
"""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 			__magic_name__       (       unittest.TestCase ):
       def 							lowercase_							(     self ,				A_ ,				A_       )     ->	Any:





         """simple docstring"""




         _lowercase:			Tuple       =     jnp.ones((batch_size, length)       ) / length
         return scores
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			List[str]       =     None
         _lowercase:			Optional[Any]       =     20

         _lowercase:			List[Any]       =     self._get_uniform_logits(batch_size=2 ,				length=__lowerCamelCase       )

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

         # compute softmax
         _lowercase:			int       =     jax.nn.softmax(__lowerCamelCase ,				axis=-1       )

         _lowercase:			Optional[int]       =     FlaxTemperatureLogitsWarper(temperature=0.5       )
         _lowercase:			Tuple       =     FlaxTemperatureLogitsWarper(temperature=1.3       )

         _lowercase:			Dict       =     jax.nn.softmax(temp_dist_warper_sharper(__lowerCamelCase ,				scores.copy() ,				cur_len=__lowerCamelCase       ) ,				axis=-1       )
         _lowercase:			Any       =     jax.nn.softmax(temp_dist_warper_smoother(__lowerCamelCase ,				scores.copy() ,				cur_len=__lowerCamelCase       ) ,				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 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Optional[int]       =     None
         _lowercase:			Optional[int]       =     10
         _lowercase:			str       =     2

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

         _lowercase:			Union[str, Any]       =     FlaxTopKLogitsWarper(3       )

         _lowercase:			Optional[int]       =     top_k_warp(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )

         # 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
         _lowercase:			str       =     5
         _lowercase:			str       =     FlaxTopKLogitsWarper(top_k=1 ,				filter_value=0.0 ,				min_tokens_to_keep=3       )

         _lowercase:			Dict       =     np.broadcast_to(np.arange(__lowerCamelCase       )[None, :] ,				(batch_size, length)       ).copy()
         _lowercase:			Any       =     top_k_warp_safety_check(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )

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





         """simple docstring"""




         _lowercase:			Dict       =     None
         _lowercase:			Dict       =     10
         _lowercase:			Optional[int]       =     2

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

         _lowercase:			Union[str, Any]       =     FlaxTopPLogitsWarper(0.8       )
         _lowercase:			Union[str, Any]       =     np.exp(top_p_warp(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )       )

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

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

         # make ramp_logits more extreme
         _lowercase:			int       =     ramp_logits[1] * 1_00.0

         # make sure at least 2 tokens are kept
         _lowercase:			Any       =     FlaxTopPLogitsWarper(0.9 ,				min_tokens_to_keep=2 ,				filter_value=0.0       )
         _lowercase:			List[Any]       =     top_p_warp(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )

         # 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 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     20
         _lowercase:			Any       =     4
         _lowercase:			Tuple       =     0

         _lowercase:			Dict       =     FlaxMinLengthLogitsProcessor(min_length=10 ,				eos_token_id=__lowerCamelCase       )

         # check that min length is applied at length 5
         _lowercase:			Any       =     ids_tensor((batch_size, 20) ,				vocab_size=20       )
         _lowercase:			Optional[Any]       =     5
         _lowercase:			str       =     self._get_uniform_logits(__lowerCamelCase ,				__lowerCamelCase       )
         _lowercase:			List[Any]       =     min_dist_processor(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
         self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() ,				4 * [-float('''inf'''       )]       )

         # check that min length is not applied anymore at length 15
         _lowercase:			Tuple       =     self._get_uniform_logits(__lowerCamelCase ,				__lowerCamelCase       )
         _lowercase:			int       =     15
         _lowercase:			Optional[int]       =     min_dist_processor(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
         self.assertFalse(jnp.isinf(__lowerCamelCase       ).any()       )
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			List[str]       =     20
         _lowercase:			Tuple       =     4
         _lowercase:			Optional[int]       =     0

         _lowercase:			List[str]       =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__lowerCamelCase       )

         # check that all scores are -inf except the bos_token_id score
         _lowercase:			Dict       =     ids_tensor((batch_size, 1) ,				vocab_size=20       )
         _lowercase:			Optional[int]       =     1
         _lowercase:			Any       =     self._get_uniform_logits(__lowerCamelCase ,				__lowerCamelCase       )
         _lowercase:			str       =     logits_processor(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
         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
         _lowercase:			Union[str, Any]       =     3
         _lowercase:			Any       =     self._get_uniform_logits(__lowerCamelCase ,				__lowerCamelCase       )
         _lowercase:			List[Any]       =     logits_processor(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
         self.assertFalse(jnp.isinf(__lowerCamelCase       ).any()       )
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			int       =     20
         _lowercase:			List[str]       =     4
         _lowercase:			Optional[int]       =     0
         _lowercase:			Dict       =     5

         _lowercase:			str       =     FlaxForcedEOSTokenLogitsProcessor(max_length=__lowerCamelCase ,				eos_token_id=__lowerCamelCase       )

         # check that all scores are -inf except the eos_token_id when max_length is reached
         _lowercase:			Any       =     ids_tensor((batch_size, 4) ,				vocab_size=20       )
         _lowercase:			List[Any]       =     4
         _lowercase:			List[Any]       =     self._get_uniform_logits(__lowerCamelCase ,				__lowerCamelCase       )
         _lowercase:			List[Any]       =     logits_processor(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
         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
         _lowercase:			Any       =     3
         _lowercase:			int       =     self._get_uniform_logits(__lowerCamelCase ,				__lowerCamelCase       )
         _lowercase:			Dict       =     logits_processor(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
         self.assertFalse(jnp.isinf(__lowerCamelCase       ).any()       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			List[str]       =     4
         _lowercase:			Optional[Any]       =     10
         _lowercase:			Optional[int]       =     15
         _lowercase:			Tuple       =     2
         _lowercase:			Union[str, Any]       =     1
         _lowercase:			Optional[Any]       =     15

         # dummy input_ids and scores
         _lowercase:			Tuple       =     ids_tensor((batch_size, sequence_length) ,				__lowerCamelCase       )
         _lowercase:			Any       =     input_ids.copy()

         _lowercase:			Tuple       =     self._get_uniform_logits(__lowerCamelCase ,				__lowerCamelCase       )
         _lowercase:			List[str]       =     scores.copy()

         # instantiate all dist processors
         _lowercase:			Tuple       =     FlaxTemperatureLogitsWarper(temperature=0.5       )
         _lowercase:			Dict       =     FlaxTopKLogitsWarper(3       )
         _lowercase:			Optional[int]       =     FlaxTopPLogitsWarper(0.8       )

         # instantiate all logits processors
         _lowercase:			str       =     FlaxMinLengthLogitsProcessor(min_length=10 ,				eos_token_id=__lowerCamelCase       )
         _lowercase:			str       =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__lowerCamelCase       )
         _lowercase:			int       =     FlaxForcedEOSTokenLogitsProcessor(max_length=__lowerCamelCase ,				eos_token_id=__lowerCamelCase       )

         _lowercase:			Any       =     10

         # no processor list
         _lowercase:			Union[str, Any]       =     temp_dist_warp(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
         _lowercase:			Any       =     top_k_warp(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
         _lowercase:			List[str]       =     top_p_warp(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
         _lowercase:			Optional[int]       =     min_dist_proc(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
         _lowercase:			Optional[int]       =     bos_dist_proc(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
         _lowercase:			Optional[int]       =     eos_dist_proc(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )

         # with processor list
         _lowercase:			List[str]       =     FlaxLogitsProcessorList(
             [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]       )
         _lowercase:			Optional[int]       =     processor(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )

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

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



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         _lowercase:			Tuple       =     4
         _lowercase:			List[Any]       =     10
         _lowercase:			str       =     15
         _lowercase:			List[Any]       =     2
         _lowercase:			Union[str, Any]       =     1
         _lowercase:			Optional[Any]       =     15

         # dummy input_ids and scores
         _lowercase:			Dict       =     ids_tensor((batch_size, sequence_length) ,				__lowerCamelCase       )
         _lowercase:			Union[str, Any]       =     input_ids.copy()

         _lowercase:			Union[str, Any]       =     self._get_uniform_logits(__lowerCamelCase ,				__lowerCamelCase       )
         _lowercase:			Optional[int]       =     scores.copy()

         # instantiate all dist processors
         _lowercase:			str       =     FlaxTemperatureLogitsWarper(temperature=0.5       )
         _lowercase:			Optional[Any]       =     FlaxTopKLogitsWarper(3       )
         _lowercase:			Optional[int]       =     FlaxTopPLogitsWarper(0.8       )

         # instantiate all logits processors
         _lowercase:			List[str]       =     FlaxMinLengthLogitsProcessor(min_length=10 ,				eos_token_id=__lowerCamelCase       )
         _lowercase:			Union[str, Any]       =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__lowerCamelCase       )
         _lowercase:			List[Any]       =     FlaxForcedEOSTokenLogitsProcessor(max_length=__lowerCamelCase ,				eos_token_id=__lowerCamelCase       )

         _lowercase:			int       =     10

         # no processor list
         def run_no_processor_list(A_ ,				A_ ,				A_       ):
           _lowercase:			List[str]       =     temp_dist_warp(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
           _lowercase:			str       =     top_k_warp(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
           _lowercase:			Union[str, Any]       =     top_p_warp(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
           _lowercase:			List[Any]       =     min_dist_proc(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
           _lowercase:			Tuple       =     bos_dist_proc(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
           _lowercase:			Any       =     eos_dist_proc(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
           return scores

         # with processor list
         def run_processor_list(A_ ,				A_ ,				A_       ):
           _lowercase:			Tuple       =     FlaxLogitsProcessorList(
               [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]       )
           _lowercase:			int       =     processor(__lowerCamelCase ,				__lowerCamelCase ,				cur_len=__lowerCamelCase       )
           return scores

         _lowercase:			Any       =     jax.jit(__lowerCamelCase       )
         _lowercase:			Any       =     jax.jit(__lowerCamelCase       )

         _lowercase:			str       =     jitted_run_no_processor_list(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase       )
         _lowercase:			int       =     jitted_run_processor_list(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase       )

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

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


"""simple docstring"""




from typing import Any

import numpy as np




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  return np.array_equal(_UpperCamelCase ,				matrix.conjugate().T						)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			str       =     v.conjugate().T
  _lowercase:			List[Any]       =     v_star.dot(_UpperCamelCase						)
  assert isinstance(_UpperCamelCase ,				np.ndarray						)
  return (v_star_dot.dot(_UpperCamelCase						)) / (v_star.dot(_UpperCamelCase						))




def  _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			int       =     np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]						)
  _lowercase:			str       =     np.array([[1], [2], [3]]						)
  assert is_hermitian(_UpperCamelCase						), f'''{a} is not hermitian.'''
  print(rayleigh_quotient(_UpperCamelCase ,				_UpperCamelCase						)						)

  _lowercase:			Any       =     np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]						)
  assert is_hermitian(_UpperCamelCase						), f'''{a} is not hermitian.'''
  assert rayleigh_quotient(_UpperCamelCase ,				_UpperCamelCase						) == float(3						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()
