




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     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







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



							'''simple docstring'''
							__lowerCamelCase		       =    jnp.ones((batch_size, length)							) / length
							return scores


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    20

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

							# 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(lowerCamelCase__			,						axis=-1							)

							__lowerCamelCase		       =    FlaxTemperatureLogitsWarper(temperature=0.5							)
							__lowerCamelCase		       =    FlaxTemperatureLogitsWarper(temperature=1.3							)

							__lowerCamelCase		       =    jax.nn.softmax(temp_dist_warper_sharper(lowerCamelCase__			,						scores.copy()			,						cur_len=lowerCamelCase__							)			,						axis=-1							)
							__lowerCamelCase		       =    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       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    None
							__lowerCamelCase		       =    10
							__lowerCamelCase		       =    2

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

							__lowerCamelCase		       =    FlaxTopKLogitsWarper(3							)

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

							__lowerCamelCase		       =    np.broadcast_to(np.arange(lowerCamelCase__							)[None, :]			,						(batch_size, length)							).copy()
							__lowerCamelCase		       =    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       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__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], [0.15, 0.3, 0.3, 0.25]]							)							)

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

							# make ramp_logits more extreme
							__lowerCamelCase		       =    ramp_logits[1] * 1_00.0

							# 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(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       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    20
							__lowerCamelCase		       =    4
							__lowerCamelCase		       =    0

							__lowerCamelCase		       =    FlaxMinLengthLogitsProcessor(min_length=10			,						eos_token_id=lowerCamelCase__							)

							# 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(lowerCamelCase__			,						lowerCamelCase__							)
							__lowerCamelCase		       =    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
							__lowerCamelCase		       =    self._get_uniform_logits(lowerCamelCase__			,						lowerCamelCase__							)
							__lowerCamelCase		       =    15
							__lowerCamelCase		       =    min_dist_processor(lowerCamelCase__			,						lowerCamelCase__			,						cur_len=lowerCamelCase__							)
							self.assertFalse(jnp.isinf(lowerCamelCase__							).any()							)


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    20
							__lowerCamelCase		       =    4
							__lowerCamelCase		       =    0

							__lowerCamelCase		       =    FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase__							)

							# 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(lowerCamelCase__			,						lowerCamelCase__							)
							__lowerCamelCase		       =    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
							__lowerCamelCase		       =    3
							__lowerCamelCase		       =    self._get_uniform_logits(lowerCamelCase__			,						lowerCamelCase__							)
							__lowerCamelCase		       =    logits_processor(lowerCamelCase__			,						lowerCamelCase__			,						cur_len=lowerCamelCase__							)
							self.assertFalse(jnp.isinf(lowerCamelCase__							).any()							)


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    20
							__lowerCamelCase		       =    4
							__lowerCamelCase		       =    0
							__lowerCamelCase		       =    5

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

							# 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(lowerCamelCase__			,						lowerCamelCase__							)
							__lowerCamelCase		       =    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
							__lowerCamelCase		       =    3
							__lowerCamelCase		       =    self._get_uniform_logits(lowerCamelCase__			,						lowerCamelCase__							)
							__lowerCamelCase		       =    logits_processor(lowerCamelCase__			,						lowerCamelCase__			,						cur_len=lowerCamelCase__							)
							self.assertFalse(jnp.isinf(lowerCamelCase__							).any()							)


			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							__lowerCamelCase		       =    4
							__lowerCamelCase		       =    10
							__lowerCamelCase		       =    15
							__lowerCamelCase		       =    2
							__lowerCamelCase		       =    1
							__lowerCamelCase		       =    15

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

							__lowerCamelCase		       =    self._get_uniform_logits(lowerCamelCase__			,						lowerCamelCase__							)
							__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=lowerCamelCase__							)
							__lowerCamelCase		       =    FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase__							)
							__lowerCamelCase		       =    FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCamelCase__			,						eos_token_id=lowerCamelCase__							)

							__lowerCamelCase		       =    10

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

							# 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(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       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							__lowerCamelCase		       =    4
							__lowerCamelCase		       =    10
							__lowerCamelCase		       =    15
							__lowerCamelCase		       =    2
							__lowerCamelCase		       =    1
							__lowerCamelCase		       =    15

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

							__lowerCamelCase		       =    self._get_uniform_logits(lowerCamelCase__			,						lowerCamelCase__							)
							__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=lowerCamelCase__							)
							__lowerCamelCase		       =    FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCamelCase__							)
							__lowerCamelCase		       =    FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCamelCase__			,						eos_token_id=lowerCamelCase__							)

							__lowerCamelCase		       =    10

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

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

							__lowerCamelCase		       =    jax.jit(lowerCamelCase__							)
							__lowerCamelCase		       =    jax.jit(lowerCamelCase__							)

							__lowerCamelCase		       =    jitted_run_no_processor_list(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)
							__lowerCamelCase		       =    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__      )) ,     )


