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


a  :      List[str]            =    getLogger(__name__)


a  :      Any            =    """cuda""" if torch.cuda.is_available() else """cpu"""
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase = 8							,				lowercase = DEFAULT_DEVICE							,				lowercase=False							,				lowercase="summarization"							,				lowercase=None							,				**lowercase							,				):
   lowerCAmelCase_:							Union[str, Any]       =	Path(lowercase					).open("w"							,				encoding="utf-8"					)
   lowerCAmelCase_:							Union[str, Any]       =	str(lowercase					)
   lowerCAmelCase_:							Optional[int]       =	AutoModelForSeqaSeqLM.from_pretrained(lowercase					).to(lowercase					)
   if fpaa:
      lowerCAmelCase_:							Optional[int]       =	model.half()

   lowerCAmelCase_:							List[Any]       =	AutoTokenizer.from_pretrained(lowercase					)
   logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''					)  # if this is wrong, check config.model_type.

   lowerCAmelCase_:							List[Any]       =	time.time()
   # update config with task specific params
   use_task_specific_params(lowercase							,				lowercase					)
   if prefix is None:
      lowerCAmelCase_:							Union[str, Any]       =	prefix or getattr(model.config							,				"prefix"							,				""					) or ""
   for examples_chunk in tqdm(list(chunks(lowercase							,				lowercase					)					)					):
      lowerCAmelCase_:							str       =	[prefix + text for text in examples_chunk]
      lowerCAmelCase_:							int       =	tokenizer(lowercase							,				return_tensors="pt"							,				truncation=lowercase							,				padding="longest"					).to(lowercase					)
      lowerCAmelCase_:							Optional[Any]       =	model.generate(
          input_ids=batch.input_ids							,				attention_mask=batch.attention_mask							,				**lowercase							,				)
      lowerCAmelCase_:							str       =	tokenizer.batch_decode(lowercase							,				skip_special_tokens=lowercase							,				clean_up_tokenization_spaces=lowercase					)
      for hypothesis in dec:
         fout.write(hypothesis + "\n"					)
         fout.flush()
   fout.close()
   lowerCAmelCase_:							Dict       =	int(time.time() - start_time					)  # seconds
   lowerCAmelCase_:							str       =	len(lowercase					)
   return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,				4					)}
def 		snake_case__	(		):
   return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"					)
def 		snake_case__	(		lowercase=True					):
   lowerCAmelCase_:							Dict       =	argparse.ArgumentParser()
   parser.add_argument("model_name"							,				type=lowercase							,				help="like facebook/bart-large-cnn,t5-base, etc."					)
   parser.add_argument("input_path"							,				type=lowercase							,				help="like cnn_dm/test.source"					)
   parser.add_argument("save_path"							,				type=lowercase							,				help="where to save summaries"					)
   parser.add_argument("--reference_path"							,				type=lowercase							,				required=lowercase							,				help="like cnn_dm/test.target"					)
   parser.add_argument("--score_path"							,				type=lowercase							,				required=lowercase							,				default="metrics.json"							,				help="where to save metrics"					)
   parser.add_argument("--device"							,				type=lowercase							,				required=lowercase							,				default=lowercase							,				help="cuda, cuda:1, cpu etc."					)
   parser.add_argument(
       "--prefix"							,				type=lowercase							,				required=lowercase							,				default=lowercase							,				help="will be added to the begininng of src examples"					)
   parser.add_argument("--task"							,				type=lowercase							,				default="summarization"							,				help="used for task_specific_params + metrics"					)
   parser.add_argument("--bs"							,				type=lowercase							,				default=8							,				required=lowercase							,				help="batch size"					)
   parser.add_argument(
       "--n_obs"							,				type=lowercase							,				default=-1							,				required=lowercase							,				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=lowercase							,				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
   lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	parser.parse_known_args()
   lowerCAmelCase_:							List[Any]       =	parse_numeric_n_bool_cl_kwargs(lowercase					)
   if parsed_args and verbose:
      print(F'''parsed the following generate kwargs: {parsed_args}'''					)
   lowerCAmelCase_:							Optional[int]       =	[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path					).readlines()]
   if args.n_obs > 0:
      lowerCAmelCase_:							List[str]       =	examples[: args.n_obs]
   Path(args.save_path					).parent.mkdir(exist_ok=lowercase					)

   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"					)

   lowerCAmelCase_:							Optional[Any]       =	generate_summaries_or_translations(
       lowercase							,				args.save_path							,				args.model_name							,				batch_size=args.bs							,				device=args.device							,				fpaa=args.fpaa							,				task=args.task							,				prefix=args.prefix							,				**lowercase							,				)

   if args.reference_path is None:
      return {}

   # Compute scores
   lowerCAmelCase_:							Any       =	calculate_bleu if "translation" in args.task else calculate_rouge
   lowerCAmelCase_:							List[Any]       =	[x.rstrip() for x in open(args.save_path					).readlines()]
   lowerCAmelCase_:							int       =	[x.rstrip() for x in open(args.reference_path					).readlines()][: len(lowercase					)]
   lowerCAmelCase_:							dict       =	score_fn(lowercase							,				lowercase					)
   scores.update(lowercase					)

   if args.dump_args:
      scores.update(lowercase					)
   if args.info:
      lowerCAmelCase_:							Dict       =	args.info

   if verbose:
      print(lowercase					)

   if args.score_path is not None:
      json.dump(lowercase							,				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 tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a  :      List[str]            =    False

class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''
    pass





@nightly
@require_torch_gpu
class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def      _a    (    self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							List[str]       =	torch.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy" ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(lowerCamelCase__ )
          lowerCAmelCase_:							int       =	VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase__ )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	generator.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy" ).images

       assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	VersatileDiffusionTextToImagePipeline.from_pretrained(
           "shi-labs/versatile-diffusion"       ,						torch_dtype=torch.floataa )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							str       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							int       =	torch.manual_seed(0 )
       lowerCAmelCase_:							List[str]       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=50       ,						output_type="numpy" ).images

       lowerCAmelCase_:							List[Any]       =	image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowerCAmelCase_:							Optional[int]       =	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

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