


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 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






     def  a_					(			self		)       ->    Any:
          _a						=		AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
          _a						=		AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(__UpperCamelCase		)
          _a						=		-1

          _a						=		ids_tensor((1, 5)     ,							vocab_size=model.config.vocab_size		).to(__UpperCamelCase		)
          _a						=		model.generate(__UpperCamelCase     ,							max_new_tokens=10     ,							do_sample=__UpperCamelCase		)
          _a						=		tokenizer.decode(greedy_ids[0]		)

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

          self.assertEqual(__UpperCamelCase     ,							__UpperCamelCase		)






     def  a_					(			self		)       ->    int:
          _a						=		AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
          _a						=		AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(__UpperCamelCase		)
          _a						=		-1

          _a						=		ids_tensor((1, 5)     ,							vocab_size=model.config.vocab_size		).to(__UpperCamelCase		)
          _a						=		model.generate(__UpperCamelCase     ,							max_new_tokens=10     ,							do_sample=__UpperCamelCase		)
          _a						=		tokenizer.decode(greedy_ids[0]		)

          _a						=		TextIteratorStreamer(__UpperCamelCase		)
          _a						=		{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
          _a						=		Thread(target=model.generate     ,							kwargs=__UpperCamelCase		)
          thread.start()
          _a						=		""
          for new_text in streamer:
               streamer_text += new_text

          self.assertEqual(__UpperCamelCase     ,							__UpperCamelCase		)






     def  a_					(			self		)       ->    str:
          _a						=		AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
          _a						=		AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(__UpperCamelCase		)
          _a						=		-1

          _a						=		ids_tensor((1, 5)     ,							vocab_size=model.config.vocab_size		).to(__UpperCamelCase		)
          _a						=		model.generate(__UpperCamelCase     ,							max_new_tokens=10     ,							do_sample=__UpperCamelCase		)
          _a						=		greedy_ids[:, input_ids.shape[1] :]
          _a						=		tokenizer.decode(new_greedy_ids[0]		)

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

          self.assertEqual(__UpperCamelCase     ,							__UpperCamelCase		)






     def  a_					(			self		)       ->    List[Any]:
          # 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
          _a						=		AutoTokenizer.from_pretrained("distilgpt2"		)
          _a						=		AutoModelForCausalLM.from_pretrained("distilgpt2"		).to(__UpperCamelCase		)
          _a						=		-1

          _a						=		torch.ones((1, 5)     ,							device=__UpperCamelCase		).long() * model.config.bos_token_id
          with CaptureStdout() as cs:
               _a						=		TextStreamer(__UpperCamelCase     ,							skip_special_tokens=__UpperCamelCase		)
               model.generate(__UpperCamelCase     ,							max_new_tokens=1     ,							do_sample=__UpperCamelCase     ,							streamer=__UpperCamelCase		)

          # 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
          _a						=		cs.out[:-1]  # Remove the final "\n"
          _a						=		tokenizer(__UpperCamelCase     ,							return_tensors="pt"		)
          self.assertEqual(streamer_text_tokenized.input_ids.shape     ,							(1, 1)		)






     def  a_					(			self		)       ->    List[Any]:
          _a						=		AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
          _a						=		AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(__UpperCamelCase		)
          _a						=		-1

          _a						=		ids_tensor((1, 5)     ,							vocab_size=model.config.vocab_size		).to(__UpperCamelCase		)
          _a						=		TextIteratorStreamer(__UpperCamelCase     ,							timeout=0.0_01		)
          _a						=		{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
          _a						=		Thread(target=model.generate     ,							kwargs=__UpperCamelCase		)
          thread.start()

          # The streamer will timeout after 0.001 seconds, so an exception will be raised
          with self.assertRaises(__UpperCamelCase		):
               _a						=		""
               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 __UpperCamelCase   (			__lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: Dict=1024				)      ->     str:







     '''simple docstring'''






     _a			,			_a						=		[], []

     _a						=		list(zip(__lowerCamelCase      ,  __lowerCamelCase				)				)
     _a			,			_a						=		sorted_examples[0]

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

     for src, tgt in tqdm(sorted_examples[1:]				):
          _a						=		new_src + " " + src
          _a						=		new_tgt + " " + tgt
          if is_too_big(__lowerCamelCase				) or is_too_big(__lowerCamelCase				):  # cant fit, finalize example
               finished_src.append(__lowerCamelCase				)
               finished_tgt.append(__lowerCamelCase				)
               _a			,			_a						=		src, tgt
          else:  # can fit, keep adding
               _a			,			_a						=		cand_src, cand_tgt

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






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: Path      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: Optional[int]				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		Path(__lowerCamelCase				)
     save_path.mkdir(exist_ok=__lowerCamelCase				)
     for split in ["train"]:
          _a			,			_a						=		data_dir / F"{split}.source", data_dir / F"{split}.target"
          _a						=		[x.rstrip() for x in Path(__lowerCamelCase				).open().readlines()]
          _a						=		[x.rstrip() for x in Path(__lowerCamelCase				).open().readlines()]
          _a			,			_a						=		pack_examples(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)
          print(F"packed {split} split from {len(__lowerCamelCase				)} examples -> {len(__lowerCamelCase				)}."				)
          Path(save_path / F"{split}.source"				).open("w"				).write("\n".join(__lowerCamelCase				)				)
          Path(save_path / F"{split}.target"				).open("w"				).write("\n".join(__lowerCamelCase				)				)
     for split in ["val", "test"]:
          _a			,			_a						=		data_dir / F"{split}.source", data_dir / F"{split}.target"
          shutil.copyfile(__lowerCamelCase      ,  save_path / F"{split}.source"				)
          shutil.copyfile(__lowerCamelCase      ,  save_path / F"{split}.target"				)






def __UpperCamelCase   (			)      ->     Any:







     '''simple docstring'''






     _a						=		argparse.ArgumentParser()
     parser.add_argument("--tok_name"      ,  type=__lowerCamelCase      ,  help="like facebook/bart-large-cnn,t5-base, etc."				)
     parser.add_argument("--max_seq_len"      ,  type=__lowerCamelCase      ,  default=128				)
     parser.add_argument("--data_dir"      ,  type=__lowerCamelCase				)
     parser.add_argument("--save_path"      ,  type=__lowerCamelCase				)
     _a						=		parser.parse_args()
     _a						=		AutoTokenizer.from_pretrained(args.tok_name				)
     return pack_data_dir(__lowerCamelCase      ,  Path(args.data_dir				)      ,  args.max_seq_len      ,  args.save_path				)


if __name__ == "__main__":
   packer_cli()

