
"""simple docstring"""

import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	PriorTransformer
      __SCREAMING_SNAKE_CASE   :							Optional[int]							      =	"hidden_states"




      @property
      def     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					4
          UpperCAmelCase									=					8
          UpperCAmelCase									=					7

          UpperCAmelCase									=					floats_tensor((batch_size, embedding_dim)							).to(lowercase_							)

          UpperCAmelCase									=					floats_tensor((batch_size, embedding_dim)							).to(lowercase_							)
          UpperCAmelCase									=					floats_tensor((batch_size, num_embeddings, embedding_dim)							).to(lowercase_							)

          return {
              "hidden_states": hidden_states,
              "timestep": 2,
              "proj_embedding": proj_embedding,
              "encoder_hidden_states": encoder_hidden_states,
          }




      def     a_      (							self   ,       lowercase_=0							)						->					Optional[Any]:
          torch.manual_seed(lowercase_							)
          UpperCAmelCase									=					4
          UpperCAmelCase									=					8
          UpperCAmelCase									=					7

          UpperCAmelCase									=					torch.randn((batch_size, embedding_dim)							).to(lowercase_							)

          UpperCAmelCase									=					torch.randn((batch_size, embedding_dim)							).to(lowercase_							)
          UpperCAmelCase									=					torch.randn((batch_size, num_embeddings, embedding_dim)							).to(lowercase_							)

          return {
              "hidden_states": hidden_states,
              "timestep": 2,
              "proj_embedding": proj_embedding,
              "encoder_hidden_states": encoder_hidden_states,
          }




      @property
      def     a_      (							self							)						->					Dict:
          return (4, 8)




      @property
      def     a_      (							self							)						->					Optional[int]:
          return (4, 8)




      def     a_      (							self							)						->					Any:
          UpperCAmelCase									=					{
              'num_attention_heads': 2,
              'attention_head_dim': 4,
              'num_layers': 2,
              'embedding_dim': 8,
              'num_embeddings': 7,
              'additional_embeddings': 4,
          }
          UpperCAmelCase									=					self.dummy_input
          return init_dict, inputs_dict




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase			,							UpperCAmelCase									=					PriorTransformer.from_pretrained(
              'hf-internal-testing/prior-dummy'   ,       output_loading_info=lowercase_							)
          self.assertIsNotNone(lowercase_							)
          self.assertEqual(len(loading_info['missing_keys']							)   ,       0							)

          model.to(lowercase_							)
          UpperCAmelCase									=					model(**self.dummy_input							)[0]

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




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase			,							UpperCAmelCase									=					self.prepare_init_args_and_inputs_for_common()

          UpperCAmelCase									=					self.model_class(**lowercase_							)
          UpperCAmelCase									=					inspect.signature(model.forward							)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          UpperCAmelCase									=					[*signature.parameters.keys()]

          UpperCAmelCase									=					['hidden_states', 'timestep']
          self.assertListEqual(arg_names[:2]   ,       lowercase_							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'							)
          UpperCAmelCase									=					model.to(lowercase_							)

          if hasattr(lowercase_   ,       'set_default_attn_processor'							):
              model.set_default_attn_processor()

          UpperCAmelCase									=					self.get_dummy_seed_input()

          with torch.no_grad():
              UpperCAmelCase									=					model(**lowercase_							)[0]

          UpperCAmelCase									=					output[0, :5].flatten().cpu()
          print(lowercase_							)

          # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
          # the expected output slices are not the same for CPU and GPU.
          UpperCAmelCase									=					torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]							)
          self.assertTrue(torch_all_close(lowercase_   ,       lowercase_   ,       rtol=1E-2							)							)

@slow
class      _UpperCAmelCase							(		unittest.TestCase							):




      def     a_      (							self   ,       lowercase_=1   ,       lowercase_=7_6_8   ,       lowercase_=7_7   ,       lowercase_=0							)						->					str:
          torch.manual_seed(lowercase_							)
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					embedding_dim
          UpperCAmelCase									=					num_embeddings

          UpperCAmelCase									=					torch.randn((batch_size, embedding_dim)							).to(lowercase_							)

          UpperCAmelCase									=					torch.randn((batch_size, embedding_dim)							).to(lowercase_							)
          UpperCAmelCase									=					torch.randn((batch_size, num_embeddings, embedding_dim)							).to(lowercase_							)

          return {
              "hidden_states": hidden_states,
              "timestep": 2,
              "proj_embedding": proj_embedding,
              "encoder_hidden_states": encoder_hidden_states,
          }




      def     a_      (							self							)						->					List[str]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




      @parameterized.expand(
          [
              # fmt: off
              [1_3, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
              [3_7, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
              # fmt: on
          ]							)
      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					Tuple:
          UpperCAmelCase									=					PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'   ,       subfolder='prior'							)
          model.to(lowercase_							)
          UpperCAmelCase									=					self.get_dummy_seed_input(seed=lowercase_							)

          with torch.no_grad():
              UpperCAmelCase									=					model(**lowercase_							)[0]

          assert list(sample.shape							) == [1, 7_6_8]

          UpperCAmelCase									=					sample[0, :8].flatten().cpu()
          print(lowercase_							)
          UpperCAmelCase									=					torch.tensor(lowercase_							)

          assert torch_all_close(lowercase_   ,       lowercase_   ,       atol=1E-3							)





"""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_    					=						Lock()




def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Optional[Any] ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					Optional[Any] ) ->      Optional[int]:






    """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(lowerCAmelCase )
            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(lowerCAmelCase ,				lowerCAmelCase )
        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(lowerCAmelCase )
            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(lowerCAmelCase ,				lowerCAmelCase )
    # after all swaps are performed, send the values back to main
    result_pipe[1].send(lowerCAmelCase )




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






    """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=lowerCAmelCase ,				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(lowerCAmelCase ) - 1 ):
        UpperCAmelCase									=					Pipe()
        UpperCAmelCase									=					Pipe()
        process_array_.append(
            Process(
                target=lowerCAmelCase ,				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=lowerCAmelCase ,				args=(
                len(lowerCAmelCase ) - 1,
                arr[len(lowerCAmelCase ) - 1],
                temp_ls,
                None,
                temp_lr,
                None,
                result_pipe[len(lowerCAmelCase ) - 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(lowerCAmelCase ) ):
        UpperCAmelCase									=					result_pipe[p][0].recv()
        process_array_[p].join()
    return arr




def     lowercase__						(					) ->      Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase									=					list(range(10 ,				0 ,				-1 ) )
    print('Initial List' )
    print(*lowerCAmelCase )
    UpperCAmelCase									=					odd_even_transposition(lowerCAmelCase )
    print('Sorted List\n' )
    print(*lowerCAmelCase )


if __name__ == "__main__":
   main()



