


'''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		=getLogger(__name__)


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







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__ = 8	, UpperCamelCase__ = DEFAULT_DEVICE	, UpperCamelCase__=False	, UpperCamelCase__="summarization"	, UpperCamelCase__=None	, **UpperCamelCase__	, ):
			UpperCAmelCase__					:   int       				=       Path(UpperCamelCase__   ).open("""w"""	, encoding="""utf-8"""   )
			UpperCAmelCase__					:   Any       				=       str(UpperCamelCase__   )
			UpperCAmelCase__					:   Dict       				=       AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__   ).to(UpperCamelCase__   )
			if fpaa:
						UpperCAmelCase__					:   Optional[Any]       				=       model.half()

			UpperCAmelCase__					:   Optional[Any]       				=       AutoTokenizer.from_pretrained(UpperCamelCase__   )
			logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''   )  # if this is wrong, check config.model_type.

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







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







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

			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__					:   Tuple       				=       generate_summaries_or_translations(
			    UpperCamelCase__	, args.save_path	, args.model_name	, batch_size=args.bs	, device=args.device	, fpaa=args.fpaa	, task=args.task	, prefix=args.prefix	, **UpperCamelCase__	, )

			if args.reference_path is None:
						return {}

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

			if args.dump_args:
						scores.update(UpperCamelCase__   )
			if args.info:
						UpperCAmelCase__					:   List[Any]       				=       args.info

			if verbose:
						print(UpperCamelCase__   )

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



'''simple docstring'''



import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roformer.modeling_flax_roformer import (
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		)


class 						_snake_case   (       unittest.TestCase		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=13							,							_lowerCamelCase=7							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=99							,							_lowerCamelCase=32							,							_lowerCamelCase=5							,							_lowerCamelCase=4							,							_lowerCamelCase=37							,							_lowerCamelCase="gelu"							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=512							,							_lowerCamelCase=16							,							_lowerCamelCase=2							,							_lowerCamelCase=0.02							,							_lowerCamelCase=4							,							):
								UpperCAmelCase__					:   Union[str, Any]       				=       parent
								UpperCAmelCase__					:   str       				=       batch_size
								UpperCAmelCase__					:   Tuple       				=       seq_length
								UpperCAmelCase__					:   Any       				=       is_training
								UpperCAmelCase__					:   Tuple       				=       use_attention_mask
								UpperCAmelCase__					:   Optional[Any]       				=       use_token_type_ids
								UpperCAmelCase__					:   Optional[Any]       				=       use_labels
								UpperCAmelCase__					:   str       				=       vocab_size
								UpperCAmelCase__					:   Dict       				=       hidden_size
								UpperCAmelCase__					:   int       				=       num_hidden_layers
								UpperCAmelCase__					:   Optional[int]       				=       num_attention_heads
								UpperCAmelCase__					:   Optional[int]       				=       intermediate_size
								UpperCAmelCase__					:   Tuple       				=       hidden_act
								UpperCAmelCase__					:   Optional[Any]       				=       hidden_dropout_prob
								UpperCAmelCase__					:   Union[str, Any]       				=       attention_probs_dropout_prob
								UpperCAmelCase__					:   List[Any]       				=       max_position_embeddings
								UpperCAmelCase__					:   Optional[int]       				=       type_vocab_size
								UpperCAmelCase__					:   str       				=       type_sequence_label_size
								UpperCAmelCase__					:   Optional[int]       				=       initializer_range
								UpperCAmelCase__					:   str       				=       num_choices




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

								UpperCAmelCase__					:   Union[str, Any]       				=       None
								if self.use_attention_mask:
											UpperCAmelCase__					:   Tuple       				=       random_attention_mask([self.batch_size, self.seq_length])

								UpperCAmelCase__					:   Dict       				=       None
								if self.use_token_type_ids:
											UpperCAmelCase__					:   Any       				=       ids_tensor([self.batch_size, self.seq_length]							,							self.type_vocab_size)

								UpperCAmelCase__					:   Optional[Any]       				=       RoFormerConfig(
								    vocab_size=self.vocab_size							,							hidden_size=self.hidden_size							,							num_hidden_layers=self.num_hidden_layers							,							num_attention_heads=self.num_attention_heads							,							intermediate_size=self.intermediate_size							,							hidden_act=self.hidden_act							,							hidden_dropout_prob=self.hidden_dropout_prob							,							attention_probs_dropout_prob=self.attention_probs_dropout_prob							,							max_position_embeddings=self.max_position_embeddings							,							type_vocab_size=self.type_vocab_size							,							is_decoder=_lowerCamelCase							,							initializer_range=self.initializer_range							,							)

								return config, input_ids, token_type_ids, attention_mask




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[Any]       				=       self.prepare_config_and_inputs()
								UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   Optional[Any]       				=       config_and_inputs
								UpperCAmelCase__					:   Dict       				=       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
								return config, inputs_dict


@require_flax
class 						_snake_case   (       a__    ,				unittest.TestCase		):
					lowerCAmelCase :Optional[int]						=   True

					lowerCAmelCase :Any						=   (
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   List[str]       				=       FlaxRoFormerModelTester(self)




					@slow
					def 			snake_case__  ( self):
								for model_class_name in self.all_model_classes:
											UpperCAmelCase__					:   List[str]       				=       model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""							,							from_pt=_lowerCamelCase)
											UpperCAmelCase__					:   Dict       				=       model(np.ones((1, 1)))
											self.assertIsNotNone(_lowerCamelCase)


@require_flax
class 						_snake_case   (       unittest.TestCase		):




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Any       				=       FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""")
								UpperCAmelCase__					:   int       				=       jnp.array([[0, 1, 2, 3, 4, 5]])
								UpperCAmelCase__					:   Optional[int]       				=       model(_lowerCamelCase)[0]

								UpperCAmelCase__					:   Union[str, Any]       				=       5_0000

								UpperCAmelCase__					:   Any       				=       (1, 6, vocab_size)
								self.assertEqual(output.shape							,							_lowerCamelCase)

								UpperCAmelCase__					:   Union[str, Any]       				=       jnp.array(
								    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]])

								self.assertTrue(jnp.allclose(output[:, :3, :3]							,							_lowerCamelCase							,							atol=1e-4))