

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 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , __UpperCamelCase: int  , __UpperCamelCase: int      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    None
						__magic_name__							=    20

						__magic_name__							=    self._get_uniform_logits(batch_size=2  , length=__UpperCamelCase      )

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

						# compute softmax
						__magic_name__							=    jax.nn.softmax(__UpperCamelCase  , axis=-1      )

						__magic_name__							=    FlaxTemperatureLogitsWarper(temperature=0.5      )
						__magic_name__							=    FlaxTemperatureLogitsWarper(temperature=1.3      )

						__magic_name__							=    jax.nn.softmax(temp_dist_warper_sharper(__UpperCamelCase  , scores.copy()  , cur_len=__UpperCamelCase      )  , axis=-1      )
						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    None
						__magic_name__							=    10
						__magic_name__							=    2

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

						__magic_name__							=    FlaxTopKLogitsWarper(3      )

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

						__magic_name__							=    np.broadcast_to(np.arange(__UpperCamelCase      )[None, :]  , (batch_size, length)      ).copy()
						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    None
						__magic_name__							=    10
						__magic_name__							=    2

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

						__magic_name__							=    FlaxTopPLogitsWarper(0.8      )
						__magic_name__							=    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
						__magic_name__							=    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
						__magic_name__							=    np.broadcast_to(np.arange(__UpperCamelCase      )[None, :]  , (batch_size, vocab_size)      ).copy() - (
						    vocab_size // 2
						)

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

						# make sure at least 2 tokens are kept
						__magic_name__							=    FlaxTopPLogitsWarper(0.9  , min_tokens_to_keep=2  , filter_value=0.0      )
						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						__magic_name__							=    20
						__magic_name__							=    4
						__magic_name__							=    0

						__magic_name__							=    FlaxMinLengthLogitsProcessor(min_length=10  , eos_token_id=__UpperCamelCase      )

						# check that min length is applied at length 5
						__magic_name__							=    ids_tensor((batch_size, 20)  , vocab_size=20      )
						__magic_name__							=    5
						__magic_name__							=    self._get_uniform_logits(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    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
						__magic_name__							=    self._get_uniform_logits(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    15
						__magic_name__							=    min_dist_processor(__UpperCamelCase  , __UpperCamelCase  , cur_len=__UpperCamelCase      )
						self.assertFalse(jnp.isinf(__UpperCamelCase      ).any()      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    20
						__magic_name__							=    4
						__magic_name__							=    0

						__magic_name__							=    FlaxForcedBOSTokenLogitsProcessor(bos_token_id=__UpperCamelCase      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    20
						__magic_name__							=    4
						__magic_name__							=    0
						__magic_name__							=    5

						__magic_name__							=    FlaxForcedEOSTokenLogitsProcessor(max_length=__UpperCamelCase  , eos_token_id=__UpperCamelCase      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    4
						__magic_name__							=    10
						__magic_name__							=    15
						__magic_name__							=    2
						__magic_name__							=    1
						__magic_name__							=    15

						# dummy input_ids and scores
						__magic_name__							=    ids_tensor((batch_size, sequence_length)  , __UpperCamelCase      )
						__magic_name__							=    input_ids.copy()

						__magic_name__							=    self._get_uniform_logits(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    scores.copy()

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

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

						__magic_name__							=    10

						# no processor list
						__magic_name__							=    temp_dist_warp(__UpperCamelCase  , __UpperCamelCase  , cur_len=__UpperCamelCase      )
						__magic_name__							=    top_k_warp(__UpperCamelCase  , __UpperCamelCase  , cur_len=__UpperCamelCase      )
						__magic_name__							=    top_p_warp(__UpperCamelCase  , __UpperCamelCase  , cur_len=__UpperCamelCase      )
						__magic_name__							=    min_dist_proc(__UpperCamelCase  , __UpperCamelCase  , cur_len=__UpperCamelCase      )
						__magic_name__							=    bos_dist_proc(__UpperCamelCase  , __UpperCamelCase  , cur_len=__UpperCamelCase      )
						__magic_name__							=    eos_dist_proc(__UpperCamelCase  , __UpperCamelCase  , cur_len=__UpperCamelCase      )

						# with processor list
						__magic_name__							=    FlaxLogitsProcessorList(
						    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]      )
						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    4
						__magic_name__							=    10
						__magic_name__							=    15
						__magic_name__							=    2
						__magic_name__							=    1
						__magic_name__							=    15

						# dummy input_ids and scores
						__magic_name__							=    ids_tensor((batch_size, sequence_length)  , __UpperCamelCase      )
						__magic_name__							=    input_ids.copy()

						__magic_name__							=    self._get_uniform_logits(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    scores.copy()

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

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

						__magic_name__							=    10

						# no processor list
						def run_no_processor_list(__UpperCamelCase: int  , __UpperCamelCase: str  , __UpperCamelCase: Optional[Any]      ):
											__magic_name__							=    temp_dist_warp(__UpperCamelCase  , __UpperCamelCase  , cur_len=__UpperCamelCase      )
											__magic_name__							=    top_k_warp(__UpperCamelCase  , __UpperCamelCase  , cur_len=__UpperCamelCase      )
											__magic_name__							=    top_p_warp(__UpperCamelCase  , __UpperCamelCase  , cur_len=__UpperCamelCase      )
											__magic_name__							=    min_dist_proc(__UpperCamelCase  , __UpperCamelCase  , cur_len=__UpperCamelCase      )
											__magic_name__							=    bos_dist_proc(__UpperCamelCase  , __UpperCamelCase  , cur_len=__UpperCamelCase      )
											__magic_name__							=    eos_dist_proc(__UpperCamelCase  , __UpperCamelCase  , cur_len=__UpperCamelCase      )
											return scores

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

						__magic_name__							=    jax.jit(__UpperCamelCase      )
						__magic_name__							=    jax.jit(__UpperCamelCase      )

						__magic_name__							=    jitted_run_no_processor_list(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    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()      )







from math import factorial, radians




def 					_lowercase     (				a_				:					float					,a_				:					int = 1_8					,a_				:					int = 1_0				)				->	float:




					'''simple docstring'''







					__magic_name__							=    angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

					# Converting from degrees to radians
					__magic_name__							=    radians(a_				)

					__magic_name__							=    angle_in_radians
					__magic_name__							=    3
					__magic_name__							=    -1

					for _ in range(a_				):
										result += (b * (angle_in_radians**a)) / factorial(a_				)

										__magic_name__							=    -b  # One positive term and the next will be negative and so on...
										a += 2  # Increased by 2 for every term.

					return round(a_					,a_				)


if __name__ == "__main__":
						__import__("doctest").testmod()




