





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 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						None
            _UpperCamelCase              =						20

            _UpperCamelCase              =						self._get_uniform_logits(batch_size=2,       length=A      )

            # 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(A,       axis=-1      )

            _UpperCamelCase              =						FlaxTemperatureLogitsWarper(temperature=0.5      )
            _UpperCamelCase              =						FlaxTemperatureLogitsWarper(temperature=1.3      )

            _UpperCamelCase              =						jax.nn.softmax(temp_dist_warper_sharper(A,       scores.copy(),       cur_len=A      ),       axis=-1      )
            _UpperCamelCase              =						jax.nn.softmax(temp_dist_warper_smoother(A,       scores.copy(),       cur_len=A      ),       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      ):





            """simple docstring"""

            _UpperCamelCase              =						None
            _UpperCamelCase              =						10
            _UpperCamelCase              =						2

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

            _UpperCamelCase              =						FlaxTopKLogitsWarper(3      )

            _UpperCamelCase              =						top_k_warp(A,       A,       cur_len=A      )

            # 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(A      )[None, :],       (batch_size, length)      ).copy()
            _UpperCamelCase              =						top_k_warp_safety_check(A,       A,       cur_len=A      )

            # 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      ):





            """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], [0.15, 0.3, 0.3, 0.25]]      )      )

            _UpperCamelCase              =						FlaxTopPLogitsWarper(0.8      )
            _UpperCamelCase              =						np.exp(top_p_warp(A,       A,       cur_len=A      )      )

            # 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, 0.25]]      )
            self.assertTrue(np.allclose(A,       A,       atol=1E-3      )      )

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

            # make ramp_logits more extreme
            _UpperCamelCase              =						ramp_logits[1] * 100.0

            # 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(A,       A,       cur_len=A      )

            # 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      ):





            """simple docstring"""

            _UpperCamelCase              =						20
            _UpperCamelCase              =						4
            _UpperCamelCase              =						0

            _UpperCamelCase              =						FlaxMinLengthLogitsProcessor(min_length=10,       eos_token_id=A      )

            # 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(A,       A      )
            _UpperCamelCase              =						min_dist_processor(A,       A,       cur_len=A      )
            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(A,       A      )
            _UpperCamelCase              =						15
            _UpperCamelCase              =						min_dist_processor(A,       A,       cur_len=A      )
            self.assertFalse(jnp.isinf(A      ).any()      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						20
            _UpperCamelCase              =						4
            _UpperCamelCase              =						0

            _UpperCamelCase              =						FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A      )

            # 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(A,       A      )
            _UpperCamelCase              =						logits_processor(A,       A,       cur_len=A      )
            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(A,       A      )
            _UpperCamelCase              =						logits_processor(A,       A,       cur_len=A      )
            self.assertFalse(jnp.isinf(A      ).any()      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						20
            _UpperCamelCase              =						4
            _UpperCamelCase              =						0
            _UpperCamelCase              =						5

            _UpperCamelCase              =						FlaxForcedEOSTokenLogitsProcessor(max_length=A,       eos_token_id=A      )

            # 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(A,       A      )
            _UpperCamelCase              =						logits_processor(A,       A,       cur_len=A      )
            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(A,       A      )
            _UpperCamelCase              =						logits_processor(A,       A,       cur_len=A      )
            self.assertFalse(jnp.isinf(A      ).any()      )

     def 	_UpperCamelCase     (      self      ):





            """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),       A      )
            _UpperCamelCase              =						input_ids.copy()

            _UpperCamelCase              =						self._get_uniform_logits(A,       A      )
            _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=A      )
            _UpperCamelCase              =						FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A      )
            _UpperCamelCase              =						FlaxForcedEOSTokenLogitsProcessor(max_length=A,       eos_token_id=A      )

            _UpperCamelCase              =						10

            # no processor list
            _UpperCamelCase              =						temp_dist_warp(A,       A,       cur_len=A      )
            _UpperCamelCase              =						top_k_warp(A,       A,       cur_len=A      )
            _UpperCamelCase              =						top_p_warp(A,       A,       cur_len=A      )
            _UpperCamelCase              =						min_dist_proc(A,       A,       cur_len=A      )
            _UpperCamelCase              =						bos_dist_proc(A,       A,       cur_len=A      )
            _UpperCamelCase              =						eos_dist_proc(A,       A,       cur_len=A      )

            # 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(A,       A,       cur_len=A      )

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

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



     def 	_UpperCamelCase     (      self      ):





            """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),       A      )
            _UpperCamelCase              =						input_ids.copy()

            _UpperCamelCase              =						self._get_uniform_logits(A,       A      )
            _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=A      )
            _UpperCamelCase              =						FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A      )
            _UpperCamelCase              =						FlaxForcedEOSTokenLogitsProcessor(max_length=A,       eos_token_id=A      )

            _UpperCamelCase              =						10

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

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

            _UpperCamelCase              =						jax.jit(A      )
            _UpperCamelCase              =						jax.jit(A      )

            _UpperCamelCase              =						jitted_run_no_processor_list(A,       A,       A      )
            _UpperCamelCase              =						jitted_run_processor_list(A,       A,       A      )

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

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













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						0
       for ch in input_str:
              _UpperCamelCase              =						ord(lowerCAmelCase					)
              _UpperCamelCase              =						pow(2    ,       lowerCAmelCase					)

              # If we already turned on bit for current character's unicode
              if bitmap >> ch_unicode & 1 == 1:
                     return False
              bitmap |= ch_bit_index_on
       return True


if __name__ == "__main__":
       import doctest

       doctest.testmod()






