




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference





class 					snake_case_   (     __lowercase   ,	unittest.TestCase	):



				'''simple docstring'''
				lowerCamelCase       =  ShapEPipeline
				lowerCamelCase       =  ['''prompt''']
				lowerCamelCase       =  ['''prompt''']
				lowerCamelCase       =  [
				    '''num_images_per_prompt''',
				    '''num_inference_steps''',
				    '''generator''',
				    '''latents''',
				    '''guidance_scale''',
				    '''frame_size''',
				    '''output_type''',
				    '''return_dict''',
				]
				lowerCamelCase       =  False





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   str							)							->			int:
											return 32





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   List[Any]							)							->			List[Any]:
											return 32





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   Union[str, Any]							)							->			Optional[Any]:
											return self.time_input_dim * 4





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   Tuple							)							->			Union[str, Any]:
											return 8





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   List[str]							)							->			Dict:
											lowerCamelCase_			:    Tuple   						=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)
											return tokenizer





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   Any							)							->			Any:
											torch.manual_seed(0							)
											lowerCamelCase_			:    int   						=      CLIPTextConfig(
											    bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=self.text_embedder_hidden_size      ,							projection_dim=self.text_embedder_hidden_size      ,							intermediate_size=37      ,							layer_norm_eps=1e-05      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=1000      ,							)
											return CLIPTextModelWithProjection(__magic_name__							)





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]							)							->			Optional[int]:
											torch.manual_seed(0							)

											lowerCamelCase_			:    Tuple   						=      {
											    """num_attention_heads""": 2,
											    """attention_head_dim""": 16,
											    """embedding_dim""": self.time_input_dim,
											    """num_embeddings""": 32,
											    """embedding_proj_dim""": self.text_embedder_hidden_size,
											    """time_embed_dim""": self.time_embed_dim,
											    """num_layers""": 1,
											    """clip_embed_dim""": self.time_input_dim * 2,
											    """additional_embeddings""": 0,
											    """time_embed_act_fn""": """gelu""",
											    """norm_in_type""": """layer""",
											    """encoder_hid_proj_type""": None,
											    """added_emb_type""": None,
											}

											lowerCamelCase_			:    Union[str, Any]   						=      PriorTransformer(**__magic_name__							)
											return model





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]							)							->			str:
											torch.manual_seed(0							)

											lowerCamelCase_			:    Dict   						=      {
											    """param_shapes""": (
											        (self.renderer_dim, 93),
											        (self.renderer_dim, 8),
											        (self.renderer_dim, 8),
											        (self.renderer_dim, 8),
											    ),
											    """d_latent""": self.time_input_dim,
											    """d_hidden""": self.renderer_dim,
											    """n_output""": 12,
											    """background""": (
											        0.1,
											        0.1,
											        0.1,
											    ),
											}
											lowerCamelCase_			:    Dict   						=      ShapERenderer(**__magic_name__							)
											return model





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]							)							->			Any:
											lowerCamelCase_			:    List[str]   						=      self.dummy_prior
											lowerCamelCase_			:    Any   						=      self.dummy_text_encoder
											lowerCamelCase_			:    Tuple   						=      self.dummy_tokenizer
											lowerCamelCase_			:    int   						=      self.dummy_renderer

											lowerCamelCase_			:    Tuple   						=      HeunDiscreteScheduler(
											    beta_schedule="exp"      ,							num_train_timesteps=1024      ,							prediction_type="sample"      ,							use_karras_sigmas=__magic_name__      ,							clip_sample=__magic_name__      ,							clip_sample_range=1.0      ,							)
											lowerCamelCase_			:    int   						=      {
											    """prior""": prior,
											    """text_encoder""": text_encoder,
											    """tokenizer""": tokenizer,
											    """renderer""": renderer,
											    """scheduler""": scheduler,
											}

											return components





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[Any]      ,							__magic_name__							:   List[Any]      ,							__magic_name__							:   Any=0							)							->			Union[str, Any]:

											if str(__magic_name__							).startswith("mps"							):
																		lowerCamelCase_			:    List[Any]   						=      torch.manual_seed(__magic_name__							)
											else:
																		lowerCamelCase_			:    Optional[int]   						=      torch.Generator(device=__magic_name__							).manual_seed(__magic_name__							)
											lowerCamelCase_			:    str   						=      {
											    """prompt""": """horse""",
											    """generator""": generator,
											    """num_inference_steps""": 1,
											    """frame_size""": 32,
											    """output_type""": """np""",
											}
											return inputs





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[Any]							)							->			Optional[Any]:
											lowerCamelCase_			:    Tuple   						=      """cpu"""

											lowerCamelCase_			:    Dict   						=      self.get_dummy_components()

											lowerCamelCase_			:    Dict   						=      self.pipeline_class(**__magic_name__							)
											lowerCamelCase_			:    List[Any]   						=      pipe.to(__magic_name__							)

											pipe.set_progress_bar_config(disable=__magic_name__							)

											lowerCamelCase_			:    Optional[Any]   						=      pipe(**self.get_dummy_inputs(__magic_name__							)							)
											lowerCamelCase_			:    str   						=      output.images[0]
											lowerCamelCase_			:    int   						=      image[0, -3:, -3:, -1]

											assert image.shape == (20, 32, 32, 3)

											lowerCamelCase_			:    int   						=      np.array(
											    [
											        0.0003_9216,
											        0.0003_9216,
											        0.0003_9216,
											        0.0003_9216,
											        0.0003_9216,
											        0.0003_9216,
											        0.0003_9216,
											        0.0003_9216,
											        0.0003_9216,
											    ]							)

											assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2





				def    __SCREAMING_SNAKE_CASE     (      self							:   Any							)							->			str:
											self._test_inference_batch_consistent(batch_sizes=[1, 2]							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   int							)							->			List[str]:
											lowerCamelCase_			:    Dict   						=      torch_device == """cpu"""
											lowerCamelCase_			:    int   						=      True

											self._test_inference_batch_single_identical(
											    batch_size=2      ,							test_max_difference=__magic_name__      ,							relax_max_difference=__magic_name__      ,							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Tuple							)							->			Tuple:
											lowerCamelCase_			:    int   						=      self.get_dummy_components()
											lowerCamelCase_			:    List[str]   						=      self.pipeline_class(**__magic_name__							)
											lowerCamelCase_			:    List[Any]   						=      pipe.to(__magic_name__							)
											pipe.set_progress_bar_config(disable=__magic_name__							)

											lowerCamelCase_			:    str   						=      1
											lowerCamelCase_			:    Optional[int]   						=      2

											lowerCamelCase_			:    int   						=      self.get_dummy_inputs(__magic_name__							)

											for key in inputs.keys():
																		if key in self.batch_params:
																									lowerCamelCase_			:    List[str]   						=      batch_size * [inputs[key]]

											lowerCamelCase_			:    Optional[Any]   						=      pipe(**__magic_name__      ,							num_images_per_prompt=__magic_name__							)[0]

											assert images.shape[0] == batch_size * num_images_per_prompt





@slow
@require_torch_gpu
class 					snake_case_   (     unittest.TestCase	):



				'''simple docstring'''





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[Any]							)							->			Optional[int]:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[str]							)							->			List[str]:
											lowerCamelCase_			:    List[Any]   						=      load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
											    "/shap_e/test_shap_e_np_out.npy"							)
											lowerCamelCase_			:    List[Any]   						=      ShapEPipeline.from_pretrained("openai/shap-e"							)
											lowerCamelCase_			:    Optional[int]   						=      pipe.to(__magic_name__							)
											pipe.set_progress_bar_config(disable=__magic_name__							)

											lowerCamelCase_			:    Optional[int]   						=      torch.Generator(device=__magic_name__							).manual_seed(0							)

											lowerCamelCase_			:    Dict   						=      pipe(
											    "a shark"      ,							generator=__magic_name__      ,							guidance_scale=15.0      ,							num_inference_steps=64      ,							frame_size=64      ,							output_type="np"      ,							).images[0]

											assert images.shape == (20, 64, 64, 3)

											assert_mean_pixel_difference(__magic_name__      ,							__magic_name__							)








'''simple docstring'''






from __future__ import annotations

from typing import Any




class  _A :

		def __init__(   self					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int  ,      __magic_name__					:   float = 0			)					->    None:


					"""simple docstring"""

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

		def __str__(   self					:   List[Any]			)					->    str:


					"""simple docstring"""

					__snake_case					:      Dict							      =     f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

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

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

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

		def __repr__(   self					:   Optional[int]			)					->    str:


					"""simple docstring"""

					return str(self			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   tuple[int, int]			)					->    bool:


					"""simple docstring"""

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

		def __getitem__(   self					:   int  ,      __magic_name__					:   tuple[int, int]			)					->    Any:


					"""simple docstring"""

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

		def __setitem__(   self					:   List[str]  ,      __magic_name__					:   tuple[int, int]  ,      __magic_name__					:   float			)					->    None:


					"""simple docstring"""

					assert self.validate_indicies(__magic_name__			)
					__snake_case					:      Optional[int]							      =     value

		def __add__(   self					:   Any  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

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

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

		def __neg__(   self					:   Tuple			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     Matrix(self.row  ,      self.column			)
					for r in range(self.row			):
								for c in range(self.column			):
											__snake_case					:      List[Any]							      =     -self[r, c]
					return result

		def __sub__(   self					:   Optional[int]  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

					return self + (-another)

		def __mul__(   self					:   List[Any]  ,      __magic_name__					:   int | float | Matrix			)					->    Matrix:


					"""simple docstring"""

					if isinstance(__magic_name__  ,      (int, float)			):  # Scalar multiplication
								__snake_case					:      Optional[int]							      =     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(__magic_name__  ,      __magic_name__			):  # Matrix multiplication
								assert self.column == another.row
								__snake_case					:      Dict							      =     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					:      Optional[int]							      =     f'''Unsupported type given for another ({type(__magic_name__			)})'''
								raise TypeError(__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    Matrix:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   Matrix  ,      __magic_name__					:   Matrix			)					->    Any:


					"""simple docstring"""

					assert isinstance(__magic_name__  ,      __magic_name__			) and isinstance(__magic_name__  ,      __magic_name__			)
					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					:      List[str]							      =     v.transpose()
					__snake_case					:      Tuple							      =     (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						(       ) ->				None:



				"""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					:      Dict							      =     Matrix(3    ,	1    ,	0 )
				__snake_case      ,     __snake_case      ,     __snake_case					:      Union[str, Any]							      =     1, 2, -3
				__snake_case					:      str							      =     Matrix(3    ,	1    ,	0 )
				__snake_case      ,     __snake_case      ,     __snake_case					:      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(_lowerCamelCase    ,	_lowerCamelCase )}''' )







	def 					_a						(       ) ->				None:



				"""simple docstring"""


				import doctest

				doctest.testmod()

	testa()


