



'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class   SCREAMING_SNAKE_CASE__				(   snake_case_     ,			unittest.TestCase					):
	"""simple docstring"""


	A__    :    Optional[int]						=				KandinskyPipeline
	A__    :    Optional[int]						=				[
	    '''prompt''',
	    '''image_embeds''',
	    '''negative_image_embeds''',
	]
	A__    :    List[Any]						=				['''prompt''', '''negative_prompt''', '''image_embeds''', '''negative_image_embeds''']
	A__    :    Optional[int]						=				[
	    '''generator''',
	    '''height''',
	    '''width''',
	    '''latents''',
	    '''guidance_scale''',
	    '''negative_prompt''',
	    '''num_inference_steps''',
	    '''return_dict''',
	    '''guidance_scale''',
	    '''num_images_per_prompt''',
	    '''output_type''',
	    '''return_dict''',
	]
	A__    :    List[str]						=				False


	@property
	def      a__ (		self			) ->							Tuple:
				return 32


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


	@property
	def      a__ (		self			) ->							Optional[int]:
				return self.time_input_dim


	@property
	def      a__ (		self			) ->							Dict:
				return self.time_input_dim * 4


	@property
	def      a__ (		self			) ->							Tuple:
				return 1_00


	@property
	def      a__ (		self			) ->							List[str]:
				A:      Any														=   XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""			)
				return tokenizer


	@property
	def      a__ (		self			) ->							Tuple:
				torch.manual_seed(0			)
				A:      List[str]														=   MCLIPConfig(
				    numDims=self.cross_attention_dim					,   transformerDimensions=self.text_embedder_hidden_size					,   hidden_size=self.text_embedder_hidden_size					,   intermediate_size=37					,   num_attention_heads=4					,   num_hidden_layers=5					,   vocab_size=10_05					,   )

				A:      List[Any]														=   MultilingualCLIP(A			)
				A:      str														=   text_encoder.eval()

				return text_encoder


	@property
	def      a__ (		self			) ->							List[Any]:
				torch.manual_seed(0			)

				A:      List[str]														=   {
				    """in_channels""": 4,
				    # Out channels is double in channels because predicts mean and variance
				    """out_channels""": 8,
				    """addition_embed_type""": """text_image""",
				    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
				    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
				    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
				    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
				    """layers_per_block""": 1,
				    """encoder_hid_dim""": self.text_embedder_hidden_size,
				    """encoder_hid_dim_type""": """text_image_proj""",
				    """cross_attention_dim""": self.cross_attention_dim,
				    """attention_head_dim""": 4,
				    """resnet_time_scale_shift""": """scale_shift""",
				    """class_embed_type""": None,
				}

				A:      List[str]														=   UNetaDConditionModel(**A			)
				return model


	@property
	def      a__ (		self			) ->							List[str]:
				return {
				    "block_out_channels": [32, 64],
				    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
				    "in_channels": 3,
				    "latent_channels": 4,
				    "layers_per_block": 1,
				    "norm_num_groups": 8,
				    "norm_type": "spatial",
				    "num_vq_embeddings": 12,
				    "out_channels": 3,
				    "up_block_types": [
				        "AttnUpDecoderBlock2D",
				        "UpDecoderBlock2D",
				    ],
				    "vq_embed_dim": 4,
				}


	@property
	def      a__ (		self			) ->							Union[str, Any]:
				torch.manual_seed(0			)
				A:      Optional[int]														=   VQModel(**self.dummy_movq_kwargs			)
				return model


	def      a__ (		self			) ->							Optional[Any]:
				A:      str														=   self.dummy_text_encoder
				A:      int														=   self.dummy_tokenizer
				A:      str														=   self.dummy_unet
				A:      List[str]														=   self.dummy_movq

				A:      Any														=   DDIMScheduler(
				    num_train_timesteps=10_00					,   beta_schedule="""linear"""					,   beta_start=0.00085					,   beta_end=0.012					,   clip_sample=A					,   set_alpha_to_one=A					,   steps_offset=1					,   prediction_type="""epsilon"""					,   thresholding=A					,   )

				A:      Any														=   {
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """unet""": unet,
				    """scheduler""": scheduler,
				    """movq""": movq,
				}
				return components


	def      a__ (		self					,   A					,   A=0			) ->							int:
				A:      Any														=   floats_tensor((1, self.cross_attention_dim)					,   rng=random.Random(A			)			).to(A			)
				A:      Optional[Any]														=   floats_tensor((1, self.cross_attention_dim)					,   rng=random.Random(seed + 1			)			).to(A			)
				if str(A			).startswith("""mps"""			):
							A:      List[str]														=   torch.manual_seed(A			)
				else:
							A:      str														=   torch.Generator(device=A			).manual_seed(A			)
				A:      Optional[int]														=   {
				    """prompt""": """horse""",
				    """image_embeds""": image_embeds,
				    """negative_image_embeds""": negative_image_embeds,
				    """generator""": generator,
				    """height""": 64,
				    """width""": 64,
				    """guidance_scale""": 4.0,
				    """num_inference_steps""": 2,
				    """output_type""": """np""",
				}
				return inputs





	def      a__ (		self			) ->							str:
				A:      List[str]														=   """cpu"""

				A:      Optional[Any]														=   self.get_dummy_components()

				A:      str														=   self.pipeline_class(**A			)
				A:      List[str]														=   pipe.to(A			)

				pipe.set_progress_bar_config(disable=A			)

				A:      List[Any]														=   pipe(**self.get_dummy_inputs(A			)			)
				A:      Optional[Any]														=   output.images

				A:      Any														=   pipe(
				    **self.get_dummy_inputs(A			)					,   return_dict=A					,   )[0]

				A:      Union[str, Any]														=   image[0, -3:, -3:, -1]
				A:      str														=   image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				A:      Union[str, Any]														=   np.array(
				    [0.328663, 1.0, 0.23216873, 1.0, 0.92717564, 0.4639046, 0.96894777, 0.31713378, 0.6293953]			)

				assert (
				    np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
				), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
				assert (
				    np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
				), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







@slow
@require_torch_gpu
class   SCREAMING_SNAKE_CASE__				(   unittest.TestCase					):
	"""simple docstring"""


	def      a__ (		self			) ->							Any:
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def      a__ (		self			) ->							Optional[Any]:
				A:      Optional[int]														=   load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""			)

				A:      Union[str, Any]														=   KandinskyPriorPipeline.from_pretrained(
				    """kandinsky-community/kandinsky-2-1-prior"""					,   torch_dtype=torch.floataa			)
				pipe_prior.to(A			)

				A:      Optional[Any]														=   KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""					,   torch_dtype=torch.floataa			)
				A:      Optional[Any]														=   pipeline.to(A			)
				pipeline.set_progress_bar_config(disable=A			)

				A:      Optional[int]														=   """red cat, 4k photo"""

				A:      Dict														=   torch.Generator(device="""cuda"""			).manual_seed(0			)
				A							,		A:      Union[str, Any]														=   pipe_prior(
				    A					,   generator=A					,   num_inference_steps=5					,   negative_prompt=""""""					,   ).to_tuple()

				A:      Optional[Any]														=   torch.Generator(device="""cuda"""			).manual_seed(0			)
				A:      Tuple														=   pipeline(
				    A					,   image_embeds=A					,   negative_image_embeds=A					,   generator=A					,   num_inference_steps=1_00					,   output_type="""np"""					,   )

				A:      int														=   output.images[0]

				assert image.shape == (5_12, 5_12, 3)

				assert_mean_pixel_difference(A					,   A			)










'''simple docstring'''
import numpy as np
import requests



def 		_SCREAMING_SNAKE_CASE							(				):


			'''simple docstring'''







			A:      List[Any]														=   requests.get(
			    """https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"""
			    """master/Week1/ADRvsRating.csv"""				)
			A:      List[str]														=   response.text.splitlines()
			A:      Dict														=   []
			for item in lines:
						A:      int														=   item.split(""","""				)
						data.append(lowerCamelCase__				)
			data.pop(0				)  # This is for removing the labels from the list
			A:      Tuple														=   np.matrix(lowerCamelCase__				)
			return dataset



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      List[str]							,							lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      Dict							,							lowerCamelCase__   :      int				):


			'''simple docstring'''







			A:      Union[str, Any]														=   len_data

			A:      Tuple														=   np.dot(lowerCamelCase__							,							data_x.transpose()				)
			prod -= data_y.transpose()
			A:      Optional[Any]														=   np.dot(lowerCamelCase__							,							lowerCamelCase__				)
			A:      Optional[Any]														=   theta - (alpha / n) * sum_grad
			return theta



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      int							,							lowerCamelCase__   :      Optional[Any]							,							lowerCamelCase__   :      Union[str, Any]							,							lowerCamelCase__   :      Union[str, Any]				):


			'''simple docstring'''







			A:      str														=   np.dot(lowerCamelCase__							,							data_x.transpose()				)
			prod -= data_y.transpose()
			A:      Any														=   np.sum(np.square(lowerCamelCase__				)				)
			A:      List[Any]														=   sum_elem / (2 * len_data)
			return error



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      List[Any]							,							lowerCamelCase__   :      Union[str, Any]				):


			'''simple docstring'''







			A:      Dict														=   100000
			A:      Dict														=   0.0_00_15_50

			A:      Union[str, Any]														=   data_x.shape[1]
			A:      Tuple														=   data_x.shape[0] - 1

			A:      str														=   np.zeros((1, no_features)				)

			for i in range(0							,							lowerCamelCase__				):
						A:      List[str]														=   run_steep_gradient_descent(lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__				)
						A:      str														=   sum_of_square_error(lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__				)
						print(f'At Iteration {i + 1} - Error is {error:.5f}'				)

			return theta



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      List[Any]							,							lowerCamelCase__   :      List[str]				):


			'''simple docstring'''







			A:      List[Any]														=   sum(abs(y - predicted_y[i]				) for i, y in enumerate(lowerCamelCase__				)				)
			return total / len(lowerCamelCase__				)



def 		_SCREAMING_SNAKE_CASE							(				):


			'''simple docstring'''







			A:      Optional[Any]														=   collect_dataset()

			A:      str														=   data.shape[0]
			A:      Union[str, Any]														=   np.c_[np.ones(lowerCamelCase__				), data[:, :-1]].astype(lowerCamelCase__				)
			A:      Any														=   data[:, -1].astype(lowerCamelCase__				)

			A:      Dict														=   run_linear_regression(lowerCamelCase__							,							lowerCamelCase__				)
			A:      Union[str, Any]														=   theta.shape[1]
			print("""Resultant Feature vector : """				)
			for i in range(0							,							lowerCamelCase__				):
						print(f'{theta[0, i]:.5f}'				)


if __name__ == "__main__":
					main()





