
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   (						__UpperCamelCase							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    Any 				=    PriorTransformer
   __UpperCamelCase		:    Tuple 				=    '''hidden_states'''


   @property
   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       4
      _A:  List[str]					=       8
      _A:  List[Any]					=       7

      _A:  List[str]					=       floats_tensor((batch_size, embedding_dim)     ).to(lowerCAmelCase_     )

      _A:  List[Any]					=       floats_tensor((batch_size, embedding_dim)     ).to(lowerCAmelCase_     )
      _A:  Optional[Any]					=       floats_tensor((batch_size, num_embeddings, embedding_dim)     ).to(lowerCAmelCase_     )

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


   def  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							Union[str, Any]=0     ):

      """simple docstring"""
      torch.manual_seed(lowerCAmelCase_     )
      _A:  int					=       4
      _A:  Dict					=       8
      _A:  Dict					=       7

      _A:  List[Any]					=       torch.randn((batch_size, embedding_dim)     ).to(lowerCAmelCase_     )

      _A:  str					=       torch.randn((batch_size, embedding_dim)     ).to(lowerCAmelCase_     )
      _A:  List[str]					=       torch.randn((batch_size, num_embeddings, embedding_dim)     ).to(lowerCAmelCase_     )

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


   @property
   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      return (4, 8)


   @property
   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      return (4, 8)


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  Tuple					=       {
          'num_attention_heads': 2,
          'attention_head_dim': 4,
          'num_layers': 2,
          'embedding_dim': 8,
          'num_embeddings': 7,
          'additional_embeddings': 4,
      }
      _A:  List[str]					=       self.dummy_input
      return init_dict, inputs_dict


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      _A:  List[str]					=       PriorTransformer.from_pretrained(
          '''hf-internal-testing/prior-dummy'''			,			output_loading_info=lowerCAmelCase_     )
      self.assertIsNotNone(lowerCAmelCase_     )
      self.assertEqual(len(loading_info['''missing_keys''']     )			,			0     )

      model.to(lowerCAmelCase_     )
      _A:  Optional[int]					=       model(**self.dummy_input     )[0]

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


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      _A:  str					=       self.prepare_init_args_and_inputs_for_common()

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

      _A:  List[Any]					=       ['hidden_states', 'timestep']
      self.assertListEqual(arg_names[:2]			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      _A:  List[str]					=       PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''     )
      _A:  int					=       model.to(lowerCAmelCase_     )

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

      _A:  Optional[int]					=       self.get_dummy_seed_input()

      with torch.no_grad():
         _A:  List[str]					=       model(**lowerCAmelCase_     )[0]

      _A:  List[str]					=       output[0, :5].flatten().cpu()
      print(lowerCAmelCase_     )

      # 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.
      _A:  str					=       torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]     )
      self.assertTrue(torch_all_close(lowerCAmelCase_			,			lowerCAmelCase_			,			rtol=1e-2     )     )






@slow
class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   def  __magic_name__				(					self     :							str			,			lowerCAmelCase_     :							Optional[Any]=1			,			lowerCAmelCase_     :							List[Any]=7_6_8			,			lowerCAmelCase_     :							Tuple=7_7			,			lowerCAmelCase_     :							List[Any]=0     ):

      """simple docstring"""
      torch.manual_seed(lowerCAmelCase_     )
      _A:  List[str]					=       batch_size
      _A:  List[str]					=       embedding_dim
      _A:  Union[str, Any]					=       num_embeddings

      _A:  Dict					=       torch.randn((batch_size, embedding_dim)     ).to(lowerCAmelCase_     )

      _A:  Any					=       torch.randn((batch_size, embedding_dim)     ).to(lowerCAmelCase_     )
      _A:  Dict					=       torch.randn((batch_size, num_embeddings, embedding_dim)     ).to(lowerCAmelCase_     )

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


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


   @parameterized.expand(
       [
           # fmt: off
           [1_3, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
           [3_7, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
           # fmt: on
       ]     )
   def  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Tuple     ):

      """simple docstring"""
      _A:  Any					=       PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''			,			subfolder='''prior'''     )
      model.to(lowerCAmelCase_     )
      _A:  Union[str, Any]					=       self.get_dummy_seed_input(seed=lowerCAmelCase_     )

      with torch.no_grad():
         _A:  List[str]					=       model(**lowerCAmelCase_     )[0]

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

      _A:  List[str]					=       sample[0, :8].flatten().cpu()
      print(lowerCAmelCase_     )
      _A:  Optional[Any]					=       torch.tensor(lowerCAmelCase_     )

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










from __future__ import annotations


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     list[tuple[int, int]]:
    lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		position
    lowerCamelCase__    :      Optional[Any]					 =		[
        (y + 1, x + 2),
        (y - 1, x + 2),
        (y + 1, x - 2),
        (y - 1, x - 2),
        (y + 2, x + 1),
        (y + 2, x - 1),
        (y - 2, x + 1),
        (y - 2, x - 1),
    ]
    lowerCamelCase__    :      Dict					 =		[]

    for position in positions:
        lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		position
        if 0 <= y_test < n and 0 <= x_test < n:
            permissible_positions.append(_UpperCAmelCase )

    return permissible_positions


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     bool:
    return not any(elem == 0 for row in board for elem in row )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     bool:

    if is_complete(_UpperCAmelCase ):
        return True

    for position in get_valid_pos(_UpperCAmelCase						,	len(_UpperCAmelCase ) ):
        lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		position

        if board[y][x] == 0:
            lowerCamelCase__    :      List[Any]					 =		curr + 1
            if open_knight_tour_helper(_UpperCAmelCase						,	_UpperCAmelCase						,	curr + 1 ):
                return True
            lowerCamelCase__    :      Optional[Any]					 =		0

    return False


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     list[list[int]]:
    lowerCamelCase__    :      Any					 =		[[0 for i in range(_UpperCAmelCase )] for j in range(_UpperCAmelCase )]

    for i in range(_UpperCAmelCase ):
        for j in range(_UpperCAmelCase ):
            lowerCamelCase__    :      Optional[int]					 =		1
            if open_knight_tour_helper(_UpperCAmelCase						,	(i, j)						,	1 ):
                return board
            lowerCamelCase__    :      Optional[Any]					 =		0

    lowerCamelCase__    :      Any					 =		F"""Open Kight Tour cannot be performed on a board of size {n}"""
    raise ValueError(_UpperCAmelCase )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





