





'''simple docstring'''






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


lowerCAmelCase       		=      logging.get_logger(__name__)  # pylint: disable=invalid-name
class 		lowerCamelCase  (      _snake_case       ):



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



	def   _lowerCamelCase    (   self		,      a_ = "auto"	):

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



	def   _lowerCamelCase    (   self	):
			self.enable_attention_slicing(a_	)





	@torch.no_grad()
	def __call__(   self		,      a_		,      a_ = 512		,      a_ = 512		,      a_ = 50		,      a_ = 7.5		,      a_ = None		,      a_ = 1		,      a_ = 0.0		,      a_ = None		,      a_ = None		,      a_ = "pil"		,      a_ = True		,      a_ = None		,      a_ = 1		,      a_ = None		,      **a_		,      ):

			if isinstance(a_		,      a_	):
					lowerCAmelCase     :    int						=   1
			elif isinstance(a_		,      a_	):
					lowerCAmelCase     :    Union[str, Any]						=   len(a_	)
			else:
					raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(a_	)}'''	)

			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(a_		,      a_	) or callback_steps <= 0)
			):
					raise ValueError(
					    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
					    F''' {type(a_	)}.'''	)

			# get prompt text embeddings
			lowerCAmelCase     :    Union[str, Any]						=   self.tokenizer(
			    a_		,      padding="max_length"		,      max_length=self.tokenizer.model_max_length		,      return_tensors="pt"		,      )
			lowerCAmelCase     :    List[str]						=   text_inputs.input_ids

			if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
					lowerCAmelCase     :    Any						=   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}'''	)
					lowerCAmelCase     :    Optional[int]						=   text_input_ids[:, : self.tokenizer.model_max_length]

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

			# duplicate text embeddings for each generation per prompt, using mps friendly method
			lowerCAmelCase	,		lowerCAmelCase	,		lowerCAmelCase     :    Optional[Any]						=   text_embeddings.shape
			lowerCAmelCase     :    Optional[int]						=   text_embeddings.repeat(1		,      a_		,      1	)
			lowerCAmelCase     :    Any						=   text_embeddings.view(bs_embed * num_images_per_prompt		,      a_		,      -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.
			lowerCAmelCase     :    Any						=   guidance_scale > 1.0
			# get unconditional embeddings for classifier free guidance
			if do_classifier_free_guidance:
					lowerCAmelCase     :    str						=   42
					if negative_prompt is None:
							lowerCAmelCase     :    Optional[int]						=   [""]
					elif type(a_	) is not type(a_	):
							raise TypeError(
							    F'''`negative_prompt` should be the same type to `prompt`, but got {type(a_	)} !='''
							    F''' {type(a_	)}.'''	)
					elif isinstance(a_		,      a_	):
							lowerCAmelCase     :    str						=   [negative_prompt]
					elif batch_size != len(a_	):
							raise ValueError(
							    F'''`negative_prompt`: {negative_prompt} has batch size {len(a_	)}, but `prompt`:'''
							    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
							    " the batch size of `prompt`."	)
					else:
							lowerCAmelCase     :    str						=   negative_prompt

					lowerCAmelCase     :    Optional[Any]						=   text_input_ids.shape[-1]
					lowerCAmelCase     :    List[Any]						=   self.tokenizer(
					    a_		,      padding="max_length"		,      max_length=a_		,      truncation=a_		,      return_tensors="pt"		,      )
					lowerCAmelCase     :    str						=   self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
					lowerCAmelCase     :    str						=   uncond_embeddings.shape[1]
					lowerCAmelCase     :    Union[str, Any]						=   uncond_embeddings.repeat(a_		,      a_		,      1	)
					lowerCAmelCase     :    Tuple						=   uncond_embeddings.view(batch_size * num_images_per_prompt		,      a_		,      -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
					lowerCAmelCase     :    Tuple						=   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`.
			lowerCAmelCase     :    List[str]						=   (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
			lowerCAmelCase     :    Union[str, Any]						=   (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
			lowerCAmelCase     :    Any						=   text_embeddings.dtype
			if latents is None:
					if self.device.type == "mps":
							# randn does not exist on mps
							lowerCAmelCase     :    Optional[int]						=   torch.randn(
							    a_		,      generator=a_		,      device="cpu"		,      dtype=a_	).to(self.device	)
							lowerCAmelCase     :    Tuple						=   torch.randn(a_		,      generator=a_		,      device="cpu"		,      dtype=a_	).to(
							    self.device	)
					else:
							lowerCAmelCase     :    int						=   torch.randn(
							    a_		,      generator=a_		,      device=self.device		,      dtype=a_	)
							lowerCAmelCase     :    Any						=   torch.randn(a_		,      generator=a_		,      device=self.device		,      dtype=a_	)
			else:
					if latents_reference.shape != latents_shape:
							raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''	)
					lowerCAmelCase     :    Dict						=   latents_reference.to(self.device	)
					lowerCAmelCase     :    Optional[Any]						=   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
			lowerCAmelCase     :    Optional[Any]						=   (latents_shape[3] - latents_shape_reference[3]) // 2
			lowerCAmelCase     :    Dict						=   (latents_shape[2] - latents_shape_reference[2]) // 2
			lowerCAmelCase     :    Dict						=   latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
			lowerCAmelCase     :    Tuple						=   latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
			lowerCAmelCase     :    Dict						=   0 if dx < 0 else dx
			lowerCAmelCase     :    Optional[Any]						=   0 if dy < 0 else dy
			lowerCAmelCase     :    int						=   max(-dx		,      0	)
			lowerCAmelCase     :    Optional[Any]						=   max(-dy		,      0	)
			# import pdb
			# pdb.set_trace()
			lowerCAmelCase     :    Optional[int]						=   latents_reference[:, :, dy : dy + h, dx : dx + w]

			# set timesteps
			self.scheduler.set_timesteps(a_	)

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

			# scale the initial noise by the standard deviation required by the scheduler
			lowerCAmelCase     :    str						=   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]
			lowerCAmelCase     :    Dict						=   "eta" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
			lowerCAmelCase     :    List[Any]						=   {}
			if accepts_eta:
					lowerCAmelCase     :    str						=   eta

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

					# predict the noise residual
					lowerCAmelCase     :    str						=   self.unet(a_		,      a_		,      encoder_hidden_states=a_	).sample

					# perform guidance
					if do_classifier_free_guidance:
							lowerCAmelCase	,		lowerCAmelCase     :    Union[str, Any]						=   noise_pred.chunk(2	)
							lowerCAmelCase     :    Union[str, Any]						=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

					# compute the previous noisy sample x_t -> x_t-1
					lowerCAmelCase     :    Any						=   self.scheduler.step(a_		,      a_		,      a_		,      **a_	).prev_sample

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

			lowerCAmelCase     :    Dict						=   1 / 0.18215 * latents
			lowerCAmelCase     :    int						=   self.vae.decode(a_	).sample

			lowerCAmelCase     :    Union[str, Any]						=   (image / 2 + 0.5).clamp(0		,      1	)

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

			if self.safety_checker is not None:
					lowerCAmelCase     :    List[str]						=   self.feature_extractor(self.numpy_to_pil(a_	)		,      return_tensors="pt"	).to(
					    self.device	)
					lowerCAmelCase	,		lowerCAmelCase     :    Union[str, Any]						=   self.safety_checker(
					    images=a_		,      clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype	)	)
			else:
					lowerCAmelCase     :    List[str]						=   None

			if output_type == "pil":
					lowerCAmelCase     :    Union[str, Any]						=   self.numpy_to_pil(a_	)

			if not return_dict:
					return (image, has_nsfw_concept)

			return StableDiffusionPipelineOutput(images=a_		,      nsfw_content_detected=a_	)










'''simple docstring'''






import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser






def      __A						(							a_						:  Any=None      ,a_						:  List[Any]=None	):
		return field(default_factory=lambda: default      ,metadata=a_	)
@dataclass
class 		lowerCamelCase  :
	snake_case_      =   field(
	    metadata={"help": "The csv file to plot."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Disable logarithmic scale when plotting"}      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={
	        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
	    }      ,   )
	snake_case_      =   field(
	    default=_A      ,   metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}      ,   )
	snake_case_      =   list_field(
	    default=_A      ,   metadata={"help": "List of model names that are used instead of the ones in the csv file."}       )






def      __A						(							a_						:  Tuple	):
		try:
				int(a_	)
				return True
		except ValueError:
				return False






def      __A						(							a_						:  int	):
		try:
				float(a_	)
				return True
		except ValueError:
				return False
class 		lowerCamelCase  :



	def __init__(   self		,      a_	):
			lowerCAmelCase     :    Optional[Any]						=   args
			lowerCAmelCase     :    List[str]						=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

			with open(self.args.csv_file		,      newline=""	) as csv_file:
					lowerCAmelCase     :    str						=   csv.DictReader(a_	)
					for row in reader:
							lowerCAmelCase     :    Tuple						=   row["model"]
							self.result_dict[model_name]["bsz"].append(int(row["batch_size"]	)	)
							self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]	)	)
							if can_convert_to_int(row["result"]	):
									# value is not None
									lowerCAmelCase     :    Union[str, Any]						=   int(row["result"]	)
							elif can_convert_to_float(row["result"]	):
									# value is not None
									lowerCAmelCase     :    Optional[int]						=   float(row["result"]	)





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase	,		lowerCAmelCase     :    Any						=   plt.subplots()
			lowerCAmelCase     :    int						=   "Time usage" if self.args.is_time else "Memory usage"
			lowerCAmelCase     :    List[Any]						=   title_str + " for training" if self.args.is_train else title_str + " for inference"

			if not self.args.no_log_scale:
					# set logarithm scales
					ax.set_xscale("log"	)
					ax.set_yscale("log"	)

			for axis in [ax.xaxis, ax.yaxis]:
					axis.set_major_formatter(ScalarFormatter()	)

			for model_name_idx, model_name in enumerate(self.result_dict.keys()	):
					lowerCAmelCase     :    str						=   sorted(set(self.result_dict[model_name]["bsz"]	)	)
					lowerCAmelCase     :    List[str]						=   sorted(set(self.result_dict[model_name]["seq_len"]	)	)
					lowerCAmelCase     :    Union[str, Any]						=   self.result_dict[model_name]["result"]

					((lowerCAmelCase)	,		(lowerCAmelCase))     :    str						=   (
					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
					)

					lowerCAmelCase     :    Union[str, Any]						=   (
					    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
					)

					for inner_loop_value in inner_loop_array:
							if self.args.plot_along_batch:
									lowerCAmelCase     :    int						=   np.asarray(
									    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,      dtype=a_		,      )
							else:
									lowerCAmelCase     :    Any						=   np.asarray(
									    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,      dtype=np.floataa		,      )

							((lowerCAmelCase)	,		(lowerCAmelCase))     :    Any						=   (
							    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
							)

							lowerCAmelCase     :    Union[str, Any]						=   np.asarray(a_		,      a_	)[: len(a_	)]
							plt.scatter(
							    a_		,      a_		,      label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''	)
							plt.plot(a_		,      a_		,      "--"	)

					title_str += F''' {label_model_name} vs.'''

			lowerCAmelCase     :    List[str]						=   title_str[:-4]
			lowerCAmelCase     :    List[Any]						=   "Time in s" if self.args.is_time else "Memory in MB"

			# plot
			plt.title(a_	)
			plt.xlabel(a_	)
			plt.ylabel(a_	)
			plt.legend()

			if self.args.figure_png_file is not None:
					plt.savefig(self.args.figure_png_file	)
			else:
					plt.show()






def      __A						(							):
		lowerCAmelCase     :    Optional[Any]						=   HfArgumentParser(a_	)
		lowerCAmelCase     :    List[Any]						=   parser.parse_args_into_dataclasses()[0]
		lowerCAmelCase     :    str						=   Plot(args=a_	)
		plot.plot()


if __name__ == "__main__":
					main()



