


'''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


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

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







  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=__lowercase      ,      speech_processor=__lowercase      ,      vae=__lowercase      ,      text_encoder=__lowercase      ,      tokenizer=__lowercase      ,      unet=__lowercase      ,      scheduler=__lowercase      ,      feature_extractor=__lowercase      ,      )


 def  snake_case__   (      self      ,      __lowercase = "auto"						):

  """simple docstring"""







  if slice_size == "auto":
   __A  :		str       =			self.unet.config.attention_head_dim // 2
  self.unet.set_attention_slice(__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  self.enable_attention_slicing(__lowercase						)







 @torch.no_grad()
 def __call__(      self      ,      __lowercase      ,      __lowercase=16_000      ,      __lowercase = 512      ,      __lowercase = 512      ,      __lowercase = 50      ,      __lowercase = 7.5      ,      __lowercase = None      ,      __lowercase = 1      ,      __lowercase = 0.0      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = "pil"      ,      __lowercase = True      ,      __lowercase = None      ,      __lowercase = 1      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		Any       =			self.speech_processor.feature_extractor(
      __lowercase      ,      return_tensors='pt'      ,      sampling_rate=__lowercase						).input_features.to(self.device						)
  __A  :		List[str]       =			self.speech_model.generate(__lowercase      ,      max_length=480_000						)

  __A  :		Union[str, Any]       =			self.speech_processor.tokenizer.batch_decode(__lowercase      ,      skip_special_tokens=__lowercase      ,      normalize=__lowercase						)[
      0
  ]

  if isinstance(__lowercase      ,      __lowercase						):
   __A  :		str       =			1
  elif isinstance(__lowercase      ,      __lowercase						):
   __A  :		List[str]       =			len(__lowercase						)
  else:
   raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__lowercase						)}"""						)

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

  # get prompt text embeddings
  __A  :		Union[str, Any]       =			self.tokenizer(
      __lowercase      ,      padding='max_length'      ,      max_length=self.tokenizer.model_max_length      ,      return_tensors='pt'      ,      )
  __A  :		Optional[Any]       =			text_inputs.input_ids

  if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
   __A  :		Dict       =			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  :		Union[str, Any]       =			text_input_ids[:, : self.tokenizer.model_max_length]
  __A  :		Optional[int]       =			self.text_encoder(text_input_ids.to(self.device						)						)[0]

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

   __A  :		Any       =			text_input_ids.shape[-1]
   __A  :		Tuple       =			self.tokenizer(
       __lowercase      ,      padding='max_length'      ,      max_length=__lowercase      ,      truncation=__lowercase      ,      return_tensors='pt'      ,      )
   __A  :		int       =			self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

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

  # set timesteps
  self.scheduler.set_timesteps(__lowercase						)

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

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

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

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

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

   # compute the previous noisy sample x_t -> x_t-1
   __A  :		str       =			self.scheduler.step(__lowercase      ,      __lowercase      ,      __lowercase      ,      **__lowercase						).prev_sample

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

  __A  :		str       =			1 / 0.1_8_2_1_5 * latents
  __A  :		Optional[int]       =			self.vae.decode(__lowercase						).sample

  __A  :		Optional[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
  __A  :		Any       =			image.cpu().permute(0      ,      2      ,      3      ,      1						).float().numpy()

  if output_type == "pil":
   __A  :		Tuple       =			self.numpy_to_pil(__lowercase						)

  if not return_dict:
   return image

  return StableDiffusionPipelineOutput(images=__lowercase      ,      nsfw_content_detected=__lowercase						)










'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


UpperCAmelCase_ :     Dict								=      getLogger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					str,				UpperCamelCase__       :					str,				UpperCamelCase__       :					int = 8,				UpperCamelCase__       :					int = 1024,				UpperCamelCase__       :					List[Any]="val",				UpperCamelCase__       :					int=None,				UpperCamelCase__       :					str=False,				UpperCamelCase__       :					int="summarization",				UpperCamelCase__       :					List[Any]=None,				UpperCamelCase__       :					List[Any]=1,				UpperCamelCase__       :					Dict = None,				UpperCamelCase__       :					Optional[int]="",				**UpperCamelCase__       :					str,				):
 __A  :		Dict       =			str(UpperCamelCase__			)
 assert local_rank is not None
 torch.distributed.init_process_group(backend='nccl',				rank=UpperCamelCase__			)

 __A  :		Union[str, Any]       =			Path(UpperCamelCase__			)
 __A  :		Optional[int]       =			save_dir.joinpath(f"""rank_{local_rank}_output.json"""			)
 torch.cuda.set_device(UpperCamelCase__			)
 __A  :		Dict       =			AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__			).cuda()
 if fpaa:
  __A  :		Optional[Any]       =			model.half()
 # determine if we need to increase num_beams
 use_task_specific_params(UpperCamelCase__,				UpperCamelCase__			)  # update config with task specific params
 __A  :		Any       =			generate_kwargs.pop('num_beams',				model.config.num_beams			)  # AttributeError risk?
 if num_return_sequences > num_beams:
  __A  :		int       =			num_return_sequences

 __A  :		Optional[Any]       =			AutoTokenizer.from_pretrained(UpperCamelCase__			)
 logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""			)  # if this is wrong, check config.model_type.

 if max_source_length is None:
  __A  :		Union[str, Any]       =			tokenizer.model_max_length
 if prefix is None:
  __A  :		List[Any]       =			prefix or getattr(model.config,				'prefix',				''			) or ''
 __A  :		Tuple       =			SeqaSeqDataset(
     UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				max_target_length=1024,				type_path=UpperCamelCase__,				n_obs=UpperCamelCase__,				prefix=UpperCamelCase__,				**UpperCamelCase__,				)
 # I set shuffle=True for a more accurate progress bar.
 # If all the longest samples are first, the prog bar estimate is too high at the beginning.
 __A  :		Any       =			ds.make_sortish_sampler(UpperCamelCase__,				distributed=UpperCamelCase__,				add_extra_examples=UpperCamelCase__,				shuffle=UpperCamelCase__			)
 __A  :		Union[str, Any]       =			DataLoader(UpperCamelCase__,				sampler=UpperCamelCase__,				batch_size=UpperCamelCase__,				collate_fn=ds.collate_fn			)
 __A  :		Tuple       =			[]
 for batch in tqdm(UpperCamelCase__			):
  __A  :		Any       =			model.generate(
      input_ids=batch['input_ids'].to(model.device			),				attention_mask=batch['attention_mask'].to(model.device			),				num_return_sequences=UpperCamelCase__,				num_beams=UpperCamelCase__,				**UpperCamelCase__,				)
  __A  :		Dict       =			tokenizer.batch_decode(UpperCamelCase__,				skip_special_tokens=UpperCamelCase__,				clean_up_tokenization_spaces=UpperCamelCase__			)
  __A  :		List[str]       =			batch['ids']
  if num_return_sequences > 1:
   __A  :		str       =			chunks(UpperCamelCase__,				UpperCamelCase__			)  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(UpperCamelCase__			):
   results.append({'pred': pred, 'id': ids[i].item()}			)
 save_json(UpperCamelCase__,				UpperCamelCase__			)
 return results, sampler.num_replicas


def 			_lowercase       (				):
 __A  :		Optional[Any]       =			argparse.ArgumentParser(
     epilog='Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'			)
 parser.add_argument('--data_dir',				type=UpperCamelCase__,				help='like cnn_dm/test.source'			)
 parser.add_argument(
     '--model_name',				type=UpperCamelCase__,				help='like facebook/bart-large-cnn,t5-base, etc.',				default='sshleifer/distilbart-xsum-12-3',				)
 parser.add_argument('--save_dir',				type=UpperCamelCase__,				help='where to save',				default='tmp_gen'			)
 parser.add_argument('--max_source_length',				type=UpperCamelCase__,				default=UpperCamelCase__			)
 parser.add_argument(
     '--type_path',				type=UpperCamelCase__,				default='test',				help='which subset to evaluate typically train/val/test'			)
 parser.add_argument('--task',				type=UpperCamelCase__,				default='summarization',				help='used for task_specific_params + metrics'			)
 parser.add_argument('--bs',				type=UpperCamelCase__,				default=8,				required=UpperCamelCase__,				help='batch size'			)
 parser.add_argument(
     '--local_rank',				type=UpperCamelCase__,				default=-1,				required=UpperCamelCase__,				help='should be passed by distributed.launch'			)

 parser.add_argument(
     '--n_obs',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__,				help='How many observations. Defaults to all.'			)
 parser.add_argument(
     '--num_return_sequences',				type=UpperCamelCase__,				default=1,				required=UpperCamelCase__,				help='How many sequences to return'			)
 parser.add_argument(
     '--sync_timeout',				type=UpperCamelCase__,				default=600,				required=UpperCamelCase__,				help='How long should master process wait for other processes to finish.',				)
 parser.add_argument('--src_lang',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__			)
 parser.add_argument('--tgt_lang',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__			)
 parser.add_argument(
     '--prefix',				type=UpperCamelCase__,				required=UpperCamelCase__,				default=UpperCamelCase__,				help='will be added to the begininng of src examples'			)
 parser.add_argument('--fp16',				action='store_true'			)
 parser.add_argument('--debug',				action='store_true'			)
 __A  :		int       =			time.time()
 __A     ,__A  :		int       =			parser.parse_known_args()
 __A  :		List[str]       =			parse_numeric_n_bool_cl_kwargs(UpperCamelCase__			)
 if generate_kwargs and args.local_rank <= 0:
  print(f"""parsed the following generate kwargs: {generate_kwargs}"""			)
 __A  :		List[str]       =			Path(args.save_dir + '_tmp'			)
 Path(UpperCamelCase__			).mkdir(exist_ok=UpperCamelCase__			)  # this handles locking.
 __A  :		Optional[Any]       =			list(json_save_dir.glob('rank_*.json'			)			)
 if intermediate_files:
  raise ValueError(f"""Found files at {json_save_dir} please move or remove them."""			)
  # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
 __A  :		List[str]       =			{}
 if args.src_lang is not None:
  __A  :		Dict       =			args.src_lang
 if args.tgt_lang is not None:
  __A  :		Optional[Any]       =			args.tgt_lang

 Path(args.save_dir			).mkdir(exist_ok=UpperCamelCase__			)
 __A     ,__A  :		List[Any]       =			eval_data_dir(
     args.data_dir,				UpperCamelCase__,				args.model_name,				type_path=args.type_path,				bs=args.bs,				fpaa=args.fpaa,				task=args.task,				local_rank=args.local_rank,				n_obs=args.n_obs,				max_source_length=args.max_source_length,				num_return_sequences=args.num_return_sequences,				prefix=args.prefix,				dataset_kwargs=UpperCamelCase__,				**UpperCamelCase__,				)

 if args.local_rank <= 0:
  __A  :		Tuple       =			Path(args.save_dir			)
  save_dir.mkdir(exist_ok=UpperCamelCase__			)
  __A  :		Dict       =			gather_results_from_each_node(UpperCamelCase__,				UpperCamelCase__,				args.sync_timeout			)
  __A  :		Union[str, Any]       =			combine_partial_results(UpperCamelCase__			)
  if args.num_return_sequences > 1:
   __A  :		Any       =			save_dir.joinpath('pseudolabel_results.json'			)
   print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""			)
   save_json(UpperCamelCase__,				UpperCamelCase__			)
   return
  __A  :		int       =			Path(args.data_dir			).joinpath(args.type_path + '.target'			)
  with open(UpperCamelCase__			) as f:
   __A  :		str       =			[x.rstrip() for x in f.readlines()][: len(UpperCamelCase__			)]

  # Calculate metrics, save metrics,  and save _generations.txt
  __A  :		Tuple       =			'translation' in args.task
  __A  :		Union[str, Any]       =			calculate_bleu if calc_bleu else calculate_rouge
  __A  :		Optional[Any]       =			'bleu' if calc_bleu else 'rouge'
  __A  :		Dict       =			score_fn(UpperCamelCase__,				UpperCamelCase__			)
  __A  :		Any       =			len(UpperCamelCase__			)
  __A  :		Tuple       =			time.time() - start_time
  __A  :		Tuple       =			round(runtime / metrics['n_obs'],				4			)
  __A  :		int       =			num_replicas
  # TODO(@stas00): add whatever metadata to metrics
  __A  :		List[Any]       =			save_dir.joinpath(f"""{args.type_path}_{metric_name}.json"""			)
  save_json(UpperCamelCase__,				UpperCamelCase__,				indent=UpperCamelCase__			)
  print(UpperCamelCase__			)
  write_txt_file(UpperCamelCase__,				save_dir.joinpath(f"""{args.type_path}_generations.txt"""			)			)
  if args.debug:
   write_txt_file(UpperCamelCase__,				save_dir.joinpath(f"""{args.type_path}.target"""			)			)
  else:
   shutil.rmtree(UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					List[str]			):
 __A  :		List[str]       =			[]
 for partial_result in partial_results:
  records.extend(UpperCamelCase__			)
 __A  :		List[Any]       =			sorted(UpperCamelCase__,				key=lambda UpperCamelCase__			: x["id"]			)
 __A  :		Dict       =			[x['pred'] for x in records]
 return preds


def 			_lowercase       (				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					int			):
 # WAIT FOR lots of .json files
 __A  :		Dict       =			time.time()
 logger.info('waiting for all nodes to finish'			)
 __A  :		int       =			None
 while (time.time() - start_wait) < timeout:
  __A  :		List[Any]       =			list(save_dir.glob('rank_*.json'			)			)
  if len(UpperCamelCase__			) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   __A  :		List[Any]       =			lmap(UpperCamelCase__,				UpperCamelCase__			)
   return json_data
  except JSONDecodeError:
   continue
 else:
  raise TimeoutError('Rank 0 gave up on waiting for other processes'			)
 # Unreachable


if __name__ == "__main__":
 # Usage for MT:
 run_generate()






