





'''simple docstring'''






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

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


A__    :       List[str]              =logging.get_logger(__name__)  # pylint: disable=invalid-name




class 			UpperCAmelCase					(      lowerCamelCase_       ):




						def __init__(      self      :			Any      ,      __snake_case      :			WhisperForConditionalGeneration      ,      __snake_case      :			WhisperProcessor      ,      __snake_case      :			AutoencoderKL      ,      __snake_case      :			CLIPTextModel      ,      __snake_case      :			CLIPTokenizer      ,      __snake_case      :			UNetaDConditionModel      ,      __snake_case      :			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,      __snake_case      :			StableDiffusionSafetyChecker      ,      __snake_case      :			CLIPImageProcessor      ,      )    ->				Tuple:
												super().__init__()

												if safety_checker is None:
																		logger.warning(
																		    f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
																		    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
																		    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
																		    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
																		    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
																		    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""  )

												self.register_modules(
												    speech_model=__snake_case      ,      speech_processor=__snake_case      ,      vae=__snake_case      ,      text_encoder=__snake_case      ,      tokenizer=__snake_case      ,      unet=__snake_case      ,      scheduler=__snake_case      ,      feature_extractor=__snake_case      ,      )




						def     lowercase__   (      self      :			str      ,      __snake_case      :			Optional[Union[str, int]] = "auto"  )    ->				str:
												if slice_size == "auto":
																		_lowerCAmelCase												=							self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(__snake_case  )




						def     lowercase__   (      self      :			Optional[int]  )    ->				Union[str, Any]:
												self.enable_attention_slicing(__snake_case  )




						@torch.no_grad()
						def __call__(      self      :			int      ,      __snake_case      :			Any      ,      __snake_case      :			List[str]=1_60_00      ,      __snake_case      :			int = 5_12      ,      __snake_case      :			int = 5_12      ,      __snake_case      :			int = 50      ,      __snake_case      :			float = 7.5      ,      __snake_case      :			Optional[Union[str, List[str]]] = None      ,      __snake_case      :			Optional[int] = 1      ,      __snake_case      :			float = 0.0      ,      __snake_case      :			Optional[torch.Generator] = None      ,      __snake_case      :			Optional[torch.FloatTensor] = None      ,      __snake_case      :			Optional[str] = "pil"      ,      __snake_case      :			bool = True      ,      __snake_case      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,      __snake_case      :			int = 1      ,      **__snake_case      :			List[str]      ,      )    ->				List[Any]:
												_lowerCAmelCase												=							self.speech_processor.feature_extractor(
												    __snake_case      ,      return_tensors="""pt"""      ,      sampling_rate=__snake_case  ).input_features.to(self.device  )
												_lowerCAmelCase												=							self.speech_model.generate(__snake_case      ,      max_length=48_00_00  )

												_lowerCAmelCase												=							self.speech_processor.tokenizer.batch_decode(__snake_case      ,      skip_special_tokens=__snake_case      ,      normalize=__snake_case  )[
												    0
												]

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

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

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

												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		_lowerCAmelCase												=							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												=							text_input_ids[:, : self.tokenizer.model_max_length]
												_lowerCAmelCase												=							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												=							text_embeddings.shape
												_lowerCAmelCase												=							text_embeddings.repeat(1      ,      __snake_case      ,      1  )
												_lowerCAmelCase												=							text_embeddings.view(bs_embed * num_images_per_prompt      ,      __snake_case      ,      -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												=							guidance_scale > 1.0
												# get unconditional embeddings for classifier free guidance
												if do_classifier_free_guidance:
																		_lowerCAmelCase												=							42
																		if negative_prompt is None:
																								_lowerCAmelCase												=							[""""""] * batch_size
																		elif type(__snake_case  ) is not type(__snake_case  ):
																								raise TypeError(
																								    f"`negative_prompt` should be the same type to `prompt`, but got {type(__snake_case  )} !="
																								    f" {type(__snake_case  )}."  )
																		elif isinstance(__snake_case      ,      __snake_case  ):
																								_lowerCAmelCase												=							[negative_prompt]
																		elif batch_size != len(__snake_case  ):
																								raise ValueError(
																								    f"`negative_prompt`: {negative_prompt} has batch size {len(__snake_case  )}, but `prompt`:"
																								    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
																								    """ the batch size of `prompt`."""  )
																		else:
																								_lowerCAmelCase												=							negative_prompt

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

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		_lowerCAmelCase												=							uncond_embeddings.shape[1]
																		_lowerCAmelCase												=							uncond_embeddings.repeat(1      ,      __snake_case      ,      1  )
																		_lowerCAmelCase												=							uncond_embeddings.view(batch_size * num_images_per_prompt      ,      __snake_case      ,      -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												=							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												=							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
												_lowerCAmelCase												=							text_embeddings.dtype
												if latents is None:
																		if self.device.type == "mps":
																								# randn does not exist on mps
																								_lowerCAmelCase												=							torch.randn(__snake_case      ,      generator=__snake_case      ,      device="""cpu"""      ,      dtype=__snake_case  ).to(
																								    self.device  )
																		else:
																								_lowerCAmelCase												=							torch.randn(__snake_case      ,      generator=__snake_case      ,      device=self.device      ,      dtype=__snake_case  )
												else:
																		if latents.shape != latents_shape:
																								raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"  )
																		_lowerCAmelCase												=							latents.to(self.device  )

												# set timesteps
												self.scheduler.set_timesteps(__snake_case  )

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

												# scale the initial noise by the standard deviation required by the scheduler
												_lowerCAmelCase												=							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												=							"""eta""" in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
												_lowerCAmelCase												=							{}
												if accepts_eta:
																		_lowerCAmelCase												=							eta

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

																		# predict the noise residual
																		_lowerCAmelCase												=							self.unet(__snake_case      ,      __snake_case      ,      encoder_hidden_states=__snake_case  ).sample

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

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

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

												_lowerCAmelCase												=							1 / 0.1_82_15 * latents
												_lowerCAmelCase												=							self.vae.decode(__snake_case  ).sample

												_lowerCAmelCase												=							(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												=							image.cpu().permute(0      ,      2      ,      3      ,      1  ).float().numpy()

												if output_type == "pil":
																		_lowerCAmelCase												=							self.numpy_to_pil(__snake_case  )

												if not return_dict:
																		return image

												return StableDiffusionPipelineOutput(images=__snake_case      ,      nsfw_content_detected=__snake_case  )













'''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        UpperCamelCase__				(		lowerCAmelCase=None							,     lowerCAmelCase=None  ):





      """simple docstring"""


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




@dataclass
class 			UpperCAmelCase					:
      _lowercase:      str											=       field(
          metadata={'''help''': '''The csv file to plot.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Disable logarithmic scale when plotting'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={
              '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
          }							,		)
      _lowercase:      Optional[str]											=       field(
          default=snake_case_							,		metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}							,		)
      _lowercase:      Optional[List[str]]											=       list_field(
          default=snake_case_							,		metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}       )




def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      try:
            int(lowerCAmelCase  )
            return True
      except ValueError:
            return False
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      try:
            float(lowerCAmelCase  )
            return True
      except ValueError:
            return False




class 			UpperCAmelCase					:




      def __init__(      self      :			List[str]      ,      __snake_case      :			Union[str, Any]  )    ->				int:
            _lowerCAmelCase												=							args
            _lowerCAmelCase												=							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

            with open(self.args.csv_file      ,      newline=""""""  ) as csv_file:
                  _lowerCAmelCase												=							csv.DictReader(__snake_case  )
                  for row in reader:
                        _lowerCAmelCase												=							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												=							int(row["""result"""]  )
                        elif can_convert_to_float(row["""result"""]  ):
                              # value is not None
                              _lowerCAmelCase												=							float(row["""result"""]  )




      def     lowercase__   (      self      :			Dict  )    ->				str:
            _lowerCAmelCase					,							_lowerCAmelCase												=							plt.subplots()
            _lowerCAmelCase												=							"""Time usage""" if self.args.is_time else """Memory usage"""
            _lowerCAmelCase												=							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												=							sorted(set(self.result_dict[model_name]["""bsz"""]  )  )
                  _lowerCAmelCase												=							sorted(set(self.result_dict[model_name]["""seq_len"""]  )  )
                  _lowerCAmelCase												=							self.result_dict[model_name]["""result"""]

                  ((_lowerCAmelCase)					,							(_lowerCAmelCase))												=							(
                      (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                  )

                  _lowerCAmelCase												=							(
                      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												=							np.asarray(
                                  [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,      dtype=__snake_case      ,      )
                        else:
                              _lowerCAmelCase												=							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))												=							(
                            ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
                        )

                        _lowerCAmelCase												=							np.asarray(__snake_case      ,      __snake_case  )[: len(__snake_case  )]
                        plt.scatter(
                            __snake_case      ,      __snake_case      ,      label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"  )
                        plt.plot(__snake_case      ,      __snake_case      ,      """--"""  )

                  title_str += f" {label_model_name} vs."

            _lowerCAmelCase												=							title_str[:-4]
            _lowerCAmelCase												=							"""Time in s""" if self.args.is_time else """Memory in MB"""

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

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




def        UpperCamelCase__				(		):





      """simple docstring"""


      _lowerCAmelCase												=							HfArgumentParser(lowerCAmelCase  )
      _lowerCAmelCase												=							parser.parse_args_into_dataclasses()[0]
      _lowerCAmelCase												=							Plot(args=lowerCAmelCase  )
      plot.plot()


if __name__ == "__main__":
      main()






