
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       _snake_case   (	UpperCAmelCase_  ,						unittest.TestCase							):
 __lowerCAmelCase  :     List[str]        =			PriorTransformer
 __lowerCAmelCase  :     List[Any]        =			'hidden_states'


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      4
   lowercase__  :   Optional[Any]    		=      8
   lowercase__  :   Tuple    		=      7

   lowercase__  :   Union[str, Any]    		=      floats_tensor((batch_size, embedding_dim)).to(SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[str]    		=      floats_tensor((batch_size, embedding_dim)).to(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Tuple    		=      floats_tensor((batch_size, num_embeddings, embedding_dim)).to(SCREAMING_SNAKE_CASE_)

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


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_=0):


   '''simple docstring'''




   torch.manual_seed(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Any    		=      4
   lowercase__  :   Union[str, Any]    		=      8
   lowercase__  :   List[Any]    		=      7

   lowercase__  :   int    		=      torch.randn((batch_size, embedding_dim)).to(SCREAMING_SNAKE_CASE_)

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

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


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return (4, 8)


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return (4, 8)


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      {
       """num_attention_heads""": 2,
       """attention_head_dim""": 4,
       """num_layers""": 2,
       """embedding_dim""": 8,
       """num_embeddings""": 7,
       """additional_embeddings""": 4,
   }
   lowercase__  :   Optional[int]    		=      self.dummy_input
   return init_dict, inputs_dict


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__		,    lowercase__  :   Optional[int]    		=      PriorTransformer.from_pretrained(
       """hf-internal-testing/prior-dummy"""					,  output_loading_info=SCREAMING_SNAKE_CASE_)
   self.assertIsNotNone(SCREAMING_SNAKE_CASE_)
   self.assertEqual(len(loading_info["""missing_keys"""])					,  0)

   model.to(SCREAMING_SNAKE_CASE_)
   lowercase__  :   str    		=      model(**self.dummy_input)[0]

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


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__		,    lowercase__  :   List[Any]    		=      self.prepare_init_args_and_inputs_for_common()

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

   lowercase__  :   List[str]    		=      ["""hidden_states""", """timestep"""]
   self.assertListEqual(arg_names[:2]					,  SCREAMING_SNAKE_CASE_)



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   str    		=      PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy""")
   lowercase__  :   Any    		=      model.to(SCREAMING_SNAKE_CASE_)

   if hasattr(SCREAMING_SNAKE_CASE_					,  """set_default_attn_processor"""):
     model.set_default_attn_processor()

   lowercase__  :   Optional[Any]    		=      self.get_dummy_seed_input()

   with torch.no_grad():
     lowercase__  :   Union[str, Any]    		=      model(**SCREAMING_SNAKE_CASE_)[0]

   lowercase__  :   str    		=      output[0, :5].flatten().cpu()
   print(SCREAMING_SNAKE_CASE_)

   # 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.
   lowercase__  :   Optional[Any]    		=      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(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  rtol=1E-2))



@slow
class       _snake_case   (	unittest.TestCase							):


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_=1					,  SCREAMING_SNAKE_CASE_=7_68					,  SCREAMING_SNAKE_CASE_=77					,  SCREAMING_SNAKE_CASE_=0):


   '''simple docstring'''




   torch.manual_seed(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Any    		=      batch_size
   lowercase__  :   int    		=      embedding_dim
   lowercase__  :   Union[str, Any]    		=      num_embeddings

   lowercase__  :   Dict    		=      torch.randn((batch_size, embedding_dim)).to(SCREAMING_SNAKE_CASE_)

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

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


 def      lowercase__     (					self):


   '''simple docstring'''




   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()



 @parameterized.expand(
     [
         # fmt: off
         [13, [-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]],
         [37, [-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      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""					,  subfolder="""prior""")
   model.to(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Union[str, Any]    		=      self.get_dummy_seed_input(seed=SCREAMING_SNAKE_CASE_)

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

   assert list(sample.shape) == [1, 7_68]

   lowercase__  :   int    		=      sample[0, :8].flatten().cpu()
   print(SCREAMING_SNAKE_CASE_)
   lowercase__  :   int    		=      torch.tensor(SCREAMING_SNAKE_CASE_)

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








from __future__ import annotations

from collections.abc import Iterator
from typing import Any



class       _snake_case   :


 def __init__(					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__  :   Any    		=      data
   lowercase__  :   Node | None    		=      None



class       _snake_case   :


 def __init__(					self):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      None
   lowercase__  :   int    		=      None


 def __iter__(					self):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      self.head
   while self.head:
     yield node.data
     lowercase__  :   str    		=      node.next
     if node == self.head:
       break


 def __len__(					self):


   '''simple docstring'''




   return sum(1 for _ in self)


 def __repr__(					self):


   '''simple docstring'''




   return "->".join(str(SCREAMING_SNAKE_CASE_) for item in iter(self))


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.insert_nth(len(self)					,  SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   self.insert_nth(0					,  SCREAMING_SNAKE_CASE_)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if index < 0 or index > len(self):
     raise IndexError("""list index out of range.""")
   lowercase__  :   Optional[int]    		=      Node(SCREAMING_SNAKE_CASE_)
   if self.head is None:
     lowercase__  :   Union[str, Any]    		=      new_node  # first node points itself
     lowercase__  :   Dict    		=      new_node
   elif index == 0:  # insert at head
     lowercase__  :   int    		=      self.head
     lowercase__  :   int    		=      new_node
   else:
     lowercase__  :   List[str]    		=      self.head
     for _ in range(index - 1):
       lowercase__  :   Union[str, Any]    		=      temp.next
     lowercase__  :   int    		=      temp.next
     lowercase__  :   Optional[Any]    		=      new_node
     if index == len(self) - 1:  # insert at tail
       lowercase__  :   Union[str, Any]    		=      new_node


 def      lowercase__     (					self):


   '''simple docstring'''




   return self.delete_nth(0)


 def      lowercase__     (					self):


   '''simple docstring'''




   return self.delete_nth(len(self) - 1)


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_ = 0):


   '''simple docstring'''




   if not 0 <= index < len(self):
     raise IndexError("""list index out of range.""")
   lowercase__  :   str    		=      self.head
   if self.head == self.tail:  # just one node
     lowercase__  :   List[Any]    		=      None
   elif index == 0:  # delete head node
     lowercase__  :   str    		=      self.tail.next.next
     lowercase__  :   List[Any]    		=      self.head.next
   else:
     lowercase__  :   Optional[int]    		=      self.head
     for _ in range(index - 1):
       lowercase__  :   List[Any]    		=      temp.next
     lowercase__  :   Optional[Any]    		=      temp.next
     lowercase__  :   Dict    		=      temp.next.next
     if index == len(self) - 1:  # delete at tail
       lowercase__  :   Tuple    		=      temp
   return delete_node.data



 def      lowercase__     (					self):


   '''simple docstring'''




   return len(self) == 0

def 		UpperCamelCase	(      )		->	None:


  '''simple docstring'''






  lowercase__  :   int    		=      CircularLinkedList()
  assert len(lowercase_   ) == 0
  assert circular_linked_list.is_empty() is True
  assert str(lowercase_   ) == ""

  try:
    circular_linked_list.delete_front()
    raise AssertionError  # This should not happen
  except IndexError:
    assert True  # This should happen

  try:
    circular_linked_list.delete_tail()
    raise AssertionError  # This should not happen
  except IndexError:
    assert True  # This should happen

  try:
    circular_linked_list.delete_nth(-1   )
    raise AssertionError
  except IndexError:
    assert True

  try:
    circular_linked_list.delete_nth(0   )
    raise AssertionError
  except IndexError:
    assert True

  assert circular_linked_list.is_empty() is True
  for i in range(5   ):
    assert len(lowercase_   ) == i
    circular_linked_list.insert_nth(lowercase_ , i + 1   )
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(1 , 6   )   )

  circular_linked_list.insert_tail(6   )
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(1 , 7   )   )
  circular_linked_list.insert_head(0   )
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(0 , 7   )   )

  assert circular_linked_list.delete_front() == 0
  assert circular_linked_list.delete_tail() == 6
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(1 , 6   )   )
  assert circular_linked_list.delete_nth(2   ) == 3

  circular_linked_list.insert_nth(2 , 3   )
  assert str(lowercase_   ) == "->".join(str(lowercase_   ) for i in range(1 , 6   )   )

  assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
     import doctest

     doctest.testmod()






