






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						):





		'''simple docstring'''



		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   jnp.ones((batch_size, length)       ) / length
						return scores


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   None
						SCREAMING_SNAKE_CASE_   :	Tuple													=   20

						SCREAMING_SNAKE_CASE_   :	Tuple													=   self._get_uniform_logits(batch_size=2 ,    length=UpperCamelCase__       )

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

						# compute softmax
						SCREAMING_SNAKE_CASE_   :	Dict													=   jax.nn.softmax(UpperCamelCase__ ,    axis=-1       )

						SCREAMING_SNAKE_CASE_   :	Dict													=   FlaxTemperatureLogitsWarper(temperature=0.5       )
						SCREAMING_SNAKE_CASE_   :	int													=   FlaxTemperatureLogitsWarper(temperature=1.3       )

						SCREAMING_SNAKE_CASE_   :	Dict													=   jax.nn.softmax(temp_dist_warper_sharper(UpperCamelCase__ ,    scores.copy() ,    cur_len=UpperCamelCase__       ) ,    axis=-1       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   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   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   None
						SCREAMING_SNAKE_CASE_   :	Any													=   10
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   2

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

						SCREAMING_SNAKE_CASE_   :	int													=   FlaxTopKLogitsWarper(3       )

						SCREAMING_SNAKE_CASE_   :	int													=   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
						SCREAMING_SNAKE_CASE_   :	Dict													=   5
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   FlaxTopKLogitsWarper(top_k=1 ,    filter_value=0.0 ,    min_tokens_to_keep=3       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   np.broadcast_to(np.arange(UpperCamelCase__       )[None, :] ,    (batch_size, length)       ).copy()
						SCREAMING_SNAKE_CASE_   :	Tuple													=   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   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   None
						SCREAMING_SNAKE_CASE_   :	str													=   10
						SCREAMING_SNAKE_CASE_   :	List[str]													=   2

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

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   FlaxTopPLogitsWarper(0.8       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   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
						SCREAMING_SNAKE_CASE_   :	str													=   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
						SCREAMING_SNAKE_CASE_   :	Tuple													=   np.broadcast_to(np.arange(UpperCamelCase__       )[None, :] ,    (batch_size, vocab_size)       ).copy() - (
						    vocab_size // 2
						)

						# make ramp_logits more extreme
						SCREAMING_SNAKE_CASE_   :	Any													=   ramp_logits[1] * 1_00.0

						# make sure at least 2 tokens are kept
						SCREAMING_SNAKE_CASE_   :	List[str]													=   FlaxTopPLogitsWarper(0.9 ,    min_tokens_to_keep=2 ,    filter_value=0.0       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   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   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   20
						SCREAMING_SNAKE_CASE_   :	Dict													=   4
						SCREAMING_SNAKE_CASE_   :	Dict													=   0

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   FlaxMinLengthLogitsProcessor(min_length=10 ,    eos_token_id=UpperCamelCase__       )

						# check that min length is applied at length 5
						SCREAMING_SNAKE_CASE_   :	List[str]													=   ids_tensor((batch_size, 20) ,    vocab_size=20       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   5
						SCREAMING_SNAKE_CASE_   :	int													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   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
						SCREAMING_SNAKE_CASE_   :	Any													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   15
						SCREAMING_SNAKE_CASE_   :	str													=   min_dist_processor(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						self.assertFalse(jnp.isinf(UpperCamelCase__       ).any()       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   20
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   4
						SCREAMING_SNAKE_CASE_   :	Dict													=   0

						SCREAMING_SNAKE_CASE_   :	str													=   FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase__       )

						# check that all scores are -inf except the bos_token_id score
						SCREAMING_SNAKE_CASE_   :	Dict													=   ids_tensor((batch_size, 1) ,    vocab_size=20       )
						SCREAMING_SNAKE_CASE_   :	int													=   1
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   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
						SCREAMING_SNAKE_CASE_   :	Dict													=   3
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   logits_processor(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						self.assertFalse(jnp.isinf(UpperCamelCase__       ).any()       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   20
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   4
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   0
						SCREAMING_SNAKE_CASE_   :	int													=   5

						SCREAMING_SNAKE_CASE_   :	Any													=   FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase__ ,    eos_token_id=UpperCamelCase__       )

						# check that all scores are -inf except the eos_token_id when max_length is reached
						SCREAMING_SNAKE_CASE_   :	Any													=   ids_tensor((batch_size, 4) ,    vocab_size=20       )
						SCREAMING_SNAKE_CASE_   :	int													=   4
						SCREAMING_SNAKE_CASE_   :	str													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   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
						SCREAMING_SNAKE_CASE_   :	List[str]													=   3
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   logits_processor(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						self.assertFalse(jnp.isinf(UpperCamelCase__       ).any()       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   4
						SCREAMING_SNAKE_CASE_   :	Any													=   10
						SCREAMING_SNAKE_CASE_   :	List[str]													=   15
						SCREAMING_SNAKE_CASE_   :	Dict													=   2
						SCREAMING_SNAKE_CASE_   :	List[str]													=   1
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   15

						# dummy input_ids and scores
						SCREAMING_SNAKE_CASE_   :	int													=   ids_tensor((batch_size, sequence_length) ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   input_ids.copy()

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   scores.copy()

						# instantiate all dist processors
						SCREAMING_SNAKE_CASE_   :	str													=   FlaxTemperatureLogitsWarper(temperature=0.5       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   FlaxTopKLogitsWarper(3       )
						SCREAMING_SNAKE_CASE_   :	int													=   FlaxTopPLogitsWarper(0.8       )

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

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   10

						# no processor list
						SCREAMING_SNAKE_CASE_   :	Tuple													=   temp_dist_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Tuple													=   top_k_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   top_p_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   min_dist_proc(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   bos_dist_proc(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	str													=   eos_dist_proc(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )

						# with processor list
						SCREAMING_SNAKE_CASE_   :	int													=   FlaxLogitsProcessorList(
						    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   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   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   4
						SCREAMING_SNAKE_CASE_   :	List[str]													=   10
						SCREAMING_SNAKE_CASE_   :	str													=   15
						SCREAMING_SNAKE_CASE_   :	int													=   2
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   1
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   15

						# dummy input_ids and scores
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   ids_tensor((batch_size, sequence_length) ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   input_ids.copy()

						SCREAMING_SNAKE_CASE_   :	str													=   self._get_uniform_logits(UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   scores.copy()

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

						# instantiate all logits processors
						SCREAMING_SNAKE_CASE_   :	Any													=   FlaxMinLengthLogitsProcessor(min_length=10 ,    eos_token_id=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase__ ,    eos_token_id=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   10

						# no processor list
						def run_no_processor_list(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Dict													=   temp_dist_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Tuple													=   top_k_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	int													=   top_p_warp(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Any													=   min_dist_proc(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Any													=   bos_dist_proc(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   eos_dist_proc(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
										return scores

						# with processor list
						def run_processor_list(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	str													=   FlaxLogitsProcessorList(
										    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]       )
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   processor(UpperCamelCase__ ,    UpperCamelCase__ ,    cur_len=UpperCamelCase__       )
										return scores

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   jax.jit(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   jax.jit(UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   jitted_run_no_processor_list(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   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()       )







import math



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				return math.pow(lowerCAmelCase__    ,    2      ) - a



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				return 2 * x



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   2.0

				while start <= a:
								SCREAMING_SNAKE_CASE_   :	str													=   math.pow(lowerCAmelCase__    ,    2      )

				return start



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		int = 9999    ,    lowerCAmelCase__				:		float = 0.00_000_000_000_001      ):





				'''simple docstring'''







				if a < 0:
								raise ValueError('math domain error'      )

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   get_initial_point(lowerCAmelCase__      )

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Tuple													=   value
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   value - fx(lowerCAmelCase__    ,    lowerCAmelCase__      ) / fx_derivative(lowerCAmelCase__      )
								if abs(prev_value - value      ) < tolerance:
												return value

				return value


if __name__ == "__main__":
				from doctest import testmod

				testmod()