




'''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    __lowercase						(  unittest.TestCase ):







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Optional[Any]:
     __a			=					jnp.ones((batch_size, length)      ) / length
     return scores







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __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 		UpperCamelCase__			(	self      )				->    Any:
     __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 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __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] * 100.0

     # 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 		UpperCamelCase__			(	self      )				->    List[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 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __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 		UpperCamelCase__			(	self      )				->    int:
     __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 		UpperCamelCase__			(	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
     __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 		UpperCamelCase__			(	self      )				->    int:
     __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'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							int			):
  return int((input_a, input_a).count(0			) != 0			)


def 						SCREAMING_SNAKE_CASE       (      ):
  assert nand_gate(0				,					0			) == 1
  assert nand_gate(0				,					1			) == 1
  assert nand_gate(1				,					0			) == 1
  assert nand_gate(1				,					1			) == 0


if __name__ == "__main__":
  print(nand_gate(0, 0))
  print(nand_gate(0, 1))
  print(nand_gate(1, 0))
  print(nand_gate(1, 1))



