

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class 							A_			(		A__ ):
					_lowercase      :					List[Any]       =     (DPMSolverMultistepScheduler,)
					_lowercase      :					Optional[int]       =     (('num_inference_steps', 2_5),)


					def   UpperCAmelCase  (							self      :		Union[str, Any]      ,  **UpperCAmelCase      :		Optional[Any] )							->     List[Any]:
						__lowerCAmelCase:       Any            =				{
						    "num_train_timesteps": 1_0_0_0,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "solver_order": 2,
						    "prediction_type": "epsilon",
						    "thresholding": False,
						    "sample_max_value": 1.0,
						    "algorithm_type": "dpmsolver++",
						    "solver_type": "midpoint",
						    "lower_order_final": False,
						    "lambda_min_clipped": -float('inf' ),
						    "variance_type": None,
						}

						config.update(**_A )
						return config


					def   UpperCAmelCase  (							self      :		Any      ,  UpperCAmelCase      :		Tuple=0      ,  **UpperCAmelCase      :		Optional[int] )							->     int:
						__lowerCAmelCase:       int            =				dict(self.forward_default_kwargs )
						__lowerCAmelCase:       List[str]            =				kwargs.pop('num_inference_steps'      ,  _A )
						__lowerCAmelCase:       List[str]            =				self.dummy_sample
						__lowerCAmelCase:       str            =				0.1 * sample
						__lowerCAmelCase:       Tuple            =				[residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
							__lowerCAmelCase:       Dict            =				self.get_scheduler_config(**_A )
							__lowerCAmelCase:       Dict            =				scheduler_class(**_A )
							scheduler.set_timesteps(_A )
							# copy over dummy past residuals
							__lowerCAmelCase:       int            =				dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(_A )
								__lowerCAmelCase:       Any            =				scheduler_class.from_pretrained(_A )
								new_scheduler.set_timesteps(_A )
								# copy over dummy past residuals
								__lowerCAmelCase:       List[Any]            =				dummy_past_residuals[: new_scheduler.config.solver_order]

							__lowerCAmelCase:       str            =				sample, sample
							for t in range(_A      ,  time_step + scheduler.config.solver_order + 1 ):
								__lowerCAmelCase:       List[str]            =				scheduler.step(_A      ,  _A      ,  _A      ,  **_A ).prev_sample
								__lowerCAmelCase:       Any            =				new_scheduler.step(_A      ,  _A      ,  _A      ,  **_A ).prev_sample

								assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"


					def   UpperCAmelCase  (							self      :		Any )							->     Optional[int]:
						pass


					def   UpperCAmelCase  (							self      :		Dict      ,  UpperCAmelCase      :		List[str]=0      ,  **UpperCAmelCase      :		Union[str, Any] )							->     str:
						__lowerCAmelCase:       Optional[int]            =				dict(self.forward_default_kwargs )
						__lowerCAmelCase:       Tuple            =				kwargs.pop('num_inference_steps'      ,  _A )
						__lowerCAmelCase:       int            =				self.dummy_sample
						__lowerCAmelCase:       str            =				0.1 * sample
						__lowerCAmelCase:       Dict            =				[residual + 0.2, residual + 0.15, residual + 0.10]

						for scheduler_class in self.scheduler_classes:
							__lowerCAmelCase:       Optional[Any]            =				self.get_scheduler_config()
							__lowerCAmelCase:       str            =				scheduler_class(**_A )
							scheduler.set_timesteps(_A )

							# copy over dummy past residuals (must be after setting timesteps)
							__lowerCAmelCase:       Union[str, Any]            =				dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(_A )
								__lowerCAmelCase:       int            =				scheduler_class.from_pretrained(_A )
								# copy over dummy past residuals
								new_scheduler.set_timesteps(_A )

								# copy over dummy past residual (must be after setting timesteps)
								__lowerCAmelCase:       List[Any]            =				dummy_past_residuals[: new_scheduler.config.solver_order]

							__lowerCAmelCase:       Dict            =				scheduler.step(_A      ,  _A      ,  _A      ,  **_A ).prev_sample
							__lowerCAmelCase:       List[Any]            =				new_scheduler.step(_A      ,  _A      ,  _A      ,  **_A ).prev_sample

							assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"


					def   UpperCAmelCase  (							self      :		Tuple      ,  UpperCAmelCase      :		List[Any]=None      ,  **UpperCAmelCase      :		int )							->     int:






						if scheduler is None:
							__lowerCAmelCase:       Union[str, Any]            =				self.scheduler_classes[0]
							__lowerCAmelCase:       Optional[int]            =				self.get_scheduler_config(**_A )
							__lowerCAmelCase:       str            =				scheduler_class(**_A )

						__lowerCAmelCase:       Any            =				1_0
						__lowerCAmelCase:       int            =				self.dummy_model()
						__lowerCAmelCase:       Optional[Any]            =				self.dummy_sample_deter
						scheduler.set_timesteps(_A )

						for i, t in enumerate(scheduler.timesteps ):
							__lowerCAmelCase:       List[str]            =				model(_A      ,  _A )
							__lowerCAmelCase:       Tuple            =				scheduler.step(_A      ,  _A      ,  _A ).prev_sample

						return sample


					def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     Any:
						__lowerCAmelCase:       str            =				dict(self.forward_default_kwargs )

						__lowerCAmelCase:       List[str]            =				kwargs.pop('num_inference_steps'      ,  _A )

						for scheduler_class in self.scheduler_classes:
							__lowerCAmelCase:       Optional[int]            =				self.get_scheduler_config()
							__lowerCAmelCase:       Dict            =				scheduler_class(**_A )

							__lowerCAmelCase:       int            =				self.dummy_sample
							__lowerCAmelCase:       Tuple            =				0.1 * sample

							if num_inference_steps is not None and hasattr(_A      ,  'set_timesteps' ):
								scheduler.set_timesteps(_A )
							elif num_inference_steps is not None and not hasattr(_A      ,  'set_timesteps' ):
								__lowerCAmelCase:       Optional[int]            =				num_inference_steps

							# copy over dummy past residuals (must be done after set_timesteps)
							__lowerCAmelCase:       Tuple            =				[residual + 0.2, residual + 0.15, residual + 0.10]
							__lowerCAmelCase:       Dict            =				dummy_past_residuals[: scheduler.config.solver_order]

							__lowerCAmelCase:       Any            =				scheduler.timesteps[5]
							__lowerCAmelCase:       Dict            =				scheduler.timesteps[6]

							__lowerCAmelCase:       List[Any]            =				scheduler.step(_A      ,  _A      ,  _A      ,  **_A ).prev_sample
							__lowerCAmelCase:       int            =				scheduler.step(_A      ,  _A      ,  _A      ,  **_A ).prev_sample

							self.assertEqual(output_a.shape      ,  sample.shape )
							self.assertEqual(output_a.shape      ,  output_a.shape )


					def   UpperCAmelCase  (							self      :		Tuple )							->     int:






						for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=_A )


					def   UpperCAmelCase  (							self      :		List[Any] )							->     Optional[int]:
						self.check_over_configs(thresholding=_A )
						for order in [1, 2, 3]:
							for solver_type in ["midpoint", "heun"]:
								for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(
										    thresholding=_A      ,  prediction_type=_A      ,  sample_max_value=_A      ,  algorithm_type='dpmsolver++'      ,  solver_order=_A      ,  solver_type=_A      ,  )


					def   UpperCAmelCase  (							self      :		Optional[Any] )							->     Optional[int]:






						for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=_A )


					def   UpperCAmelCase  (							self      :		int )							->     str:






						for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
							for solver_type in ["midpoint", "heun"]:
								for order in [1, 2, 3]:
									for prediction_type in ["epsilon", "sample"]:
										if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
											if order == 3:
												continue
										else:
											self.check_over_configs(
											    solver_order=_A      ,  solver_type=_A      ,  prediction_type=_A      ,  algorithm_type=_A      ,  )
										__lowerCAmelCase:       Dict            =				self.full_loop(
										    solver_order=_A      ,  solver_type=_A      ,  prediction_type=_A      ,  algorithm_type=_A      ,  )
										assert not torch.isnan(_A ).any(), "Samples have nan numbers"


					def   UpperCAmelCase  (							self      :		List[str] )							->     Union[str, Any]:
						self.check_over_configs(lower_order_final=_A )
						self.check_over_configs(lower_order_final=_A )


					def   UpperCAmelCase  (							self      :		Dict )							->     Optional[int]:
						self.check_over_configs(lambda_min_clipped=-float('inf' ) )
						self.check_over_configs(lambda_min_clipped=-5.1 )


					def   UpperCAmelCase  (							self      :		Optional[int] )							->     Tuple:
						self.check_over_configs(variance_type=_A )
						self.check_over_configs(variance_type='learned_range' )


					def   UpperCAmelCase  (							self      :		Optional[int] )							->     str:






						for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_forward(num_inference_steps=_A      ,  time_step=0 )


					def   UpperCAmelCase  (							self      :		int )							->     List[str]:
						__lowerCAmelCase:       Dict            =				self.full_loop()
						__lowerCAmelCase:       Optional[int]            =				torch.mean(torch.abs(_A ) )

						assert abs(result_mean.item() - 0.3301 ) < 1E-3


					def   UpperCAmelCase  (							self      :		Any )							->     Any:
						__lowerCAmelCase:       int            =				self.full_loop(thresholding=_A      ,  dynamic_thresholding_ratio=0.87      ,  sample_max_value=0.5 )
						__lowerCAmelCase:       List[Any]            =				torch.mean(torch.abs(_A ) )

						assert abs(result_mean.item() - 1.1364 ) < 1E-3


					def   UpperCAmelCase  (							self      :		Optional[Any] )							->     Optional[int]:
						__lowerCAmelCase:       Union[str, Any]            =				self.full_loop(prediction_type='v_prediction' )
						__lowerCAmelCase:       Optional[Any]            =				torch.mean(torch.abs(_A ) )

						assert abs(result_mean.item() - 0.2251 ) < 1E-3


					def   UpperCAmelCase  (							self      :		List[str] )							->     List[Any]:
						__lowerCAmelCase:       List[Any]            =				self.full_loop(prediction_type='v_prediction'      ,  use_karras_sigmas=_A )
						__lowerCAmelCase:       List[str]            =				torch.mean(torch.abs(_A ) )

						assert abs(result_mean.item() - 0.2096 ) < 1E-3


					def   UpperCAmelCase  (							self      :		Tuple )							->     Optional[Any]:
						__lowerCAmelCase:       List[Any]            =				DPMSolverMultistepScheduler(**self.get_scheduler_config() )
						__lowerCAmelCase:       List[Any]            =				self.full_loop(scheduler=_A )
						__lowerCAmelCase:       List[Any]            =				torch.mean(torch.abs(_A ) )

						assert abs(result_mean.item() - 0.3301 ) < 1E-3

						__lowerCAmelCase:       Dict            =				DPMSolverSinglestepScheduler.from_config(scheduler.config )
						__lowerCAmelCase:       Union[str, Any]            =				UniPCMultistepScheduler.from_config(scheduler.config )
						__lowerCAmelCase:       Union[str, Any]            =				DEISMultistepScheduler.from_config(scheduler.config )
						__lowerCAmelCase:       int            =				DPMSolverMultistepScheduler.from_config(scheduler.config )

						__lowerCAmelCase:       int            =				self.full_loop(scheduler=_A )
						__lowerCAmelCase:       List[Any]            =				torch.mean(torch.abs(_A ) )

						assert abs(result_mean.item() - 0.3301 ) < 1E-3


					def   UpperCAmelCase  (							self      :		int )							->     Union[str, Any]:
						__lowerCAmelCase:       Tuple            =				self.scheduler_classes[0]
						__lowerCAmelCase:       List[str]            =				self.get_scheduler_config(thresholding=_A      ,  dynamic_thresholding_ratio=0 )
						__lowerCAmelCase:       List[str]            =				scheduler_class(**_A )

						__lowerCAmelCase:       int            =				1_0
						__lowerCAmelCase:       List[str]            =				self.dummy_model()
						__lowerCAmelCase:       List[Any]            =				self.dummy_sample_deter.half()
						scheduler.set_timesteps(_A )

						for i, t in enumerate(scheduler.timesteps ):
							__lowerCAmelCase:       Union[str, Any]            =				model(_A      ,  _A )
							__lowerCAmelCase:       int            =				scheduler.step(_A      ,  _A      ,  _A ).prev_sample

						assert sample.dtype == torch.floataa


					def   UpperCAmelCase  (							self      :		Tuple      ,  **UpperCAmelCase      :		List[str] )							->     List[str]:






						for scheduler_class in self.scheduler_classes:
							__lowerCAmelCase:       int            =				self.get_scheduler_config(**_A )
							__lowerCAmelCase:       Union[str, Any]            =				scheduler_class(**_A )

							scheduler.set_timesteps(scheduler.config.num_train_timesteps )
							assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps








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			=       getLogger(__name__)


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







def _a			(	SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      str   , SCREAMING_SNAKE_CASE		:      int = 8   , SCREAMING_SNAKE_CASE		:      str = DEFAULT_DEVICE   , SCREAMING_SNAKE_CASE		:      Union[str, Any]=False   , SCREAMING_SNAKE_CASE		:      Tuple="summarization"   , SCREAMING_SNAKE_CASE		:      int=None   , **SCREAMING_SNAKE_CASE		:      Optional[Any]   , )   ->   Dict:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				Path(SCREAMING_SNAKE_CASE		).open('w'   , encoding='utf-8'		)
 __lowerCAmelCase:       Tuple            =				str(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE		).to(SCREAMING_SNAKE_CASE		)
 if fpaa:
  __lowerCAmelCase:       List[Any]            =				model.half()

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

 __lowerCAmelCase:       Any            =				time.time()
 # update config with task specific params
 use_task_specific_params(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 if prefix is None:
  __lowerCAmelCase:       Optional[Any]            =				prefix or getattr(model.config   , 'prefix'   , ''		) or ''
 for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)		)		):
  __lowerCAmelCase:       str            =				[prefix + text for text in examples_chunk]
  __lowerCAmelCase:       Optional[Any]            =				tokenizer(SCREAMING_SNAKE_CASE   , return_tensors='pt'   , truncation=SCREAMING_SNAKE_CASE   , padding='longest'		).to(SCREAMING_SNAKE_CASE		)
  __lowerCAmelCase:       List[str]            =				model.generate(
      input_ids=batch.input_ids   , attention_mask=batch.attention_mask   , **SCREAMING_SNAKE_CASE   , )
  __lowerCAmelCase:       Dict            =				tokenizer.batch_decode(SCREAMING_SNAKE_CASE   , skip_special_tokens=SCREAMING_SNAKE_CASE   , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE		)
  for hypothesis in dec:
   fout.write(hypothesis + '\n'		)
   fout.flush()
 fout.close()
 __lowerCAmelCase:       str            =				int(time.time() - start_time		)  # seconds
 __lowerCAmelCase:       Tuple            =				len(SCREAMING_SNAKE_CASE		)
 return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs   , 4		)}







def _a			(	)   ->   Dict:

 """simple docstring"""






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







def _a			(	SCREAMING_SNAKE_CASE		:      Tuple=True		)   ->   int:

 """simple docstring"""






 __lowerCAmelCase:       str            =				argparse.ArgumentParser()
 parser.add_argument('model_name'   , type=SCREAMING_SNAKE_CASE   , help='like facebook/bart-large-cnn,t5-base, etc.'		)
 parser.add_argument('input_path'   , type=SCREAMING_SNAKE_CASE   , help='like cnn_dm/test.source'		)
 parser.add_argument('save_path'   , type=SCREAMING_SNAKE_CASE   , help='where to save summaries'		)
 parser.add_argument('--reference_path'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , help='like cnn_dm/test.target'		)
 parser.add_argument('--score_path'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , default='metrics.json'   , help='where to save metrics'		)
 parser.add_argument('--device'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , default=SCREAMING_SNAKE_CASE   , help='cuda, cuda:1, cpu etc.'		)
 parser.add_argument(
     '--prefix'   , type=SCREAMING_SNAKE_CASE   , required=SCREAMING_SNAKE_CASE   , default=SCREAMING_SNAKE_CASE   , help='will be added to the begininng of src examples'		)
 parser.add_argument('--task'   , type=SCREAMING_SNAKE_CASE   , default='summarization'   , help='used for task_specific_params + metrics'		)
 parser.add_argument('--bs'   , type=SCREAMING_SNAKE_CASE   , default=8   , required=SCREAMING_SNAKE_CASE   , help='batch size'		)
 parser.add_argument(
     '--n_obs'   , type=SCREAMING_SNAKE_CASE   , default=-1   , required=SCREAMING_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=SCREAMING_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
 __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				parser.parse_known_args()
 __lowerCAmelCase:       Union[str, Any]            =				parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE		)
 if parsed_args and verbose:
  print(f'''parsed the following generate kwargs: {parsed_args}'''		)
 with open(args.input_path		) as f:
  __lowerCAmelCase:       Optional[int]            =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
 if args.n_obs > 0:
  __lowerCAmelCase:       Tuple            =				examples[: args.n_obs]
 Path(args.save_path		).parent.mkdir(exist_ok=SCREAMING_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.'''		)
 __lowerCAmelCase:       Tuple            =				generate_summaries_or_translations(
     SCREAMING_SNAKE_CASE   , args.save_path   , args.model_name   , batch_size=args.bs   , device=args.device   , fpaa=args.fpaa   , task=args.task   , prefix=args.prefix   , **SCREAMING_SNAKE_CASE   , )

 if args.reference_path is None:
  return {}

 # Compute scores
 __lowerCAmelCase:       Dict            =				calculate_bleu if 'translation' in args.task else calculate_rouge
 __lowerCAmelCase:       Optional[Any]            =				[x.rstrip() for x in open(args.save_path		).readlines()]
 __lowerCAmelCase:       Union[str, Any]            =				[x.rstrip() for x in open(args.reference_path		).readlines()][: len(SCREAMING_SNAKE_CASE		)]
 __lowerCAmelCase:       dict            =				score_fn(SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE		)
 scores.update(SCREAMING_SNAKE_CASE		)

 if args.dump_args:
  scores.update(SCREAMING_SNAKE_CASE		)
 if args.info:
  __lowerCAmelCase:       Any            =				args.info

 if verbose:
  print(SCREAMING_SNAKE_CASE		)

 if args.score_path is not None:
  json.dump(SCREAMING_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)





