





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 					__a     (       self	,   _a	,   _a						)			->					int:
							lowerCAmelCase_												=				jnp.ones((batch_size, length)						) / length
							return scores




					def 					__a     (       self						)			->					Optional[int]:
							lowerCAmelCase_												=				None
							lowerCAmelCase_												=				20

							lowerCAmelCase_												=				self._get_uniform_logits(batch_size=2	,   length=snake_case__						)

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

							# compute softmax
							lowerCAmelCase_												=				jax.nn.softmax(snake_case__	,   axis=-1						)

							lowerCAmelCase_												=				FlaxTemperatureLogitsWarper(temperature=0.5						)
							lowerCAmelCase_												=				FlaxTemperatureLogitsWarper(temperature=1.3						)

							lowerCAmelCase_												=				jax.nn.softmax(temp_dist_warper_sharper(snake_case__	,   scores.copy()	,   cur_len=snake_case__						)	,   axis=-1						)
							lowerCAmelCase_												=				jax.nn.softmax(temp_dist_warper_smoother(snake_case__	,   scores.copy()	,   cur_len=snake_case__						)	,   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 					__a     (       self						)			->					Optional[int]:
							lowerCAmelCase_												=				None
							lowerCAmelCase_												=				10
							lowerCAmelCase_												=				2

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

							lowerCAmelCase_												=				FlaxTopKLogitsWarper(3						)

							lowerCAmelCase_												=				top_k_warp(snake_case__	,   snake_case__	,   cur_len=snake_case__						)

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

							lowerCAmelCase_												=				np.broadcast_to(np.arange(snake_case__						)[None, :]	,   (batch_size, length)						).copy()
							lowerCAmelCase_												=				top_k_warp_safety_check(snake_case__	,   snake_case__	,   cur_len=snake_case__						)

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




					def 					__a     (       self						)			->					Optional[int]:
							lowerCAmelCase_												=				None
							lowerCAmelCase_												=				10
							lowerCAmelCase_												=				2

							# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
							lowerCAmelCase_												=				np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.1_5, 0.3, 0.3, 0.2_5]]						)						)

							lowerCAmelCase_												=				FlaxTopPLogitsWarper(0.8						)
							lowerCAmelCase_												=				np.exp(top_p_warp(snake_case__	,   snake_case__	,   cur_len=snake_case__						)						)

							# dist should be filtered to keep min num values so that sum is >= top_p
							# exp (-inf) => 0
							lowerCAmelCase_												=				np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.2_5]]						)
							self.assertTrue(np.allclose(snake_case__	,   snake_case__	,   atol=1E-3						)						)

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

							# make ramp_logits more extreme
							lowerCAmelCase_												=				ramp_logits[1] * 1_0_0.0

							# make sure at least 2 tokens are kept
							lowerCAmelCase_												=				FlaxTopPLogitsWarper(0.9	,   min_tokens_to_keep=2	,   filter_value=0.0						)
							lowerCAmelCase_												=				top_p_warp(snake_case__	,   snake_case__	,   cur_len=snake_case__						)

							# 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 					__a     (       self						)			->					Union[str, Any]:
							lowerCAmelCase_												=				20
							lowerCAmelCase_												=				4
							lowerCAmelCase_												=				0

							lowerCAmelCase_												=				FlaxMinLengthLogitsProcessor(min_length=10	,   eos_token_id=snake_case__						)

							# check that min length is applied at length 5
							lowerCAmelCase_												=				ids_tensor((batch_size, 20)	,   vocab_size=20						)
							lowerCAmelCase_												=				5
							lowerCAmelCase_												=				self._get_uniform_logits(snake_case__	,   snake_case__						)
							lowerCAmelCase_												=				min_dist_processor(snake_case__	,   snake_case__	,   cur_len=snake_case__						)
							self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()	,   4 * [-float("inf"						)]						)

							# check that min length is not applied anymore at length 15
							lowerCAmelCase_												=				self._get_uniform_logits(snake_case__	,   snake_case__						)
							lowerCAmelCase_												=				15
							lowerCAmelCase_												=				min_dist_processor(snake_case__	,   snake_case__	,   cur_len=snake_case__						)
							self.assertFalse(jnp.isinf(snake_case__						).any()						)




					def 					__a     (       self						)			->					List[str]:
							lowerCAmelCase_												=				20
							lowerCAmelCase_												=				4
							lowerCAmelCase_												=				0

							lowerCAmelCase_												=				FlaxForcedBOSTokenLogitsProcessor(bos_token_id=snake_case__						)

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




					def 					__a     (       self						)			->					str:
							lowerCAmelCase_												=				20
							lowerCAmelCase_												=				4
							lowerCAmelCase_												=				0
							lowerCAmelCase_												=				5

							lowerCAmelCase_												=				FlaxForcedEOSTokenLogitsProcessor(max_length=snake_case__	,   eos_token_id=snake_case__						)

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




					def 					__a     (       self						)			->					List[str]:
							lowerCAmelCase_												=				4
							lowerCAmelCase_												=				10
							lowerCAmelCase_												=				15
							lowerCAmelCase_												=				2
							lowerCAmelCase_												=				1
							lowerCAmelCase_												=				15

							# dummy input_ids and scores
							lowerCAmelCase_												=				ids_tensor((batch_size, sequence_length)	,   snake_case__						)
							lowerCAmelCase_												=				input_ids.copy()

							lowerCAmelCase_												=				self._get_uniform_logits(snake_case__	,   snake_case__						)
							lowerCAmelCase_												=				scores.copy()

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

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

							lowerCAmelCase_												=				10

							# no processor list
							lowerCAmelCase_												=				temp_dist_warp(snake_case__	,   snake_case__	,   cur_len=snake_case__						)
							lowerCAmelCase_												=				top_k_warp(snake_case__	,   snake_case__	,   cur_len=snake_case__						)
							lowerCAmelCase_												=				top_p_warp(snake_case__	,   snake_case__	,   cur_len=snake_case__						)
							lowerCAmelCase_												=				min_dist_proc(snake_case__	,   snake_case__	,   cur_len=snake_case__						)
							lowerCAmelCase_												=				bos_dist_proc(snake_case__	,   snake_case__	,   cur_len=snake_case__						)
							lowerCAmelCase_												=				eos_dist_proc(snake_case__	,   snake_case__	,   cur_len=snake_case__						)

							# with processor list
							lowerCAmelCase_												=				FlaxLogitsProcessorList(
							    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]						)
							lowerCAmelCase_												=				processor(snake_case__	,   snake_case__	,   cur_len=snake_case__						)

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

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




					def 					__a     (       self						)			->					Dict:
							lowerCAmelCase_												=				4
							lowerCAmelCase_												=				10
							lowerCAmelCase_												=				15
							lowerCAmelCase_												=				2
							lowerCAmelCase_												=				1
							lowerCAmelCase_												=				15

							# dummy input_ids and scores
							lowerCAmelCase_												=				ids_tensor((batch_size, sequence_length)	,   snake_case__						)
							lowerCAmelCase_												=				input_ids.copy()

							lowerCAmelCase_												=				self._get_uniform_logits(snake_case__	,   snake_case__						)
							lowerCAmelCase_												=				scores.copy()

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

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

							lowerCAmelCase_												=				10

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

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

							lowerCAmelCase_												=				jax.jit(snake_case__						)
							lowerCAmelCase_												=				jax.jit(snake_case__						)

							lowerCAmelCase_												=				jitted_run_no_processor_list(snake_case__	,   snake_case__	,   snake_case__						)
							lowerCAmelCase_												=				jitted_run_processor_list(snake_case__	,   snake_case__	,   snake_case__						)

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

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







def        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     Optional[Any]  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[Any]					  =      [0 for i in range(r + 1  )]
			# nc0 = 1
			SCREAMING_SNAKE_CASE_   : Dict					  =      1
			for i in range(1  ,							n + 1  ):
						# to compute current row from previous row.
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      min(lowerCamelCase_  ,							lowerCamelCase_  )
						while j > 0:
									c[j] += c[j - 1]
									j -= 1
			return c[r]




print(binomial_coefficient(n=10, r=5))



