


'''simple docstring'''







from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class 					lowercase_     (    a__   ):
    __UpperCAmelCase   			=	42

class 					lowercase_     (    a__  ,						a__   ):
    __UpperCAmelCase   			=	True


    @register_to_config
    def __init__(     self  ,					a = 3  ,					a = 3  ,					a = ("DownEncoderBlock2D",)  ,					a = ("UpDecoderBlock2D",)  ,					a = (64,)  ,					a = 1  ,					a = "silu"  ,					a = 4  ,					a = 32  ,					a = 32  ,					a = 0.1_8215  ,					):
          super().__init__()

          # pass init params to Encoder
          UpperCamelCase__										=				Encoder(
              in_channels=a  ,					out_channels=a  ,					down_block_types=a  ,					block_out_channels=a  ,					layers_per_block=a  ,					act_fn=a  ,					norm_num_groups=a  ,					double_z=a  ,					)

          # pass init params to Decoder
          UpperCamelCase__										=				Decoder(
              in_channels=a  ,					out_channels=a  ,					up_block_types=a  ,					block_out_channels=a  ,					layers_per_block=a  ,					norm_num_groups=a  ,					act_fn=a  ,					)

          UpperCamelCase__										=				nn.Convad(2 * latent_channels  ,					2 * latent_channels  ,					1    )
          UpperCamelCase__										=				nn.Convad(a  ,					a  ,					1    )

          UpperCamelCase__										=				False
          UpperCamelCase__										=				False

          # only relevant if vae tiling is enabled
          UpperCamelCase__										=				self.config.sample_size
          UpperCamelCase__										=				(
              self.config.sample_size[0]
              if isinstance(self.config.sample_size  ,					(list, tuple)    )
              else self.config.sample_size
          )
          UpperCamelCase__										=				int(sample_size / (2 ** (len(self.config.block_out_channels    ) - 1))    )
          UpperCamelCase__										=				0.25


    def    __a							(     self  ,					a  ,					a=False    ):
          if isinstance(a  ,					(Encoder, Decoder)    ):
                UpperCamelCase__										=				value


    def    __a							(     self  ,					a = True    ):
          UpperCamelCase__										=				use_tiling


    def    __a							(     self    ):
          self.enable_tiling(a    )


    def    __a							(     self    ):
          UpperCamelCase__										=				True


    def    __a							(     self    ):
          UpperCamelCase__										=				False


    @property
    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
    def    __a							(     self    ):
          UpperCamelCase__										=				{}

          def fn_recursive_add_processors(a  ,					a  ,					a    ):
                if hasattr(a  ,					"set_processor"    ):
                      UpperCamelCase__										=				module.processor

                for sub_name, child in module.named_children():
                      fn_recursive_add_processors(f'''{name}.{sub_name}'''  ,					a  ,					a    )

                return processors

          for name, module in self.named_children():
                fn_recursive_add_processors(a  ,					a  ,					a    )

          return processors


    def    __a							(     self  ,					a    ):
          UpperCamelCase__										=				len(self.attn_processors.keys()    )

          if isinstance(a  ,					a    ) and len(a    ) != count:
                raise ValueError(
                    f'''A dict of processors was passed, but the number of processors {len(a    )} does not match the'''
                    f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''    )

          def fn_recursive_attn_processor(a  ,					a  ,					a    ):
                if hasattr(a  ,					"set_processor"    ):
                      if not isinstance(a  ,					a    ):
                            module.set_processor(a    )
                      else:
                            module.set_processor(processor.pop(f'''{name}.processor'''    )    )

                for sub_name, child in module.named_children():
                      fn_recursive_attn_processor(f'''{name}.{sub_name}'''  ,					a  ,					a    )

          for name, module in self.named_children():
                fn_recursive_attn_processor(a  ,					a  ,					a    )


    def    __a							(     self    ):
          self.set_attn_processor(AttnProcessor()    )


    @apply_forward_hook
    def    __a							(     self  ,					a  ,					a = True    ):
          if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
                return self.tiled_encode(a  ,					return_dict=a    )

          if self.use_slicing and x.shape[0] > 1:
                UpperCamelCase__										=				[self.encoder(a    ) for x_slice in x.split(1    )]
                UpperCamelCase__										=				torch.cat(a    )
          else:
                UpperCamelCase__										=				self.encoder(a    )

          UpperCamelCase__										=				self.quant_conv(a    )
          UpperCamelCase__										=				DiagonalGaussianDistribution(a    )

          if not return_dict:
                return (posterior,)

          return AutoencoderKLOutput(latent_dist=a    )


    def    __a							(     self  ,					a  ,					a = True    ):
          if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
                return self.tiled_decode(a  ,					return_dict=a    )

          UpperCamelCase__										=				self.post_quant_conv(a    )
          UpperCamelCase__										=				self.decoder(a    )

          if not return_dict:
                return (dec,)

          return DecoderOutput(sample=a    )


    @apply_forward_hook
    def    __a							(     self  ,					a  ,					a = True    ):
          if self.use_slicing and z.shape[0] > 1:
                UpperCamelCase__										=				[self._decode(a    ).sample for z_slice in z.split(1    )]
                UpperCamelCase__										=				torch.cat(a    )
          else:
                UpperCamelCase__										=				self._decode(a    ).sample

          if not return_dict:
                return (decoded,)

          return DecoderOutput(sample=a    )


    def    __a							(     self  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				min(a.shape[2]  ,					b.shape[2]  ,					a    )
          for y in range(a    ):
                UpperCamelCase__										=				a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
          return b


    def    __a							(     self  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				min(a.shape[3]  ,					b.shape[3]  ,					a    )
          for x in range(a    ):
                UpperCamelCase__										=				a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
          return b


    def    __a							(     self  ,					a  ,					a = True    ):
          UpperCamelCase__										=				int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)    )
          UpperCamelCase__										=				int(self.tile_latent_min_size * self.tile_overlap_factor    )
          UpperCamelCase__										=				self.tile_latent_min_size - blend_extent

          # Split the image into 512x512 tiles and encode them separately.
          UpperCamelCase__										=				[]
          for i in range(0  ,					x.shape[2]  ,					a    ):
                UpperCamelCase__										=				[]
                for j in range(0  ,					x.shape[3]  ,					a    ):
                      UpperCamelCase__										=				x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                      UpperCamelCase__										=				self.encoder(a    )
                      UpperCamelCase__										=				self.quant_conv(a    )
                      row.append(a    )
                rows.append(a    )
          UpperCamelCase__										=				[]
          for i, row in enumerate(a    ):
                UpperCamelCase__										=				[]
                for j, tile in enumerate(a    ):
                      # blend the above tile and the left tile
                      # to the current tile and add the current tile to the result row
                      if i > 0:
                            UpperCamelCase__										=				self.blend_v(rows[i - 1][j]  ,					a  ,					a    )
                      if j > 0:
                            UpperCamelCase__										=				self.blend_h(row[j - 1]  ,					a  ,					a    )
                      result_row.append(tile[:, :, :row_limit, :row_limit]    )
                result_rows.append(torch.cat(a  ,					dim=3    )    )

          UpperCamelCase__										=				torch.cat(a  ,					dim=2    )
          UpperCamelCase__										=				DiagonalGaussianDistribution(a    )

          if not return_dict:
                return (posterior,)

          return AutoencoderKLOutput(latent_dist=a    )


    def    __a							(     self  ,					a  ,					a = True    ):
          UpperCamelCase__										=				int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)    )
          UpperCamelCase__										=				int(self.tile_sample_min_size * self.tile_overlap_factor    )
          UpperCamelCase__										=				self.tile_sample_min_size - blend_extent

          # Split z into overlapping 64x64 tiles and decode them separately.
          # The tiles have an overlap to avoid seams between tiles.
          UpperCamelCase__										=				[]
          for i in range(0  ,					z.shape[2]  ,					a    ):
                UpperCamelCase__										=				[]
                for j in range(0  ,					z.shape[3]  ,					a    ):
                      UpperCamelCase__										=				z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                      UpperCamelCase__										=				self.post_quant_conv(a    )
                      UpperCamelCase__										=				self.decoder(a    )
                      row.append(a    )
                rows.append(a    )
          UpperCamelCase__										=				[]
          for i, row in enumerate(a    ):
                UpperCamelCase__										=				[]
                for j, tile in enumerate(a    ):
                      # blend the above tile and the left tile
                      # to the current tile and add the current tile to the result row
                      if i > 0:
                            UpperCamelCase__										=				self.blend_v(rows[i - 1][j]  ,					a  ,					a    )
                      if j > 0:
                            UpperCamelCase__										=				self.blend_h(row[j - 1]  ,					a  ,					a    )
                      result_row.append(tile[:, :, :row_limit, :row_limit]    )
                result_rows.append(torch.cat(a  ,					dim=3    )    )

          UpperCamelCase__										=				torch.cat(a  ,					dim=2    )
          if not return_dict:
                return (dec,)

          return DecoderOutput(sample=a    )


    def    __a							(     self  ,					a  ,					a = False  ,					a = True  ,					a = None  ,					):
          UpperCamelCase__										=				sample
          UpperCamelCase__										=				self.encode(a    ).latent_dist
          if sample_posterior:
                UpperCamelCase__										=				posterior.sample(generator=a    )
          else:
                UpperCamelCase__										=				posterior.mode()
          UpperCamelCase__										=				self.decode(a    ).sample

          if not return_dict:
                return (dec,)

          return DecoderOutput(sample=a    )








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   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: str  		=       jnp.ones((batch_size, length)) / length
									return scores



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Dict  		=       None
									SCREAMING_SNAKE_CASE_: str  		=       20

									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._get_uniform_logits(batch_size=2   ,						length=lowerCAmelCase__)

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

									# compute softmax
									SCREAMING_SNAKE_CASE_: Dict  		=       jax.nn.softmax(lowerCAmelCase__   ,						axis=-1)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       FlaxTemperatureLogitsWarper(temperature=0.5)
									SCREAMING_SNAKE_CASE_: List[str]  		=       FlaxTemperatureLogitsWarper(temperature=1.3)

									SCREAMING_SNAKE_CASE_: str  		=       jax.nn.softmax(temp_dist_warper_sharper(lowerCAmelCase__   ,						scores.copy()   ,						cur_len=lowerCAmelCase__)   ,						axis=-1)
									SCREAMING_SNAKE_CASE_: int  		=       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        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       None
									SCREAMING_SNAKE_CASE_: str  		=       10
									SCREAMING_SNAKE_CASE_: Tuple  		=       2

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

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       FlaxTopKLogitsWarper(3)

									SCREAMING_SNAKE_CASE_: Dict  		=       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
									SCREAMING_SNAKE_CASE_: Any  		=       5
									SCREAMING_SNAKE_CASE_: str  		=       FlaxTopKLogitsWarper(top_k=1   ,						filter_value=0.0   ,						min_tokens_to_keep=3)

									SCREAMING_SNAKE_CASE_: Any  		=       np.broadcast_to(np.arange(lowerCAmelCase__)[None, :]   ,						(batch_size, length)).copy()
									SCREAMING_SNAKE_CASE_: 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        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Tuple  		=       None
									SCREAMING_SNAKE_CASE_: Dict  		=       10
									SCREAMING_SNAKE_CASE_: Dict  		=       2

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

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

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

									# make sure at least 2 tokens are kept
									SCREAMING_SNAKE_CASE_: str  		=       FlaxTopPLogitsWarper(0.9   ,						min_tokens_to_keep=2   ,						filter_value=0.0)
									SCREAMING_SNAKE_CASE_: 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        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Tuple  		=       20
									SCREAMING_SNAKE_CASE_: List[str]  		=       4
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       0

									SCREAMING_SNAKE_CASE_: str  		=       FlaxMinLengthLogitsProcessor(min_length=10   ,						eos_token_id=lowerCAmelCase__)

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: int  		=       20
									SCREAMING_SNAKE_CASE_: str  		=       4
									SCREAMING_SNAKE_CASE_: List[Any]  		=       0

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase__)

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



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: Any  		=       20
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       4
									SCREAMING_SNAKE_CASE_: Dict  		=       0
									SCREAMING_SNAKE_CASE_: List[Any]  		=       5

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase__   ,						eos_token_id=lowerCAmelCase__)

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: int  		=       4
									SCREAMING_SNAKE_CASE_: List[Any]  		=       10
									SCREAMING_SNAKE_CASE_: int  		=       15
									SCREAMING_SNAKE_CASE_: Dict  		=       2
									SCREAMING_SNAKE_CASE_: int  		=       1
									SCREAMING_SNAKE_CASE_: List[Any]  		=       15

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

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

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

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

									SCREAMING_SNAKE_CASE_: str  		=       10

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

									# with processor list
									SCREAMING_SNAKE_CASE_: str  		=       FlaxLogitsProcessorList(
									    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
									SCREAMING_SNAKE_CASE_: Tuple  		=       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        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       4
									SCREAMING_SNAKE_CASE_: int  		=       10
									SCREAMING_SNAKE_CASE_: List[str]  		=       15
									SCREAMING_SNAKE_CASE_: List[Any]  		=       2
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       1
									SCREAMING_SNAKE_CASE_: str  		=       15

									# dummy input_ids and scores
									SCREAMING_SNAKE_CASE_: Tuple  		=       ids_tensor((batch_size, sequence_length)   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       input_ids.copy()

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

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

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

									SCREAMING_SNAKE_CASE_: str  		=       10

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

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

									SCREAMING_SNAKE_CASE_: str  		=       jax.jit(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       jax.jit(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Dict  		=       jitted_run_no_processor_list(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       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())






