






'''simple docstring'''

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     a_						(      unittest.TestCase    ):

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = jnp.ones((batch_size, length)				) / length
        return scores

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = None
        UpperCamelCase      = 20

        UpperCamelCase      = self._get_uniform_logits(batch_size=2      ,		length=_SCREAMING_SNAKE_CASE				)

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

        # compute softmax
        UpperCamelCase      = jax.nn.softmax(_SCREAMING_SNAKE_CASE      ,		axis=-1				)

        UpperCamelCase      = FlaxTemperatureLogitsWarper(temperature=0.5				)
        UpperCamelCase      = FlaxTemperatureLogitsWarper(temperature=1.3				)

        UpperCamelCase      = jax.nn.softmax(temp_dist_warper_sharper(_SCREAMING_SNAKE_CASE      ,		scores.copy()      ,		cur_len=_SCREAMING_SNAKE_CASE				)      ,		axis=-1				)
        UpperCamelCase      = jax.nn.softmax(temp_dist_warper_smoother(_SCREAMING_SNAKE_CASE      ,		scores.copy()      ,		cur_len=_SCREAMING_SNAKE_CASE				)      ,		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				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = None
        UpperCamelCase      = 10
        UpperCamelCase      = 2

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

        UpperCamelCase      = FlaxTopKLogitsWarper(3				)

        UpperCamelCase      = top_k_warp(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)

        # 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
        UpperCamelCase      = 5
        UpperCamelCase      = FlaxTopKLogitsWarper(top_k=1      ,		filter_value=0.0      ,		min_tokens_to_keep=3				)

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

        # 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				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = None
        UpperCamelCase      = 10
        UpperCamelCase      = 2

        # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
        UpperCamelCase      = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.1_5, 0.3, 0.3, 0.2_5]]				)				)

        UpperCamelCase      = FlaxTopPLogitsWarper(0.8				)
        UpperCamelCase      = np.exp(top_p_warp(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)				)

        # dist should be filtered to keep min num values so that sum is >= top_p
        # exp (-inf) => 0
        UpperCamelCase      = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.2_5]]				)
        self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)

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

        # make ramp_logits more extreme
        UpperCamelCase      = ramp_logits[1] * 1_0_0.0

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

        # 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]:



        """simple docstring"""






        UpperCamelCase      = 20
        UpperCamelCase      = 4
        UpperCamelCase      = 0

        UpperCamelCase      = FlaxMinLengthLogitsProcessor(min_length=10      ,		eos_token_id=_SCREAMING_SNAKE_CASE				)

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

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

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = 20
        UpperCamelCase      = 4
        UpperCamelCase      = 0

        UpperCamelCase      = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE				)

        # check that all scores are -inf except the bos_token_id score
        UpperCamelCase      = ids_tensor((batch_size, 1)      ,		vocab_size=20				)
        UpperCamelCase      = 1
        UpperCamelCase      = self._get_uniform_logits(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = logits_processor(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
        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
        UpperCamelCase      = 3
        UpperCamelCase      = self._get_uniform_logits(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = logits_processor(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
        self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE				).any()				)

  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = 20
        UpperCamelCase      = 4
        UpperCamelCase      = 0
        UpperCamelCase      = 5

        UpperCamelCase      = FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE      ,		eos_token_id=_SCREAMING_SNAKE_CASE				)

        # check that all scores are -inf except the eos_token_id when max_length is reached
        UpperCamelCase      = ids_tensor((batch_size, 4)      ,		vocab_size=20				)
        UpperCamelCase      = 4
        UpperCamelCase      = self._get_uniform_logits(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = logits_processor(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
        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
        UpperCamelCase      = 3
        UpperCamelCase      = self._get_uniform_logits(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = logits_processor(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
        self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE				).any()				)

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = 4
        UpperCamelCase      = 10
        UpperCamelCase      = 15
        UpperCamelCase      = 2
        UpperCamelCase      = 1
        UpperCamelCase      = 15

        # dummy input_ids and scores
        UpperCamelCase      = ids_tensor((batch_size, sequence_length)      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = input_ids.copy()

        UpperCamelCase      = self._get_uniform_logits(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = scores.copy()

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

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

        UpperCamelCase      = 10

        # no processor list
        UpperCamelCase      = temp_dist_warp(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = top_k_warp(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = top_p_warp(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = min_dist_proc(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = bos_dist_proc(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = eos_dist_proc(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		cur_len=_SCREAMING_SNAKE_CASE				)

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

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

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


  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = 4
        UpperCamelCase      = 10
        UpperCamelCase      = 15
        UpperCamelCase      = 2
        UpperCamelCase      = 1
        UpperCamelCase      = 15

        # dummy input_ids and scores
        UpperCamelCase      = ids_tensor((batch_size, sequence_length)      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = input_ids.copy()

        UpperCamelCase      = self._get_uniform_logits(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = scores.copy()

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

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

        UpperCamelCase      = 10

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

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

        UpperCamelCase      = jax.jit(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = jax.jit(_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = jitted_run_no_processor_list(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = jitted_run_processor_list(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

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

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








'''simple docstring'''

from __future__ import annotations

import collections
import pprint
from pathlib import Path







def    lowercase__			(				__UpperCamelCase   )->			str:
      return "".join(sorted(__UpperCamelCase   )   )







def    lowercase__			(				__UpperCamelCase   )->			list[str]:
      return word_by_signature[signature(__UpperCamelCase   )]


SCREAMING_SNAKE_CASE__     =       Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
SCREAMING_SNAKE_CASE__     =       sorted({word.strip().lower() for word in data.splitlines()})

SCREAMING_SNAKE_CASE__     =       collections.defaultdict(list)
for word in word_list:
     word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

     with open('anagrams.txt', 'w') as file:
          file.write('all_anagrams = \n ')
          file.write(pprint.pformat(all_anagrams))
