




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        __snake_case  (unittest.TestCase		):



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      jnp.ones((batch_size, length)      ) / length
								return scores



				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      None
								_lowerCAmelCase    :    str										=      20

								_lowerCAmelCase    :    List[Any]										=      self._get_uniform_logits(batch_size=2		,	length=_UpperCAmelCase      )

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

								# compute softmax
								_lowerCAmelCase    :    List[Any]										=      jax.nn.softmax(_UpperCAmelCase		,	axis=-1      )

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

								_lowerCAmelCase    :    Optional[int]										=      jax.nn.softmax(temp_dist_warper_sharper(_UpperCAmelCase		,	scores.copy()		,	cur_len=_UpperCAmelCase      )		,	axis=-1      )
								_lowerCAmelCase    :    List[Any]										=      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      :		str      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      None
								_lowerCAmelCase    :    Optional[int]										=      10
								_lowerCAmelCase    :    List[str]										=      2

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

								_lowerCAmelCase    :    Optional[Any]										=      FlaxTopKLogitsWarper(3      )

								_lowerCAmelCase    :    Any										=      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
								_lowerCAmelCase    :    Tuple										=      5
								_lowerCAmelCase    :    Optional[int]										=      FlaxTopKLogitsWarper(top_k=1		,	filter_value=0.0		,	min_tokens_to_keep=3      )

								_lowerCAmelCase    :    Dict										=      np.broadcast_to(np.arange(_UpperCAmelCase      )[None, :]		,	(batch_size, length)      ).copy()
								_lowerCAmelCase    :    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       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      None
								_lowerCAmelCase    :    Tuple										=      10
								_lowerCAmelCase    :    List[str]										=      2

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

								_lowerCAmelCase    :    Union[str, Any]										=      FlaxTopPLogitsWarper(0.8      )
								_lowerCAmelCase    :    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
								_lowerCAmelCase    :    Optional[int]										=      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
								_lowerCAmelCase    :    Dict										=      np.broadcast_to(np.arange(_UpperCAmelCase      )[None, :]		,	(batch_size, vocab_size)      ).copy() - (
								    vocab_size // 2
								)

								# make ramp_logits more extreme
								_lowerCAmelCase    :    Optional[int]										=      ramp_logits[1] * 100.0

								# make sure at least 2 tokens are kept
								_lowerCAmelCase    :    List[str]										=      FlaxTopPLogitsWarper(0.9		,	min_tokens_to_keep=2		,	filter_value=0.0      )
								_lowerCAmelCase    :    str										=      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      :		List[Any]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      20
								_lowerCAmelCase    :    Optional[int]										=      4
								_lowerCAmelCase    :    int										=      0

								_lowerCAmelCase    :    Tuple										=      FlaxMinLengthLogitsProcessor(min_length=10		,	eos_token_id=_UpperCAmelCase      )

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



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      20
								_lowerCAmelCase    :    Any										=      4
								_lowerCAmelCase    :    Any										=      0

								_lowerCAmelCase    :    Union[str, Any]										=      FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_UpperCAmelCase      )

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



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Optional[Any]:





								'''simple docstring'''




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

								_lowerCAmelCase    :    Tuple										=      FlaxForcedEOSTokenLogitsProcessor(max_length=_UpperCAmelCase		,	eos_token_id=_UpperCAmelCase      )

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      4
								_lowerCAmelCase    :    Dict										=      10
								_lowerCAmelCase    :    Optional[Any]										=      15
								_lowerCAmelCase    :    str										=      2
								_lowerCAmelCase    :    Any										=      1
								_lowerCAmelCase    :    List[str]										=      15

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

								_lowerCAmelCase    :    List[str]										=      self._get_uniform_logits(_UpperCAmelCase		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[int]										=      scores.copy()

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

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

								_lowerCAmelCase    :    List[Any]										=      10

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

								# with processor list
								_lowerCAmelCase    :    List[Any]										=      FlaxLogitsProcessorList(
								    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]      )
								_lowerCAmelCase    :    Dict										=      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      :		Any      )  ->    Any:





								'''simple docstring'''




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

								# dummy input_ids and scores
								_lowerCAmelCase    :    Optional[Any]										=      ids_tensor((batch_size, sequence_length)		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      input_ids.copy()

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

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

								# instantiate all logits processors
								_lowerCAmelCase    :    List[str]										=      FlaxMinLengthLogitsProcessor(min_length=10		,	eos_token_id=_UpperCAmelCase      )
								_lowerCAmelCase    :    Tuple										=      FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_UpperCAmelCase      )
								_lowerCAmelCase    :    str										=      FlaxForcedEOSTokenLogitsProcessor(max_length=_UpperCAmelCase		,	eos_token_id=_UpperCAmelCase      )

								_lowerCAmelCase    :    Optional[int]										=      10

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

								# with processor list
								def run_processor_list(_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		Tuple      ):
												_lowerCAmelCase    :    List[str]										=      FlaxLogitsProcessorList(
												    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]      )
												_lowerCAmelCase    :    Dict										=      processor(_UpperCAmelCase		,	_UpperCAmelCase		,	cur_len=_UpperCAmelCase      )
												return scores

								_lowerCAmelCase    :    Optional[Any]										=      jax.jit(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[int]										=      jax.jit(_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      jitted_run_no_processor_list(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    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 cva
import numpy as np
class        __snake_case  :



				def __init__(      self      :		List[str]		,	_UpperCAmelCase      :		float		,	_UpperCAmelCase      :		int      )  ->    int:





								'''simple docstring'''




								if k in (0.04, 0.06):
												_lowerCAmelCase    :    str										=      k
												_lowerCAmelCase    :    Optional[Any]										=      window_size
								else:
												raise ValueError("""invalid k value"""      )



				def __str__(      self      :		int      )  ->    str:





								'''simple docstring'''




								return str(self.k      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		str      )  ->    tuple[cva.Mat, list[list[int]]]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      cva.imread(_UpperCAmelCase		,	0      )
								_lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      img.shape
								_lowerCAmelCase    :    list[list[int]]										=      []
								_lowerCAmelCase    :    int										=      img.copy()
								_lowerCAmelCase    :    str										=      cva.cvtColor(_UpperCAmelCase		,	cva.COLOR_GRAY2RGB      )
								_lowerCAmelCase					,    _lowerCAmelCase    :    int										=      np.gradient(_UpperCAmelCase      )
								_lowerCAmelCase    :    Any										=      dx**2
								_lowerCAmelCase    :    Optional[int]										=      dy**2
								_lowerCAmelCase    :    Optional[Any]										=      dx * dy
								_lowerCAmelCase    :    Dict										=      0.04
								_lowerCAmelCase    :    Tuple										=      self.window_size // 2
								for y in range(_UpperCAmelCase		,	h - offset      ):
												for x in range(_UpperCAmelCase		,	w - offset      ):
																_lowerCAmelCase    :    Optional[int]										=      ixx[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()
																_lowerCAmelCase    :    int										=      iyy[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()
																_lowerCAmelCase    :    Any										=      ixy[
																    y - offset : y + offset + 1, x - offset : x + offset + 1
																].sum()

																_lowerCAmelCase    :    Dict										=      (wxx * wyy) - (wxy**2)
																_lowerCAmelCase    :    Union[str, Any]										=      wxx + wyy
																_lowerCAmelCase    :    int										=      det - k * (trace**2)
																# Can change the value
																if r > 0.5:
																				corner_list.append([x, y, r]      )
																				color_img.itemset((y, x, 0)		,	0      )
																				color_img.itemset((y, x, 1)		,	0      )
																				color_img.itemset((y, x, 2)		,	255      )
								return color_img, corner_list


if __name__ == "__main__":
	_lowerCamelCase      :  int      						=						HarrisCorner(0.0_4, 3)
	_lowerCamelCase						, _lowerCamelCase      :  Any      						=						edge_detect.detect("path_to_image")
	cva.imwrite("detect.png", color_img)

