




"""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


_lowerCamelCase					:     Dict				       =    getLogger(__name__)


_lowerCamelCase					:     List[str]				       =    'cuda' if torch.cuda.is_available() else 'cpu'

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase = 8					, _UpperCAmelCase = DEFAULT_DEVICE					, _UpperCAmelCase=False					, _UpperCAmelCase="summarization"					, _UpperCAmelCase=None					, **_UpperCAmelCase					, ):




	"""simple docstring"""






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

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

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

def 						lowercase_ (						):




	"""simple docstring"""






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

def 						lowercase_ (						_UpperCAmelCase=True  ):




	"""simple docstring"""






	A_					:			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
	A_					:			Union[str, Any]						=						parser.parse_known_args()
	A_					:			Union[str, Any]						=						parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase  )
	if parsed_args and verbose:
		print(f"""parsed the following generate kwargs: {parsed_args}"""  )
	with open(args.input_path  ) as f:
		A_					:			Optional[int]						=						[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
	if args.n_obs > 0:
		A_					:			Tuple						=						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."""  )
	A_					:			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
	A_					:			Dict						=						calculate_bleu if 'translation' in args.task else calculate_rouge
	A_					:			Optional[Any]						=						[x.rstrip() for x in open(args.save_path  ).readlines()]
	A_					:			Union[str, Any]						=						[x.rstrip() for x in open(args.reference_path  ).readlines()][: len(_UpperCAmelCase  )]
	A_					:			dict						=						score_fn(_UpperCAmelCase					, _UpperCAmelCase  )
	scores.update(_UpperCAmelCase  )

	if args.dump_args:
		scores.update(_UpperCAmelCase  )
	if args.info:
		A_					:			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 shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class     lowercase (      unittest.TestCase):







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			List[str]						=						tempfile.mkdtemp()

					A_					:			List[str]						=						CLIPImageProcessor()
					A_					:			Any						=						BertTokenizer.from_pretrained(
					    '''hf-internal-testing/tiny-random-BertModel''' ,       model_input_names=['''input_ids''', '''attention_mask''']    )

					A_					:			str						=						GitProcessor(_lowerCamelCase ,       _lowerCamelCase    )

					processor.save_pretrained(self.tmpdirname    )







				def    a_					(							self				:   Tuple ,       **_lowerCamelCase				:   List[str]    ):



					"""simple docstring"""

					return AutoProcessor.from_pretrained(self.tmpdirname ,       **_lowerCamelCase    ).tokenizer







				def    a_					(							self				:   List[Any] ,       **_lowerCamelCase				:   int    ):



					"""simple docstring"""

					return AutoProcessor.from_pretrained(self.tmpdirname ,       **_lowerCamelCase    ).image_processor







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					shutil.rmtree(self.tmpdirname    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			List[str]						=						[np.random.randint(2_55 ,       size=(3, 30, 4_00) ,       dtype=np.uinta    )]

					A_					:			Any						=						[Image.fromarray(np.moveaxis(_lowerCamelCase ,       0 ,       -1    )    ) for x in image_inputs]

					return image_inputs







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						GitProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()    )
					processor.save_pretrained(self.tmpdirname    )

					A_					:			Union[str, Any]						=						self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''    )
					A_					:			List[Any]						=						self.get_image_processor(do_normalize=_lowerCamelCase ,       padding_value=1.0    )

					A_					:			List[str]						=						GitProcessor.from_pretrained(
					    self.tmpdirname ,       bos_token='''(BOS)''' ,       eos_token='''(EOS)''' ,       do_normalize=_lowerCamelCase ,       padding_value=1.0    )

					self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer_add_kwargs.get_vocab()    )
					self.assertIsInstance(processor.tokenizer ,       _lowerCamelCase    )

					self.assertEqual(processor.image_processor.to_json_string() ,       image_processor_add_kwargs.to_json_string()    )
					self.assertIsInstance(processor.image_processor ,       _lowerCamelCase    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Tuple						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						self.prepare_image_inputs()

					A_					:			Any						=						image_processor(_lowerCamelCase ,       return_tensors='''np'''    )
					A_					:			Optional[Any]						=						processor(images=_lowerCamelCase ,       return_tensors='''np'''    )

					for key in input_feat_extract.keys():
						self.assertAlmostEqual(input_feat_extract[key].sum() ,       input_processor[key].sum() ,       delta=1E-2    )







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						self.get_image_processor()
					A_					:			Union[str, Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			List[str]						=						'''lower newer'''

					A_					:			Tuple						=						processor(text=_lowerCamelCase    )

					A_					:			List[str]						=						tokenizer(_lowerCamelCase ,       return_token_type_ids=_lowerCamelCase    )

					for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key] ,       encoded_processor[key]    )







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						'''lower newer'''
					A_					:			str						=						self.prepare_image_inputs()

					A_					:			Optional[int]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

					self.assertListEqual(list(inputs.keys()    ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']    )

					# test if it raises when no input is passed
					with pytest.raises(_lowerCamelCase    ):
						processor()







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			List[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Dict						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					A_					:			str						=						processor.batch_decode(_lowerCamelCase    )
					A_					:			int						=						tokenizer.batch_decode(_lowerCamelCase    )

					self.assertListEqual(_lowerCamelCase ,       _lowerCamelCase    )







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Dict						=						self.get_image_processor()
					A_					:			List[str]						=						self.get_tokenizer()

					A_					:			Any						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			int						=						'''lower newer'''
					A_					:			Union[str, Any]						=						self.prepare_image_inputs()

					A_					:			List[str]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

					# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
					self.assertListEqual(list(inputs.keys()    ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']    )





