





'''simple docstring'''






import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
					import torch

					from transformers import AutoModelForCausalLM
@require_torch
class 		lowerCamelCase  (      unittest.TestCase       ):



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
			lowerCAmelCase     :    Any						=   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(a_	)
			lowerCAmelCase     :    Dict						=   -1

			lowerCAmelCase     :    Optional[Any]						=   ids_tensor((1, 5)		,      vocab_size=model.config.vocab_size	).to(a_	)
			lowerCAmelCase     :    List[Any]						=   model.generate(a_		,      max_new_tokens=10		,      do_sample=a_	)
			lowerCAmelCase     :    Union[str, Any]						=   tokenizer.decode(greedy_ids[0]	)

			with CaptureStdout() as cs:
					lowerCAmelCase     :    List[Any]						=   TextStreamer(a_	)
					model.generate(a_		,      max_new_tokens=10		,      do_sample=a_		,      streamer=a_	)
			# The greedy text should be printed to stdout, except for the final "\n" in the streamer
			lowerCAmelCase     :    Tuple						=   cs.out[:-1]

			self.assertEqual(a_		,      a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
			lowerCAmelCase     :    Tuple						=   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(a_	)
			lowerCAmelCase     :    Any						=   -1

			lowerCAmelCase     :    str						=   ids_tensor((1, 5)		,      vocab_size=model.config.vocab_size	).to(a_	)
			lowerCAmelCase     :    Tuple						=   model.generate(a_		,      max_new_tokens=10		,      do_sample=a_	)
			lowerCAmelCase     :    Tuple						=   tokenizer.decode(greedy_ids[0]	)

			lowerCAmelCase     :    Union[str, Any]						=   TextIteratorStreamer(a_	)
			lowerCAmelCase     :    str						=   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
			lowerCAmelCase     :    Dict						=   Thread(target=model.generate		,      kwargs=a_	)
			thread.start()
			lowerCAmelCase     :    List[str]						=   ""
			for new_text in streamer:
					streamer_text += new_text

			self.assertEqual(a_		,      a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
			lowerCAmelCase     :    List[str]						=   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(a_	)
			lowerCAmelCase     :    Any						=   -1

			lowerCAmelCase     :    List[str]						=   ids_tensor((1, 5)		,      vocab_size=model.config.vocab_size	).to(a_	)
			lowerCAmelCase     :    Dict						=   model.generate(a_		,      max_new_tokens=10		,      do_sample=a_	)
			lowerCAmelCase     :    Optional[int]						=   greedy_ids[:, input_ids.shape[1] :]
			lowerCAmelCase     :    int						=   tokenizer.decode(new_greedy_ids[0]	)

			with CaptureStdout() as cs:
					lowerCAmelCase     :    Optional[Any]						=   TextStreamer(a_		,      skip_prompt=a_	)
					model.generate(a_		,      max_new_tokens=10		,      do_sample=a_		,      streamer=a_	)
			# The greedy text should be printed to stdout, except for the final "\n" in the streamer
			lowerCAmelCase     :    Dict						=   cs.out[:-1]

			self.assertEqual(a_		,      a_	)



	def   _lowerCamelCase    (   self	):
			# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
			# with actual models -- the dummy models' tokenizers are not aligned with their models, and
			# `skip_special_tokens=True` has no effect on them
			lowerCAmelCase     :    int						=   AutoTokenizer.from_pretrained("distilgpt2"	)
			lowerCAmelCase     :    Union[str, Any]						=   AutoModelForCausalLM.from_pretrained("distilgpt2"	).to(a_	)
			lowerCAmelCase     :    List[Any]						=   -1

			lowerCAmelCase     :    int						=   torch.ones((1, 5)		,      device=a_	).long() * model.config.bos_token_id
			with CaptureStdout() as cs:
					lowerCAmelCase     :    List[str]						=   TextStreamer(a_		,      skip_special_tokens=a_	)
					model.generate(a_		,      max_new_tokens=1		,      do_sample=a_		,      streamer=a_	)

			# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
			# re-tokenized, must only contain one token
			lowerCAmelCase     :    Optional[Any]						=   cs.out[:-1]  # Remove the final "\n"
			lowerCAmelCase     :    str						=   tokenizer(a_		,      return_tensors="pt"	)
			self.assertEqual(streamer_text_tokenized.input_ids.shape		,      (1, 1)	)





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
			lowerCAmelCase     :    Optional[Any]						=   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(a_	)
			lowerCAmelCase     :    Any						=   -1

			lowerCAmelCase     :    Dict						=   ids_tensor((1, 5)		,      vocab_size=model.config.vocab_size	).to(a_	)
			lowerCAmelCase     :    List[Any]						=   TextIteratorStreamer(a_		,      timeout=0.001	)
			lowerCAmelCase     :    int						=   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
			lowerCAmelCase     :    List[str]						=   Thread(target=model.generate		,      kwargs=a_	)
			thread.start()

			# The streamer will timeout after 0.001 seconds, so an exception will be raised
			with self.assertRaises(a_	):
					lowerCAmelCase     :    Any						=   ""
					for new_text in streamer:
							streamer_text += new_text










'''simple docstring'''






import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def      __A						(							a_						:  List[str]      ,a_						:  Dict      ,a_						:  List[Any]      ,a_						:  Optional[int]=1_0_2_4	):
		lowerCAmelCase	,		lowerCAmelCase     :    Dict						=   [], []

		lowerCAmelCase     :    Optional[Any]						=   list(zip(a_      ,a_	)	)
		lowerCAmelCase	,		lowerCAmelCase     :    Any						=   sorted_examples[0]

		def is_too_big(a_						:  Any	):
				return tok(a_      ,return_tensors="pt"	).input_ids.shape[1] > max_tokens

		for src, tgt in tqdm(sorted_examples[1:]	):
				lowerCAmelCase     :    Optional[int]						=   new_src + " " + src
				lowerCAmelCase     :    Union[str, Any]						=   new_tgt + " " + tgt
				if is_too_big(a_	) or is_too_big(a_	):  # cant fit, finalize example
						finished_src.append(a_	)
						finished_tgt.append(a_	)
						lowerCAmelCase	,		lowerCAmelCase     :    str						=   src, tgt
				else:  # can fit, keep adding
						lowerCAmelCase	,		lowerCAmelCase     :    List[str]						=   cand_src, cand_tgt

    # cleanup
		if new_src:
				assert new_tgt
				finished_src.append(a_	)
				finished_tgt.append(a_	)
		return finished_src, finished_tgt






def      __A						(							a_						:  List[str]      ,a_						:  Path      ,a_						:  Any      ,a_						:  int	):
		lowerCAmelCase     :    Optional[int]						=   Path(a_	)
		save_path.mkdir(exist_ok=a_	)
		for split in ["train"]:
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
				lowerCAmelCase     :    Tuple						=   [x.rstrip() for x in Path(a_	).open().readlines()]
				lowerCAmelCase     :    str						=   [x.rstrip() for x in Path(a_	).open().readlines()]
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   pack_examples(a_      ,a_      ,a_      ,a_	)
				print(f'''packed {split} split from {len(a_	)} examples -> {len(a_	)}.'''	)
				Path(save_path / f'''{split}.source'''	).open("w"	).write("\n".join(a_	)	)
				Path(save_path / f'''{split}.target'''	).open("w"	).write("\n".join(a_	)	)
		for split in ["val", "test"]:
				lowerCAmelCase	,		lowerCAmelCase     :    Optional[Any]						=   data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
				shutil.copyfile(a_      ,save_path / f'''{split}.source'''	)
				shutil.copyfile(a_      ,save_path / f'''{split}.target'''	)






def      __A						(							):
		lowerCAmelCase     :    List[str]						=   argparse.ArgumentParser()
		parser.add_argument("--tok_name"      ,type=a_      ,help="like facebook/bart-large-cnn,t5-base, etc."	)
		parser.add_argument("--max_seq_len"      ,type=a_      ,default=1_2_8	)
		parser.add_argument("--data_dir"      ,type=a_	)
		parser.add_argument("--save_path"      ,type=a_	)
		lowerCAmelCase     :    Optional[Any]						=   parser.parse_args()
		lowerCAmelCase     :    List[str]						=   AutoTokenizer.from_pretrained(args.tok_name	)
		return pack_data_dir(a_      ,Path(args.data_dir	)      ,args.max_seq_len      ,args.save_path	)


if __name__ == "__main__":
					packer_cli()



