


'''simple docstring'''
import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np
class    lowercase	:

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__   :int												= TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''							)

							torch.manual_seed(0							)
							UpperCamelCase__   :int												= AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''							)

							torch.manual_seed(0							)
							UpperCamelCase__   :int												= UNetaDConditionModel(
							    sample_size=32    ,   layers_per_block=1    ,   block_out_channels=[32, 64]    ,   down_block_types=[
							        '''ResnetDownsampleBlock2D''',
							        '''SimpleCrossAttnDownBlock2D''',
							    ]    ,   mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''    ,   up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']    ,   in_channels=3    ,   out_channels=6    ,   cross_attention_dim=32    ,   encoder_hid_dim=32    ,   attention_head_dim=8    ,   addition_embed_type='''text'''    ,   addition_embed_type_num_heads=2    ,   cross_attention_norm='''group_norm'''    ,   resnet_time_scale_shift='''scale_shift'''    ,   act_fn='''gelu'''    ,   )
							unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

							torch.manual_seed(0							)
							UpperCamelCase__   :int												= DDPMScheduler(
							    num_train_timesteps=1000    ,   beta_schedule='''squaredcos_cap_v2'''    ,   beta_start=0.0001    ,   beta_end=0.02    ,   thresholding=UpperCamelCase_    ,   dynamic_thresholding_ratio=0.95    ,   sample_max_value=1.0    ,   prediction_type='''epsilon'''    ,   variance_type='''learned_range'''    ,   )

							torch.manual_seed(0							)
							UpperCamelCase__   :Optional[Any]												= IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__   :int												= TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5'''							)

							torch.manual_seed(0							)
							UpperCamelCase__   :List[str]												= AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5'''							)

							torch.manual_seed(0							)
							UpperCamelCase__   :List[str]												= UNetaDConditionModel(
							    sample_size=32    ,   layers_per_block=[1, 2]    ,   block_out_channels=[32, 64]    ,   down_block_types=[
							        '''ResnetDownsampleBlock2D''',
							        '''SimpleCrossAttnDownBlock2D''',
							    ]    ,   mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''    ,   up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']    ,   in_channels=6    ,   out_channels=6    ,   cross_attention_dim=32    ,   encoder_hid_dim=32    ,   attention_head_dim=8    ,   addition_embed_type='''text'''    ,   addition_embed_type_num_heads=2    ,   cross_attention_norm='''group_norm'''    ,   resnet_time_scale_shift='''scale_shift'''    ,   act_fn='''gelu'''    ,   class_embed_type='''timestep'''    ,   mid_block_scale_factor=1.414    ,   time_embedding_act_fn='''gelu'''    ,   time_embedding_dim=32    ,   )
							unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

							torch.manual_seed(0							)
							UpperCamelCase__   :Optional[int]												= DDPMScheduler(
							    num_train_timesteps=1000    ,   beta_schedule='''squaredcos_cap_v2'''    ,   beta_start=0.0001    ,   beta_end=0.02    ,   thresholding=UpperCamelCase_    ,   dynamic_thresholding_ratio=0.95    ,   sample_max_value=1.0    ,   prediction_type='''epsilon'''    ,   variance_type='''learned_range'''    ,   )

							torch.manual_seed(0							)
							UpperCamelCase__   :Tuple												= DDPMScheduler(
							    num_train_timesteps=1000    ,   beta_schedule='''squaredcos_cap_v2'''    ,   beta_start=0.0001    ,   beta_end=0.02    ,   )

							torch.manual_seed(0							)
							UpperCamelCase__   :str												= IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= self.get_dummy_components()
							UpperCamelCase__   :List[Any]												= self.pipeline_class(**UpperCamelCase_							)
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :int												= self.get_dummy_inputs(UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= inputs['''prompt''']
							UpperCamelCase__   :Tuple												= inputs['''generator''']
							UpperCamelCase__   :Optional[Any]												= inputs['''num_inference_steps''']
							UpperCamelCase__   :List[str]												= inputs['''output_type''']

							if "image" in inputs:
												UpperCamelCase__   :Optional[int]												= inputs['''image''']
							else:
												UpperCamelCase__   :Optional[Any]												= None

							if "mask_image" in inputs:
												UpperCamelCase__   :List[Any]												= inputs['''mask_image''']
							else:
												UpperCamelCase__   :Union[str, Any]												= None

							if "original_image" in inputs:
												UpperCamelCase__   :Optional[Any]												= inputs['''original_image''']
							else:
												UpperCamelCase__   :Optional[Any]												= None

							UpperCamelCase__					,				UpperCamelCase__   :Tuple												= pipe.encode_prompt(UpperCamelCase_							)

							# inputs with prompt converted to embeddings
							UpperCamelCase__   :List[str]												= {
							    '''prompt_embeds''': prompt_embeds,
							    '''negative_prompt_embeds''': negative_prompt_embeds,
							    '''generator''': generator,
							    '''num_inference_steps''': num_inference_steps,
							    '''output_type''': output_type,
							}

							if image is not None:
												UpperCamelCase__   :List[str]												= image

							if mask_image is not None:
												UpperCamelCase__   :List[Any]												= mask_image

							if original_image is not None:
												UpperCamelCase__   :Optional[Any]												= original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
												setattr(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)

							UpperCamelCase__   :List[Any]												= pipe(**UpperCamelCase_							)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
												pipe.save_pretrained(UpperCamelCase_							)
												UpperCamelCase__   :Dict												= self.pipeline_class.from_pretrained(UpperCamelCase_							)
												pipe_loaded.to(UpperCamelCase_							)
												pipe_loaded.set_progress_bar_config(disable=UpperCamelCase_							)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

							for optional_component in pipe._optional_components:
												self.assertTrue(
												    getattr(UpperCamelCase_    ,   UpperCamelCase_							) is None    ,   F'''`{optional_component}` did not stay set to None after loading.'''    ,   )

							UpperCamelCase__   :List[Any]												= self.get_dummy_inputs(UpperCamelCase_							)

							UpperCamelCase__   :str												= inputs['''generator''']
							UpperCamelCase__   :Optional[int]												= inputs['''num_inference_steps''']
							UpperCamelCase__   :Any												= inputs['''output_type''']

							# inputs with prompt converted to embeddings
							UpperCamelCase__   :List[str]												= {
							    '''prompt_embeds''': prompt_embeds,
							    '''negative_prompt_embeds''': negative_prompt_embeds,
							    '''generator''': generator,
							    '''num_inference_steps''': num_inference_steps,
							    '''output_type''': output_type,
							}

							if image is not None:
												UpperCamelCase__   :Dict												= image

							if mask_image is not None:
												UpperCamelCase__   :int												= mask_image

							if original_image is not None:
												UpperCamelCase__   :Optional[int]												= original_image

							UpperCamelCase__   :str												= pipe_loaded(**UpperCamelCase_							)[0]

							UpperCamelCase__   :Optional[Any]												= np.abs(to_np(UpperCamelCase_							) - to_np(UpperCamelCase_							)							).max()
							self.assertLess(UpperCamelCase_    ,   1e-4							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Union[str, Any]												= self.get_dummy_components()
							UpperCamelCase__   :List[str]												= self.pipeline_class(**UpperCamelCase_							)
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :Optional[Any]												= self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__   :Optional[int]												= pipe(**UpperCamelCase_							)[0]

							with tempfile.TemporaryDirectory() as tmpdir:
												pipe.save_pretrained(UpperCamelCase_							)
												UpperCamelCase__   :Tuple												= self.pipeline_class.from_pretrained(UpperCamelCase_							)
												pipe_loaded.to(UpperCamelCase_							)
												pipe_loaded.set_progress_bar_config(disable=UpperCamelCase_							)

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()							)  # For reproducibility tests

							UpperCamelCase__   :Tuple												= self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__   :int												= pipe_loaded(**UpperCamelCase_							)[0]

							UpperCamelCase__   :Tuple												= np.abs(to_np(UpperCamelCase_							) - to_np(UpperCamelCase_							)							).max()
							self.assertLess(UpperCamelCase_    ,   1e-4							)
'''simple docstring'''





from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE_:     Optional[int]  =Lock()





def 	lowerCAmelCase_				(					snake_case_       : Dict					,    snake_case_       : Dict					,    snake_case_       : Optional[int]					,    snake_case_       : Tuple					,    snake_case_       : Any					,    snake_case_       : Optional[Any]					,    snake_case_       : Union[str, Any]			)    ->      Optional[Any]:


     '''simple docstring'''




     global process_lock

     # we perform n swaps since after n swaps we know we are sorted
     # we *could* stop early if we are sorted already, but it takes as long to
     # find out we are sorted as it does to sort the list with this algorithm
     for i in range(0					,    10			):
          if (i + position) % 2 == 0 and r_send is not None:
               # send your value to your right neighbor
               process_lock.acquire()
               r_send[1].send(snake_case_			)
               process_lock.release()

               # receive your right neighbor's value
               process_lock.acquire()
               UpperCAmelCase_           =   rr_cv[0].recv()
               process_lock.release()

               # take the lower value since you are on the left
               UpperCAmelCase_           =   min(snake_case_					,    snake_case_			)
          elif (i + position) % 2 != 0 and l_send is not None:
               # send your value to your left neighbor
               process_lock.acquire()
               l_send[1].send(snake_case_			)
               process_lock.release()

               # receive your left neighbor's value
               process_lock.acquire()
               UpperCAmelCase_           =   lr_cv[0].recv()
               process_lock.release()

               # take the higher value since you are on the right
               UpperCAmelCase_           =   max(snake_case_					,    snake_case_			)
    # after all swaps are performed, send the values back to main
     result_pipe[1].send(snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]			)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   []
     UpperCAmelCase_           =   []
     # initialize the list of pipes where the values will be retrieved
     for _ in arr:
          result_pipe.append(Pipe()			)
     # creates the processes
     # the first and last process only have one neighbor so they are made outside
     # of the loop
     UpperCAmelCase_           =   Pipe()
     UpperCAmelCase_           =   Pipe()
     process_array_.append(
         Process(
             target=snake_case_					,    args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])					,    )			)
     UpperCAmelCase_           =   temp_rs
     UpperCAmelCase_           =   temp_rr

     for i in range(1					,    len(snake_case_			) - 1			):
          UpperCAmelCase_           =   Pipe()
          UpperCAmelCase_           =   Pipe()
          process_array_.append(
              Process(
                  target=snake_case_					,    args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])					,    )			)
          UpperCAmelCase_           =   temp_rs
          UpperCAmelCase_           =   temp_rr

     process_array_.append(
         Process(
             target=snake_case_					,    args=(
                 len(snake_case_			) - 1,
                 arr[len(snake_case_			) - 1],
                 temp_ls,
                 None,
                 temp_lr,
                 None,
                 result_pipe[len(snake_case_			) - 1],
             )					,    )			)

     # start the processes
     for p in process_array_:
          p.start()

     # wait for the processes to end and write their values to the list
     for p in range(0					,    len(snake_case_			)			):
          UpperCAmelCase_           =   result_pipe[p][0].recv()
          process_array_[p].join()
     return arr





def 	lowerCAmelCase_				(					)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   list(range(10					,    0					,    -1			)			)
     print("Initial List"			)
     print(*snake_case_			)
     UpperCAmelCase_           =   odd_even_transposition(snake_case_			)
     print("Sorted List\n"			)
     print(*snake_case_			)


if __name__ == "__main__":
    main()




