



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


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





def 	__snake_case      (     self  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase  ,					_UpperCAmelCase							):
    __a					=							image.to(device=_UpperCAmelCase							)
    __a					=							image  # take image as image_embeddings
    __a					=							image_embeddings.unsqueeze(1							)

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

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

    return image_embeddings

class        _A			(      __UpperCAmelCase       ):



       def __init__(						self				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	PriorTransformer	,						__SCREAMING_SNAKE_CASE				:	CLIPTokenizer	,						__SCREAMING_SNAKE_CASE				:	CLIPTextModelWithProjection	,						__SCREAMING_SNAKE_CASE				:	UnCLIPScheduler	,						__SCREAMING_SNAKE_CASE				:	Optional[dict] = None	,						):


           '''simple docstring'''

           super().__init__()

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

           __a					=							decoder_pipe_kwargs.get('''torch_dtype'''	,						__SCREAMING_SNAKE_CASE) or prior.dtype

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

           # replace `_encode_image` method
           __a					=							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 						_lowerCamelCase     (						self				:	Any	,						__SCREAMING_SNAKE_CASE				:	Any	,						__SCREAMING_SNAKE_CASE				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	Optional[Union[CLIPTextModelOutput, Tuple]] = None	,						__SCREAMING_SNAKE_CASE				:	Optional[torch.Tensor] = None	,						):


           '''simple docstring'''

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

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

               __a					=							self.text_encoder(text_input_ids.to(__SCREAMING_SNAKE_CASE))

               __a					=							text_encoder_output.text_embeds
               __a					=							text_encoder_output.last_hidden_state

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

           __a					=							text_embeddings.repeat_interleave(__SCREAMING_SNAKE_CASE	,						dim=0)
           __a					=							text_encoder_hidden_states.repeat_interleave(__SCREAMING_SNAKE_CASE	,						dim=0)
           __a					=							text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE	,						dim=0)

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

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

               __a					=							uncond_embeddings_text_encoder_output.text_embeds
               __a					=							uncond_embeddings_text_encoder_output.last_hidden_state

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

               __a					=							uncond_embeddings.shape[1]
               __a					=							uncond_embeddings.repeat(1	,						__SCREAMING_SNAKE_CASE)
               __a					=							uncond_embeddings.view(batch_size * num_images_per_prompt	,						__SCREAMING_SNAKE_CASE)

               __a					=							uncond_text_encoder_hidden_states.shape[1]
               __a					=							uncond_text_encoder_hidden_states.repeat(1	,						__SCREAMING_SNAKE_CASE	,						1)
               __a					=							uncond_text_encoder_hidden_states.view(
                   batch_size * num_images_per_prompt	,						__SCREAMING_SNAKE_CASE	,						-1)
               __a					=							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
               __a					=							torch.cat([uncond_embeddings, text_embeddings])
               __a					=							torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])

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

           return text_embeddings, text_encoder_hidden_states, text_mask



       @property
       def 						_lowerCamelCase     (						self				:	int):


           '''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 						_lowerCamelCase     (						self				:	List[str]	,						__SCREAMING_SNAKE_CASE				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	Optional[int]):


           '''simple docstring'''

           if latents is None:
               __a					=							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}')
               __a					=							latents.to(__SCREAMING_SNAKE_CASE)

           __a					=							latents * scheduler.init_noise_sigma
           return latents



       def 						_lowerCamelCase     (						self				:	str	,						__SCREAMING_SNAKE_CASE				:	Optional[Union[str, torch.device]] = None):


           '''simple docstring'''

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





       @torch.no_grad()
       def __call__(						self				:	Any	,						__SCREAMING_SNAKE_CASE				:	Optional[Union[str, List[str]]] = None	,						__SCREAMING_SNAKE_CASE				:	Optional[int] = None	,						__SCREAMING_SNAKE_CASE				:	Optional[int] = None	,						__SCREAMING_SNAKE_CASE				:	int = 1	,						__SCREAMING_SNAKE_CASE				:	int = 25	,						__SCREAMING_SNAKE_CASE				:	Optional[torch.Generator] = None	,						__SCREAMING_SNAKE_CASE				:	Optional[torch.FloatTensor] = None	,						__SCREAMING_SNAKE_CASE				:	Optional[Union[CLIPTextModelOutput, Tuple]] = None	,						__SCREAMING_SNAKE_CASE				:	Optional[torch.Tensor] = None	,						__SCREAMING_SNAKE_CASE				:	float = 4.0	,						__SCREAMING_SNAKE_CASE				:	float = 8.0	,						__SCREAMING_SNAKE_CASE				:	int = 50	,						__SCREAMING_SNAKE_CASE				:	Optional[int] = 1	,						__SCREAMING_SNAKE_CASE				:	float = 0.0	,						__SCREAMING_SNAKE_CASE				:	Optional[str] = "pil"	,						__SCREAMING_SNAKE_CASE				:	bool = True	,						):


           '''simple docstring'''

           if prompt is not None:
               if isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE):
                   __a					=							1
               elif isinstance(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE):
                   __a					=							len(__SCREAMING_SNAKE_CASE)
               else:
                   raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(__SCREAMING_SNAKE_CASE)}')
           else:
               __a					=							text_model_output[0].shape[0]

           __a					=							self._execution_device

           __a					=							batch_size * num_images_per_prompt

           __a					=							prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

           __a			,  __a			,  __a					=							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)
           __a					=							self.prior_scheduler.timesteps

           __a					=							self.prior.config.embedding_dim

           __a					=							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
               __a					=							torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents

               __a					=							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:
                   __a			,  __a					=							predicted_image_embedding.chunk(2)
                   __a					=							predicted_image_embedding_uncond + prior_guidance_scale * (
                       predicted_image_embedding_text - predicted_image_embedding_uncond
                   )

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

               __a					=							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

           __a					=							self.prior.post_process_latents(__SCREAMING_SNAKE_CASE)

           __a					=							prior_latents

           __a					=							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










def 	__snake_case      (     _UpperCAmelCase							):
    __a					=							1
    __a					=							0
    __a					=							1
    __a					=							1
    __a					=							3
    __a					=							3
    __a					=							limit
    __a					=							0

    __a					=							''''''
    while counter != decimal + 1:
        if 4 * q + r - t < n * t:
            result += str(_UpperCAmelCase							)
            if counter == 0:
                result += "."

            if decimal == counter:
                break

            counter += 1
            __a					=							10 * (r - n * t)
            __a					=							((10 * (3 * q + r)) // t) - 10 * n
            q *= 10
            __a					=							nr
        else:
            __a					=							(2 * q + r) * l
            __a					=							(q * (7 * k) + 2 + (r * l)) // (t * l)
            q *= k
            t *= l
            l += 2
            k += 1
            __a					=							nn
            __a					=							nr
    return result





def 	__snake_case      (     ):
    print(f'{calculate_pi(50							) = }'							)
    import doctest

    doctest.testmod()


if __name__ == "__main__":
 main()





