
'''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     a			(				SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    PriorTransformer
						__UpperCAmelCase			     =    """hidden_states"""







						@property
						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  4
								snake_case__						:       List[str]       						=  8
								snake_case__						:       Any       						=  7

								snake_case__						:       Any       						=  floats_tensor((batch_size, embedding_dim)  ).to(snake_case_  )

								snake_case__						:       Optional[Any]       						=  floats_tensor((batch_size, embedding_dim)  ).to(snake_case_  )
								snake_case__						:       Optional[int]       						=  floats_tensor((batch_size, num_embeddings, embedding_dim)  ).to(snake_case_  )

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







						def       __magic_name__							(							self					:      str			,			snake_case_					:      List[str]=0  ):



								'''simple docstring'''



								torch.manual_seed(snake_case_  )
								snake_case__						:       Optional[Any]       						=  4
								snake_case__						:       List[str]       						=  8
								snake_case__						:       str       						=  7

								snake_case__						:       Any       						=  torch.randn((batch_size, embedding_dim)  ).to(snake_case_  )

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

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







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



								'''simple docstring'''



								return (4, 8)







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



								'''simple docstring'''



								return (4, 8)







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  {
								    '''num_attention_heads''': 2,
								    '''attention_head_dim''': 4,
								    '''num_layers''': 2,
								    '''embedding_dim''': 8,
								    '''num_embeddings''': 7,
								    '''additional_embeddings''': 4,
								}
								snake_case__						:       str       						=  self.dummy_input
								return init_dict, inputs_dict







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  PriorTransformer.from_pretrained(
								    '''hf-internal-testing/prior-dummy'''			,			output_loading_info=snake_case_  )
								self.assertIsNotNone(snake_case_  )
								self.assertEqual(len(loading_info['''missing_keys''']  )			,			0  )

								model.to(snake_case_  )
								snake_case__						:       str       						=  model(**self.dummy_input  )[0]

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







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



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.prepare_init_args_and_inputs_for_common()

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

								snake_case__						:       Optional[Any]       						=  ['''hidden_states''', '''timestep''']
								self.assertListEqual(arg_names[:2]			,			snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''  )
								snake_case__						:       List[str]       						=  model.to(snake_case_  )

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

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

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

								snake_case__						:       Optional[int]       						=  output[0, :5].flatten().cpu()
								print(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.
								snake_case__						:       List[str]       						=  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(snake_case_			,			snake_case_			,			rtol=1e-2  )  )





@slow
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      int=1			,			snake_case_					:      Any=7_6_8			,			snake_case_					:      str=7_7			,			snake_case_					:      int=0  ):



								'''simple docstring'''



								torch.manual_seed(snake_case_  )
								snake_case__						:       Tuple       						=  batch_size
								snake_case__						:       Any       						=  embedding_dim
								snake_case__						:       Any       						=  num_embeddings

								snake_case__						:       Tuple       						=  torch.randn((batch_size, embedding_dim)  ).to(snake_case_  )

								snake_case__						:       Optional[Any]       						=  torch.randn((batch_size, embedding_dim)  ).to(snake_case_  )
								snake_case__						:       List[str]       						=  torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(snake_case_  )

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







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								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       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      Any			,			snake_case_					:      Any  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''			,			subfolder='''prior'''  )
								model.to(snake_case_  )
								snake_case__						:       List[Any]       						=  self.get_dummy_seed_input(seed=snake_case_  )

								with torch.no_grad():
										snake_case__						:       str       						=  model(**snake_case_  )[0]

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

								snake_case__						:       List[str]       						=  sample[0, :8].flatten().cpu()
								print(snake_case_  )
								snake_case__						:       Optional[int]       						=  torch.tensor(snake_case_  )

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






'''simple docstring'''


from __future__ import annotations

from typing import Any




class     a			:






						"""simple docstring"""




						def __init__(							self					:      Tuple			,			snake_case_					:      int			,			snake_case_					:      int			,			snake_case_					:      float = 0  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						:       Optional[Any]       						=  row, column
								snake_case__						:       Dict       						=  [[default_value for c in range(snake_case_  )] for r in range(snake_case_  )]







						def __str__(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

								# Make string identifier
								snake_case__						:       List[str]       						=  0
								for row_vector in self.array:
										for obj in row_vector:
												snake_case__						:       str       						=  max(snake_case_			,			len(str(snake_case_  )  )  )
								snake_case__						:       Dict       						=  F"""%{max_element_length}s"""

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

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







						def __repr__(							self					:      Optional[int]  ):



								'''simple docstring'''



								return str(self  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      tuple[int, int]  ):



								'''simple docstring'''



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







						def __getitem__(							self					:      List[str]			,			snake_case_					:      tuple[int, int]  ):



								'''simple docstring'''



								assert self.validate_indicies(snake_case_  )
								return self.array[loc[0]][loc[1]]







						def __setitem__(							self					:      int			,			snake_case_					:      tuple[int, int]			,			snake_case_					:      float  ):



								'''simple docstring'''



								assert self.validate_indicies(snake_case_  )
								snake_case__						:       Optional[Any]       						=  value







						def __add__(							self					:      Dict			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								assert isinstance(snake_case_			,			snake_case_  )
								assert self.row == another.row and self.column == another.column

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







						def __neg__(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  Matrix(self.row			,			self.column  )
								for r in range(self.row  ):
										for c in range(self.column  ):
												snake_case__						:       str       						=  -self[r, c]
								return result







						def __sub__(							self					:      Optional[int]			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								return self + (-another)







						def __mul__(							self					:      int			,			snake_case_					:      int | float | Matrix  ):



								'''simple docstring'''



								if isinstance(snake_case_			,			(int, float)  ):  # Scalar multiplication
										snake_case__						:       Dict       						=  Matrix(self.row			,			self.column  )
										for r in range(self.row  ):
												for c in range(self.column  ):
														snake_case__						:       Tuple       						=  self[r, c] * another
										return result
								elif isinstance(snake_case_			,			snake_case_  ):  # Matrix multiplication
										assert self.column == another.row
										snake_case__						:       List[Any]       						=  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:
										snake_case__						:       Union[str, Any]       						=  F"""Unsupported type given for another ({type(snake_case_  )})"""
										raise TypeError(snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Matrix			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								assert isinstance(snake_case_			,			snake_case_  ) and isinstance(snake_case_			,			snake_case_  )
								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
								snake_case__						:       Tuple       						=  v.transpose()
								snake_case__						:       Optional[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 	_a       (				):

							"""simple docstring"""

							snake_case__						:       Tuple       						=  Matrix(3   ,							3   ,							0     )
							for i in range(3     ):
									snake_case__						:       Any       						=  1
							print(F"""a^(-1) is {ainv}"""     )
							# u, v
							snake_case__						:       int       						=  Matrix(3   ,							1   ,							0     )
							snake_case__						,       snake_case__						,       snake_case__						:       List[Any]       						=  1, 2, -3
							snake_case__						:       Any       						=  Matrix(3   ,							1   ,							0     )
							snake_case__						,       snake_case__						,       snake_case__						:       Optional[int]       						=  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(__lowerCAmelCase   ,							__lowerCAmelCase     )}"""     )




					def 	_a       (				):

							"""simple docstring"""

							import doctest

							doctest.testmod()





					testa()



