


import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.generation import (
       FlaxForcedBOSTokenLogitsProcessor,
       FlaxForcedEOSTokenLogitsProcessor,
       FlaxLogitsProcessorList,
       FlaxMinLengthLogitsProcessor,
       FlaxTemperatureLogitsWarper,
       FlaxTopKLogitsWarper,
       FlaxTopPLogitsWarper,
   )





@require_flax
class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Union[str, Any]:
          _a						=		jnp.ones((batch_size, length)		) / length
          return scores






     def  a_					(			self		)       ->    str:
          _a						=		None
          _a						=		20

          _a						=		self._get_uniform_logits(batch_size=2     ,							length=__UpperCamelCase		)

          # tweak scores to not be uniform anymore
          _a						=		scores.at[1, 5].set((1 / length) + 0.1		)  # peak, 1st batch
          _a						=		scores.at[1, 10].set((1 / length) - 0.4		)  # valley, 1st batch

          # compute softmax
          _a						=		jax.nn.softmax(__UpperCamelCase     ,							axis=-1		)

          _a						=		FlaxTemperatureLogitsWarper(temperature=0.5		)
          _a						=		FlaxTemperatureLogitsWarper(temperature=1.3		)

          _a						=		jax.nn.softmax(temp_dist_warper_sharper(__UpperCamelCase     ,							scores.copy()     ,							cur_len=__UpperCamelCase		)     ,							axis=-1		)
          _a						=		jax.nn.softmax(temp_dist_warper_smoother(__UpperCamelCase     ,							scores.copy()     ,							cur_len=__UpperCamelCase		)     ,							axis=-1		)

          # uniform distribution stays uniform
          self.assertTrue(jnp.allclose(probs[0, :]     ,							warped_prob_sharp[0, :]     ,							atol=1e-3		)		)
          self.assertTrue(jnp.allclose(probs[0, :]     ,							warped_prob_smooth[0, :]     ,							atol=1e-3		)		)

          # sharp peaks get higher, valleys get lower
          self.assertLess(probs[1, :].max()     ,							warped_prob_sharp[1, :].max()		)
          self.assertGreater(probs[1, :].min()     ,							warped_prob_sharp[1, :].min()		)

          # smooth peaks get lower, valleys get higher
          self.assertGreater(probs[1, :].max()     ,							warped_prob_smooth[1, :].max()		)
          self.assertLess(probs[1, :].min()     ,							warped_prob_smooth[1, :].min()		)






     def  a_					(			self		)       ->    str:
          _a						=		None
          _a						=		10
          _a						=		2

          # create ramp distribution
          _a						=		np.broadcast_to(np.arange(__UpperCamelCase		)[None, :]     ,							(batch_size, vocab_size)		).copy()
          _a						=		ramp_logits[1:, : vocab_size // 2] + vocab_size

          _a						=		FlaxTopKLogitsWarper(3		)

          _a						=		top_k_warp(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)

          # check that correct tokens are filtered
          self.assertListEqual(jnp.isinf(scores[0]		).tolist()     ,							7 * [True] + 3 * [False]		)
          self.assertListEqual(jnp.isinf(scores[1]		).tolist()     ,							2 * [True] + 3 * [False] + 5 * [True]		)

          # check special case
          _a						=		5
          _a						=		FlaxTopKLogitsWarper(top_k=1     ,							filter_value=0.0     ,							min_tokens_to_keep=3		)

          _a						=		np.broadcast_to(np.arange(__UpperCamelCase		)[None, :]     ,							(batch_size, length)		).copy()
          _a						=		top_k_warp_safety_check(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)

          # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
          self.assertListEqual((scores == 0.0).sum(axis=-1		).tolist()     ,							[2, 2]		)






     def  a_					(			self		)       ->    Any:
          _a						=		None
          _a						=		10
          _a						=		2

          # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
          _a						=		np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]		)		)

          _a						=		FlaxTopPLogitsWarper(0.8		)
          _a						=		np.exp(top_p_warp(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)		)

          # dist should be filtered to keep min num values so that sum is >= top_p
          # exp (-inf) => 0
          _a						=		np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]		)
          self.assertTrue(np.allclose(__UpperCamelCase     ,							__UpperCamelCase     ,							atol=1e-3		)		)

          # check edge cases with negative and extreme logits
          _a						=		np.broadcast_to(np.arange(__UpperCamelCase		)[None, :]     ,							(batch_size, vocab_size)		).copy() - (
              vocab_size // 2
          )

          # make ramp_logits more extreme
          _a						=		ramp_logits[1] * 1_00.0

          # make sure at least 2 tokens are kept
          _a						=		FlaxTopPLogitsWarper(0.9     ,							min_tokens_to_keep=2     ,							filter_value=0.0		)
          _a						=		top_p_warp(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)

          # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
          self.assertListEqual((filtered_dist != 0.0).sum(axis=-1		).tolist()     ,							[3, 2]		)






     def  a_					(			self		)       ->    Optional[Any]:
          _a						=		20
          _a						=		4
          _a						=		0

          _a						=		FlaxMinLengthLogitsProcessor(min_length=10     ,							eos_token_id=__UpperCamelCase		)

          # check that min length is applied at length 5
          _a						=		ids_tensor((batch_size, 20)     ,							vocab_size=20		)
          _a						=		5
          _a						=		self._get_uniform_logits(__UpperCamelCase     ,							__UpperCamelCase		)
          _a						=		min_dist_processor(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
          self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()     ,							4 * [-float("inf"		)]		)

          # check that min length is not applied anymore at length 15
          _a						=		self._get_uniform_logits(__UpperCamelCase     ,							__UpperCamelCase		)
          _a						=		15
          _a						=		min_dist_processor(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
          self.assertFalse(jnp.isinf(__UpperCamelCase		).any()		)






     def  a_					(			self		)       ->    List[Any]:
          _a						=		20
          _a						=		4
          _a						=		0

          _a						=		FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__UpperCamelCase		)

          # check that all scores are -inf except the bos_token_id score
          _a						=		ids_tensor((batch_size, 1)     ,							vocab_size=20		)
          _a						=		1
          _a						=		self._get_uniform_logits(__UpperCamelCase     ,							__UpperCamelCase		)
          _a						=		logits_processor(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
          self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]		).all()		)
          self.assertListEqual(scores[:, bos_token_id].tolist()     ,							4 * [0]		)  # score for bos_token_id shold be zero

          # check that bos_token_id is not forced if current length is greater than 1
          _a						=		3
          _a						=		self._get_uniform_logits(__UpperCamelCase     ,							__UpperCamelCase		)
          _a						=		logits_processor(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
          self.assertFalse(jnp.isinf(__UpperCamelCase		).any()		)






     def  a_					(			self		)       ->    List[Any]:
          _a						=		20
          _a						=		4
          _a						=		0
          _a						=		5

          _a						=		FlaxForcedEOSTokenLogitsProcessor(max_length=__UpperCamelCase     ,							eos_token_id=__UpperCamelCase		)

          # check that all scores are -inf except the eos_token_id when max_length is reached
          _a						=		ids_tensor((batch_size, 4)     ,							vocab_size=20		)
          _a						=		4
          _a						=		self._get_uniform_logits(__UpperCamelCase     ,							__UpperCamelCase		)
          _a						=		logits_processor(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
          self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]		).all()		)
          self.assertListEqual(scores[:, eos_token_id].tolist()     ,							4 * [0]		)  # score for eos_token_id should be zero

          # check that eos_token_id is not forced if max_length is not reached
          _a						=		3
          _a						=		self._get_uniform_logits(__UpperCamelCase     ,							__UpperCamelCase		)
          _a						=		logits_processor(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
          self.assertFalse(jnp.isinf(__UpperCamelCase		).any()		)






     def  a_					(			self		)       ->    Tuple:
          _a						=		4
          _a						=		10
          _a						=		15
          _a						=		2
          _a						=		1
          _a						=		15

          # dummy input_ids and scores
          _a						=		ids_tensor((batch_size, sequence_length)     ,							__UpperCamelCase		)
          _a						=		input_ids.copy()

          _a						=		self._get_uniform_logits(__UpperCamelCase     ,							__UpperCamelCase		)
          _a						=		scores.copy()

          # instantiate all dist processors
          _a						=		FlaxTemperatureLogitsWarper(temperature=0.5		)
          _a						=		FlaxTopKLogitsWarper(3		)
          _a						=		FlaxTopPLogitsWarper(0.8		)

          # instantiate all logits processors
          _a						=		FlaxMinLengthLogitsProcessor(min_length=10     ,							eos_token_id=__UpperCamelCase		)
          _a						=		FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__UpperCamelCase		)
          _a						=		FlaxForcedEOSTokenLogitsProcessor(max_length=__UpperCamelCase     ,							eos_token_id=__UpperCamelCase		)

          _a						=		10

          # no processor list
          _a						=		temp_dist_warp(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
          _a						=		top_k_warp(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
          _a						=		top_p_warp(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
          _a						=		min_dist_proc(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
          _a						=		bos_dist_proc(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
          _a						=		eos_dist_proc(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)

          # with processor list
          _a						=		FlaxLogitsProcessorList(
              [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
          _a						=		processor(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)

          # scores should be equal
          self.assertTrue(jnp.allclose(__UpperCamelCase     ,							__UpperCamelCase     ,							atol=1e-3		)		)

          # input_ids should never be changed
          self.assertListEqual(input_ids.tolist()     ,							input_ids_comp.tolist()		)






     def  a_					(			self		)       ->    str:
          _a						=		4
          _a						=		10
          _a						=		15
          _a						=		2
          _a						=		1
          _a						=		15

          # dummy input_ids and scores
          _a						=		ids_tensor((batch_size, sequence_length)     ,							__UpperCamelCase		)
          _a						=		input_ids.copy()

          _a						=		self._get_uniform_logits(__UpperCamelCase     ,							__UpperCamelCase		)
          _a						=		scores.copy()

          # instantiate all dist processors
          _a						=		FlaxTemperatureLogitsWarper(temperature=0.5		)
          _a						=		FlaxTopKLogitsWarper(3		)
          _a						=		FlaxTopPLogitsWarper(0.8		)

          # instantiate all logits processors
          _a						=		FlaxMinLengthLogitsProcessor(min_length=10     ,							eos_token_id=__UpperCamelCase		)
          _a						=		FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__UpperCamelCase		)
          _a						=		FlaxForcedEOSTokenLogitsProcessor(max_length=__UpperCamelCase     ,							eos_token_id=__UpperCamelCase		)

          _a						=		10

          # no processor list
          def run_no_processor_list(__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		):
               _a						=		temp_dist_warp(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
               _a						=		top_k_warp(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
               _a						=		top_p_warp(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
               _a						=		min_dist_proc(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
               _a						=		bos_dist_proc(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
               _a						=		eos_dist_proc(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
               return scores

          # with processor list
          def run_processor_list(__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		):
               _a						=		FlaxLogitsProcessorList(
                   [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]		)
               _a						=		processor(__UpperCamelCase     ,							__UpperCamelCase     ,							cur_len=__UpperCamelCase		)
               return scores

          _a						=		jax.jit(__UpperCamelCase		)
          _a						=		jax.jit(__UpperCamelCase		)

          _a						=		jitted_run_no_processor_list(__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)
          _a						=		jitted_run_processor_list(__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)

          # scores should be equal
          self.assertTrue(jnp.allclose(__UpperCamelCase     ,							__UpperCamelCase     ,							atol=1e-3		)		)

          # input_ids should never be changed
          self.assertListEqual(input_ids.tolist()     ,							input_ids_comp.tolist()		)





'''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()

