



from __future__ import annotations

import os
class  _snake_case	:
				UpperCamelCase__ :    str   						=(
				    """High card""",
				    """One pair""",
				    """Two pairs""",
				    """Three of a kind""",
				    """Straight""",
				    """Flush""",
				    """Full house""",
				    """Four of a kind""",
				    """Straight flush""",
				    """Royal flush""",
				)

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




				def __init__(  self    :  str,    __lowercase    :  str  ):
							if not isinstance(__lowercase,    __lowercase  ):
										lowercase__         = F'''Hand should be of type \'str\': {hand!r}'''
										raise TypeError(__lowercase  )
							# split removes duplicate whitespaces so no need of strip
							if len(hand.split(" "  )  ) != 5:
										lowercase__         = F'''Hand should contain only 5 cards: {hand!r}'''
										raise ValueError(__lowercase  )
							lowercase__         = hand
							lowercase__         = 0
							lowercase__         = 0
							lowercase__         = self._internal_state()
							lowercase__         = self._get_hand_type()
							lowercase__         = self._card_values[0]




				@property
				def 							A__					(  self    :  Union[str, Any]  ):
							return self._hand




				def 							A__					(  self    :  Any,    __lowercase    :  PokerHand  ):
							# Breaking the tie works on the following order of precedence:
							# 1. First pair (default 0)
							# 2. Second pair (default 0)
							# 3. Compare all cards in reverse order because they are sorted.

							# First pair and second pair will only be a non-zero value if the card
							# type is either from the following:
							# 21: Four of a kind
							# 20: Full house
							# 17: Three of a kind
							# 16: Two pairs
							# 15: One pair
							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(__lowercase  )
										else:
													return "Win" if self._second_pair > other._second_pair else "Loss"
							return "Win" if self._first_pair > other._first_pair else "Loss"




				def 							A__					(  self    :  int  ):
							lowercase__         = PokerHand._HAND_NAME[self._hand_type - 14]
							lowercase__         = PokerHand._CARD_NAME[self._high_card]
							lowercase__         = PokerHand._CARD_NAME[self._first_pair]
							lowercase__         = 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]:
										lowercase__         = '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 							A__					(  self    :  Tuple,    __lowercase    :  PokerHand  ):
							# Enumerate gives us the index as well as the element of a list
							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 							A__					(  self    :  Any  ):
							# Number representing the type of hand internally:
							# 23: Royal flush
							# 22: Straight flush
							# 21: Four of a kind
							# 20: Full house
							# 19: Flush
							# 18: Straight
							# 17: Three of a kind
							# 16: Two pairs
							# 15: One pair
							# 14: High card
							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 							A__					(  self    :  int  ):
							return len(self._card_suit  ) == 1




				def 							A__					(  self    :  Any  ):
							# If a card is a five high straight (low ace) change the location of
							# ace from the start of the list to the end. Check whether the first
							# element is ace or not. (Don't want to change again)
							# Five high straight (low ace): AH 2H 3S 4C 5D
							# Why use sorted here? One call to this function will mutate the list to
							# [5, 4, 3, 2, 14] and so for subsequent calls (which will be rare) we
							# need to compare the sorted version.
							# Refer test_multiple_calls_five_high_straight in test_poker_hand.py
							if sorted(self._card_values  ) == [2, 3, 4, 5, 14]:
										if self._card_values[0] == 14:
													# Remember, our list is sorted in reverse order
													lowercase__         = self._card_values.pop(0  )
													self._card_values.append(__lowercase  )
										return True
							return False




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




				def 							A__					(  self    :  Union[str, Any]  ):
							# Kind Values for internal use:
							# 7: Four of a kind
							# 6: Full house
							# 3: Three of a kind
							# 2: Two pairs
							# 1: One pair
							# 0: False
							lowercase__         = 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:
																lowercase__         = self._card_values[i]
																kind += 1
													elif vala == self._card_values[i]:
																kind += 2
													elif not vala:
																lowercase__         = 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)
							lowercase__         = kind + 2 if kind in [4, 5] else kind
							# first meaning first pair to compare in 'compare_with'
							lowercase__         = max(__lowercase,    __lowercase  )
							lowercase__         = min(__lowercase,    __lowercase  )
							# 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(__lowercase  ) != 3:
										lowercase__         = second, first
							lowercase__         = first
							lowercase__         = second
							return kind




				def 							A__					(  self    :  Optional[Any]  ):
							# Internal representation of hand as a list of card values and
							# a set of card suit
							lowercase__         = {"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
							lowercase__         = self._hand.translate(str.maketrans(__lowercase  )  ).split()
							lowercase__         = [int(card[:-1]  ) for card in new_hand]
							lowercase__         = {card[-1] for card in new_hand}
							return sorted(__lowercase,    reverse=__lowercase  ), card_suit




				def __repr__(  self    :  Tuple  ):
							return F'''{self.__class__}("{self._hand}")'''




				def __str__(  self    :  str  ):
							return self._hand




				def __eq__(  self    :  str,    __lowercase    :  Optional[Any]  ):
							if isinstance(__lowercase,    __lowercase  ):
										return self.compare_with(__lowercase  ) == "Tie"
							return NotImplemented




				def __lt__(  self    :  Tuple,    __lowercase    :  Union[str, Any]  ):
							if isinstance(__lowercase,    __lowercase  ):
										return self.compare_with(__lowercase  ) == "Loss"
							return NotImplemented




				def __le__(  self    :  Optional[Any],    __lowercase    :  Optional[Any]  ):
							if isinstance(__lowercase,    __lowercase  ):
										return self < other or self == other
							return NotImplemented




				def __gt__(  self    :  str,    __lowercase    :  Any  ):
							if isinstance(__lowercase,    __lowercase  ):
										return not self < other and self != other
							return NotImplemented




				def __ge__(  self    :  Any,    __lowercase    :  List[str]  ):
							if isinstance(__lowercase,    __lowercase  ):
										return not self < other
							return NotImplemented




				def __hash__(  self    :  Any  ):
							return object.__hash__(self  )



def __lowerCAmelCase       (   ):
			# Solution for problem number 54 from Project Euler
			# Input from poker_hands.txt file
			lowercase__         = 0
			lowercase__         = os.path.abspath(os.path.dirname(SCREAMING_SNAKE_CASE_     )     )
			lowercase__         = os.path.join(SCREAMING_SNAKE_CASE_  ,     "poker_hands.txt"     )
			with open(SCREAMING_SNAKE_CASE_     ) as file_hand:
						for line in file_hand:
									lowercase__         = line[:14].strip()
									lowercase__         = line[15:].strip()
									lowercase__         = PokerHand(SCREAMING_SNAKE_CASE_     ), PokerHand(SCREAMING_SNAKE_CASE_     )
									lowercase__         = player.compare_with(SCREAMING_SNAKE_CASE_     )
									if output == "Win":
												answer += 1
			return answer


if __name__ == "__main__":
							solution()








'''simple docstring'''







import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

A      :     Optional[int]										= logging.get_logger(__name__)






def       lowerCAmelCase__       (				lowerCamelCase     :			int  ,lowerCamelCase     :			bool = True   ):
       _A     :	Any      =					{}
       for name in model.attn_processors.keys():
              _A     :	Any      =					name.endswith('attn2.processor'   ) and not (
                  name.startswith('transformer_in'   ) or 'temp_attentions' in name.split('.'   )
              )
              _A     :	Dict      =					model.config.cross_attention_dim if has_cross_attention else None
              if name.startswith('mid_block'   ):
                     _A     :	Optional[int]      =					model.config.block_out_channels[-1]
              elif name.startswith('up_blocks'   ):
                     _A     :	str      =					int(name[len('up_blocks.'   )]   )
                     _A     :	Any      =					list(reversed(model.config.block_out_channels   )   )[block_id]
              elif name.startswith('down_blocks'   ):
                     _A     :	str      =					int(name[len('down_blocks.'   )]   )
                     _A     :	int      =					model.config.block_out_channels[block_id]
              elif name.startswith('transformer_in'   ):
                     # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
                     _A     :	Dict      =					8 * model.config.attention_head_dim

              _A     :	int      =					LoRAAttnProcessor(hidden_size=lowerCamelCase  ,cross_attention_dim=lowerCamelCase   )
              _A     :	Tuple      =					lora_attn_procs[name].to(model.device   )

              if mock_weights:
                     # add 1 to weights to mock trained weights
                     with torch.no_grad():
                            lora_attn_procs[name].to_q_lora.up.weight += 1
                            lora_attn_procs[name].to_k_lora.up.weight += 1
                            lora_attn_procs[name].to_v_lora.up.weight += 1
                            lora_attn_procs[name].to_out_lora.up.weight += 1

       return lora_attn_procs


@skip_mps
class  __lowerCamelCase (       a_				,					a_				,					unittest.TestCase       ):
    """simple docstring"""
    a													=    UNetaDConditionModel
    a													=    "sample"


    @property
    def    A    (  self			:							Tuple):
           _A     :	List[Any]      =					4
           _A     :	Tuple      =					4
           _A     :	int      =					4
           _A     :	Dict      =					(32, 32)

           _A     :	Any      =					floats_tensor((batch_size, num_channels, num_frames) + sizes).to(SCREAMING_SNAKE_CASE)
           _A     :	Optional[Any]      =					torch.tensor([10]).to(SCREAMING_SNAKE_CASE)
           _A     :	Optional[Any]      =					floats_tensor((batch_size, 4, 32)).to(SCREAMING_SNAKE_CASE)

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


    @property
    def    A    (  self			:							Tuple):
           return (4, 4, 32, 32)


    @property
    def    A    (  self			:							str):
           return (4, 4, 32, 32)


    def    A    (  self			:							List[Any]):
           _A     :	List[str]      =					{
               'block_out_channels': (32, 64),
               'down_block_types': (
                   'CrossAttnDownBlock3D',
                   'DownBlock3D',
               ),
               'up_block_types': ('UpBlock3D', 'CrossAttnUpBlock3D'),
               'cross_attention_dim': 32,
               'attention_head_dim': 8,
               'out_channels': 4,
               'in_channels': 4,
               'layers_per_block': 1,
               'sample_size': 32,
           }
           _A     :	int      =					self.dummy_input
           return init_dict, inputs_dict


    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()      ,							reason='XFormers attention is only available with CUDA and `xformers` installed'      ,							)
    def    A    (  self			:							Tuple):
           _A  , _A     :	Dict      =					self.prepare_init_args_and_inputs_for_common()
           _A     :	Any      =					self.model_class(**SCREAMING_SNAKE_CASE)

           model.enable_xformers_memory_efficient_attention()

           assert (
               model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
               == "XFormersAttnProcessor"
           ), "xformers is not enabled"


    def    A    (  self			:							str):
           _A  , _A     :	Union[str, Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Union[str, Any]      =					32

           _A     :	int      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           with torch.no_grad():
                  _A     :	List[str]      =					model(**SCREAMING_SNAKE_CASE)

                  if isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         _A     :	int      =					output.sample

           self.assertIsNotNone(SCREAMING_SNAKE_CASE)
           _A     :	Optional[int]      =					inputs_dict['sample'].shape
           self.assertEqual(output.shape      ,							SCREAMING_SNAKE_CASE      ,							'Input and output shapes do not match')


    def    A    (  self			:							Dict):
           _A  , _A     :	List[str]      =					self.prepare_init_args_and_inputs_for_common()
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           with torch.no_grad():
                  # Warmup pass when using mps (see #372)
                  if torch_device == "mps" and isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         model(**self.dummy_input)

                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE)
                  if isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         _A     :	Optional[int]      =					first.sample

                  _A     :	Union[str, Any]      =					model(**SCREAMING_SNAKE_CASE)
                  if isinstance(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                         _A     :	Dict      =					second.sample

           _A     :	Dict      =					first.cpu().numpy()
           _A     :	Dict      =					second.cpu().numpy()
           _A     :	Optional[int]      =					out_a[~np.isnan(SCREAMING_SNAKE_CASE)]
           _A     :	Dict      =					out_a[~np.isnan(SCREAMING_SNAKE_CASE)]
           _A     :	List[Any]      =					np.amax(np.abs(out_a - out_a))
           self.assertLessEqual(SCREAMING_SNAKE_CASE      ,							1e-5)


    def    A    (  self			:							Optional[int]):
           _A  , _A     :	Union[str, Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Optional[int]      =					8

           _A     :	Optional[Any]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           model.set_attention_slice('auto')
           with torch.no_grad():
                  _A     :	Any      =					model(**SCREAMING_SNAKE_CASE)
           assert output is not None

           model.set_attention_slice('max')
           with torch.no_grad():
                  _A     :	Optional[Any]      =					model(**SCREAMING_SNAKE_CASE)
           assert output is not None

           model.set_attention_slice(2)
           with torch.no_grad():
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE)
           assert output is not None


    def    A    (  self			:							List[Any]):
           _A  , _A     :	Tuple      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Dict      =					8

           _A     :	Any      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Tuple      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	List[str]      =					create_lora_layers(SCREAMING_SNAKE_CASE)

           # make sure we can set a list of attention processors
           model.set_attn_processor(SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           # test that attn processors can be set to itself
           model.set_attn_processor(model.attn_processors)

           with torch.no_grad():
                  _A     :	Union[str, Any]      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.0}).sample
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample
                  _A     :	Optional[int]      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           assert (samplea - samplea).abs().max() < 3e-3
           assert (samplea - samplea).abs().max() < 3e-3

           # sample 2 and sample 3 should be different
           assert (samplea - samplea).abs().max() > 3e-3


    def    A    (  self			:							str):
           _A  , _A     :	Optional[Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Tuple      =					8

           torch.manual_seed(0)
           _A     :	List[str]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	Optional[int]      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Optional[int]      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(SCREAMING_SNAKE_CASE)
                  self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE      ,							'pytorch_lora_weights.bin')))
                  torch.manual_seed(0)
                  _A     :	Dict      =					self.model_class(**SCREAMING_SNAKE_CASE)
                  new_model.to(SCREAMING_SNAKE_CASE)
                  new_model.load_attn_procs(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	List[Any]      =					new_model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           assert (sample - new_sample).abs().max() < 1e-3

           # LoRA and no LoRA should NOT be the same
           assert (sample - old_sample).abs().max() > 1e-4


    def    A    (  self			:							Union[str, Any]):
           _A  , _A     :	Any      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Any      =					8

           torch.manual_seed(0)
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	Any      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Tuple      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(SCREAMING_SNAKE_CASE      ,							safe_serialization=SCREAMING_SNAKE_CASE)
                  self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE      ,							'pytorch_lora_weights.safetensors')))
                  torch.manual_seed(0)
                  _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
                  new_model.to(SCREAMING_SNAKE_CASE)
                  new_model.load_attn_procs(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Optional[int]      =					new_model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.5}).sample

           assert (sample - new_sample).abs().max() < 3e-3

           # LoRA and no LoRA should NOT be the same
           assert (sample - old_sample).abs().max() > 1e-4


    def    A    (  self			:							Optional[int]):
           # enable deterministic behavior for gradient checkpointing
           _A  , _A     :	List[Any]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	int      =					8

           torch.manual_seed(0)
           _A     :	List[str]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           _A     :	List[Any]      =					create_lora_layers(SCREAMING_SNAKE_CASE      ,							mock_weights=SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)
           # Saving as torch, properly reloads with directly filename
           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(SCREAMING_SNAKE_CASE)
                  self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE      ,							'pytorch_lora_weights.bin')))
                  torch.manual_seed(0)
                  _A     :	Dict      =					self.model_class(**SCREAMING_SNAKE_CASE)
                  new_model.to(SCREAMING_SNAKE_CASE)
                  new_model.load_attn_procs(SCREAMING_SNAKE_CASE      ,							weight_name='pytorch_lora_weights.bin')


    def    A    (  self			:							List[Any]):
           _A  , _A     :	Dict      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	Optional[Any]      =					8

           torch.manual_seed(0)
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           _A     :	Dict      =					create_lora_layers(SCREAMING_SNAKE_CASE      ,							mock_weights=SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)
           # Saving as torch, properly reloads with directly filename
           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_attn_procs(SCREAMING_SNAKE_CASE)
                  self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE      ,							'pytorch_lora_weights.bin')))
                  torch.manual_seed(0)
                  _A     :	Union[str, Any]      =					self.model_class(**SCREAMING_SNAKE_CASE)
                  new_model.to(SCREAMING_SNAKE_CASE)
                  with self.assertRaises(SCREAMING_SNAKE_CASE) as e:
                         new_model.load_attn_procs(SCREAMING_SNAKE_CASE      ,							use_safetensors=SCREAMING_SNAKE_CASE)
                  self.assertIn('Error no file named pytorch_lora_weights.safetensors'      ,							str(e.exception))


    def    A    (  self			:							Optional[int]):
           _A  , _A     :	str      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	str      =					8

           torch.manual_seed(0)
           _A     :	Optional[int]      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	Optional[int]      =					model(**SCREAMING_SNAKE_CASE).sample

           _A     :	Optional[int]      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           with torch.no_grad():
                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE      ,							cross_attention_kwargs={'scale': 0.0}).sample

           model.set_attn_processor(AttnProcessor())

           with torch.no_grad():
                  _A     :	List[Any]      =					model(**SCREAMING_SNAKE_CASE).sample

           assert (sample - new_sample).abs().max() < 1e-4
           assert (sample - old_sample).abs().max() < 3e-3


    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()      ,							reason='XFormers attention is only available with CUDA and `xformers` installed'      ,							)
    def    A    (  self			:							List[str]):
           # enable deterministic behavior for gradient checkpointing
           _A  , _A     :	List[str]      =					self.prepare_init_args_and_inputs_for_common()

           _A     :	int      =					4

           torch.manual_seed(0)
           _A     :	str      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           _A     :	str      =					create_lora_layers(SCREAMING_SNAKE_CASE)
           model.set_attn_processor(SCREAMING_SNAKE_CASE)

           # default
           with torch.no_grad():
                  _A     :	str      =					model(**SCREAMING_SNAKE_CASE).sample

                  model.enable_xformers_memory_efficient_attention()
                  _A     :	Tuple      =					model(**SCREAMING_SNAKE_CASE).sample

                  model.disable_xformers_memory_efficient_attention()
                  _A     :	int      =					model(**SCREAMING_SNAKE_CASE).sample

           assert (sample - on_sample).abs().max() < 1e-4
           assert (sample - off_sample).abs().max() < 1e-4


    def    A    (  self			:							Optional[Any]):
           _A  , _A     :	Dict      =					self.prepare_init_args_and_inputs_for_common()
           _A     :	List[Any]      =					32

           _A     :	Any      =					self.model_class(**SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()

           with torch.no_grad():
                  _A     :	Any      =					model(**SCREAMING_SNAKE_CASE)[0]

           model.enable_forward_chunking()
           with torch.no_grad():
                  _A     :	Dict      =					model(**SCREAMING_SNAKE_CASE)[0]

           self.assertEqual(output.shape      ,							output_a.shape      ,							'Shape doesn\'t match')
           assert np.abs(output.cpu() - output_a.cpu()).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.






