

"""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      __lowercase			(    __lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       PriorTransformer
  snake_case_				       =       """hidden_states"""

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






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					4
   UpperCAmelCase__					:  int					   =					8
   UpperCAmelCase__					:  Tuple					   =					7

   UpperCAmelCase__					:  List[Any]					   =					floats_tensor((batch_size, embedding_dim)    ).to(__A    )

   UpperCAmelCase__					:  List[Any]					   =					floats_tensor((batch_size, embedding_dim)    ).to(__A    )
   UpperCAmelCase__					:  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    __lowercase       (  self		:			Union[str, Any]					,A		:			List[Any]=0    ):






   '''simple docstring'''
   torch.manual_seed(__A    )
   UpperCAmelCase__					:  Dict					   =					4
   UpperCAmelCase__					:  Union[str, Any]					   =					8
   UpperCAmelCase__					:  Optional[Any]					   =					7

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

   UpperCAmelCase__					:  List[str]					   =					torch.randn((batch_size, embedding_dim)    ).to(__A    )
   UpperCAmelCase__					:  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,
   }

  @property
  def    __lowercase       (  self		:			Dict    ):






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

  @property
  def    __lowercase       (  self		:			Any    ):






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

  def    __lowercase       (  self		:			List[Any]    ):






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

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Union[str, 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    )
   UpperCAmelCase__					:  Optional[Any]					   =					model(**self.dummy_input    )[0]

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

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					self.prepare_init_args_and_inputs_for_common()

   UpperCAmelCase__					:  Any					   =					self.model_class(**__A    )
   UpperCAmelCase__					:  Any					   =					inspect.signature(model.forward    )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   UpperCAmelCase__					:  Tuple					   =					[*signature.parameters.keys()]

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




  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""    )
   UpperCAmelCase__					:  Optional[int]					   =					model.to(__A    )

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

   UpperCAmelCase__					:  List[str]					   =					self.get_dummy_seed_input()

   with torch.no_grad():
    UpperCAmelCase__					:  int					   =					model(**__A    )[0]

   UpperCAmelCase__					:  List[str]					   =					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.
   UpperCAmelCase__					:  int					   =					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(__A					,__A					,rtol=1e-2    )    )







@slow
class      __lowercase			(    unittest.TestCase					):

  def    __lowercase       (  self		:			int					,A		:			Optional[Any]=1					,A		:			List[Any]=768					,A		:			List[str]=77					,A		:			List[Any]=0    ):






   '''simple docstring'''
   torch.manual_seed(__A    )
   UpperCAmelCase__					:  str					   =					batch_size
   UpperCAmelCase__					:  Union[str, Any]					   =					embedding_dim
   UpperCAmelCase__					:  Any					   =					num_embeddings

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

   UpperCAmelCase__					:  Dict					   =					torch.randn((batch_size, embedding_dim)    ).to(__A    )
   UpperCAmelCase__					:  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    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   # clean up the VRAM after each test
   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		:			Any					,A		:			Tuple					,A		:			int    ):






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

   with torch.no_grad():
    UpperCAmelCase__					:  Optional[Any]					   =					model(**__A    )[0]

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

   UpperCAmelCase__					:  Union[str, Any]					   =					sample[0, :8].flatten().cpu()
   print(__A    )
   UpperCAmelCase__					:  Dict					   =					torch.tensor(__A    )

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





"""simple docstring"""




from __future__ import annotations

from typing import Any







class      __lowercase			:

  def __init__(  self		:			Optional[int]					,A		:			int					,A		:			int					,A		:			float = 0    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  str					   =					row, column
   UpperCAmelCase__					:  int					   =					[[default_value for c in range(A    )] for r in range(A    )]

  def __str__(  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					f"Matrix consist of {self.row} rows and {self.column} columns\n"

   # Make string identifier
   UpperCAmelCase__					:  Optional[Any]					   =					0
   for row_vector in self.array:
    for obj in row_vector:
     UpperCAmelCase__					:  Optional[int]					   =					max(A					,len(str(A    )    )    )
   UpperCAmelCase__					:  int					   =					f"%{max_element_length}s"

   # Make string and return
   def single_line(A		:			list[float]    ) -> str:
    nonlocal string_format_identifier
    UpperCAmelCase__					:  List[str]					   =					"""["""
    line += ", ".join(string_format_identifier % (obj,) for obj in row_vector    )
    line += "]"
    return line

   s += "\n".join(single_line(A    ) for row_vector in self.array    )
   return s

  def __repr__(  self		:			Dict    ):






   '''simple docstring'''
   return str(self    )

  def    __lowercase       (  self		:			int					,A		:			tuple[int, int]    ):






   '''simple docstring'''
   if not (isinstance(A					,(list, tuple)    ) and len(A    ) == 2):
    return False
   elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
    return False
   else:
    return True

  def __getitem__(  self		:			Optional[int]					,A		:			tuple[int, int]    ):






   '''simple docstring'''
   assert self.validate_indicies(A    )
   return self.array[loc[0]][loc[1]]

  def __setitem__(  self		:			List[Any]					,A		:			tuple[int, int]					,A		:			float    ):






   '''simple docstring'''
   assert self.validate_indicies(A    )
   UpperCAmelCase__					:  str					   =					value

  def __add__(  self		:			Any					,A		:			Matrix    ):






   '''simple docstring'''
   assert isinstance(A					,A    )
   assert self.row == another.row and self.column == another.column

   # Add
   UpperCAmelCase__					:  Optional[int]					   =					Matrix(self.row					,self.column    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     UpperCAmelCase__					:  Union[str, Any]					   =					self[r, c] + another[r, c]
   return result

  def __neg__(  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					Matrix(self.row					,self.column    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     UpperCAmelCase__					:  int					   =					-self[r, c]
   return result

  def __sub__(  self		:			str					,A		:			Matrix    ):






   '''simple docstring'''
   return self + (-another)

  def __mul__(  self		:			Union[str, Any]					,A		:			int | float | Matrix    ):






   '''simple docstring'''

   if isinstance(A					,(int, float)    ):  # Scalar multiplication
    UpperCAmelCase__					:  Dict					   =					Matrix(self.row					,self.column    )
    for r in range(self.row    ):
     for c in range(self.column    ):
      UpperCAmelCase__					:  Any					   =					self[r, c] * another
    return result
   elif isinstance(A					,A    ):  # Matrix multiplication
    assert self.column == another.row
    UpperCAmelCase__					:  Optional[int]					   =					Matrix(self.row					,another.column    )
    for r in range(self.row    ):
     for c in range(another.column    ):
      for i in range(self.column    ):
       result[r, c] += self[r, i] * another[i, c]
    return result
   else:
    UpperCAmelCase__					:  Union[str, Any]					   =					f"Unsupported type given for another ({type(A    )})"
    raise TypeError(A    )

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					Matrix(self.column					,self.row    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     UpperCAmelCase__					:  Optional[int]					   =					self[r, c]
   return result




  def    __lowercase       (  self		:			Any					,A		:			Matrix					,A		:			Matrix    ):






   '''simple docstring'''
   assert isinstance(A					,A    ) and isinstance(A					,A    )
   assert self.row == self.column == u.row == v.row  # u, v should be column vector
   assert u.column == v.column == 1  # u, v should be column vector

   # Calculate
   UpperCAmelCase__					:  Optional[Any]					   =					v.transpose()
   UpperCAmelCase__					:  Union[str, Any]					   =					(v_t * self * u)[0, 0] + 1
   if numerator_factor == 0:
    return None  # It's not invertable
   return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":
      def   lowerCAmelCase						(					):
       '''simple docstring'''





       UpperCAmelCase__					:  List[str]					   =					Matrix(3							,    3							,    0      )
       for i in range(3      ):
        UpperCAmelCase__					:  List[str]					   =					1
       print(F"a^(-1) is {ainv}"      )
       # u, v
       UpperCAmelCase__					:  List[Any]					   =					Matrix(3							,    1							,    0      )
       UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					1, 2, -3
       UpperCAmelCase__					:  List[str]					   =					Matrix(3							,    1							,    0      )
       UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					4, -2, 5
       print(F"u is {u}"      )
       print(F"v is {v}"      )
       print(F"uv^T is {u * v.transpose()}"      )
       # Sherman Morrison
       print(F"(a + uv^T)^(-1) is {ainv.sherman_morrison(__UpperCamelCase							,    __UpperCamelCase      )}"      )
      def   lowerCAmelCase						(					):
       '''simple docstring'''





       import doctest

       doctest.testmod()




      testa()





