


from __future__ import annotations







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				list:
  snake_case__						= []
  snake_case__				,				snake_case__						= input_list[low:mid], input_list[mid : high + 1]
  while left and right:
    result.append((left if left[0] <= right[0] else right).pop(0				)				)
  snake_case__						= result + left + right
  return input_list







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				list:
  if len(__lowerCAmelCase				) <= 1:
    return input_list
  snake_case__						= list(__lowerCAmelCase				)

  # iteration for two-way merging
  snake_case__						= 2
  while p <= len(__lowerCAmelCase				):
    # getting low, high and middle value for merge-sort of single list
    for i in range(0							,   len(__lowerCAmelCase				)							,   __lowerCAmelCase				):
      snake_case__						= i
      snake_case__						= i + p - 1
      snake_case__						= (low + high + 1) // 2
      snake_case__						= merge(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)
    # final merge of last two parts
    if p * 2 >= len(__lowerCAmelCase				):
      snake_case__						= i
      snake_case__						= merge(__lowerCAmelCase							,   0							,   __lowerCAmelCase							,   len(__lowerCAmelCase				) - 1				)
      break
    p *= 2

  return input_list


if __name__ == "__main__":
   lowerCamelCase__							:						Optional[Any]							     =   input("""Enter numbers separated by a comma:\n""").strip()
   if user_input == "":
      lowerCamelCase__							:						str							     =   []
   else:
      lowerCamelCase__							:						List[Any]							     =   [int(item.strip()) for item in user_input.split(""",""")]
   print(iter_merge_sort(unsorted))



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 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						PriorTransformer
       __lowerCamelCase				       =						"hidden_states"


       @property
       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=4
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=8
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=7

              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=floats_tensor((batch_size, num_embeddings, embedding_dim)				).to(__A				)

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


       def 	_snake_case				(    self						,       __A=0				)						->    int:
              torch.manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				str		=4
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=8
              SCREAMING_SNAKE_CASE_							:				List[Any]		=7

              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.randn((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				int		=torch.randn((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.randn((batch_size, num_embeddings, embedding_dim)				).to(__A				)

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


       @property
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return (4, 8)


       @property
       def 	_snake_case				(    self				)						->    int:
              return (4, 8)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				List[Any]		={
                  '''num_attention_heads''': 2,
                  '''attention_head_dim''': 4,
                  '''num_layers''': 2,
                  '''embedding_dim''': 8,
                  '''num_embeddings''': 7,
                  '''additional_embeddings''': 4,
              }
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.dummy_input
              return init_dict, inputs_dict


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

              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model(**self.dummy_input				)[0]

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


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.prepare_init_args_and_inputs_for_common()

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.model_class(**__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=['''hidden_states''', '''timestep''']
              self.assertListEqual(arg_names[:2]						,       __A				)





       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Dict		=PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model.to(__A				)

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

              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_dummy_seed_input()

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				str		=model(**__A				)[0]

              SCREAMING_SNAKE_CASE_							:				Any		=output[0, :5].flatten().cpu()
              print(__A				)

              # 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.
              SCREAMING_SNAKE_CASE_							:				int		=torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]				)
              self.assertTrue(torch_all_close(__A						,       __A						,       rtol=1e-2				)				)






@slow
class 			lowercase_		(      unittest.TestCase       ):


       def 	_snake_case				(    self						,       __A=1						,       __A=768						,       __A=77						,       __A=0				)						->    str:
              torch.manual_seed(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=batch_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=embedding_dim
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=num_embeddings

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.randn((batch_size, embedding_dim)				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.randn((batch_size, embedding_dim)				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.randn((batch_size, num_embeddings, embedding_dim)				).to(__A				)

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


       def 	_snake_case				(    self				)						->    Dict:
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()





       @parameterized.expand(
           [
               # fmt: off
               [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
               [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
               # fmt: on
           ]				)
       def 	_snake_case				(    self						,       __A						,       __A				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Dict		=PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''						,       subfolder='''prior'''				)
              model.to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_dummy_seed_input(seed=__A				)

              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				Dict		=model(**__A				)[0]

              assert list(sample.shape				) == [1, 768]

              SCREAMING_SNAKE_CASE_							:				Dict		=sample[0, :8].flatten().cpu()
              print(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor(__A				)

              assert torch_all_close(__A						,       __A						,       atol=1e-3				)
