



"""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       SCREAMING_SNAKE_CASE__       (							unittest.TestCase      ):

    def     _UpperCAmelCase		( self	: Union[str, Any]       ,     lowerCAmelCase_	: str       ,     lowerCAmelCase_	: Dict):







      """simple docstring"""







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

    def     _UpperCAmelCase		( self	: Union[str, Any]):







      """simple docstring"""







      lowercase_	    =       None
      lowercase_	    =       2_0

      lowercase_	    =       self._get_uniform_logits(batch_size=2       ,     length=lowerCamelCase__)

      # tweak scores to not be uniform anymore
      lowercase_	    =       scores.at[1, 5].set((1 / length) + 0.1)  # peak, 1st batch
      lowercase_	    =       scores.at[1, 1_0].set((1 / length) - 0.4)  # valley, 1st batch

      # compute softmax
      lowercase_	    =       jax.nn.softmax(lowerCamelCase__       ,     axis=-1)

      lowercase_	    =       FlaxTemperatureLogitsWarper(temperature=0.5)
      lowercase_	    =       FlaxTemperatureLogitsWarper(temperature=1.3)

      lowercase_	    =       jax.nn.softmax(temp_dist_warper_sharper(lowerCamelCase__       ,     scores.copy()       ,     cur_len=lowerCamelCase__)       ,     axis=-1)
      lowercase_	    =       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     _UpperCAmelCase		( self	: Optional[int]):







      """simple docstring"""







      lowercase_	    =       None
      lowercase_	    =       1_0
      lowercase_	    =       2

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

      lowercase_	    =       FlaxTopKLogitsWarper(3)

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

      lowercase_	    =       np.broadcast_to(np.arange(lowerCamelCase__)[None, :]       ,     (batch_size, length)).copy()
      lowercase_	    =       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     _UpperCAmelCase		( self	: str):







      """simple docstring"""







      lowercase_	    =       None
      lowercase_	    =       1_0
      lowercase_	    =       2

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

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

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

      # make sure at least 2 tokens are kept
      lowercase_	    =       FlaxTopPLogitsWarper(0.9       ,     min_tokens_to_keep=2       ,     filter_value=0.0)
      lowercase_	    =       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     _UpperCAmelCase		( self	: Union[str, Any]):







      """simple docstring"""







      lowercase_	    =       2_0
      lowercase_	    =       4
      lowercase_	    =       0

      lowercase_	    =       FlaxMinLengthLogitsProcessor(min_length=1_0       ,     eos_token_id=lowerCamelCase__)

      # check that min length is applied at length 5
      lowercase_	    =       ids_tensor((batch_size, 2_0)       ,     vocab_size=2_0)
      lowercase_	    =       5
      lowercase_	    =       self._get_uniform_logits(lowerCamelCase__       ,     lowerCamelCase__)
      lowercase_	    =       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
      lowercase_	    =       self._get_uniform_logits(lowerCamelCase__       ,     lowerCamelCase__)
      lowercase_	    =       1_5
      lowercase_	    =       min_dist_processor(lowerCamelCase__       ,     lowerCamelCase__       ,     cur_len=lowerCamelCase__)
      self.assertFalse(jnp.isinf(lowerCamelCase__).any())

    def     _UpperCAmelCase		( self	: str):







      """simple docstring"""







      lowercase_	    =       2_0
      lowercase_	    =       4
      lowercase_	    =       0

      lowercase_	    =       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase__)

      # check that all scores are -inf except the bos_token_id score
      lowercase_	    =       ids_tensor((batch_size, 1)       ,     vocab_size=2_0)
      lowercase_	    =       1
      lowercase_	    =       self._get_uniform_logits(lowerCamelCase__       ,     lowerCamelCase__)
      lowercase_	    =       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
      lowercase_	    =       3
      lowercase_	    =       self._get_uniform_logits(lowerCamelCase__       ,     lowerCamelCase__)
      lowercase_	    =       logits_processor(lowerCamelCase__       ,     lowerCamelCase__       ,     cur_len=lowerCamelCase__)
      self.assertFalse(jnp.isinf(lowerCamelCase__).any())

    def     _UpperCAmelCase		( self	: int):







      """simple docstring"""







      lowercase_	    =       2_0
      lowercase_	    =       4
      lowercase_	    =       0
      lowercase_	    =       5

      lowercase_	    =       FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCamelCase__       ,     eos_token_id=lowerCamelCase__)

      # check that all scores are -inf except the eos_token_id when max_length is reached
      lowercase_	    =       ids_tensor((batch_size, 4)       ,     vocab_size=2_0)
      lowercase_	    =       4
      lowercase_	    =       self._get_uniform_logits(lowerCamelCase__       ,     lowerCamelCase__)
      lowercase_	    =       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
      lowercase_	    =       3
      lowercase_	    =       self._get_uniform_logits(lowerCamelCase__       ,     lowerCamelCase__)
      lowercase_	    =       logits_processor(lowerCamelCase__       ,     lowerCamelCase__       ,     cur_len=lowerCamelCase__)
      self.assertFalse(jnp.isinf(lowerCamelCase__).any())

    def     _UpperCAmelCase		( self	: Optional[int]):







      """simple docstring"""







      lowercase_	    =       4
      lowercase_	    =       1_0
      lowercase_	    =       1_5
      lowercase_	    =       2
      lowercase_	    =       1
      lowercase_	    =       1_5

      # dummy input_ids and scores
      lowercase_	    =       ids_tensor((batch_size, sequence_length)       ,     lowerCamelCase__)
      lowercase_	    =       input_ids.copy()

      lowercase_	    =       self._get_uniform_logits(lowerCamelCase__       ,     lowerCamelCase__)
      lowercase_	    =       scores.copy()

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

      # instantiate all logits processors
      lowercase_	    =       FlaxMinLengthLogitsProcessor(min_length=1_0       ,     eos_token_id=lowerCamelCase__)
      lowercase_	    =       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase__)
      lowercase_	    =       FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCamelCase__       ,     eos_token_id=lowerCamelCase__)

      lowercase_	    =       1_0

      # no processor list
      lowercase_	    =       temp_dist_warp(lowerCamelCase__       ,     lowerCamelCase__       ,     cur_len=lowerCamelCase__)
      lowercase_	    =       top_k_warp(lowerCamelCase__       ,     lowerCamelCase__       ,     cur_len=lowerCamelCase__)
      lowercase_	    =       top_p_warp(lowerCamelCase__       ,     lowerCamelCase__       ,     cur_len=lowerCamelCase__)
      lowercase_	    =       min_dist_proc(lowerCamelCase__       ,     lowerCamelCase__       ,     cur_len=lowerCamelCase__)
      lowercase_	    =       bos_dist_proc(lowerCamelCase__       ,     lowerCamelCase__       ,     cur_len=lowerCamelCase__)
      lowercase_	    =       eos_dist_proc(lowerCamelCase__       ,     lowerCamelCase__       ,     cur_len=lowerCamelCase__)

      # with processor list
      lowercase_	    =       FlaxLogitsProcessorList(
          [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
      lowercase_	    =       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     _UpperCAmelCase		( self	: Optional[Any]):







      """simple docstring"""







      lowercase_	    =       4
      lowercase_	    =       1_0
      lowercase_	    =       1_5
      lowercase_	    =       2
      lowercase_	    =       1
      lowercase_	    =       1_5

      # dummy input_ids and scores
      lowercase_	    =       ids_tensor((batch_size, sequence_length)       ,     lowerCamelCase__)
      lowercase_	    =       input_ids.copy()

      lowercase_	    =       self._get_uniform_logits(lowerCamelCase__       ,     lowerCamelCase__)
      lowercase_	    =       scores.copy()

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

      # instantiate all logits processors
      lowercase_	    =       FlaxMinLengthLogitsProcessor(min_length=1_0       ,     eos_token_id=lowerCamelCase__)
      lowercase_	    =       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase__)
      lowercase_	    =       FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCamelCase__       ,     eos_token_id=lowerCamelCase__)

      lowercase_	    =       1_0

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

      # with processor list
      def run_processor_list(lowerCAmelCase_	: Dict       ,     lowerCAmelCase_	: List[str]       ,     lowerCAmelCase_	: Optional[int]):
        lowercase_	    =       FlaxLogitsProcessorList(
            [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
        lowercase_	    =       processor(lowerCamelCase__       ,     lowerCamelCase__       ,     cur_len=lowerCamelCase__)
        return scores

      lowercase_	    =       jax.jit(lowerCamelCase__)
      lowercase_	    =       jax.jit(lowerCamelCase__)

      lowercase_	    =       jitted_run_no_processor_list(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__)
      lowercase_	    =       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())







from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class     __lowerCAmelCase :

			"""simple docstring"""







			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42  # [batch_size x 3]
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42
			snake_case_   		=       42


			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
							assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
							assert len(self.x.shape							) == len(self.y.shape							) == len(self.z.shape							) == len(self.origin.shape							) == 2


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							return torch.from_numpy(np.array([self.width, self.height]			,						dtype=np.floataa							)							)


			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							return torch.from_numpy(np.array([self.x_fov, self.y_fov]			,						dtype=np.floataa							)							)


			def       lowercase_    (      self							)    ->      torch.Tensor:



							'''simple docstring'''
							__lowerCamelCase		       =    torch.arange(self.height * self.width							)
							__lowerCamelCase		       =    torch.stack(
							    [
							        pixel_indices % self.width,
							        torch.div(lowerCamelCase__			,						self.width			,						rounding_mode='trunc'							),
							    ]			,						axis=1			,						)
							return coords


			@property
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase       ,						*__lowerCamelCase		       =    self.shape
							__lowerCamelCase		       =    int(np.prod(lowerCamelCase__							)							)

							__lowerCamelCase		       =    self.get_image_coords()
							__lowerCamelCase		       =    torch.broadcast_to(coords.unsqueeze(0							)			,						[batch_size * inner_batch_size, *coords.shape]							)
							__lowerCamelCase		       =    self.get_camera_rays(lowerCamelCase__							)

							__lowerCamelCase		       =    rays.view(lowerCamelCase__			,						inner_batch_size * self.height * self.width			,						2			,						3							)

							return rays


			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      torch.Tensor:



							'''simple docstring'''
							__lowerCamelCase       ,						*__lowerCamelCase       ,						__lowerCamelCase		       =    coords.shape
							assert n_coords == 2
							assert batch_size == self.origin.shape[0]

							__lowerCamelCase		       =    coords.view(lowerCamelCase__			,						-1			,						2							)

							__lowerCamelCase		       =    self.resolution()
							__lowerCamelCase		       =    self.fov()

							__lowerCamelCase		       =    (flat.float() / (res - 1)) * 2 - 1
							__lowerCamelCase		       =    fracs * torch.tan(fov / 2							)

							__lowerCamelCase		       =    fracs.view(lowerCamelCase__			,						-1			,						2							)
							__lowerCamelCase		       =    (
							    self.z.view(lowerCamelCase__			,						1			,						3							)
							    + self.x.view(lowerCamelCase__			,						1			,						3							) * fracs[:, :, :1]
							    + self.y.view(lowerCamelCase__			,						1			,						3							) * fracs[:, :, 1:]
							)
							__lowerCamelCase		       =    directions / directions.norm(dim=-1			,						keepdim=lowerCamelCase__							)
							__lowerCamelCase		       =    torch.stack(
							    [
							        torch.broadcast_to(self.origin.view(lowerCamelCase__			,						1			,						3							)			,						[batch_size, directions.shape[1], 3]							),
							        directions,
							    ]			,						dim=2			,						)
							return rays.view(lowerCamelCase__			,						*lowerCamelCase__			,						2			,						3							)






			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      "DifferentiableProjectiveCamera":



							'''simple docstring'''
							assert width * self.height == height * self.width, "The aspect ratio should not change."
							return DifferentiableProjectiveCamera(
							    origin=self.origin			,						x=self.x			,						y=self.y			,						z=self.z			,						width=lowerCamelCase__			,						height=lowerCamelCase__			,						x_fov=self.x_fov			,						y_fov=self.y_fov			,						)







def 			lowerCamelCase_		(       UpperCamelCase__				:      int      )     ->       DifferentiableProjectiveCamera:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    []
				for theta in np.linspace(0 ,     2 * np.pi ,     num=20      ):
								__lowerCamelCase		       =    np.array([np.sin(UpperCamelCase__      ), np.cos(UpperCamelCase__      ), -0.5]      )
								z /= np.sqrt(np.sum(z**2      )      )
								__lowerCamelCase		       =    -z * 4
								__lowerCamelCase		       =    np.array([np.cos(UpperCamelCase__      ), -np.sin(UpperCamelCase__      ), 0.0]      )
								__lowerCamelCase		       =    np.cross(UpperCamelCase__ ,     UpperCamelCase__      )
								origins.append(UpperCamelCase__      )
								xs.append(UpperCamelCase__      )
								ys.append(UpperCamelCase__      )
								zs.append(UpperCamelCase__      )
				return DifferentiableProjectiveCamera(
				    origin=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     x=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     y=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     z=torch.from_numpy(np.stack(UpperCamelCase__ ,     axis=0      )      ).float() ,     width=UpperCamelCase__ ,     height=UpperCamelCase__ ,     x_fov=0.7 ,     y_fov=0.7 ,     shape=(1, len(UpperCamelCase__      )) ,     )


