





"""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  __UpperCamelCase       (		unittest.TestCase		):






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				List[str]:
								a   :	Optional[Any]			=       jnp.ones((batch_size, length)   ) / length
								return scores






							def 					__a     (     self   )			->				List[Any]:
								a   :	Optional[Any]			=       None
								a   :	Optional[int]			=       20

								a   :	Tuple			=       self._get_uniform_logits(batch_size=2   ,      length=lowerCAmelCase__   )

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

								# compute softmax
								a   :	Any			=       jax.nn.softmax(lowerCAmelCase__   ,      axis=-1   )

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

								a   :	Union[str, Any]			=       jax.nn.softmax(temp_dist_warper_sharper(lowerCAmelCase__   ,      scores.copy()   ,      cur_len=lowerCAmelCase__   )   ,      axis=-1   )
								a   :	Tuple			=       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 					__a     (     self   )			->				Any:
								a   :	Tuple			=       None
								a   :	Any			=       10
								a   :	int			=       2

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

								a   :	Dict			=       FlaxTopKLogitsWarper(3   )

								a   :	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
								a   :	str			=       5
								a   :	Optional[int]			=       FlaxTopKLogitsWarper(top_k=1   ,      filter_value=0.0   ,      min_tokens_to_keep=3   )

								a   :	Optional[Any]			=       np.broadcast_to(np.arange(lowerCAmelCase__   )[None, :]   ,      (batch_size, length)   ).copy()
								a   :	Union[str, 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 					__a     (     self   )			->				List[str]:
								a   :	Optional[Any]			=       None
								a   :	List[str]			=       10
								a   :	Tuple			=       2

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

								a   :	int			=       FlaxTopPLogitsWarper(0.8   )
								a   :	Dict			=       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
								a   :	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
								a   :	Dict			=       np.broadcast_to(np.arange(lowerCAmelCase__   )[None, :]   ,      (batch_size, vocab_size)   ).copy() - (
								    vocab_size // 2
								)

								# make ramp_logits more extreme
								a   :	Dict			=       ramp_logits[1] * 100.0

								# make sure at least 2 tokens are kept
								a   :	int			=       FlaxTopPLogitsWarper(0.9   ,      min_tokens_to_keep=2   ,      filter_value=0.0   )
								a   :	Optional[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 					__a     (     self   )			->				List[Any]:
								a   :	Union[str, Any]			=       20
								a   :	Optional[int]			=       4
								a   :	Dict			=       0

								a   :	List[str]			=       FlaxMinLengthLogitsProcessor(min_length=10   ,      eos_token_id=lowerCAmelCase__   )

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






							def 					__a     (     self   )			->				List[Any]:
								a   :	Union[str, Any]			=       20
								a   :	Tuple			=       4
								a   :	Dict			=       0

								a   :	List[str]			=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase__   )

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






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Dict			=       20
								a   :	Union[str, Any]			=       4
								a   :	int			=       0
								a   :	Union[str, Any]			=       5

								a   :	int			=       FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase__   ,      eos_token_id=lowerCAmelCase__   )

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






							def 					__a     (     self   )			->				int:
								a   :	Optional[Any]			=       4
								a   :	Optional[Any]			=       10
								a   :	Union[str, Any]			=       15
								a   :	Union[str, Any]			=       2
								a   :	Any			=       1
								a   :	Any			=       15

								# dummy input_ids and scores
								a   :	str			=       ids_tensor((batch_size, sequence_length)   ,      lowerCAmelCase__   )
								a   :	int			=       input_ids.copy()

								a   :	List[Any]			=       self._get_uniform_logits(lowerCAmelCase__   ,      lowerCAmelCase__   )
								a   :	List[Any]			=       scores.copy()

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

								# instantiate all logits processors
								a   :	Any			=       FlaxMinLengthLogitsProcessor(min_length=10   ,      eos_token_id=lowerCAmelCase__   )
								a   :	str			=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase__   )
								a   :	Optional[Any]			=       FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase__   ,      eos_token_id=lowerCAmelCase__   )

								a   :	Optional[Any]			=       10

								# no processor list
								a   :	List[Any]			=       temp_dist_warp(lowerCAmelCase__   ,      lowerCAmelCase__   ,      cur_len=lowerCAmelCase__   )
								a   :	List[Any]			=       top_k_warp(lowerCAmelCase__   ,      lowerCAmelCase__   ,      cur_len=lowerCAmelCase__   )
								a   :	List[Any]			=       top_p_warp(lowerCAmelCase__   ,      lowerCAmelCase__   ,      cur_len=lowerCAmelCase__   )
								a   :	Optional[Any]			=       min_dist_proc(lowerCAmelCase__   ,      lowerCAmelCase__   ,      cur_len=lowerCAmelCase__   )
								a   :	int			=       bos_dist_proc(lowerCAmelCase__   ,      lowerCAmelCase__   ,      cur_len=lowerCAmelCase__   )
								a   :	Optional[int]			=       eos_dist_proc(lowerCAmelCase__   ,      lowerCAmelCase__   ,      cur_len=lowerCAmelCase__   )

								# with processor list
								a   :	List[str]			=       FlaxLogitsProcessorList(
								    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]   )
								a   :	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 					__a     (     self   )			->				Optional[Any]:
								a   :	Optional[Any]			=       4
								a   :	Any			=       10
								a   :	Optional[Any]			=       15
								a   :	int			=       2
								a   :	List[Any]			=       1
								a   :	Dict			=       15

								# dummy input_ids and scores
								a   :	List[str]			=       ids_tensor((batch_size, sequence_length)   ,      lowerCAmelCase__   )
								a   :	List[str]			=       input_ids.copy()

								a   :	int			=       self._get_uniform_logits(lowerCAmelCase__   ,      lowerCAmelCase__   )
								a   :	List[str]			=       scores.copy()

								# instantiate all dist processors
								a   :	Union[str, Any]			=       FlaxTemperatureLogitsWarper(temperature=0.5   )
								a   :	Union[str, Any]			=       FlaxTopKLogitsWarper(3   )
								a   :	Tuple			=       FlaxTopPLogitsWarper(0.8   )

								# instantiate all logits processors
								a   :	int			=       FlaxMinLengthLogitsProcessor(min_length=10   ,      eos_token_id=lowerCAmelCase__   )
								a   :	Optional[int]			=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase__   )
								a   :	Any			=       FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase__   ,      eos_token_id=lowerCAmelCase__   )

								a   :	List[Any]			=       10

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

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

								a   :	Tuple			=       jax.jit(lowerCAmelCase__   )
								a   :	str			=       jax.jit(lowerCAmelCase__   )

								a   :	Any			=       jitted_run_no_processor_list(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )
								a   :	Dict			=       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"""




def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int = 10					, _lowercase		:					int = 1000					, _lowercase		:					bool = True      )				->int:






	'''simple docstring'''






	assert (
	    isinstance(_lowercase					, _lowercase      )
	    and isinstance(_lowercase					, _lowercase      )
	    and isinstance(_lowercase					, _lowercase      )
	), "Invalid type of value(s) specified to function!"

	if min_val > max_val:
		raise ValueError("Invalid value for min_val or max_val (min_value < max_value)"      )
	return min_val if option else max_val






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					int      )				->int:






	'''simple docstring'''






	return int((number_a + number_a) / 2      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					int					, _lowercase		:					int      )				->None:






	'''simple docstring'''






	assert (
	    isinstance(_lowercase					, _lowercase      ) and isinstance(_lowercase					, _lowercase      ) and isinstance(_lowercase					, _lowercase      )
	), 'argument values must be type of "int"'

	if lower > higher:
		raise ValueError("argument value for lower and higher must be(lower > higher)"      )

	if not lower < to_guess < higher:
		raise ValueError(
		    "guess value must be within the range of lower and higher value"      )

	def answer(_lowercase		:					int      ) -> str:
		if number > to_guess:
			return "high"
		elif number < to_guess:
			return "low"
		else:
			return "same"

	print("started..."      )

	a   :	Optional[Any]			=       lower
	a   :	List[Any]			=       higher

	a   :	Tuple			=       []

	while True:
		a   :	List[Any]			=       get_avg(_lowercase					, _lowercase      )
		last_numbers.append(_lowercase      )

		if answer(_lowercase      ) == "low":
			a   :	Optional[int]			=       number
		elif answer(_lowercase      ) == "high":
			a   :	Tuple			=       number
		else:
			break

	print(F"""guess the number : {last_numbers[-1]}"""      )
	print(F"""details : {last_numbers!s}"""      )






def    _SCREAMING_SNAKE_CASE	(			)				->None:






	'''simple docstring'''






	a   :	Tuple			=       int(input("Enter lower value : "      ).strip()      )
	a   :	Dict			=       int(input("Enter high value : "      ).strip()      )
	a   :	Optional[int]			=       int(input("Enter value to guess : "      ).strip()      )
	guess_the_number(_lowercase					, _lowercase					, _lowercase      )


if __name__ == "__main__":
		main()


