





from typing import Any

import numpy as np
class 	UpperCamelCase_						:




					def __init__(						self ,  lowerCAmelCase_ ,  lowerCAmelCase_     )						->							int:

										if np.any(tableau[:, -1] ,  where=tableau[:, -1] < 0     ):
															raise ValueError('RHS must be > 0'     )

										_snake_case													= tableau
										_snake_case							,   _snake_case													= tableau.shape

										# Number of decision variables x1, x2, x3...
										_snake_case													= n_vars

										# Number of artificial variables to be minimised
										_snake_case													= len(np.where(tableau[self.n_vars : -1] == -1     )[0]     )

										# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
										_snake_case													= (self.n_art_vars > 0) + 1

										# Number of slack variables added to make inequalities into equalities
										_snake_case													= self.n_rows - self.n_stages

										# Objectives for each stage
										_snake_case													= ['max']

										# In two stage simplex, first minimise then maximise
										if self.n_art_vars:
															self.objectives.append('min'     )

										_snake_case													= ['']

										# Index of current pivot row and column
										_snake_case													= None
										_snake_case													= None

										# Does objective row only contain (non)-negative values?
										_snake_case													= False




					@staticmethod
					def 		lowerCAmelCase	(						*lowerCAmelCase_     )						->							Union[str, Any]:

										if len(__SCREAMING_SNAKE_CASE     ) != 3:
															raise ValueError('Must provide n_vars, n_slack, and n_art_vars'     )

										if not all(x >= 0 and isinstance(__SCREAMING_SNAKE_CASE ,  __SCREAMING_SNAKE_CASE     ) for x in args     ):
															raise ValueError('All arguments must be non-negative integers'     )

										# decision | slack | artificial
										_snake_case													= ['x', 's', 'a']
										_snake_case													= []
										for i in range(3     ):
															for j in range(args[i]     ):
																				titles.append(string_starts[i] + str(j + 1     )     )
										titles.append('RHS'     )
										return titles




					def 		lowerCAmelCase	(						self ,  lowerCAmelCase_     )						->							Any:
										_snake_case													= self.objectives[-1]

										# Find entries of highest magnitude in objective rows
										_snake_case													= (objective == 'min') - (objective == 'max')
										_snake_case													= np.argmax(sign * tableau[0, : self.n_vars]     )

										# Choice is only valid if below 0 for maximise, and above for minimise
										if sign * self.tableau[0, col_idx] <= 0:
															_snake_case													= True
															return 0, 0

										# Pivot row is chosen as having the lowest quotient when elements of
										# the pivot column divide the right-hand side

										# Slice excluding the objective rows
										_snake_case													= slice(self.n_stages ,  self.n_rows     )

										# RHS
										_snake_case													= tableau[s, -1]

										# Elements of pivot column within slice
										_snake_case													= tableau[s, col_idx]

										# Array filled with nans
										_snake_case													= np.full(self.n_rows - self.n_stages ,  np.nan     )

										# If element in pivot column is greater than zeron_stages, return
										# quotient or nan otherwise
										_snake_case													= np.divide(__SCREAMING_SNAKE_CASE ,  __SCREAMING_SNAKE_CASE ,  out=__SCREAMING_SNAKE_CASE ,  where=divisor > 0     )

										# Arg of minimum quotient excluding the nan values. n_stages is added
										# to compensate for earlier exclusion of objective columns
										_snake_case													= np.nanargmin(__SCREAMING_SNAKE_CASE     ) + self.n_stages
										return row_idx, col_idx




					def 		lowerCAmelCase	(						self ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_     )						->							List[str]:
										_snake_case													= tableau[row_idx].copy()

										_snake_case													= piv_row[col_idx]

										# Entry becomes 1
										piv_row *= 1 / piv_val

										# Variable in pivot column becomes basic, ie the only non-zero entry
										for idx, coeff in enumerate(tableau[:, col_idx]     ):
															tableau[idx] += -coeff * piv_row
										_snake_case													= piv_row
										return tableau




					def 		lowerCAmelCase	(						self ,  lowerCAmelCase_     )						->							Union[str, Any]:
										self.objectives.pop()

										if not self.objectives:
															return tableau

										# Slice containing ids for artificial columns
										_snake_case													= slice(-self.n_art_vars - 1 ,  -1     )

										# Delete the artificial variable columns
										_snake_case													= np.delete(__SCREAMING_SNAKE_CASE ,  __SCREAMING_SNAKE_CASE ,  axis=1     )

										# Delete the objective row of the first stage
										_snake_case													= np.delete(__SCREAMING_SNAKE_CASE ,  0 ,  axis=0     )

										_snake_case													= 1
										self.n_rows -= 1
										_snake_case													= 0
										_snake_case													= False
										return tableau




					def 		lowerCAmelCase	(						self     )						->							List[Any]:

										for _ in range(100     ):
															# Completion of each stage removes an objective. If both stages
															# are complete, then no objectives are left
															if not self.objectives:
																				_snake_case													= self.generate_col_titles(
																				    self.n_vars ,  self.n_slack ,  self.n_art_vars     )

																				# Find the values of each variable at optimal solution
																				return self.interpret_tableau(self.tableau ,  self.col_titles     )

															_snake_case							,   _snake_case													= self.find_pivot(self.tableau     )

															# If there are no more negative values in objective row
															if self.stop_iter:
																				# Delete artificial variable columns and rows. Update attributes
																				_snake_case													= self.change_stage(self.tableau     )
															else:
																				_snake_case													= self.pivot(self.tableau ,  __SCREAMING_SNAKE_CASE ,  __SCREAMING_SNAKE_CASE     )
										return {}






					def 		lowerCAmelCase	(						self ,  lowerCAmelCase_ ,  lowerCAmelCase_     )						->							Optional[int]:
										_snake_case													= {'P': abs(tableau[0, -1]     )}

										for i in range(self.n_vars     ):
															# Gives ids of nonzero entries in the ith column
															_snake_case													= np.nonzero(tableau[:, i]     )
															_snake_case													= len(nonzero[0]     )

															# First entry in the nonzero ids
															_snake_case													= nonzero[0][0]
															_snake_case													= tableau[nonzero_rowidx, i]

															# If there is only one nonzero value in column, which is one
															if n_nonzero == nonzero_val == 1:
																				_snake_case													= tableau[nonzero_rowidx, -1]
																				_snake_case													= rhs_val

        # Check for basic variables
										for title in col_titles:
															# Don't add RHS or slack variables to output dict
															if title[0] not in "R-s-a":
																				output_dict.setdefault(__SCREAMING_SNAKE_CASE ,  0     )
										return output_dict


if __name__ == "__main__":
					import doctest

					doctest.testmod()










"""simple docstring"""


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


a__    :   Optional[int]    						= logging.get_logger(__name__)  # pylint: disable=invalid-name





def 							a__	(     self		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     List[str]:

 '''simple docstring'''






 snake_case__										= image.to(device=_UpperCAmelCase		)
 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		,   _UpperCAmelCase		,   1		)
 snake_case__										= image_embeddings.view(bs_embed * num_images_per_prompt		,   _UpperCAmelCase		,   -1		)

 if do_classifier_free_guidance:
  snake_case__										= torch.zeros_like(_UpperCAmelCase		)

  # 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    lowercase_ (   __lowerCAmelCase							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        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"      ,    __SCREAMING_SNAKE_CASE   ) or prior.dtype

        snake_case__										= StableDiffusionImageVariationPipeline.from_pretrained(
            "lambdalabs/sd-image-variations-diffusers"      ,    **__SCREAMING_SNAKE_CASE   )

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

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

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        if text_model_output is None:
         snake_case__										= len(__SCREAMING_SNAKE_CASE   ) if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ) else 1
         # get prompt text embeddings
         snake_case__										= self.tokenizer(
             __SCREAMING_SNAKE_CASE      ,    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(__SCREAMING_SNAKE_CASE   )

         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(__SCREAMING_SNAKE_CASE   )   )

         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(__SCREAMING_SNAKE_CASE      ,    dim=0   )
        snake_case__										= text_encoder_hidden_states.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )
        snake_case__										= text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    dim=0   )

        if do_classifier_free_guidance:
         snake_case__										= [""] * batch_size

         snake_case__										= self.tokenizer(
             __SCREAMING_SNAKE_CASE      ,    padding="max_length"      ,    max_length=self.tokenizer.model_max_length      ,    truncation=__SCREAMING_SNAKE_CASE      ,    return_tensors="pt"      ,    )
         snake_case__										= uncond_input.attention_mask.bool().to(__SCREAMING_SNAKE_CASE   )
         snake_case__										= self.text_encoder(uncond_input.input_ids.to(__SCREAMING_SNAKE_CASE   )   )

         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      ,    __SCREAMING_SNAKE_CASE   )
         snake_case__										= uncond_embeddings.view(batch_size * num_images_per_prompt      ,    __SCREAMING_SNAKE_CASE   )

         snake_case__										= uncond_text_encoder_hidden_states.shape[1]
         snake_case__										= uncond_text_encoder_hidden_states.repeat(1      ,    __SCREAMING_SNAKE_CASE      ,    1   )
         snake_case__										= uncond_text_encoder_hidden_states.view(
             batch_size * num_images_per_prompt      ,    __SCREAMING_SNAKE_CASE      ,    -1   )
         snake_case__										= uncond_text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE      ,    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 						_SCREAMING_SNAKE_CASE(	self   ):






        '''simple docstring'''

        if self.device != torch.device("meta"   ) or not hasattr(self.prior      ,    "_hf_hook"   ):
         return self.device
        for module in self.prior.modules():
         if (
             hasattr(__SCREAMING_SNAKE_CASE      ,    "_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 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        if latents is None:
         snake_case__										= randn_tensor(__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE      ,    dtype=__SCREAMING_SNAKE_CASE   )
        else:
         if latents.shape != shape:
          raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
         snake_case__										= latents.to(__SCREAMING_SNAKE_CASE   )

        snake_case__										= latents * scheduler.init_noise_sigma
        return latents

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE = None   ):






        '''simple docstring'''

        self.decoder_pipe.to(__SCREAMING_SNAKE_CASE   )
        super().to(__SCREAMING_SNAKE_CASE   )

       @torch.no_grad()
       def __call__(	self      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = 1      ,    __SCREAMING_SNAKE_CASE = 25      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = 4.0      ,    __SCREAMING_SNAKE_CASE = 8.0      ,    __SCREAMING_SNAKE_CASE = 50      ,    __SCREAMING_SNAKE_CASE = 1      ,    __SCREAMING_SNAKE_CASE = 0.0      ,    __SCREAMING_SNAKE_CASE = "pil"      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        if prompt is not None:
         if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
          snake_case__										= 1
         elif isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):
          snake_case__										= len(__SCREAMING_SNAKE_CASE   )
         else:
          raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(__SCREAMING_SNAKE_CASE   )}"   )
        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(
            __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   )

        # prior

        self.prior_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE   )
        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      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    self.prior_scheduler      ,    )

        for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE   )   ):
         # 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(
             __SCREAMING_SNAKE_CASE      ,    timestep=__SCREAMING_SNAKE_CASE      ,    proj_embedding=__SCREAMING_SNAKE_CASE      ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE      ,    attention_mask=__SCREAMING_SNAKE_CASE      ,    ).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(
             __SCREAMING_SNAKE_CASE      ,    timestep=__SCREAMING_SNAKE_CASE      ,    sample=__SCREAMING_SNAKE_CASE      ,    generator=__SCREAMING_SNAKE_CASE      ,    prev_timestep=__SCREAMING_SNAKE_CASE      ,    ).prev_sample

        snake_case__										= self.prior.post_process_latents(__SCREAMING_SNAKE_CASE   )

        snake_case__										= prior_latents

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

