




'''simple docstring'''

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

							a  :				int									=  '0.12'  # assumed parallelism: 8


if is_torch_available():
							import torch


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase=None	)		->					List[Any]:

					'''simple docstring'''

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

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

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

					snake_case_									=				np.array(__UpperCAmelCase,      dtype=jnp.intaa	).reshape(__UpperCAmelCase	)

					return output


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase=None	)		->					List[Any]:

					'''simple docstring'''

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






@require_flax
class 						a				:
				snake_case_      =		None
				snake_case_      =		()


				def   A_ (		self    :					Tuple					):
									snake_case_			,snake_case_									=				self.model_tester.prepare_config_and_inputs_for_common()

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

									snake_case_									=				jnp.ones_like(lowercase_					)
									snake_case_									=				attention_mask[:max_batch_size, :sequence_length]

									# generate max 5 tokens
									snake_case_									=				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()`
														snake_case_									=				config.eos_token_id
									return config, input_ids, attention_mask, max_length


				@is_pt_flax_cross_test
				def   A_ (		self    :					int					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self._get_input_ids_and_config()
									snake_case_									=				False
									snake_case_									=				max_length
									snake_case_									=				0

									for model_class in self.all_generative_model_classes:
														snake_case_									=				model_class(lowercase_					)

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

														snake_case_									=				flax_model.generate(lowercase_					).sequences
														snake_case_									=				pt_model.generate(torch.tensor(lowercase_       ,				dtype=torch.long					)					)

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

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


				def   A_ (		self    :					Dict					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self._get_input_ids_and_config()
									snake_case_									=				False
									snake_case_									=				max_length

									for model_class in self.all_generative_model_classes:
														snake_case_									=				model_class(lowercase_					)

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

														snake_case_									=				jit(model.generate					)
														snake_case_									=				jit_generate(lowercase_					).sequences

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


				def   A_ (		self    :					Optional[int]					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self._get_input_ids_and_config()
									snake_case_									=				True
									snake_case_									=				max_length

									for model_class in self.all_generative_model_classes:
														snake_case_									=				model_class(lowercase_					)

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

														snake_case_									=				jit(model.generate					)
														snake_case_									=				jit_generate(lowercase_					).sequences

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


				def   A_ (		self    :					Optional[Any]					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self._get_input_ids_and_config()
									snake_case_									=				False
									snake_case_									=				max_length
									snake_case_									=				2

									for model_class in self.all_generative_model_classes:
														snake_case_									=				model_class(lowercase_					)

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

														snake_case_									=				jit(model.generate					)
														snake_case_									=				jit_generate(lowercase_					).sequences

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


				def   A_ (		self    :					Optional[int]					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self._get_input_ids_and_config()
									snake_case_									=				False
									snake_case_									=				max_length
									snake_case_									=				2
									snake_case_									=				2

									for model_class in self.all_generative_model_classes:
														snake_case_									=				model_class(lowercase_					)

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


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self._get_input_ids_and_config()
									snake_case_									=				True
									snake_case_									=				max_length
									snake_case_									=				0.8
									snake_case_									=				10
									snake_case_									=				0.3
									snake_case_									=				1
									snake_case_									=				8
									snake_case_									=				9

									for model_class in self.all_generative_model_classes:
														snake_case_									=				model_class(lowercase_					)

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

														snake_case_									=				jit(model.generate					)
														snake_case_									=				jit_generate(lowercase_					).sequences

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


				def   A_ (		self    :					Tuple					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self._get_input_ids_and_config()
									snake_case_									=				max_length
									snake_case_									=				1
									snake_case_									=				8
									snake_case_									=				9

									for model_class in self.all_generative_model_classes:
														snake_case_									=				model_class(lowercase_					)

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

														snake_case_									=				jit(model.generate					)
														snake_case_									=				jit_generate(lowercase_					).sequences

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


				def   A_ (		self    :					Dict					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self._get_input_ids_and_config()
									snake_case_									=				max_length
									snake_case_									=				2
									snake_case_									=				1
									snake_case_									=				8
									snake_case_									=				9

									for model_class in self.all_generative_model_classes:
														snake_case_									=				model_class(lowercase_					)

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

														snake_case_									=				jit(model.generate					)
														snake_case_									=				jit_generate(lowercase_					).sequences

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


				def   A_ (		self    :					int					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self._get_input_ids_and_config()

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

									snake_case_									=				False
									snake_case_									=				max_length

									for model_class in self.all_generative_model_classes:
														snake_case_									=				model_class(lowercase_					)

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

														snake_case_									=				jit(model.generate					)
														snake_case_									=				jit_generate(lowercase_       ,				attention_mask=lowercase_					).sequences

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


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self._get_input_ids_and_config()

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

									snake_case_									=				True
									snake_case_									=				max_length

									for model_class in self.all_generative_model_classes:
														snake_case_									=				model_class(lowercase_					)

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

														snake_case_									=				jit(model.generate					)
														snake_case_									=				jit_generate(lowercase_       ,				attention_mask=lowercase_					).sequences

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


				def   A_ (		self    :					str					):
									snake_case_			,snake_case_			,snake_case_			,snake_case_									=				self._get_input_ids_and_config()

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

									snake_case_									=				2
									snake_case_									=				max_length

									for model_class in self.all_generative_model_classes:
														snake_case_									=				model_class(lowercase_					)

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

														snake_case_									=				jit(model.generate					)
														snake_case_									=				jit_generate(lowercase_       ,				attention_mask=lowercase_					).sequences

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







@require_flax
class 						a				(   unittest.TestCase     ):


				def   A_ (		self    :					int					):
									snake_case_									=				AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''					)
									snake_case_									=				FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''					)

									snake_case_									=				'''Hello world'''
									snake_case_									=				tokenizer(lowercase_       ,				return_tensors='''np'''					).input_ids

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

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


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


__UpperCamelCase		:       Any									=     getLogger(__name__)


__UpperCamelCase		:       int									=     'cuda' if torch.cuda.is_available() else 'cpu'
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase = 8		,  _lowercase = DEFAULT_DEVICE		,  _lowercase=False		,  _lowercase="summarization"		,  _lowercase=None		,  **_lowercase		,  ):
						SCREAMING_SNAKE_CASE							:    List[str]							=							Path(_lowercase						).open('''w'''		,  encoding='''utf-8'''						)
						SCREAMING_SNAKE_CASE							:    int							=							str(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							AutoModelForSeqaSeqLM.from_pretrained(_lowercase						).to(_lowercase						)
						if fpaa:
												SCREAMING_SNAKE_CASE							:    Dict							=							model.half()

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

						SCREAMING_SNAKE_CASE							:    str							=							time.time()
						# update config with task specific params
						use_task_specific_params(_lowercase		,  _lowercase						)
						if prefix is None:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							prefix or getattr(model.config		,  '''prefix'''		,  ''''''						) or ''''''
						for examples_chunk in tqdm(list(chunks(_lowercase		,  _lowercase						)						)						):
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[prefix + text for text in examples_chunk]
												SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer(_lowercase		,  return_tensors='''pt'''		,  truncation=_lowercase		,  padding='''longest'''						).to(_lowercase						)
												SCREAMING_SNAKE_CASE							:    str							=							model.generate(
												    input_ids=batch.input_ids		,  attention_mask=batch.attention_mask		,  **_lowercase		,  )
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							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()
						SCREAMING_SNAKE_CASE							:    Tuple							=							int(time.time() - start_time						)  # seconds
						SCREAMING_SNAKE_CASE							:    str							=							len(_lowercase						)
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		,  4						)}
def       A	(    ):
						return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''						)
def       A	(    _lowercase=True						):
						SCREAMING_SNAKE_CASE							:    int							=							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
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							parser.parse_known_args()
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							parse_numeric_n_bool_cl_kwargs(_lowercase						)
						if parsed_args and verbose:
												print(f"""parsed the following generate kwargs: {parsed_args}"""						)
						SCREAMING_SNAKE_CASE							:    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:
												SCREAMING_SNAKE_CASE							:    Any							=							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'''						)

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

						if args.dump_args:
												scores.update(_lowercase						)
						if args.info:
												SCREAMING_SNAKE_CASE							:    Tuple							=							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)





