
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






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






	"""simple docstring"""


	__lowerCAmelCase         =							[tensor.shape for tensor in tensor_list]
	return all(shape == shapes[0] for shape in shapes[1:]  )

class       a		(   __UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	unittest.TestCase					):
							lowercase_		:						Optional[Any]   							=  StableDiffusionLatentUpscalePipeline
							lowercase_		:						Optional[int]   							=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
							    'height',
							    'width',
							    'cross_attention_kwargs',
							    'negative_prompt_embeds',
							    'prompt_embeds',
							}
							lowercase_		:						Dict   							=  PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
							lowercase_		:						Optional[int]   							=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							lowercase_		:						List[str]   							=  frozenset(
							    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowercase_		:						List[str]   							=  frozenset([]					)

							lowercase_		:						Optional[Any]   							=  True







							@property
							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							1
								__lowerCAmelCase         =							4
								__lowerCAmelCase         =							(16, 16)

								__lowerCAmelCase         =							floats_tensor((batch_size, num_channels) + sizes      ,				rng=random.Random(0 ) ).to(snake_case__ )
								return image







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								torch.manual_seed(0 )
								__lowerCAmelCase         =							UNetaDConditionModel(
								    act_fn="gelu"      ,				attention_head_dim=8      ,				norm_num_groups=snake_case__      ,				block_out_channels=[32, 32, 64, 64]      ,				time_cond_proj_dim=160      ,				conv_in_kernel=1      ,				conv_out_kernel=1      ,				cross_attention_dim=32      ,				down_block_types=(
								        "KDownBlock2D",
								        "KCrossAttnDownBlock2D",
								        "KCrossAttnDownBlock2D",
								        "KCrossAttnDownBlock2D",
								    )      ,				in_channels=8      ,				mid_block_type=snake_case__      ,				only_cross_attention=snake_case__      ,				out_channels=5      ,				resnet_time_scale_shift="scale_shift"      ,				time_embedding_type="fourier"      ,				timestep_post_act="gelu"      ,				up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")      ,				)
								__lowerCAmelCase         =							AutoencoderKL(
								    block_out_channels=[32, 32, 64, 64]      ,				in_channels=3      ,				out_channels=3      ,				down_block_types=[
								        "DownEncoderBlock2D",
								        "DownEncoderBlock2D",
								        "DownEncoderBlock2D",
								        "DownEncoderBlock2D",
								    ]      ,				up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]      ,				latent_channels=4      ,				)
								__lowerCAmelCase         =							EulerDiscreteScheduler(prediction_type="sample" )
								__lowerCAmelCase         =							CLIPTextConfig(
								    bos_token_id=0      ,				eos_token_id=2      ,				hidden_size=32      ,				intermediate_size=37      ,				layer_norm_eps=1E-05      ,				num_attention_heads=4      ,				num_hidden_layers=5      ,				pad_token_id=1      ,				vocab_size=1_000      ,				hidden_act="quick_gelu"      ,				projection_dim=512      ,				)
								__lowerCAmelCase         =							CLIPTextModel(snake_case__ )
								__lowerCAmelCase         =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

								__lowerCAmelCase         =							{
								    "unet": model.eval(),
								    "vae": vae.eval(),
								    "scheduler": scheduler,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								}

								return components







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  Tuple=0 ):
								"""simple docstring"""







								if str(snake_case__ ).startswith("mps" ):
									__lowerCAmelCase         =							torch.manual_seed(snake_case__ )
								else:
									__lowerCAmelCase         =							torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
								__lowerCAmelCase         =							{
								    "prompt": "A painting of a squirrel eating a burger",
								    "image": self.dummy_image.cpu(),
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "numpy",
								}
								return inputs







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							"cpu"

								__lowerCAmelCase         =							self.get_dummy_components()
								__lowerCAmelCase         =							self.pipeline_class(**snake_case__ )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							self.get_dummy_inputs(snake_case__ )
								__lowerCAmelCase         =							pipe(**snake_case__ ).images
								__lowerCAmelCase         =							image[0, -3:, -3:, -1]

								self.assertEqual(image.shape      ,				(1, 256, 256, 3) )
								__lowerCAmelCase         =							np.array(
								    [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5] )
								__lowerCAmelCase         =							np.abs(image_slice.flatten() - expected_slice ).max()
								self.assertLessEqual(snake_case__      ,				1E-3 )







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								super().test_attention_slicing_forward_pass(expected_max_diff=7E-3 )







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								super().test_cpu_offload_forward_pass(expected_max_diff=3E-3 )







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								super().test_inference_batch_single_identical(expected_max_diff=7E-3 )







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3 )







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								super().test_save_load_local(expected_max_difference=3E-3 )







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								super().test_save_load_optional_components(expected_max_difference=3E-3 )







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							[
								    "DDIMScheduler",
								    "DDPMScheduler",
								    "PNDMScheduler",
								    "HeunDiscreteScheduler",
								    "EulerAncestralDiscreteScheduler",
								    "KDPM2DiscreteScheduler",
								    "KDPM2AncestralDiscreteScheduler",
								    "DPMSolverSDEScheduler",
								]
								__lowerCAmelCase         =							self.get_dummy_components()
								__lowerCAmelCase         =							self.pipeline_class(**snake_case__ )

								# make sure that PNDM does not need warm-up
								pipe.scheduler.register_to_config(skip_prk_steps=snake_case__ )

								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )
								__lowerCAmelCase         =							self.get_dummy_inputs(snake_case__ )
								__lowerCAmelCase         =							2

								__lowerCAmelCase         =							[]
								for scheduler_enum in KarrasDiffusionSchedulers:
									if scheduler_enum.name in skip_schedulers:
										# no sigma schedulers are not supported
										# no schedulers
										continue

									__lowerCAmelCase         =							getattr(snake_case__      ,				scheduler_enum.name )
									__lowerCAmelCase         =							scheduler_cls.from_config(pipe.scheduler.config )
									__lowerCAmelCase         =							pipe(**snake_case__ )[0]
									outputs.append(snake_case__ )

								assert check_same_shape(snake_case__ )

@require_torch_gpu
@slow
class       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							torch.manual_seed(33 )

								__lowerCAmelCase         =							StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"      ,				torch_dtype=torch.floataa )
								pipe.to("cuda" )

								__lowerCAmelCase         =							StableDiffusionLatentUpscalePipeline.from_pretrained(
								    "stabilityai/sd-x2-latent-upscaler"      ,				torch_dtype=torch.floataa )
								upscaler.to("cuda" )

								__lowerCAmelCase         =							"a photo of an astronaut high resolution, unreal engine, ultra realistic"

								__lowerCAmelCase         =							pipe(snake_case__      ,				generator=snake_case__      ,				output_type="latent" ).images

								__lowerCAmelCase         =							upscaler(
								    prompt=snake_case__      ,				image=snake_case__      ,				num_inference_steps=20      ,				guidance_scale=0      ,				generator=snake_case__      ,				output_type="np"      ,				).images[0]

								__lowerCAmelCase         =							load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy" )
								assert np.abs((expected_image - image).mean() ) < 5E-2







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							torch.manual_seed(33 )

								__lowerCAmelCase         =							StableDiffusionLatentUpscalePipeline.from_pretrained(
								    "stabilityai/sd-x2-latent-upscaler"      ,				torch_dtype=torch.floataa )
								upscaler.to("cuda" )

								__lowerCAmelCase         =							"the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png" )

								__lowerCAmelCase         =							upscaler(
								    prompt=snake_case__      ,				image=snake_case__      ,				num_inference_steps=20      ,				guidance_scale=0      ,				generator=snake_case__      ,				output_type="np"      ,				).images[0]

								__lowerCAmelCase         =							load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy" )
								assert np.abs((expected_image - image).max() ) < 5E-2








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       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  int      ,				snake_case__  :  int ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							None
								__lowerCAmelCase         =							20

								__lowerCAmelCase         =							self._get_uniform_logits(batch_size=2      ,				length=snake_case__ )

								# 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(snake_case__      ,				axis=-1 )

								__lowerCAmelCase         =							FlaxTemperatureLogitsWarper(temperature=0.5 )
								__lowerCAmelCase         =							FlaxTemperatureLogitsWarper(temperature=1.3 )

								__lowerCAmelCase         =							jax.nn.softmax(temp_dist_warper_sharper(snake_case__      ,				scores.copy()      ,				cur_len=snake_case__ )      ,				axis=-1 )
								__lowerCAmelCase         =							jax.nn.softmax(temp_dist_warper_smoother(snake_case__      ,				scores.copy()      ,				cur_len=snake_case__ )      ,				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  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							None
								__lowerCAmelCase         =							10
								__lowerCAmelCase         =							2

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

								__lowerCAmelCase         =							FlaxTopKLogitsWarper(3 )

								__lowerCAmelCase         =							top_k_warp(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )

								# 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(snake_case__ )[None, :]      ,				(batch_size, length) ).copy()
								__lowerCAmelCase         =							top_k_warp_safety_check(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )

								# 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  :  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], [0.1_5, 0.3, 0.3, 0.2_5]] ) )

								__lowerCAmelCase         =							FlaxTopPLogitsWarper(0.8 )
								__lowerCAmelCase         =							np.exp(top_p_warp(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ ) )

								# 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, 0.2_5]] )
								self.assertTrue(np.allclose(snake_case__      ,				snake_case__      ,				atol=1E-3 ) )

								# check edge cases with negative and extreme logits
								__lowerCAmelCase         =							np.broadcast_to(np.arange(snake_case__ )[None, :]      ,				(batch_size, vocab_size) ).copy() - (
								    vocab_size // 2
								)

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

								# 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(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )

								# 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  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							20
								__lowerCAmelCase         =							4
								__lowerCAmelCase         =							0

								__lowerCAmelCase         =							FlaxMinLengthLogitsProcessor(min_length=10      ,				eos_token_id=snake_case__ )

								# 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(snake_case__      ,				snake_case__ )
								__lowerCAmelCase         =							min_dist_processor(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
								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(snake_case__      ,				snake_case__ )
								__lowerCAmelCase         =							15
								__lowerCAmelCase         =							min_dist_processor(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
								self.assertFalse(jnp.isinf(snake_case__ ).any() )







							def       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							20
								__lowerCAmelCase         =							4
								__lowerCAmelCase         =							0

								__lowerCAmelCase         =							FlaxForcedBOSTokenLogitsProcessor(bos_token_id=snake_case__ )

								# 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(snake_case__      ,				snake_case__ )
								__lowerCAmelCase         =							logits_processor(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
								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(snake_case__      ,				snake_case__ )
								__lowerCAmelCase         =							logits_processor(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
								self.assertFalse(jnp.isinf(snake_case__ ).any() )







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							20
								__lowerCAmelCase         =							4
								__lowerCAmelCase         =							0
								__lowerCAmelCase         =							5

								__lowerCAmelCase         =							FlaxForcedEOSTokenLogitsProcessor(max_length=snake_case__      ,				eos_token_id=snake_case__ )

								# 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(snake_case__      ,				snake_case__ )
								__lowerCAmelCase         =							logits_processor(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
								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(snake_case__      ,				snake_case__ )
								__lowerCAmelCase         =							logits_processor(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
								self.assertFalse(jnp.isinf(snake_case__ ).any() )







							def       UpperCAmelCase__					(						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)      ,				snake_case__ )
								__lowerCAmelCase         =							input_ids.copy()

								__lowerCAmelCase         =							self._get_uniform_logits(snake_case__      ,				snake_case__ )
								__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=snake_case__ )
								__lowerCAmelCase         =							FlaxForcedBOSTokenLogitsProcessor(bos_token_id=snake_case__ )
								__lowerCAmelCase         =							FlaxForcedEOSTokenLogitsProcessor(max_length=snake_case__      ,				eos_token_id=snake_case__ )

								__lowerCAmelCase         =							10

								# no processor list
								__lowerCAmelCase         =							temp_dist_warp(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
								__lowerCAmelCase         =							top_k_warp(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
								__lowerCAmelCase         =							top_p_warp(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
								__lowerCAmelCase         =							min_dist_proc(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
								__lowerCAmelCase         =							bos_dist_proc(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
								__lowerCAmelCase         =							eos_dist_proc(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )

								# 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(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )

								# scores should be equal
								self.assertTrue(jnp.allclose(snake_case__      ,				snake_case__      ,				atol=1E-3 ) )

								# input_ids should never be changed
								self.assertListEqual(input_ids.tolist()      ,				input_ids_comp.tolist() )







							def       UpperCAmelCase__					(						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)      ,				snake_case__ )
								__lowerCAmelCase         =							input_ids.copy()

								__lowerCAmelCase         =							self._get_uniform_logits(snake_case__      ,				snake_case__ )
								__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=snake_case__ )
								__lowerCAmelCase         =							FlaxForcedBOSTokenLogitsProcessor(bos_token_id=snake_case__ )
								__lowerCAmelCase         =							FlaxForcedEOSTokenLogitsProcessor(max_length=snake_case__      ,				eos_token_id=snake_case__ )

								__lowerCAmelCase         =							10

								# no processor list
								def run_no_processor_list(snake_case__  :  int      ,				snake_case__  :  Any      ,				snake_case__  :  int ):
									__lowerCAmelCase         =							temp_dist_warp(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
									__lowerCAmelCase         =							top_k_warp(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
									__lowerCAmelCase         =							top_p_warp(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
									__lowerCAmelCase         =							min_dist_proc(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
									__lowerCAmelCase         =							bos_dist_proc(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
									__lowerCAmelCase         =							eos_dist_proc(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
									return scores

								# with processor list
								def run_processor_list(snake_case__  :  Optional[Any]      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  str ):
									__lowerCAmelCase         =							FlaxLogitsProcessorList(
									    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
									__lowerCAmelCase         =							processor(snake_case__      ,				snake_case__      ,				cur_len=snake_case__ )
									return scores

								__lowerCAmelCase         =							jax.jit(snake_case__ )
								__lowerCAmelCase         =							jax.jit(snake_case__ )

								__lowerCAmelCase         =							jitted_run_no_processor_list(snake_case__      ,				snake_case__      ,				snake_case__ )
								__lowerCAmelCase         =							jitted_run_processor_list(snake_case__      ,				snake_case__      ,				snake_case__ )

								# scores should be equal
								self.assertTrue(jnp.allclose(snake_case__      ,				snake_case__      ,				atol=1E-3 ) )

								# input_ids should never be changed
								self.assertListEqual(input_ids.tolist()      ,				input_ids_comp.tolist() )






