





from __future__ import annotations



class    _a     :






							"""simple docstring"""

							def __init__(      self				:     int							,	a				:     list[list[int]]    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					TypeError(
													    "Matrices must be formed from a list of zero or more lists containing at "
													    "least one and the same number of values, each of which must be of type "
													    "int or float."    )
													if len(a    ) != 0:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					len(rows[0]    )
																			if cols == 0:
																									raise error
																			for row in rows:
																									if len(a    ) != cols:
																															raise error
																									for value in row:
																															if not isinstance(a							,	(int, float)    ):
																																					raise error
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					rows
													else:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					[]

							def      A_  (      self				:     Dict    )	->list[list[int]]:
													return [[row[i] for row in self.rows] for i in range(len(self.rows[0]    )    )]

							@property
							def      A_  (      self				:     str    )	->int:
													return len(self.rows    )

							@property
							def      A_  (      self				:     Optional[int]    )	->int:
													return len(self.rows[0]    )

							@property
							def      A_  (      self				:     Optional[int]    )	->tuple[int, int]:
													return (self.num_rows, self.num_columns)

							@property
							def      A_  (      self				:     Union[str, Any]    )	->bool:
													return self.order[0] == self.order[1]

							def      A_  (      self				:     str    )	->Matrix:
													SCREAMING_SNAKE_CASE__     :				int   =					[
													    [0 if column_num != row_num else 1 for column_num in range(self.num_rows    )]
													    for row_num in range(self.num_rows    )
													]
													return Matrix(a    )

							def      A_  (      self				:     List[str]    )	->int:
													if not self.is_square:
																			return 0
													if self.order == (0, 0):
																			return 1
													if self.order == (1, 1):
																			return int(self.rows[0][0]    )
													if self.order == (2, 2):
																			return int(
																			    (self.rows[0][0] * self.rows[1][1])
																			    - (self.rows[0][1] * self.rows[1][0])    )
													else:
																			return sum(
																			    self.rows[0][column] * self.cofactors().rows[0][column]
																			    for column in range(self.num_columns    )    )

							def      A_  (      self				:     Dict    )	->bool:
													return bool(self.determinant()    )

							def      A_  (      self				:     Tuple							,	a				:     int							,	a				:     int    )	->int:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[
													    [
													        self.rows[other_row][other_column]
													        for other_column in range(self.num_columns    )
													        if other_column != column
													    ]
													    for other_row in range(self.num_rows    )
													    if other_row != row
													]
													return Matrix(a    ).determinant()

							def      A_  (      self				:     Any							,	a				:     int							,	a				:     int    )	->int:
													if (row + column) % 2 == 0:
																			return self.get_minor(a							,	a    )
													return -1 * self.get_minor(a							,	a    )

							def      A_  (      self				:     Optional[int]    )	->Matrix:
													return Matrix(
													    [
													        [self.get_minor(a							,	a    ) for column in range(self.num_columns    )]
													        for row in range(self.num_rows    )
													    ]    )

							def      A_  (      self				:     List[str]    )	->Matrix:
													return Matrix(
													    [
													        [
													            self.minors().rows[row][column]
													            if (row + column) % 2 == 0
													            else self.minors().rows[row][column] * -1
													            for column in range(self.minors().num_columns    )
													        ]
													        for row in range(self.minors().num_rows    )
													    ]    )

							def      A_  (      self				:     Optional[int]    )	->Matrix:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					[
													    [self.cofactors().rows[column][row] for column in range(self.num_columns    )]
													    for row in range(self.num_rows    )
													]
													return Matrix(a    )

							def      A_  (      self				:     Optional[Any]    )	->Matrix:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					self.determinant()
													if not determinant:
																			raise TypeError("Only matrices with a non-zero determinant have an inverse"    )
													return self.adjugate() * (1 / determinant)

							def __repr__(      self				:     Optional[Any]    )	->str:
													return str(self.rows    )

							def __str__(      self				:     str    )	->str:
													if self.num_rows == 0:
																			return "[]"
													if self.num_rows == 1:
																			return "[[" + ". ".join(str(self.rows[0]    )    ) + "]]"
													return (
													    "["
													    + "\n ".join(
													        [
													            "[" + ". ".join([str(a    ) for value in row]    ) + ".]"
													            for row in self.rows
													        ]    )
													    + "]"
													)

							def      A_  (      self				:     Optional[Any]							,	a				:     list[int]							,	a				:     int | None = None    )	->None:
													SCREAMING_SNAKE_CASE__     :				Dict   =					TypeError("Row must be a list containing all ints and/or floats"    )
													if not isinstance(a							,	a    ):
																			raise type_error
													for value in row:
																			if not isinstance(a							,	(int, float)    ):
																									raise type_error
													if len(a    ) != self.num_columns:
																			raise ValueError(
																			    "Row must be equal in length to the other rows in the matrix"    )
													if position is None:
																			self.rows.append(a    )
													else:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					self.rows[0:position] + [row] + self.rows[position:]

							def      A_  (      self				:     Optional[Any]							,	a				:     list[int]							,	a				:     int | None = None    )	->None:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					TypeError(
													    "Column must be a list containing all ints and/or floats"    )
													if not isinstance(a							,	a    ):
																			raise type_error
													for value in column:
																			if not isinstance(a							,	(int, float)    ):
																									raise type_error
													if len(a    ) != self.num_rows:
																			raise ValueError(
																			    "Column must be equal in length to the other columns in the matrix"    )
													if position is None:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					[self.rows[i] + [column[i]] for i in range(self.num_rows    )]
													else:
																			SCREAMING_SNAKE_CASE__     :				str   =					[
																			    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
																			    for i in range(self.num_rows    )
																			]

							def __eq__(      self				:     Tuple							,	a				:     object    )	->bool:
													if not isinstance(a							,	a    ):
																			return NotImplemented
													return self.rows == other.rows

							def __ne__(      self				:     List[Any]							,	a				:     object    )	->bool:
													return not self == other

							def __neg__(      self				:     List[str]    )	->Matrix:
													return self * -1

							def __add__(      self				:     Union[str, Any]							,	a				:     Matrix    )	->Matrix:
													if self.order != other.order:
																			raise ValueError("Addition requires matrices of the same order"    )
													return Matrix(
													    [
													        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns    )]
													        for i in range(self.num_rows    )
													    ]    )

							def __sub__(      self				:     Optional[Any]							,	a				:     Matrix    )	->Matrix:
													if self.order != other.order:
																			raise ValueError("Subtraction requires matrices of the same order"    )
													return Matrix(
													    [
													        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns    )]
													        for i in range(self.num_rows    )
													    ]    )

							def __mul__(      self				:     Tuple							,	a				:     Matrix | int | float    )	->Matrix:
													if isinstance(a							,	(int, float)    ):
																			return Matrix(
																			    [[int(element * other    ) for element in row] for row in self.rows]    )
													elif isinstance(a							,	a    ):
																			if self.num_columns != other.num_rows:
																									raise ValueError(
																									    "The number of columns in the first matrix must "
																									    "be equal to the number of rows in the second"    )
																			return Matrix(
																			    [
																			        [Matrix.dot_product(a							,	a    ) for column in other.columns()]
																			        for row in self.rows
																			    ]    )
													else:
																			raise TypeError(
																			    "A Matrix can only be multiplied by an int, float, or another matrix"    )

							def __pow__(      self				:     List[Any]							,	a				:     int    )	->Matrix:
													if not isinstance(a							,	a    ):
																			raise TypeError("A Matrix can only be raised to the power of an int"    )
													if not self.is_square:
																			raise ValueError("Only square matrices can be raised to a power"    )
													if other == 0:
																			return self.identity()
													if other < 0:
																			if self.is_invertable():
																									return self.inverse() ** (-other)
																			raise ValueError(
																			    "Only invertable matrices can be raised to a negative power"    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self
													for _ in range(other - 1    ):
																			result *= self
													return result






							@classmethod
							def      A_  (      cls				:     List[Any]							,	a				:     list[int]							,	a				:     list[int]    )	->int:
													return sum(row[i] * column[i] for i in range(len(a    )    )    )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax
				import jax.numpy as jnp
				from flax.jax_utils import replicate
				from flax.training.common_utils import shard



@slow
@require_flax
class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def      A_  (      self				:     Dict    )	->List[Any]:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()

							def      A_  (      self				:     Dict    )	->Tuple:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-canny"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Dict   =					"bird"
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe.prepare_image_inputs([canny_image] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				int   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Tuple   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					jnp.array(
													    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2






							def      A_  (      self				:     List[Any]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				int   =					FlaxControlNetModel.from_pretrained(
													    "lllyasviel/sd-controlnet-openpose"							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					FlaxStableDiffusionControlNetPipeline.from_pretrained(
													    "runwayml/stable-diffusion-v1-5"							,	controlnet=a							,	from_pt=a							,	dtype=jnp.bfloataa    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					controlnet_params

													SCREAMING_SNAKE_CASE__     :				Any   =					"Chef in the kitchen"
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jax.device_count()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					pipe.prepare_text_inputs([prompts] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"    )
													SCREAMING_SNAKE_CASE__     :				str   =					pipe.prepare_image_inputs([pose_image] * num_samples    )

													SCREAMING_SNAKE_CASE__     :				Any   =					jax.random.PRNGKey(0    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					jax.random.split(a							,	jax.device_count()    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					replicate(a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					shard(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					shard(a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pipe(
													    prompt_ids=a							,	image=a							,	params=a							,	prng_seed=a							,	num_inference_steps=50							,	jit=a							,	).images
													assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
													SCREAMING_SNAKE_CASE__     :				str   =					images[0, 2_53:2_56, 2_53:2_56, -1]

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.asarray(jax.device_get(image_slice.flatten()    )    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					jnp.array(
													    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]    )
													print(f"""output_slice: {output_slice}"""    )
													assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2