



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         =			getLogger(__name__)


__lowerCamelCase         =			'''cuda''' if torch.cuda.is_available() else '''cpu'''







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case = 8					,  __snake_case = DEFAULT_DEVICE					,  __snake_case=False					,  __snake_case="summarization"					,  __snake_case=None					,  **__snake_case					,  )     ->     Dict:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[int]				      =		Path(__snake_case    ).open("w"					,  encoding="utf-8"    )
      UpperCAmelCase_  :	Optional[Any]				      =		str(__snake_case    )
      UpperCAmelCase_  :	List[str]				      =		AutoModelForSeqaSeqLM.from_pretrained(__snake_case    ).to(__snake_case    )
      if fpaa:
            UpperCAmelCase_  :	Dict				      =		model.half()

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

      UpperCAmelCase_  :	Optional[Any]				      =		time.time()
      # update config with task specific params
      use_task_specific_params(__snake_case					,  __snake_case    )
      if prefix is None:
            UpperCAmelCase_  :	int				      =		prefix or getattr(model.config					,  "prefix"					,  ""    ) or ""
      for examples_chunk in tqdm(list(chunks(__snake_case					,  __snake_case    )    )    ):
            UpperCAmelCase_  :	int				      =		[prefix + text for text in examples_chunk]
            UpperCAmelCase_  :	List[str]				      =		tokenizer(__snake_case					,  return_tensors="pt"					,  truncation=__snake_case					,  padding="longest"    ).to(__snake_case    )
            UpperCAmelCase_  :	Optional[Any]				      =		model.generate(
                input_ids=batch.input_ids					,  attention_mask=batch.attention_mask					,  **__snake_case					,  )
            UpperCAmelCase_  :	List[Any]				      =		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()
      UpperCAmelCase_  :	str				      =		int(time.time() - start_time    )  # seconds
      UpperCAmelCase_  :	Optional[Any]				      =		len(__snake_case    )
      return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs					,  4    )}







def    _snake_case (       )     ->     Optional[Any]:






      '''simple docstring'''







      return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"    )







def    _snake_case (       __snake_case=True    )     ->     Dict:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		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
      UpperCAmelCase_      ,      UpperCAmelCase_  :	int				      =		parser.parse_known_args()
      UpperCAmelCase_  :	List[Any]				      =		parse_numeric_n_bool_cl_kwargs(__snake_case    )
      if parsed_args and verbose:
            print(F"""parsed the following generate kwargs: {parsed_args}"""    )
      UpperCAmelCase_  :	Dict				      =		[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path    ).readlines()]
      if args.n_obs > 0:
            UpperCAmelCase_  :	Optional[int]				      =		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."""    )

      if args.device == "cpu" and args.fpaa:
            # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
            raise ValueError("Can't mix --fp16 and --device cpu"    )

      UpperCAmelCase_  :	Optional[Any]				      =		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
      UpperCAmelCase_  :	List[Any]				      =		calculate_bleu if "translation" in args.task else calculate_rouge
      UpperCAmelCase_  :	Optional[Any]				      =		[x.rstrip() for x in open(args.save_path    ).readlines()]
      UpperCAmelCase_  :	List[Any]				      =		[x.rstrip() for x in open(args.reference_path    ).readlines()][: len(__snake_case    )]
      UpperCAmelCase_  :	dict				      =		score_fn(__snake_case					,  __snake_case    )
      scores.update(__snake_case    )

      if args.dump_args:
            scores.update(__snake_case    )
      if args.info:
            UpperCAmelCase_  :	List[Any]				      =		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)





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class   snake_case_ (lowercase__ , lowercase__ , lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				AltDiffusionPipeline
       _lowerCamelCase			    =				TEXT_TO_IMAGE_PARAMS
       _lowerCamelCase			    =				TEXT_TO_IMAGE_BATCH_PARAMS
       _lowerCamelCase			    =				TEXT_TO_IMAGE_IMAGE_PARAMS
       _lowerCamelCase			    =				TEXT_TO_IMAGE_IMAGE_PARAMS



       def  A_   (     self):

             """simple docstring"""





             torch.manual_seed(0)
             UpperCAmelCase_  :	Optional[Any]				      =		UNetaDConditionModel(
                 block_out_channels=(32, 64)     ,layers_per_block=2     ,sample_size=32     ,in_channels=4     ,out_channels=4     ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")     ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")     ,cross_attention_dim=32     ,)
             UpperCAmelCase_  :	Any				      =		DDIMScheduler(
                 beta_start=0.0_0085     ,beta_end=0.012     ,beta_schedule="scaled_linear"     ,clip_sample=lowercase     ,set_alpha_to_one=lowercase     ,)
             torch.manual_seed(0)
             UpperCAmelCase_  :	Any				      =		AutoencoderKL(
                 block_out_channels=[32, 64]     ,in_channels=3     ,out_channels=3     ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,latent_channels=4     ,)

             # TODO: address the non-deterministic text encoder (fails for save-load tests)
             # torch.manual_seed(0)
             # text_encoder_config = RobertaSeriesConfig(
             #     hidden_size=32,
             #     project_dim=32,
             #     intermediate_size=37,
             #     layer_norm_eps=1e-05,
             #     num_attention_heads=4,
             #     num_hidden_layers=5,
             #     vocab_size=5002,
             # )
             # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

             torch.manual_seed(0)
             UpperCAmelCase_  :	Tuple				      =		CLIPTextConfig(
                 bos_token_id=0     ,eos_token_id=2     ,hidden_size=32     ,projection_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=5002     ,)
             UpperCAmelCase_  :	Dict				      =		CLIPTextModel(lowercase)

             UpperCAmelCase_  :	List[Any]				      =		XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
             UpperCAmelCase_  :	List[str]				      =		77

             UpperCAmelCase_  :	Union[str, Any]				      =		{
                 "unet": unet,
                 "scheduler": scheduler,
                 "vae": vae,
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "safety_checker": None,
                 "feature_extractor": None,
             }
             return components



       def  A_   (     self     ,lowercase     ,lowercase=0):

             """simple docstring"""





             if str(lowercase).startswith("mps"):
                   UpperCAmelCase_  :	Any				      =		torch.manual_seed(lowercase)
             else:
                   UpperCAmelCase_  :	Dict				      =		torch.Generator(device=lowercase).manual_seed(lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		{
                 "prompt": "A painting of a squirrel eating a burger",
                 "generator": generator,
                 "num_inference_steps": 2,
                 "guidance_scale": 6.0,
                 "output_type": "numpy",
             }
             return inputs



       def  A_   (     self):

             """simple docstring"""





             super().test_attention_slicing_forward_pass(expected_max_diff=3E-3)



       def  A_   (     self):

             """simple docstring"""





             super().test_inference_batch_single_identical(expected_max_diff=3E-3)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		"cpu"  # ensure determinism for the device-dependent torch.Generator

             UpperCAmelCase_  :	Any				      =		self.get_dummy_components()
             torch.manual_seed(0)
             UpperCAmelCase_  :	List[Any]				      =		RobertaSeriesConfig(
                 hidden_size=32     ,project_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=5002     ,)
             # TODO: remove after fixing the non-deterministic text encoder
             UpperCAmelCase_  :	Any				      =		RobertaSeriesModelWithTransformation(lowercase)
             UpperCAmelCase_  :	Optional[Any]				      =		text_encoder

             UpperCAmelCase_  :	List[Any]				      =		AltDiffusionPipeline(**lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	Tuple				      =		self.get_dummy_inputs(lowercase)
             UpperCAmelCase_  :	Optional[Any]				      =		"A photo of an astronaut"
             UpperCAmelCase_  :	str				      =		alt_pipe(**lowercase)
             UpperCAmelCase_  :	List[str]				      =		output.images
             UpperCAmelCase_  :	Dict				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             UpperCAmelCase_  :	Optional[Any]				      =		np.array(
                 [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093])

             assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		"cpu"  # ensure determinism for the device-dependent torch.Generator

             UpperCAmelCase_  :	int				      =		self.get_dummy_components()
             UpperCAmelCase_  :	Optional[Any]				      =		PNDMScheduler(skip_prk_steps=lowercase)
             torch.manual_seed(0)
             UpperCAmelCase_  :	Any				      =		RobertaSeriesConfig(
                 hidden_size=32     ,project_dim=32     ,intermediate_size=37     ,layer_norm_eps=1E-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,vocab_size=5002     ,)
             # TODO: remove after fixing the non-deterministic text encoder
             UpperCAmelCase_  :	Union[str, Any]				      =		RobertaSeriesModelWithTransformation(lowercase)
             UpperCAmelCase_  :	Dict				      =		text_encoder
             UpperCAmelCase_  :	Optional[int]				      =		AltDiffusionPipeline(**lowercase)
             UpperCAmelCase_  :	List[Any]				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	str				      =		self.get_dummy_inputs(lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		alt_pipe(**lowercase)
             UpperCAmelCase_  :	int				      =		output.images
             UpperCAmelCase_  :	Dict				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             UpperCAmelCase_  :	Tuple				      =		np.array(
                 [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237])

             assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2


@slow
@require_torch_gpu
class   snake_case_ (unittest.TestCase  ):




       """simple docstring"""






       def  A_   (     self):

             """simple docstring"""





             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"     ,safety_checker=lowercase)
             UpperCAmelCase_  :	Any				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	Any				      =		"A painting of a squirrel eating a burger"
             UpperCAmelCase_  :	Any				      =		torch.manual_seed(0)
             UpperCAmelCase_  :	Optional[int]				      =		alt_pipe([prompt]     ,generator=lowercase     ,guidance_scale=6.0     ,num_inference_steps=20     ,output_type="np")

             UpperCAmelCase_  :	Dict				      =		output.images

             UpperCAmelCase_  :	List[str]				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_  :	Dict				      =		np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586])

             assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		DDIMScheduler.from_pretrained("BAAI/AltDiffusion"     ,subfolder="scheduler")

             UpperCAmelCase_  :	Optional[int]				      =		AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"     ,scheduler=lowercase     ,safety_checker=lowercase)
             UpperCAmelCase_  :	List[str]				      =		alt_pipe.to(lowercase)
             alt_pipe.set_progress_bar_config(disable=lowercase)

             UpperCAmelCase_  :	str				      =		"A painting of a squirrel eating a burger"
             UpperCAmelCase_  :	Optional[Any]				      =		torch.manual_seed(0)

             UpperCAmelCase_  :	List[str]				      =		alt_pipe([prompt]     ,generator=lowercase     ,num_inference_steps=2     ,output_type="numpy")
             UpperCAmelCase_  :	int				      =		output.images

             UpperCAmelCase_  :	str				      =		image[0, -3:, -3:, -1]

             assert image.shape == (1, 512, 512, 3)
             UpperCAmelCase_  :	List[Any]				      =		np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323])

             assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
