

import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


lowerCAmelCase_ 			=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def 						__SCREAMING_SNAKE_CASE      (self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ ):
						snake_case_     =			image.to(device=SCREAMING_SNAKE_CASE__ )
						snake_case_     =			image  # take image as image_embeddings
						snake_case_     =			image_embeddings.unsqueeze(1 )

						# duplicate image embeddings for each generation per prompt, using mps friendly method
						snake_case_,					snake_case_,					snake_case_     =			image_embeddings.shape
						snake_case_     =			image_embeddings.repeat(1						,	SCREAMING_SNAKE_CASE__						,	1 )
						snake_case_     =			image_embeddings.view(bs_embed * num_images_per_prompt						,	SCREAMING_SNAKE_CASE__						,	-1 )

						if do_classifier_free_guidance:
												snake_case_     =			torch.zeros_like(SCREAMING_SNAKE_CASE__ )

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												snake_case_     =			torch.cat([uncond_embeddings, image_embeddings] )

						return image_embeddings





class 			snake_case_     (	__A      ):







			'''simple docstring'''

			def __init__(  self				:       Tuple							,     _UpperCamelCase				:       PriorTransformer							,     _UpperCamelCase				:       CLIPTokenizer							,     _UpperCamelCase				:       CLIPTextModelWithProjection							,     _UpperCamelCase				:       UnCLIPScheduler							,     _UpperCamelCase				:       Optional[dict] = None							,     )   ->Tuple:
									super().__init__()

									snake_case_     =			{'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

									snake_case_     =			decoder_pipe_kwargs.get('''torch_dtype'''							,     _UpperCamelCase							) or prior.dtype

									snake_case_     =			StableDiffusionImageVariationPipeline.from_pretrained(
									    '''lambdalabs/sd-image-variations-diffusers'''							,     **_UpperCamelCase							)

									# replace `_encode_image` method
									snake_case_     =			types.MethodType(_encode_image							,     self.decoder_pipe							)

									self.register_modules(
									    prior=_UpperCamelCase							,     tokenizer=_UpperCamelCase							,     text_encoder=_UpperCamelCase							,     prior_scheduler=_UpperCamelCase							,     )







			def        snake_case__(  self				:       int							,     _UpperCamelCase				:       Union[str, Any]							,     _UpperCamelCase				:       List[str]							,     _UpperCamelCase				:       List[Any]							,     _UpperCamelCase				:       List[str]							,     _UpperCamelCase				:       Optional[Union[CLIPTextModelOutput, Tuple]] = None							,     _UpperCamelCase				:       Optional[torch.Tensor] = None							,     )   ->Optional[int]:
									if text_model_output is None:
															snake_case_     =			len(_UpperCamelCase							) if isinstance(_UpperCamelCase							,     _UpperCamelCase							) else 1
															# get prompt text embeddings
															snake_case_     =			self.tokenizer(
															    _UpperCamelCase							,     padding='''max_length'''							,     max_length=self.tokenizer.model_max_length							,     return_tensors='''pt'''							,     )
															snake_case_     =			text_inputs.input_ids
															snake_case_     =			text_inputs.attention_mask.bool().to(_UpperCamelCase							)

															if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																					snake_case_     =			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
																					logger.warning(
																					    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																					    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''							)
																					snake_case_     =			text_input_ids[:, : self.tokenizer.model_max_length]

															snake_case_     =			self.text_encoder(text_input_ids.to(_UpperCamelCase							)							)

															snake_case_     =			text_encoder_output.text_embeds
															snake_case_     =			text_encoder_output.last_hidden_state

									else:
															snake_case_     =			text_model_output[0].shape[0]
															snake_case_,					snake_case_     =			text_model_output[0], text_model_output[1]
															snake_case_     =			text_attention_mask

									snake_case_     =			text_embeddings.repeat_interleave(_UpperCamelCase							,     dim=0							)
									snake_case_     =			text_encoder_hidden_states.repeat_interleave(_UpperCamelCase							,     dim=0							)
									snake_case_     =			text_mask.repeat_interleave(_UpperCamelCase							,     dim=0							)

									if do_classifier_free_guidance:
															snake_case_     =			[''''''] * batch_size

															snake_case_     =			self.tokenizer(
															    _UpperCamelCase							,     padding='''max_length'''							,     max_length=self.tokenizer.model_max_length							,     truncation=_UpperCamelCase							,     return_tensors='''pt'''							,     )
															snake_case_     =			uncond_input.attention_mask.bool().to(_UpperCamelCase							)
															snake_case_     =			self.text_encoder(uncond_input.input_ids.to(_UpperCamelCase							)							)

															snake_case_     =			uncond_embeddings_text_encoder_output.text_embeds
															snake_case_     =			uncond_embeddings_text_encoder_output.last_hidden_state

															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

															snake_case_     =			uncond_embeddings.shape[1]
															snake_case_     =			uncond_embeddings.repeat(1							,     _UpperCamelCase							)
															snake_case_     =			uncond_embeddings.view(batch_size * num_images_per_prompt							,     _UpperCamelCase							)

															snake_case_     =			uncond_text_encoder_hidden_states.shape[1]
															snake_case_     =			uncond_text_encoder_hidden_states.repeat(1							,     _UpperCamelCase							,     1							)
															snake_case_     =			uncond_text_encoder_hidden_states.view(
															    batch_size * num_images_per_prompt							,     _UpperCamelCase							,     -1							)
															snake_case_     =			uncond_text_mask.repeat_interleave(_UpperCamelCase							,     dim=0							)

															# done duplicates

															# For classifier free guidance, we need to do two forward passes.
															# Here we concatenate the unconditional and text embeddings into a single batch
															# to avoid doing two forward passes
															snake_case_     =			torch.cat([uncond_embeddings, text_embeddings]							)
															snake_case_     =			torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]							)

															snake_case_     =			torch.cat([uncond_text_mask, text_mask]							)

									return text_embeddings, text_encoder_hidden_states, text_mask







			@property
			def        snake_case__(  self				:       Dict							)   ->int:
									if self.device != torch.device('''meta'''							) or not hasattr(self.prior							,     '''_hf_hook'''							):
															return self.device
									for module in self.prior.modules():
															if (
															    hasattr(_UpperCamelCase							,     '''_hf_hook'''							)
															    and hasattr(module._hf_hook							,     '''execution_device'''							)
															    and module._hf_hook.execution_device is not None
															):
																					return torch.device(module._hf_hook.execution_device							)
									return self.device







			def        snake_case__(  self				:       str							,     _UpperCamelCase				:       Dict							,     _UpperCamelCase				:       int							,     _UpperCamelCase				:       List[str]							,     _UpperCamelCase				:       Optional[Any]							,     _UpperCamelCase				:       int							,     _UpperCamelCase				:       List[str]							)   ->Any:
									if latents is None:
															snake_case_     =			randn_tensor(_UpperCamelCase							,     generator=_UpperCamelCase							,     device=_UpperCamelCase							,     dtype=_UpperCamelCase							)
									else:
															if latents.shape != shape:
																					raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''							)
															snake_case_     =			latents.to(_UpperCamelCase							)

									snake_case_     =			latents * scheduler.init_noise_sigma
									return latents







			def        snake_case__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       Optional[Union[str, torch.device]] = None							)   ->List[str]:
									self.decoder_pipe.to(_UpperCamelCase							)
									super().to(_UpperCamelCase							)







			@torch.no_grad()
			def __call__(  self				:       List[Any]							,     _UpperCamelCase				:       Optional[Union[str, List[str]]] = None							,     _UpperCamelCase				:       Optional[int] = None							,     _UpperCamelCase				:       Optional[int] = None							,     _UpperCamelCase				:       int = 1							,     _UpperCamelCase				:       int = 2_5							,     _UpperCamelCase				:       Optional[torch.Generator] = None							,     _UpperCamelCase				:       Optional[torch.FloatTensor] = None							,     _UpperCamelCase				:       Optional[Union[CLIPTextModelOutput, Tuple]] = None							,     _UpperCamelCase				:       Optional[torch.Tensor] = None							,     _UpperCamelCase				:       float = 4.0							,     _UpperCamelCase				:       float = 8.0							,     _UpperCamelCase				:       int = 5_0							,     _UpperCamelCase				:       Optional[int] = 1							,     _UpperCamelCase				:       float = 0.0							,     _UpperCamelCase				:       Optional[str] = "pil"							,     _UpperCamelCase				:       bool = True							,     )   ->Optional[int]:
									if prompt is not None:
															if isinstance(_UpperCamelCase							,     _UpperCamelCase							):
																					snake_case_     =			1
															elif isinstance(_UpperCamelCase							,     _UpperCamelCase							):
																					snake_case_     =			len(_UpperCamelCase							)
															else:
																					raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase							)}'''							)
									else:
															snake_case_     =			text_model_output[0].shape[0]

									snake_case_     =			self._execution_device

									snake_case_     =			batch_size * num_images_per_prompt

									snake_case_     =			prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

									snake_case_,					snake_case_,					snake_case_     =			self._encode_prompt(
									    _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							)

									# prior

									self.prior_scheduler.set_timesteps(_UpperCamelCase							,     device=_UpperCamelCase							)
									snake_case_     =			self.prior_scheduler.timesteps

									snake_case_     =			self.prior.config.embedding_dim

									snake_case_     =			self.prepare_latents(
									    (batch_size, embedding_dim)							,     text_embeddings.dtype							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     self.prior_scheduler							,     )

									for i, t in enumerate(self.progress_bar(_UpperCamelCase							)							):
															# expand the latents if we are doing classifier free guidance
															snake_case_     =			torch.cat([prior_latents] * 2							) if do_classifier_free_guidance else prior_latents

															snake_case_     =			self.prior(
															    _UpperCamelCase							,     timestep=_UpperCamelCase							,     proj_embedding=_UpperCamelCase							,     encoder_hidden_states=_UpperCamelCase							,     attention_mask=_UpperCamelCase							,     ).predicted_image_embedding

															if do_classifier_free_guidance:
																					snake_case_,					snake_case_     =			predicted_image_embedding.chunk(2							)
																					snake_case_     =			predicted_image_embedding_uncond + prior_guidance_scale * (
																					    predicted_image_embedding_text - predicted_image_embedding_uncond
																					)

															if i + 1 == prior_timesteps_tensor.shape[0]:
																					snake_case_     =			None
															else:
																					snake_case_     =			prior_timesteps_tensor[i + 1]

															snake_case_     =			self.prior_scheduler.step(
															    _UpperCamelCase							,     timestep=_UpperCamelCase							,     sample=_UpperCamelCase							,     generator=_UpperCamelCase							,     prev_timestep=_UpperCamelCase							,     ).prev_sample

									snake_case_     =			self.prior.post_process_latents(_UpperCamelCase							)

									snake_case_     =			prior_latents

									snake_case_     =			self.decoder_pipe(
									    image=_UpperCamelCase							,     height=_UpperCamelCase							,     width=_UpperCamelCase							,     num_inference_steps=_UpperCamelCase							,     guidance_scale=_UpperCamelCase							,     generator=_UpperCamelCase							,     output_type=_UpperCamelCase							,     return_dict=_UpperCamelCase							,     num_images_per_prompt=_UpperCamelCase							,     eta=_UpperCamelCase							,     )
									return output


import random





class 			snake_case_     :







			'''simple docstring'''

			def __init__(  self				:       List[str]							,     _UpperCamelCase				:       list[list[float]]							,     _UpperCamelCase				:       list[int]							,     _UpperCamelCase				:       float = 0.01							,     _UpperCamelCase				:       int = 1_0_0_0							,     _UpperCamelCase				:       float = -1							,     )   ->None:
									snake_case_     =			sample
									if len(self.sample							) == 0:
															raise ValueError('''Sample data can not be empty'''							)
									snake_case_     =			target
									if len(self.target							) == 0:
															raise ValueError('''Target data can not be empty'''							)
									if len(self.sample							) != len(self.target							):
															raise ValueError('''Sample data and Target data do not have matching lengths'''							)
									snake_case_     =			learning_rate
									snake_case_     =			epoch_number
									snake_case_     =			bias
									snake_case_     =			len(_UpperCamelCase							)
									snake_case_     =			len(sample[0]							)  # number of columns in dataset
									snake_case_     =			[]







			def        snake_case__(  self				:       List[str]							)   ->None:
									for sample in self.sample:
															sample.insert(0							,     self.bias							)

									for _ in range(self.col_sample							):
															self.weight.append(random.random()							)

									self.weight.insert(0							,     self.bias							)

									snake_case_     =			0

									while True:
															snake_case_     =			False
															for i in range(self.number_sample							):
																					snake_case_     =			0
																					for j in range(self.col_sample + 1							):
																											snake_case_     =			u + self.weight[j] * self.sample[i][j]
																					snake_case_     =			self.sign(_UpperCamelCase							)
																					if y != self.target[i]:
																											for j in range(self.col_sample + 1							):
																																	snake_case_     =			(
																																	    self.weight[j]
																																	    + self.learning_rate
																																	    * (self.target[i] - y)
																																	    * self.sample[i][j]
																																	)
																											snake_case_     =			True
            # print('Epoch: \n',epoch_count)
															snake_case_     =			epoch_count + 1
															# if you want control the epoch or just by error
															if not has_misclassified:
																					print(('''\nEpoch:\n''', epoch_count)							)
																					print('''------------------------\n'''							)
																					# if epoch_count > self.epoch_number or not error:
																					break







			def        snake_case__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       list[float]							)   ->None:
									if len(self.sample							) == 0:
															raise ValueError('''Sample data can not be empty'''							)
									sample.insert(0							,     self.bias							)
									snake_case_     =			0
									for i in range(self.col_sample + 1							):
															snake_case_     =			u + self.weight[i] * sample[i]

									snake_case_     =			self.sign(_UpperCamelCase							)

									if y == -1:
															print(('''Sample: ''', sample)							)
															print('''classification: P1'''							)
									else:
															print(('''Sample: ''', sample)							)
															print('''classification: P2'''							)







			def        snake_case__(  self				:       str							,     _UpperCamelCase				:       float							)   ->int:
									return 1 if u >= 0 else -1






lowerCAmelCase_ 			=			[
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

lowerCAmelCase_ 			=			[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					lowerCAmelCase_ 			=			Perceptron(
					    sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, bias=-1
					)
					network.training()
					print('''Finished training perceptron''')
					print('''Enter values to predict or q to exit''')
					while True:
										lowerCAmelCase_ 			=			[]
										for i in range(len(samples[0])):
															lowerCAmelCase_ 			=			input('''value: ''').strip()
															if user_input == "q":
																				break
															lowerCAmelCase_ 			=			float(user_input)
															sample.insert(i, observation)
										network.sort(sample)