





'''simple docstring'''






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


A__				:   Optional[Any]      ='cuda' if torch.cuda.is_available() else 'cpu'
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase = 8							,	lowerCAmelCase = DEFAULT_DEVICE							,	lowerCAmelCase=False							,	lowerCAmelCase="summarization"							,	lowerCAmelCase=None							,	**lowerCAmelCase							,	):





      """simple docstring"""


      _lowerCAmelCase							     =							Path(lowerCAmelCase  ).open("""w"""							,	encoding="""utf-8"""  )
      _lowerCAmelCase							     =							str(lowerCAmelCase  )
      _lowerCAmelCase							     =							AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase  ).to(lowerCAmelCase  )
      if fpaa:
            _lowerCAmelCase							     =							model.half()

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

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





      """simple docstring"""


      return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""  )
def    UpperCamelCase__      (		lowerCAmelCase=True  ):





      """simple docstring"""


      _lowerCAmelCase							     =							argparse.ArgumentParser()
      parser.add_argument("""model_name"""							,	type=lowerCAmelCase							,	help="""like facebook/bart-large-cnn,t5-base, etc."""  )
      parser.add_argument("""input_path"""							,	type=lowerCAmelCase							,	help="""like cnn_dm/test.source"""  )
      parser.add_argument("""save_path"""							,	type=lowerCAmelCase							,	help="""where to save summaries"""  )
      parser.add_argument("""--reference_path"""							,	type=lowerCAmelCase							,	required=lowerCAmelCase							,	help="""like cnn_dm/test.target"""  )
      parser.add_argument("""--score_path"""							,	type=lowerCAmelCase							,	required=lowerCAmelCase							,	default="""metrics.json"""							,	help="""where to save metrics"""  )
      parser.add_argument("""--device"""							,	type=lowerCAmelCase							,	required=lowerCAmelCase							,	default=lowerCAmelCase							,	help="""cuda, cuda:1, cpu etc."""  )
      parser.add_argument(
          """--prefix"""							,	type=lowerCAmelCase							,	required=lowerCAmelCase							,	default=lowerCAmelCase							,	help="""will be added to the begininng of src examples"""  )
      parser.add_argument("""--task"""							,	type=lowerCAmelCase							,	default="""summarization"""							,	help="""used for task_specific_params + metrics"""  )
      parser.add_argument("""--bs"""							,	type=lowerCAmelCase							,	default=8							,	required=lowerCAmelCase							,	help="""batch size"""  )
      parser.add_argument(
          """--n_obs"""							,	type=lowerCAmelCase							,	default=-1							,	required=lowerCAmelCase							,	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=lowerCAmelCase							,	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							     =							parser.parse_known_args()
      _lowerCAmelCase							     =							parse_numeric_n_bool_cl_kwargs(lowerCAmelCase  )
      if parsed_args and verbose:
            print(f"parsed the following generate kwargs: {parsed_args}"  )
      _lowerCAmelCase							     =							[""" """ + 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							     =							examples[: args.n_obs]
      Path(args.save_path  ).parent.mkdir(exist_ok=lowerCAmelCase  )

      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							     =							generate_summaries_or_translations(
          lowerCAmelCase							,	args.save_path							,	args.model_name							,	batch_size=args.bs							,	device=args.device							,	fpaa=args.fpaa							,	task=args.task							,	prefix=args.prefix							,	**lowerCAmelCase							,	)

      if args.reference_path is None:
            return {}

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

      if args.dump_args:
            scores.update(lowerCAmelCase  )
      if args.info:
            _lowerCAmelCase							     =							args.info

      if verbose:
            print(lowerCAmelCase  )

      if args.score_path is not None:
            json.dump(lowerCAmelCase							,	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 random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
		import os

		import jax.numpy as jnp
		from jax import jit

		from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
		from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

		__lowerCAmelCase		:				List[Any]       			=		'0.12'  # assumed parallelism: 8


if is_torch_available():
		import torch




def 			__magic_name__				( A		:					Dict,  A		:					Union[str, Any],  A		:					Optional[int]=None   ):






			'''simple docstring'''







			if rng is None:
						a    =			random.Random()

			a    =			1
			for dim in shape:
						total_dims *= dim

			a    =			[]
			for _ in range(A   ):
						values.append(rng.randint(0,  vocab_size - 1   )   )

			a    =			np.array(A,  dtype=jnp.intaa   ).reshape(A   )

			return output




def 			__magic_name__				( A		:					Dict,  A		:					Union[str, Any]=None   ):






			'''simple docstring'''







			a    =			ids_tensor(A,  vocab_size=2,  rng=A   )
			# make sure that at least one token is attended to for each batch
			a    =			1
			return attn_mask




@require_flax
class     snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							List[str]							 =					None
				SCREAMING_SNAKE_CASE_       :							Any							 =					()





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[str]:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

							# cut to half length & take max batch_size 3
							a    =			2
							a    =			inputs["input_ids"].shape[-1] // 2
							a    =			inputs["input_ids"][:max_batch_size, :sequence_length]

							a    =			jnp.ones_like(__lowerCamelCase							)
							a    =			attention_mask[:max_batch_size, :sequence_length]

							# generate max 5 tokens
							a    =			input_ids.shape[-1] + 5
							if config.eos_token_id is not None and config.pad_token_id is None:
										# hack to allow generate for models such as GPT2 as is done in `generate()`
										a    =			config.eos_token_id
							return config, input_ids, attention_mask, max_length





				@is_pt_flax_cross_test
				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     int:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			False
							a    =			max_length
							a    =			0

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
										a    =			getattr(__lowerCamelCase					,      __lowerCamelCase							)
										a    =			pt_model_class(__lowerCamelCase							).eval()
										a    =			load_flax_weights_in_pytorch_model(__lowerCamelCase					,      flax_model.params							)

										a    =			flax_model.generate(__lowerCamelCase							).sequences
										a    =			pt_model.generate(torch.tensor(__lowerCamelCase					,      dtype=torch.long							)							)

										if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
													a    =			flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

										self.assertListEqual(pt_generation_outputs.numpy().tolist()					,      flax_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[int]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			False
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Any:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			True
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     Dict:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			False
							a    =			max_length
							a    =			2

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Union[str, Any]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			False
							a    =			max_length
							a    =			2
							a    =			2

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[0]					,      input_ids.shape[0] * config.num_return_sequences							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Dict:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			True
							a    =			max_length
							a    =			0.8
							a    =			10
							a    =			0.3
							a    =			1
							a    =			8
							a    =			9

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Optional[Any]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			max_length
							a    =			1
							a    =			8
							a    =			9

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Union[str, Any]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			max_length
							a    =			2
							a    =			1
							a    =			8
							a    =			9

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Dict:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()

							# pad attention mask on the left
							a    =			attention_mask.at[(0, 0)].set(0							)

							a    =			False
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Tuple:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()

							# pad attention mask on the left
							a    =			attention_mask.at[(0, 0)].set(0							)

							a    =			True
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     List[Any]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()

							# pad attention mask on the left
							a    =			attention_mask.at[(0, 0)].set(0							)

							a    =			2
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)




@require_flax
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Optional[Any]:
							a    =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"							)
							a    =			FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"							)

							a    =			"Hello world"
							a    =			tokenizer(__lowerCamelCase					,      return_tensors="np"							).input_ids

							# typos are quickly detected (the correct argument is `do_sample`)
							with self.assertRaisesRegex(__lowerCamelCase					,      "do_samples"							):
										model.generate(__lowerCamelCase					,      do_samples=__lowerCamelCase							)

							# arbitrary arguments that will not be used anywhere are also not accepted
							with self.assertRaisesRegex(__lowerCamelCase					,      "foo"							):
										a    =			{"foo": "bar"}
										model.generate(__lowerCamelCase					,      **__lowerCamelCase							)






