
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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

class 						lowercase_				(				__SCREAMING_SNAKE_CASE ):

 def __init__(					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		):




        """simple docstring"""


        super().__init__()
        self.register_modules(
            vae=__lowerCAmelCase   ,		text_encoder=__lowerCAmelCase   ,		tokenizer=__lowerCAmelCase   ,		unet=__lowerCAmelCase   ,		scheduler=__lowerCAmelCase   ,		safety_checker=__lowerCAmelCase   ,		feature_extractor=__lowerCAmelCase   ,		)

 def lowerCamelCase_ (					self   ,		__UpperCamelCase = "auto"   ):




        """simple docstring"""


        if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               UpperCamelCase_											=				self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(__lowerCAmelCase   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        self.enable_attention_slicing(__lowerCAmelCase   )

 @torch.no_grad()
 def __call__(					self   ,		__UpperCamelCase   ,		__UpperCamelCase = 5_1_2   ,		__UpperCamelCase = 5_1_2   ,		__UpperCamelCase = 5_0   ,		__UpperCamelCase = 7.5   ,		__UpperCamelCase = None   ,		__UpperCamelCase = 1   ,		__UpperCamelCase = 0.0   ,		__UpperCamelCase = None   ,		__UpperCamelCase = None   ,		__UpperCamelCase = "pil"   ,		__UpperCamelCase = True   ,		__UpperCamelCase = None   ,		__UpperCamelCase = 1   ,		__UpperCamelCase = None   ,		**__UpperCamelCase   ,		):




        """simple docstring"""


        if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase   ):
               UpperCamelCase_											=				1
        elif isinstance(__lowerCAmelCase   ,		__lowerCAmelCase   ):
               UpperCamelCase_											=				len(__lowerCAmelCase   )
        else:
               raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(__lowerCAmelCase   )}'''   )

        if height % 8 != 0 or width % 8 != 0:
               raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''   )

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(__lowerCAmelCase   ,		__lowerCAmelCase   ) or callback_steps <= 0)
        ):
               raise ValueError(
                   f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                   f''' {type(__lowerCAmelCase   )}.'''   )

        # get prompt text embeddings
        UpperCamelCase_											=				self.tokenizer(
            __lowerCAmelCase   ,		padding="""max_length"""   ,		max_length=self.tokenizer.model_max_length   ,		return_tensors="""pt"""   ,		)
        UpperCamelCase_											=				text_inputs.input_ids

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

        if text_embeddings is None:
               UpperCamelCase_											=				self.text_encoder(text_input_ids.to(self.device   )   )[0]

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_											=				text_embeddings.shape
        UpperCamelCase_											=				text_embeddings.repeat(1   ,		__lowerCAmelCase   ,		1   )
        UpperCamelCase_											=				text_embeddings.view(bs_embed * num_images_per_prompt   ,		__lowerCAmelCase   ,		-1   )

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        UpperCamelCase_											=				guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
               UpperCamelCase_											=				4_2
               if negative_prompt is None:
                      UpperCamelCase_											=				[""""""]
               elif type(__lowerCAmelCase   ) is not type(__lowerCAmelCase   ):
                      raise TypeError(
                          f'''`negative_prompt` should be the same type to `prompt`, but got {type(__lowerCAmelCase   )} !='''
                          f''' {type(__lowerCAmelCase   )}.'''   )
               elif isinstance(__lowerCAmelCase   ,		__lowerCAmelCase   ):
                      UpperCamelCase_											=				[negative_prompt]
               elif batch_size != len(__lowerCAmelCase   ):
                      raise ValueError(
                          f'''`negative_prompt`: {negative_prompt} has batch size {len(__lowerCAmelCase   )}, but `prompt`:'''
                          f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                          """ the batch size of `prompt`."""   )
               else:
                      UpperCamelCase_											=				negative_prompt

               UpperCamelCase_											=				text_input_ids.shape[-1]
               UpperCamelCase_											=				self.tokenizer(
                   __lowerCAmelCase   ,		padding="""max_length"""   ,		max_length=__lowerCAmelCase   ,		truncation=__lowerCAmelCase   ,		return_tensors="""pt"""   ,		)
               UpperCamelCase_											=				self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

               # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
               UpperCamelCase_											=				uncond_embeddings.shape[1]
               UpperCamelCase_											=				uncond_embeddings.repeat(__lowerCAmelCase   ,		__lowerCAmelCase   ,		1   )
               UpperCamelCase_											=				uncond_embeddings.view(batch_size * num_images_per_prompt   ,		__lowerCAmelCase   ,		-1   )

               # 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
               UpperCamelCase_											=				torch.cat([uncond_embeddings, text_embeddings]   )

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        UpperCamelCase_											=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        UpperCamelCase_											=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
        UpperCamelCase_											=				text_embeddings.dtype
        if latents is None:
               if self.device.type == "mps":
                      # randn does not exist on mps
                      UpperCamelCase_											=				torch.randn(
                          __lowerCAmelCase   ,		generator=__lowerCAmelCase   ,		device="""cpu"""   ,		dtype=__lowerCAmelCase   ).to(self.device   )
                      UpperCamelCase_											=				torch.randn(__lowerCAmelCase   ,		generator=__lowerCAmelCase   ,		device="""cpu"""   ,		dtype=__lowerCAmelCase   ).to(
                          self.device   )
               else:
                      UpperCamelCase_											=				torch.randn(
                          __lowerCAmelCase   ,		generator=__lowerCAmelCase   ,		device=self.device   ,		dtype=__lowerCAmelCase   )
                      UpperCamelCase_											=				torch.randn(__lowerCAmelCase   ,		generator=__lowerCAmelCase   ,		device=self.device   ,		dtype=__lowerCAmelCase   )
        else:
               if latents_reference.shape != latents_shape:
                      raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
               UpperCamelCase_											=				latents_reference.to(self.device   )
               UpperCamelCase_											=				latents.to(self.device   )

        # This is the key part of the pipeline where we
        # try to ensure that the generated images w/ the same seed
        # but different sizes actually result in similar images
        UpperCamelCase_											=				(latents_shape[3] - latents_shape_reference[3]) // 2
        UpperCamelCase_											=				(latents_shape[2] - latents_shape_reference[2]) // 2
        UpperCamelCase_											=				latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
        UpperCamelCase_											=				latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
        UpperCamelCase_											=				0 if dx < 0 else dx
        UpperCamelCase_											=				0 if dy < 0 else dy
        UpperCamelCase_											=				max(-dx   ,		0   )
        UpperCamelCase_											=				max(-dy   ,		0   )
        # import pdb
        # pdb.set_trace()
        UpperCamelCase_											=				latents_reference[:, :, dy : dy + h, dx : dx + w]

        # set timesteps
        self.scheduler.set_timesteps(__lowerCAmelCase   )

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        UpperCamelCase_											=				self.scheduler.timesteps.to(self.device   )

        # scale the initial noise by the standard deviation required by the scheduler
        UpperCamelCase_											=				latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        UpperCamelCase_											=				"""eta""" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
        UpperCamelCase_											=				{}
        if accepts_eta:
               UpperCamelCase_											=				eta

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

               # predict the noise residual
               UpperCamelCase_											=				self.unet(__lowerCAmelCase   ,		__lowerCAmelCase   ,		encoder_hidden_states=__lowerCAmelCase   ).sample

               # perform guidance
               if do_classifier_free_guidance:
                      UpperCamelCase_				, UpperCamelCase_											=				noise_pred.chunk(2   )
                      UpperCamelCase_											=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

               # compute the previous noisy sample x_t -> x_t-1
               UpperCamelCase_											=				self.scheduler.step(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   ,		**__lowerCAmelCase   ).prev_sample

               # call the callback, if provided
               if callback is not None and i % callback_steps == 0:
                      callback(__lowerCAmelCase   ,		__lowerCAmelCase   ,		__lowerCAmelCase   )

        UpperCamelCase_											=				1 / 0.18_215 * latents
        UpperCamelCase_											=				self.vae.decode(__lowerCAmelCase   ).sample

        UpperCamelCase_											=				(image / 2 + 0.5).clamp(0   ,		1   )

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        UpperCamelCase_											=				image.cpu().permute(0   ,		2   ,		3   ,		1   ).float().numpy()

        if self.safety_checker is not None:
               UpperCamelCase_											=				self.feature_extractor(self.numpy_to_pil(__lowerCAmelCase   )   ,		return_tensors="""pt"""   ).to(
                   self.device   )
               UpperCamelCase_				, UpperCamelCase_											=				self.safety_checker(
                   images=__lowerCAmelCase   ,		clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype   )   )
        else:
               UpperCamelCase_											=				None

        if output_type == "pil":
               UpperCamelCase_											=				self.numpy_to_pil(__lowerCAmelCase   )

        if not return_dict:
               return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=__lowerCAmelCase   ,		nsfw_content_detected=__lowerCAmelCase   )










import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


A    :					Dict =      logging.get_logger(__name__)







def    __lowerCamelCase					(							__a   :int=None    ,     __a   :Optional[Any]=None    )		->				int:






		"""simple docstring"""

		return field(default_factory=lambda: default    ,     metadata=__a    )






@dataclass
class   A			:
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    list_field(
			    default=[]   ,     metadata={
			        '''help''': (
			            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
			            ''' of all available models'''
			        )
			    }   ,     )

			__lowerCamelCase	:       List[int]							=    list_field(
			    default=[8]   ,     metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}    )

			__lowerCamelCase	:       List[int]							=    list_field(
			    default=[8, 32, 128, 512]   ,     metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}   ,     )

			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}   ,     )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}   ,     )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Use FP16 to accelerate inference.'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Benchmark training of model'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Verbose memory tracing'''}    )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}   ,     )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
			    }   ,     )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Trace memory line by line'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Save result to a CSV file'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Save all print statements in a log file'''}    )
			__lowerCamelCase	:       bool							=    field(default=SCREAMING_SNAKE_CASE   ,     metadata={'''help''': '''Whether to print environment information'''}    )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': (
			            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
			            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
			            ''' for debugging / testing and on TPU.'''
			        )
			    }   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''inference_time_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving time results to csv.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''inference_memory_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''train_time_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''train_memory_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''env_info_{round(time()    )}.csv'''   ,     metadata={'''help''': '''CSV filename used if saving environment information.'''}   ,     )
			__lowerCamelCase	:       str							=    field(
			    default=F'''log_{round(time()    )}.csv'''   ,     metadata={'''help''': '''Log filename used if print statements are saved in log.'''}   ,     )
			__lowerCamelCase	:       int							=    field(default=3   ,     metadata={'''help''': '''Times an experiment will be run.'''}    )
			__lowerCamelCase	:       bool							=    field(
			    default=SCREAMING_SNAKE_CASE   ,     metadata={
			        '''help''': (
			            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
			            ''' model weights.'''
			        )
			    }   ,     )






			def    a_       (			self   :    Dict   )	->	Union[str, Any]:







					"""simple docstring"""






					warnings.warn(
					    f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
					    """ are deprecated in general and it is advised to use external Benchmarking libraries """
					    """ to benchmark Transformer models."""							,	__lowerCAmelCase							,	)






			def    a_       (			self   :    Union[str, Any]   )	->	List[str]:







					"""simple docstring"""






					return json.dumps(dataclasses.asdict(self   )							,	indent=2   )






			@property
			def    a_       (			self   :    Tuple   )	->	List[str]:







					"""simple docstring"""






					if len(self.models   ) <= 0:
							raise ValueError(
							    """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
							    """ bert-base-cased` or `args.models = ['bert-base-cased']."""   )
					return self.models







			@property
			def    a_       (			self   :    Union[str, Any]   )	->	Optional[Any]:







					"""simple docstring"""






					if not self.multi_process:
							return False
					elif self.is_tpu:
							logger.info("""Multiprocessing is currently not possible on TPU."""   )
							return False
					else:
							return True



