






"""simple docstring"""


from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


_UpperCamelCase							:       Union[str, Any]       					=		getLogger(__name__)


_UpperCamelCase							:       Union[str, Any]       					=		4
_UpperCamelCase							:       Tuple       					=		7

_UpperCamelCase							:       str       					=		{
    "float16": torch.floataa,
    "float32": torch.floataa,
}




def      snake_case    (A_ :Optional[Any] ):







  '''simple docstring'''

  if isinstance(__snake_case      ,     torch.Tensor ):
    return image
  elif isinstance(__snake_case      ,     PIL.Image.Image ):
    a	:     Optional[int]							=  [image]

  if isinstance(image[0]      ,     PIL.Image.Image ):
    a,	a	:     Union[str, Any]							=  image[0].size
    a,	a	:     Optional[int]							=  (x - x % 6_4 for x in (w, h))  # resize to integer multiple of 32

    a	:     Optional[Any]							=  [np.array(i.resize((w, h) ) )[None, :] for i in image]
    a	:     Dict							=  np.concatenate(__snake_case      ,     axis=0 )
    a	:     Dict							=  np.array(__snake_case ).astype(np.floataa ) / 2_5_5.0
    a	:     Any							=  image.transpose(0      ,     3      ,     1      ,     2 )
    a	:     int							=  2.0 * image - 1.0
    a	:     int							=  torch.from_numpy(__snake_case )
  elif isinstance(image[0]      ,     torch.Tensor ):
    a	:     str							=  torch.cat(__snake_case      ,     dim=0 )

  return image






class 		snake_case    (    UpperCAmelCase			):





    def __init__(				self      :   int							,						A      :   List[Any]							,						A      :   int							,						A      :   Optional[Any]							,						A      :   str							,						A      :   Tuple							,						A      :   Union[str, Any]							,						A      :   Optional[Any] = 3_5_0							,						):


      '''simple docstring'''




      super().__init__(
          vae=SCREAMING_SNAKE_CASE_							,						text_encoder=SCREAMING_SNAKE_CASE_							,						tokenizer=SCREAMING_SNAKE_CASE_							,						unet=SCREAMING_SNAKE_CASE_							,						low_res_scheduler=SCREAMING_SNAKE_CASE_							,						scheduler=SCREAMING_SNAKE_CASE_							,						safety_checker=SCREAMING_SNAKE_CASE_							,						feature_extractor=SCREAMING_SNAKE_CASE_							,						watermarker=SCREAMING_SNAKE_CASE_							,						max_noise_level=SCREAMING_SNAKE_CASE_							,						)





    def __call__(				self      :   Tuple							,						A      :   Optional[int]							,						A      :   List[Any]							,						A      :   Tuple = 7_5							,						A      :   str = 9.0							,						A      :   Any = 2_0							,						A      :   Optional[Any] = None							,						A      :   int = 1							,						A      :   Union[str, Any] = 0.0							,						A      :   Optional[Any] = None							,						A      :   Any = None							,						A      :   List[Any] = None							,						A      :   List[Any] = None							,						A      :   Tuple = "pil"							,						A      :   Union[str, Any] = True							,						A      :   Union[str, Any] = None							,						A      :   List[Any] = 1							,						):


      '''simple docstring'''




      self.check_inputs(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     )

      # 2. Define call parameters
      if prompt is not None and isinstance(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     ):
        a	:     Optional[int]							=  1
      elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     ):
        a	:     Optional[int]							=  len(SCREAMING_SNAKE_CASE_     )
      else:
        a	:     int							=  prompt_embeds.shape[0]

      a	:     Optional[Any]							=  self._execution_device
      # 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	:     Optional[Any]							=  guidance_scale > 1.0

      # 3. Encode input prompt
      a	:     List[str]							=  self._encode_prompt(
          SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						prompt_embeds=SCREAMING_SNAKE_CASE_							,						negative_prompt_embeds=SCREAMING_SNAKE_CASE_							,						)

      a	:     Any							=  ORT_TO_PT_TYPE[str(text_embeddings.dtype     )]

      # 4. Preprocess image
      a	:     Union[str, Any]							=  preprocess(SCREAMING_SNAKE_CASE_     )
      a	:     str							=  image.cpu()

      # 5. set timesteps
      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_							,						device=SCREAMING_SNAKE_CASE_     )
      a	:     int							=  self.scheduler.timesteps

      # 5. Add noise to image
      a	:     List[Any]							=  torch.tensor([noise_level]							,						dtype=torch.long							,						device=SCREAMING_SNAKE_CASE_     )
      a	:     List[Any]							=  torch.randn(image.shape							,						generator=SCREAMING_SNAKE_CASE_							,						device=SCREAMING_SNAKE_CASE_							,						dtype=SCREAMING_SNAKE_CASE_     )
      a	:     str							=  self.low_res_scheduler.add_noise(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     )

      a	:     str							=  2 if do_classifier_free_guidance else 1
      a	:     str							=  np.concatenate([image] * batch_multiplier * num_images_per_prompt     )
      a	:     Dict							=  np.concatenate([noise_level] * image.shape[0]     )

      # 6. Prepare latent variables
      a,	a	:     List[Any]							=  image.shape[2:]
      a	:     List[Any]							=  self.prepare_latents(
          batch_size * num_images_per_prompt							,						SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						)

      # 7. Check that sizes of image and latents match
      a	:     Optional[int]							=  image.shape[1]
      if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
        raise ValueError(
            'Incorrect configuration settings! The config of `pipeline.unet` expects'
            F''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
            F''' `num_channels_image`: {num_channels_image} '''
            F''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
            ' `pipeline.unet` or your `image` input.'     )

      # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
      a	:     str							=  self.prepare_extra_step_kwargs(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     )

      a	:     Optional[Any]							=  next(
          (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep')							,						'tensor(float)'     )
      a	:     str							=  ORT_TO_NP_TYPE[timestep_dtype]

      # 9. Denoising loop
      a	:     Optional[Any]							=  len(SCREAMING_SNAKE_CASE_     ) - num_inference_steps * self.scheduler.order
      with self.progress_bar(total=SCREAMING_SNAKE_CASE_     ) as progress_bar:
        for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
          # expand the latents if we are doing classifier free guidance
          a	:     List[Any]							=  np.concatenate([latents] * 2     ) if do_classifier_free_guidance else latents

          # concat latents, mask, masked_image_latents in the channel dimension
          a	:     int							=  self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     )
          a	:     Any							=  np.concatenate([latent_model_input, image]							,						axis=1     )

          # timestep to tensor
          a	:     List[str]							=  np.array([t]							,						dtype=SCREAMING_SNAKE_CASE_     )

          # predict the noise residual
          a	:     Optional[int]							=  self.unet(
              sample=SCREAMING_SNAKE_CASE_							,						timestep=SCREAMING_SNAKE_CASE_							,						encoder_hidden_states=SCREAMING_SNAKE_CASE_							,						class_labels=noise_level.astype(np.intaa     )							,						)[0]

          # perform guidance
          if do_classifier_free_guidance:
            a,	a	:     Optional[Any]							=  np.split(SCREAMING_SNAKE_CASE_							,						2     )
          a	:     str							=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

          # compute the previous noisy sample x_t -> x_t-1
          a	:     Union[str, Any]							=  self.scheduler.step(
              torch.from_numpy(SCREAMING_SNAKE_CASE_     )							,						SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						**SCREAMING_SNAKE_CASE_     ).prev_sample

          # call the callback, if provided
          if i == len(SCREAMING_SNAKE_CASE_     ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
            progress_bar.update()
            if callback is not None and i % callback_steps == 0:
              callback(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     )

        # 10. Post-processing
      a	:     int							=  self.decode_latents(latents.float()     )

      # 11. Convert to PIL
      if output_type == "pil":
        a	:     Optional[Any]							=  self.numpy_to_pil(SCREAMING_SNAKE_CASE_     )

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_     )





    def 							lowerCamelCase__ (				self      :   Union[str, Any]							,						A      :   Any     ):


      '''simple docstring'''




      a	:     str							=  1 / 0.0_83_33 * latents
      a	:     Union[str, Any]							=  self.vae(latent_sample=SCREAMING_SNAKE_CASE_     )[0]
      a	:     Any							=  np.clip(image / 2 + 0.5							,						0							,						1     )
      a	:     Union[str, Any]							=  image.transpose((0, 2, 3, 1)     )
      return image





    def 							lowerCamelCase__ (				self      :   Any							,						A      :   Optional[Any]							,						A      :   Dict							,						A      :   Any							,						A      :   List[str]							,						A      :   Union[str, Any]							,						A      :   List[str] = None							,						A      :   Optional[Any] = None							,						):


      '''simple docstring'''




      if prompt is not None and isinstance(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     ):
        a	:     Union[str, Any]							=  1
      elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     ):
        a	:     List[Any]							=  len(SCREAMING_SNAKE_CASE_     )
      else:
        a	:     int							=  prompt_embeds.shape[0]

      if prompt_embeds is None:
        a	:     Any							=  self.tokenizer(
            SCREAMING_SNAKE_CASE_							,						padding='max_length'							,						max_length=self.tokenizer.model_max_length							,						truncation=SCREAMING_SNAKE_CASE_							,						return_tensors='pt'							,						)
        a	:     Tuple							=  text_inputs.input_ids
        a	:     Union[str, Any]							=  self.tokenizer(SCREAMING_SNAKE_CASE_							,						padding='longest'							,						return_tensors='pt'     ).input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
            SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     ):
          a	:     Optional[Any]							=  self.tokenizer.batch_decode(
              untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]     )
          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}'''     )

        # no positional arguments to text_encoder
        a	:     Optional[Any]							=  self.text_encoder(
            input_ids=text_input_ids.int().to(SCREAMING_SNAKE_CASE_     )							,						)
        a	:     int							=  prompt_embeds[0]

      a,	a,	a	:     Optional[Any]							=  prompt_embeds.shape
      # duplicate text embeddings for each generation per prompt, using mps friendly method
      a	:     Any							=  prompt_embeds.repeat(1							,						SCREAMING_SNAKE_CASE_     )
      a	:     str							=  prompt_embeds.reshape(bs_embed * num_images_per_prompt							,						SCREAMING_SNAKE_CASE_							,						-1     )

      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance and negative_prompt_embeds is None:
        a	:     Optional[Any]							=  4_2
        if negative_prompt is None:
          a	:     Tuple							=  [''] * batch_size
        elif type(SCREAMING_SNAKE_CASE_     ) is not type(SCREAMING_SNAKE_CASE_     ):
          raise TypeError(
              F'''`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_     )} !='''
              F''' {type(SCREAMING_SNAKE_CASE_     )}.'''     )
        elif isinstance(SCREAMING_SNAKE_CASE_							,						SCREAMING_SNAKE_CASE_     ):
          a	:     Tuple							=  [negative_prompt]
        elif batch_size != len(SCREAMING_SNAKE_CASE_     ):
          raise ValueError(
              F'''`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_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:
          a	:     Optional[int]							=  negative_prompt

        a	:     str							=  text_input_ids.shape[-1]
        a	:     Any							=  self.tokenizer(
            SCREAMING_SNAKE_CASE_							,						padding='max_length'							,						max_length=SCREAMING_SNAKE_CASE_							,						truncation=SCREAMING_SNAKE_CASE_							,						return_tensors='pt'							,						)

        # if hasattr(uncond_input, "attention_mask"):
        #     attention_mask = uncond_input.attention_mask.to(device)
        # else:
        #     attention_mask = None

        a	:     Tuple							=  self.text_encoder(
            input_ids=uncond_input.input_ids.int().to(SCREAMING_SNAKE_CASE_     )							,						)
        a	:     Optional[int]							=  uncond_embeddings[0]

      if do_classifier_free_guidance:
        a	:     Any							=  uncond_embeddings.shape[1]
        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        a	:     Union[str, Any]							=  uncond_embeddings.repeat(1							,						SCREAMING_SNAKE_CASE_     )
        a	:     Optional[int]							=  uncond_embeddings.reshape(batch_size * num_images_per_prompt							,						SCREAMING_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
        a	:     Tuple							=  np.concatenate([uncond_embeddings, prompt_embeds]     )

      return prompt_embeds






import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCamelCase_				:							List[str]       =    getLogger(__name__)


lowerCamelCase_				:							Dict       =    "cuda" if torch.cuda.is_available() else "cpu"
def        __lowercase(							__snake_case				:		List[str]    ,__snake_case				:		str    ,__snake_case				:		str    ,__snake_case				:		int = 8    ,__snake_case				:		str = DEFAULT_DEVICE    ,__snake_case				:		str=False    ,__snake_case				:		Tuple="summarization"    ,__snake_case				:		List[str]=None    ,**__snake_case				:		Dict    ,)       ->   Dict:
       __snake_case						       =   Path(__snake_case					).open('w'    ,encoding='utf-8'					)
       __snake_case						       =   str(__snake_case					)
       __snake_case						       =   AutoModelForSeqaSeqLM.from_pretrained(__snake_case					).to(__snake_case					)
       if fpaa:
              __snake_case						       =   model.half()

       __snake_case						       =   AutoTokenizer.from_pretrained(__snake_case					)
       logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''					)  # if this is wrong, check config.model_type.

       __snake_case						       =   time.time()
       # update config with task specific params
       use_task_specific_params(__snake_case    ,__snake_case					)
       if prefix is None:
              __snake_case						       =   prefix or getattr(model.config    ,'prefix'    ,''					) or ''
       for examples_chunk in tqdm(list(chunks(__snake_case    ,__snake_case					)					)					):
              __snake_case						       =   [prefix + text for text in examples_chunk]
              __snake_case						       =   tokenizer(__snake_case    ,return_tensors='pt'    ,truncation=__snake_case    ,padding='longest'					).to(__snake_case					)
              __snake_case						       =   model.generate(
                  input_ids=batch.input_ids    ,attention_mask=batch.attention_mask    ,**__snake_case    ,)
              __snake_case						       =   tokenizer.batch_decode(__snake_case    ,skip_special_tokens=__snake_case    ,clean_up_tokenization_spaces=__snake_case					)
              for hypothesis in dec:
                     fout.write(hypothesis + '\n'					)
                     fout.flush()
       fout.close()
       __snake_case						       =   int(time.time() - start_time					)  # seconds
       __snake_case						       =   len(__snake_case					)
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs    ,4					)}
def        __lowercase(							)       ->   Optional[int]:
       return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'					)
def        __lowercase(							__snake_case				:		Optional[int]=True					)       ->   Dict:
       __snake_case						       =   argparse.ArgumentParser()
       parser.add_argument('model_name'    ,type=__snake_case    ,help='like facebook/bart-large-cnn,t5-base, etc.'					)
       parser.add_argument('input_path'    ,type=__snake_case    ,help='like cnn_dm/test.source'					)
       parser.add_argument('save_path'    ,type=__snake_case    ,help='where to save summaries'					)
       parser.add_argument('--reference_path'    ,type=__snake_case    ,required=__snake_case    ,help='like cnn_dm/test.target'					)
       parser.add_argument('--score_path'    ,type=__snake_case    ,required=__snake_case    ,default='metrics.json'    ,help='where to save metrics'					)
       parser.add_argument('--device'    ,type=__snake_case    ,required=__snake_case    ,default=__snake_case    ,help='cuda, cuda:1, cpu etc.'					)
       parser.add_argument(
           '--prefix'    ,type=__snake_case    ,required=__snake_case    ,default=__snake_case    ,help='will be added to the begininng of src examples'					)
       parser.add_argument('--task'    ,type=__snake_case    ,default='summarization'    ,help='used for task_specific_params + metrics'					)
       parser.add_argument('--bs'    ,type=__snake_case    ,default=8    ,required=__snake_case    ,help='batch size'					)
       parser.add_argument(
           '--n_obs'    ,type=__snake_case    ,default=-1    ,required=__snake_case    ,help='How many observations. Defaults to all.'					)
       parser.add_argument('--fp16'    ,action='store_true'					)
       parser.add_argument('--dump-args'    ,action='store_true'    ,help='print the custom hparams with the results'					)
       parser.add_argument(
           '--info'    ,nargs='?'    ,type=__snake_case    ,const=datetime_now()    ,help=(
               'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
               ' lang=en-ru. If no value is passed, the current datetime string will be used.'
           )    ,)
       # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
       __snake_case       ,       __snake_case						       =   parser.parse_known_args()
       __snake_case						       =   parse_numeric_n_bool_cl_kwargs(__snake_case					)
       if parsed_args and verbose:
              print(f'''parsed the following generate kwargs: {parsed_args}'''					)
       with open(args.input_path					) as f:
              __snake_case						       =   [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
       if args.n_obs > 0:
              __snake_case						       =   examples[: args.n_obs]
       Path(args.save_path					).parent.mkdir(exist_ok=__snake_case					)
       if args.reference_path is None and Path(args.score_path					).exists():
              warnings.warn(f'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''					)
       __snake_case						       =   generate_summaries_or_translations(
           __snake_case    ,args.save_path    ,args.model_name    ,batch_size=args.bs    ,device=args.device    ,fpaa=args.fpaa    ,task=args.task    ,prefix=args.prefix    ,**__snake_case    ,)

       if args.reference_path is None:
              return {}

       # Compute scores
       __snake_case						       =   calculate_bleu if 'translation' in args.task else calculate_rouge
       __snake_case						       =   [x.rstrip() for x in open(args.save_path					).readlines()]
       __snake_case						       =   [x.rstrip() for x in open(args.reference_path					).readlines()][: len(__snake_case					)]
       __snake_case						       =   score_fn(__snake_case    ,__snake_case					)
       scores.update(__snake_case					)

       if args.dump_args:
              scores.update(__snake_case					)
       if args.info:
              __snake_case						       =   args.info

       if verbose:
              print(__snake_case					)

       if args.score_path is not None:
              json.dump(__snake_case    ,open(args.score_path    ,'w'					)					)

       return scores


if __name__ == "__main__":
 # Usage for MT:
 # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
 run_generate(verbose=True)
