
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


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






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

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

 if do_classifier_free_guidance:
  lowercase					  =   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
  lowercase					  =   torch.cat([uncond_embeddings, image_embeddings]							)

 return image_embeddings







class 	A_       (      __lowerCamelCase       ):




 '''simple docstring'''


 def __init__(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case = None		,   ):
  super().__init__()

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

  lowercase					  =   decoder_pipe_kwargs.get('torch_dtype'		,   snake_case	) or prior.dtype

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

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

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





 def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case = None		,   snake_case = None		,   ):
  if text_model_output is None:
   lowercase					  =   len(snake_case	) if isinstance(snake_case		,   snake_case	) else 1
   # get prompt text embeddings
   lowercase					  =   self.tokenizer(
       snake_case		,   padding='max_length'		,   max_length=self.tokenizer.model_max_length		,   return_tensors='pt'		,   )
   lowercase					  =   text_inputs.input_ids
   lowercase					  =   text_inputs.attention_mask.bool().to(snake_case	)

   if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
    lowercase					  =   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}'''	)
    lowercase					  =   text_input_ids[:, : self.tokenizer.model_max_length]

   lowercase					  =   self.text_encoder(text_input_ids.to(snake_case	)	)

   lowercase					  =   text_encoder_output.text_embeds
   lowercase					  =   text_encoder_output.last_hidden_state

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

  lowercase					  =   text_embeddings.repeat_interleave(snake_case		,   dim=0	)
  lowercase					  =   text_encoder_hidden_states.repeat_interleave(snake_case		,   dim=0	)
  lowercase					  =   text_mask.repeat_interleave(snake_case		,   dim=0	)

  if do_classifier_free_guidance:
   lowercase					  =   [''] * batch_size

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

   lowercase					  =   uncond_embeddings_text_encoder_output.text_embeds
   lowercase					  =   uncond_embeddings_text_encoder_output.last_hidden_state

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

   lowercase					  =   uncond_embeddings.shape[1]
   lowercase					  =   uncond_embeddings.repeat(1		,   snake_case	)
   lowercase					  =   uncond_embeddings.view(batch_size * num_images_per_prompt		,   snake_case	)

   lowercase					  =   uncond_text_encoder_hidden_states.shape[1]
   lowercase					  =   uncond_text_encoder_hidden_states.repeat(1		,   snake_case		,   1	)
   lowercase					  =   uncond_text_encoder_hidden_states.view(
       batch_size * num_images_per_prompt		,   snake_case		,   -1	)
   lowercase					  =   uncond_text_mask.repeat_interleave(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
   lowercase					  =   torch.cat([uncond_embeddings, text_embeddings]	)
   lowercase					  =   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]	)

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

  return text_embeddings, text_encoder_hidden_states, text_mask





 @property
 def      SCREAMING_SNAKE_CASE__						(	self	):
  if self.device != torch.device('meta'	) or not hasattr(self.prior		,   '_hf_hook'	):
   return self.device
  for module in self.prior.modules():
   if (
       hasattr(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		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case	):
  if latents is None:
   lowercase					  =   randn_tensor(snake_case		,   generator=snake_case		,   device=snake_case		,   dtype=snake_case	)
  else:
   if latents.shape != shape:
    raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''	)
   lowercase					  =   latents.to(snake_case	)

  lowercase					  =   latents * scheduler.init_noise_sigma
  return latents





 def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case = None	):
  self.decoder_pipe.to(snake_case	)
  super().to(snake_case	)





 @torch.no_grad()
 def __call__(	self		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = 1		,   snake_case = 25		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = None		,   snake_case = 4.0		,   snake_case = 8.0		,   snake_case = 50		,   snake_case = 1		,   snake_case = 0.0		,   snake_case = "pil"		,   snake_case = True		,   ):
  if prompt is not None:
   if isinstance(snake_case		,   snake_case	):
    lowercase					  =   1
   elif isinstance(snake_case		,   snake_case	):
    lowercase					  =   len(snake_case	)
   else:
    raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case	)}'''	)
  else:
   lowercase					  =   text_model_output[0].shape[0]

  lowercase					  =   self._execution_device

  lowercase					  =   batch_size * num_images_per_prompt

  lowercase					  =   prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

  lowercase  , lowercase  , lowercase					  =   self._encode_prompt(
      snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case	)

  # prior

  self.prior_scheduler.set_timesteps(snake_case		,   device=snake_case	)
  lowercase					  =   self.prior_scheduler.timesteps

  lowercase					  =   self.prior.config.embedding_dim

  lowercase					  =   self.prepare_latents(
      (batch_size, embedding_dim)		,   text_embeddings.dtype		,   snake_case		,   snake_case		,   snake_case		,   self.prior_scheduler		,   )

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

   lowercase					  =   self.prior(
       snake_case		,   timestep=snake_case		,   proj_embedding=snake_case		,   encoder_hidden_states=snake_case		,   attention_mask=snake_case		,   ).predicted_image_embedding

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

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

   lowercase					  =   self.prior_scheduler.step(
       snake_case		,   timestep=snake_case		,   sample=snake_case		,   generator=snake_case		,   prev_timestep=snake_case		,   ).prev_sample

  lowercase					  =   self.prior.post_process_latents(snake_case	)

  lowercase					  =   prior_latents

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



import unittest
from timeit import timeit






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   first_num if first_num >= second_num else second_num
	lowercase					  =   max_num
	while (common_mult % first_num > 0) or (common_mult % second_num > 0):
		common_mult += max_num
	return common_mult






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	return b if a == 0 else greatest_common_divisor(b % a			,							__SCREAMING_SNAKE_CASE							)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	return first_num // greatest_common_divisor(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							) * second_num






def 							UpperCAmelCase_		(							):
	lowercase					  =   (
	    'from __main__ import least_common_multiple_slow, least_common_multiple_fast'
	)
	print(
	    'least_common_multiple_slow():'			,							timeit('least_common_multiple_slow(1000, 999)'			,							setup=__SCREAMING_SNAKE_CASE							)			,							)
	print(
	    'least_common_multiple_fast():'			,							timeit('least_common_multiple_fast(1000, 999)'			,							setup=__SCREAMING_SNAKE_CASE							)			,							)







class 	A_       (      unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Any										=	(
	    (10, 20),
	    (13, 15),
	    (4, 31),
	    (10, 42),
	    (43, 34),
	    (5, 12),
	    (12, 25),
	    (10, 25),
	    (6, 9),
	)
	_UpperCamelCase					:					int										=	(20, 195, 124, 210, 1462, 60, 300, 50, 18)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		for i, (first_num, second_num) in enumerate(self.test_inputs	):
			lowercase					  =   least_common_multiple_slow(snake_case		,   snake_case	)
			lowercase					  =   least_common_multiple_fast(snake_case		,   snake_case	)
			with self.subTest(i=snake_case	):
				self.assertEqual(snake_case		,   self.expected_results[i]	)
				self.assertEqual(snake_case		,   self.expected_results[i]	)


if __name__ == "__main__":
	benchmark()
	unittest.main()

