union[str, Any]						      =(
      "High card",
      "One pair",
      "Two pairs",
      "Three of a kind",
      "Straight",
      "Flush",
      "Full house",
      "Four of a kind",
      "Straight flush",
      "Royal flush",
  )

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







  def __init__(			self :    Tuple       ,							lowerCAmelCase :    str    )							->	None:






     """simple docstring"""







     if not isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        __lowerCAmelCase  :			Tuple					    =						f'''Hand should be of type \'str\': {hand!r}'''
        raise TypeError(lowerCAmelCase    )
     # split removes duplicate whitespaces so no need of strip
     if len(hand.split(""" """    )    ) != 5:
        __lowerCAmelCase  :			Any					    =						f'''Hand should contain only 5 cards: {hand!r}'''
        raise ValueError(lowerCAmelCase    )
     __lowerCAmelCase  :			int					    =						hand
     __lowerCAmelCase  :			Any					    =						0
     __lowerCAmelCase  :			str					    =						0
     __lowerCAmelCase    ,__lowerCAmelCase  :			List[str]					    =						self._internal_state()
     __lowerCAmelCase  :			int					    =						self._get_hand_type()
     __lowerCAmelCase  :			Any					    =						self._card_values[0]







  @property
  def        SCREAMING_SNAKE_CASE    (			self :    Union[str, Any]    )							->	Optional[int]:






     """simple docstring"""







     return self._hand







  def        SCREAMING_SNAKE_CASE    (			self :    Dict       ,							lowerCAmelCase :    PokerHand    )							->	str:






     """simple docstring"""







     if self._hand_type > other._hand_type:
        return "Win"
     elif self._hand_type < other._hand_type:
        return "Loss"
     elif self._first_pair == other._first_pair:
        if self._second_pair == other._second_pair:
           return self._compare_cards(lowerCAmelCase    )
        else:
           return "Win" if self._second_pair > other._second_pair else "Loss"
     return "Win" if self._first_pair > other._first_pair else "Loss"







  def        SCREAMING_SNAKE_CASE    (			self :    int    )							->	str:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[int]					    =						PokerHand._HAND_NAME[self._hand_type - 14]
     __lowerCAmelCase  :			str					    =						PokerHand._CARD_NAME[self._high_card]
     __lowerCAmelCase  :			Optional[Any]					    =						PokerHand._CARD_NAME[self._first_pair]
     __lowerCAmelCase  :			Any					    =						PokerHand._CARD_NAME[self._second_pair]
     if self._hand_type in [22, 19, 18]:
        return name + f''', {high}-high'''
     elif self._hand_type in [21, 17, 15]:
        return name + f''', {paira}s'''
     elif self._hand_type in [20, 16]:
        __lowerCAmelCase  :			List[str]					    =						"""over""" if self._hand_type == 20 else """and"""
        return name + f''', {paira}s {join} {paira}s'''
     elif self._hand_type == 23:
        return name
     else:
        return name + f''', {high}'''







  def        SCREAMING_SNAKE_CASE    (			self :    Dict       ,							lowerCAmelCase :    PokerHand    )							->	str:






     """simple docstring"""







     for index, card_value in enumerate(self._card_values    ):
        if card_value != other._card_values[index]:
           return "Win" if card_value > other._card_values[index] else "Loss"
     return "Tie"







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[int]    )							->	int:






     """simple docstring"""







     if self._is_flush():
        if self._is_five_high_straight() or self._is_straight():
           return 23 if sum(self._card_values    ) == 60 else 22
        return 19
     elif self._is_five_high_straight() or self._is_straight():
        return 18
     return 14 + self._is_same_kind()







  def        SCREAMING_SNAKE_CASE    (			self :    List[Any]    )							->	bool:






     """simple docstring"""







     return len(self._card_suit    ) == 1







  def        SCREAMING_SNAKE_CASE    (			self :    List[str]    )							->	bool:






     """simple docstring"""







     if sorted(self._card_values    ) == [2, 3, 4, 5, 14]:
        if self._card_values[0] == 14:
           # Remember, our list is sorted in reverse order
           __lowerCAmelCase  :			Optional[Any]					    =						self._card_values.pop(0    )
           self._card_values.append(lowerCAmelCase    )
        return True
     return False







  def        SCREAMING_SNAKE_CASE    (			self :    List[str]    )							->	bool:






     """simple docstring"""







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







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[int]    )							->	int:






     """simple docstring"""







     __lowerCAmelCase  :			str					    =						0
     for i in range(4    ):
        # Compare two cards at a time, if they are same increase 'kind',
        # add the value of the card to val1, if it is repeating again we
        # will add 2 to 'kind' as there are now 3 cards with same value.
        # If we get card of different value than val1, we will do the same
        # thing with val2
        if self._card_values[i] == self._card_values[i + 1]:
           if not vala:
              __lowerCAmelCase  :			List[str]					    =						self._card_values[i]
              kind += 1
           elif vala == self._card_values[i]:
              kind += 2
           elif not vala:
              __lowerCAmelCase  :			Optional[Any]					    =						self._card_values[i]
              kind += 1
           elif vala == self._card_values[i]:
              kind += 2
        # For consistency in hand type (look at note in _get_hand_type function)
     __lowerCAmelCase  :			List[Any]					    =						kind + 2 if kind in [4, 5] else kind
     # first meaning first pair to compare in 'compare_with'
     __lowerCAmelCase  :			Optional[int]					    =						max(lowerCAmelCase       ,							lowerCAmelCase    )
     __lowerCAmelCase  :			Optional[Any]					    =						min(lowerCAmelCase       ,							lowerCAmelCase    )
     # If it's full house (three count pair + two count pair), make sure
     # first pair is three count and if not then switch them both.
     if kind == 6 and self._card_values.count(lowerCAmelCase    ) != 3:
        __lowerCAmelCase    ,__lowerCAmelCase  :			Tuple					    =						second, first
     __lowerCAmelCase  :			Optional[Any]					    =						first
     __lowerCAmelCase  :			Union[str, Any]					    =						second
     return kind







  def        SCREAMING_SNAKE_CASE    (			self :    List[Any]    )							->	tuple[list[int], set[str]]:






     """simple docstring"""







     __lowerCAmelCase  :			dict					    =						{"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
     __lowerCAmelCase  :			Optional[Any]					    =						self._hand.translate(str.maketrans(lowerCAmelCase    )    ).split()
     __lowerCAmelCase  :			List[str]					    =						[int(card[:-1]    ) for card in new_hand]
     __lowerCAmelCase  :			str					    =						{card[-1] for card in new_hand}
     return sorted(lowerCAmelCase       ,							reverse=lowerCAmelCase    ), card_suit







  def __repr__(			self :    List[str]    )							->	Any:






     """simple docstring"""







     return f'''{self.__class__}("{self._hand}")'''







  def __str__(			self :    Any    )							->	Any:






     """simple docstring"""







     return self._hand







  def __eq__(			self :    Union[str, Any]       ,							lowerCAmelCase :    List[Any]    )							->	Union[str, Any]:






     """simple docstring"""







     if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        return self.compare_with(lowerCAmelCase    ) == "Tie"
     return NotImplemented







  def __lt__(			self :    Dict       ,							lowerCAmelCase :    str    )							->	List[str]:






     """simple docstring"""







     if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        return self.compare_with(lowerCAmelCase    ) == "Loss"
     return NotImplemented







  def __le__(			self :    str       ,							lowerCAmelCase :    int    )							->	Union[str, Any]:






     """simple docstring"""







     if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        return self < other or self == other
     return NotImplemented







  def __gt__(			self :    Dict       ,							lowerCAmelCase :    str    )							->	Optional[Any]:






     """simple docstring"""







     if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        return not self < other and self != other
     return NotImplemented







  def __ge__(			self :    Tuple       ,							lowerCAmelCase :    Tuple    )							->	Dict:






     """simple docstring"""







     if isinstance(lowerCAmelCase       ,							lowerCAmelCase    ):
        return not self < other
     return NotImplemented







  def __hash__(			self :    List[str]    )							->	str:






     """simple docstring"""







     return object.__hash__(self    )






def 	snake_case_      ()			->	int:
   # Solution for problem number 54 from Project Euler
   # Input from poker_hands.txt file
   __lowerCAmelCase  :			str					    =						0
   __lowerCAmelCase  :			List[str]					    =						os.path.abspath(os.path.dirname(__A     )     )
   __lowerCAmelCase  :			Optional[Any]					    =						os.path.join(__A					,   """poker_hands.txt"""     )
   with open(__A     ) as file_hand:
      for line in file_hand:
         __lowerCAmelCase  :			Union[str, Any]					    =						line[:1_4].strip()
         __lowerCAmelCase  :			Optional[int]					    =						line[1_5:].strip()
         __lowerCAmelCase    ,__lowerCAmelCase  :			List[str]					    =						PokerHand(__A     ), PokerHand(__A     )
         __lowerCAmelCase  :			List[str]					    =						player.compare_with(__A     )
         if output == "Win":
            answer += 1
   return answer


if __name__ == "__main__":
   solution()


