





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class SCREAMING_SNAKE_CASE_ (					lowercase_							,       lowercase_							,       lowercase_							,       unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    StableDiffusionInpaintPipeline
					A__				 =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					A__				 =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__				 =    frozenset(
					    []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					A__				 =    frozenset([]   )





					def 					__magic_name__ ( self	):
										torch.manual_seed(0	)
										lowerCamelCase__       =       UNetaDConditionModel(
										    block_out_channels=(32, 64)	,  layers_per_block=2	,  sample_size=32	,  in_channels=9	,  out_channels=4	,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,  cross_attention_dim=32	,  attention_head_dim=(2, 4)	,  use_linear_projection=_lowerCAmelCase	,  )
										lowerCamelCase__       =       PNDMScheduler(skip_prk_steps=_lowerCAmelCase	)
										torch.manual_seed(0	)
										lowerCamelCase__       =       AutoencoderKL(
										    block_out_channels=[32, 64]	,  in_channels=3	,  out_channels=3	,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,  latent_channels=4	,  sample_size=128	,  )
										torch.manual_seed(0	)
										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=1000	,  hidden_act="gelu"	,  projection_dim=512	,  )
										lowerCamelCase__       =       CLIPTextModel(_lowerCAmelCase	)
										lowerCamelCase__       =       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

										lowerCamelCase__       =       {
										    "unet": unet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "safety_checker": None,
										    "feature_extractor": None,
										}
										return components





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase=0	):
										# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
										lowerCamelCase__       =       floats_tensor((1, 3, 32, 32)	,  rng=random.Random(_lowerCAmelCase	)	).to(_lowerCAmelCase	)
										lowerCamelCase__       =       image.cpu().permute(0	,  2	,  3	,  1	)[0]
										lowerCamelCase__       =       Image.fromarray(np.uinta(_lowerCAmelCase	)	).convert("RGB"	).resize((64, 64)	)
										lowerCamelCase__       =       Image.fromarray(np.uinta(image + 4	)	).convert("RGB"	).resize((64, 64)	)
										if str(_lowerCAmelCase	).startswith("mps"	):
															lowerCamelCase__       =       torch.manual_seed(_lowerCAmelCase	)
										else:
															lowerCamelCase__       =       torch.Generator(device=_lowerCAmelCase	).manual_seed(_lowerCAmelCase	)
										lowerCamelCase__       =       {
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": init_image,
										    "mask_image": mask_image,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "guidance_scale": 6.0,
										    "output_type": "numpy",
										}
										return inputs





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       "cpu"  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__       =       self.get_dummy_components()
										lowerCamelCase__       =       StableDiffusionInpaintPipeline(**_lowerCAmelCase	)
										lowerCamelCase__       =       sd_pipe.to(_lowerCAmelCase	)
										sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase	)

										lowerCamelCase__       =       self.get_dummy_inputs(_lowerCAmelCase	)
										lowerCamelCase__       =       sd_pipe(**_lowerCAmelCase	).images
										lowerCamelCase__       =       image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										lowerCamelCase__       =       np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2






					def 					__magic_name__ ( self	):
										super().test_inference_batch_single_identical(expected_max_diff=3E-3	)

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





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-inpaint/init_image.png"	)
										lowerCamelCase__       =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"	)
										lowerCamelCase__       =       load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
										    "/yellow_cat_sitting_on_a_park_bench.npy"	)

										lowerCamelCase__       =       "stabilityai/stable-diffusion-2-inpainting"
										lowerCamelCase__       =       StableDiffusionInpaintPipeline.from_pretrained(_lowerCAmelCase	,  safety_checker=_lowerCAmelCase	)
										pipe.to(_lowerCAmelCase	)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase	)
										pipe.enable_attention_slicing()

										lowerCamelCase__       =       "Face of a yellow cat, high resolution, sitting on a park bench"

										lowerCamelCase__       =       torch.manual_seed(0	)
										lowerCamelCase__       =       pipe(
										    prompt=_lowerCAmelCase	,  image=_lowerCAmelCase	,  mask_image=_lowerCAmelCase	,  generator=_lowerCAmelCase	,  output_type="np"	,  )
										lowerCamelCase__       =       output.images[0]

										assert image.shape == (512, 512, 3)
										assert np.abs(expected_image - image	).max() < 9E-3





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-inpaint/init_image.png"	)
										lowerCamelCase__       =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"	)
										lowerCamelCase__       =       load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
										    "/yellow_cat_sitting_on_a_park_bench_fp16.npy"	)

										lowerCamelCase__       =       "stabilityai/stable-diffusion-2-inpainting"
										lowerCamelCase__       =       StableDiffusionInpaintPipeline.from_pretrained(
										    _lowerCAmelCase	,  torch_dtype=torch.floataa	,  safety_checker=_lowerCAmelCase	,  )
										pipe.to(_lowerCAmelCase	)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase	)
										pipe.enable_attention_slicing()

										lowerCamelCase__       =       "Face of a yellow cat, high resolution, sitting on a park bench"

										lowerCamelCase__       =       torch.manual_seed(0	)
										lowerCamelCase__       =       pipe(
										    prompt=_lowerCAmelCase	,  image=_lowerCAmelCase	,  mask_image=_lowerCAmelCase	,  generator=_lowerCAmelCase	,  output_type="np"	,  )
										lowerCamelCase__       =       output.images[0]

										assert image.shape == (512, 512, 3)
										assert np.abs(expected_image - image	).max() < 5E-1






					def 					__magic_name__ ( self	):
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										lowerCamelCase__       =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-inpaint/init_image.png"	)
										lowerCamelCase__       =       load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"	)

										lowerCamelCase__       =       "stabilityai/stable-diffusion-2-inpainting"
										lowerCamelCase__       =       PNDMScheduler.from_pretrained(_lowerCAmelCase	,  subfolder="scheduler"	)
										lowerCamelCase__       =       StableDiffusionInpaintPipeline.from_pretrained(
										    _lowerCAmelCase	,  safety_checker=_lowerCAmelCase	,  scheduler=_lowerCAmelCase	,  torch_dtype=torch.floataa	,  )
										pipe.to(_lowerCAmelCase	)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase	)
										pipe.enable_attention_slicing(1	)
										pipe.enable_sequential_cpu_offload()

										lowerCamelCase__       =       "Face of a yellow cat, high resolution, sitting on a park bench"

										lowerCamelCase__       =       torch.manual_seed(0	)
										lowerCamelCase__       =       pipe(
										    prompt=_lowerCAmelCase	,  image=_lowerCAmelCase	,  mask_image=_lowerCAmelCase	,  generator=_lowerCAmelCase	,  num_inference_steps=2	,  output_type="np"	,  )

										lowerCamelCase__       =       torch.cuda.max_memory_allocated()
										# make sure that less than 2.65 GB is allocated
										assert mem_bytes < 2.65 * 10**9








import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


A_												=     get_logger(__name__)


A_												=     r"\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					@add_start_docstrings(_lowerCAmelCase	)
					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										raise NotImplementedError(
										    F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."	)

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					@add_start_docstrings(_lowerCAmelCase	)
					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										raise NotImplementedError(
										    F"{self.__class__} is an abstract class. Only classes inheriting this class can be called."	)

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					@add_start_docstrings(_lowerCAmelCase	)
					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  **_lowerCAmelCase	):
										for processor in self:
															lowerCamelCase__       =       inspect.signature(processor.__call__	).parameters
															if len(_lowerCAmelCase	) > 3:
																				if not all(arg in kwargs for arg in list(function_args.keys()	)[2:]	):
																									raise ValueError(
																									    F"Make sure that all the required parameters: {list(function_args.keys()	)} for "
																									    F"{processor.__class__} are passed to the logits processor."	)
																				lowerCamelCase__       =       processor(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  **_lowerCAmelCase	)
															else:
																				lowerCamelCase__       =       processor(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	)
										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	):
										if not isinstance(_lowerCAmelCase	,  _lowerCAmelCase	) or not (temperature > 0):
															raise ValueError(F"`temperature` has to be a strictly positive float, but is {temperature}"	)

										lowerCamelCase__       =       temperature






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       scores / self.temperature
										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase = -float("Inf"	)	,  _lowerCAmelCase = 1	):
										if not isinstance(_lowerCAmelCase	,  _lowerCAmelCase	) or (top_p < 0 or top_p > 1.0):
															raise ValueError(F"`top_p` has to be a float > 0 and < 1, but is {top_p}"	)
										if not isinstance(_lowerCAmelCase	,  _lowerCAmelCase	) or (min_tokens_to_keep < 1):
															raise ValueError(F"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"	)

										lowerCamelCase__       =       top_p
										lowerCamelCase__       =       filter_value
										lowerCamelCase__       =       min_tokens_to_keep






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__    ,		lowerCamelCase__       =       lax.top_k(_lowerCAmelCase	,  scores.shape[-1]	)

										lowerCamelCase__       =       jnp.full_like(_lowerCAmelCase	,  self.filter_value	)
										lowerCamelCase__       =       jax.nn.softmax(_lowerCAmelCase	,  axis=-1	).cumsum(axis=-1	)
										lowerCamelCase__       =       cumulative_probs < self.top_p

										# include the token that is higher than top_p as well
										lowerCamelCase__       =       jnp.roll(_lowerCAmelCase	,  1	)
										score_mask |= score_mask.at[:, 0].set(_lowerCAmelCase	)

										# min tokens to keep
										lowerCamelCase__       =       score_mask.at[:, : self.min_tokens_to_keep].set(_lowerCAmelCase	)

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       jax.lax.sort_key_val(_lowerCAmelCase	,  _lowerCAmelCase	)[-1]

										return next_scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase = -float("Inf"	)	,  _lowerCAmelCase = 1	):
										if not isinstance(_lowerCAmelCase	,  _lowerCAmelCase	) or top_k <= 0:
															raise ValueError(F"`top_k` has to be a strictly positive integer, but is {top_k}"	)

										lowerCamelCase__       =       max(_lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       filter_value






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__    ,		lowerCamelCase__       =       scores.shape
										lowerCamelCase__       =       jnp.full(batch_size * vocab_size	,  self.filter_value	)

										lowerCamelCase__       =       min(self.top_k	,  scores.shape[-1]	)  # Safety check
										lowerCamelCase__    ,		lowerCamelCase__       =       lax.top_k(_lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       jnp.broadcast_to((jnp.arange(_lowerCAmelCase	) * vocab_size)[:, None]	,  (batch_size, topk)	).flatten()
										lowerCamelCase__       =       topk_scores.flatten()
										lowerCamelCase__       =       topk_indices.flatten() + shift

										lowerCamelCase__       =       next_scores_flat.at[topk_indices_flat].set(_lowerCAmelCase	)
										lowerCamelCase__       =       next_scores_flat.reshape(_lowerCAmelCase	,  _lowerCAmelCase	)
										return next_scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       bos_token_id






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       jnp.full(scores.shape	,  -float("inf"	)	)

										lowerCamelCase__       =       1 - jnp.bool_(cur_len - 1	)

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  new_scores.at[:, self.bos_token_id].set(0	)	,  _lowerCAmelCase	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       max_length
										lowerCamelCase__       =       eos_token_id






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       jnp.full(scores.shape	,  -float("inf"	)	)

										lowerCamelCase__       =       1 - jnp.bool_(cur_len - self.max_length + 1	)

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  new_scores.at[:, self.eos_token_id].set(0	)	,  _lowerCAmelCase	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										if not isinstance(_lowerCAmelCase	,  _lowerCAmelCase	) or min_length < 0:
															raise ValueError(F"`min_length` has to be a positive integer, but is {min_length}"	)

										if not isinstance(_lowerCAmelCase	,  _lowerCAmelCase	) or eos_token_id < 0:
															raise ValueError(F"`eos_token_id` has to be a positive integer, but is {eos_token_id}"	)

										lowerCamelCase__       =       min_length
										lowerCamelCase__       =       eos_token_id






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										# create boolean flag to decide if min length penalty should be applied
										lowerCamelCase__       =       1 - jnp.clip(cur_len - self.min_length	,  0	,  1	)

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  scores.at[:, self.eos_token_id].set(-float("inf"	)	)	,  _lowerCAmelCase	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       list(_lowerCAmelCase	)
										lowerCamelCase__       =       begin_index






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       1 - jnp.bool_(cur_len - self.begin_index	)

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  scores.at[:, self.begin_suppress_tokens].set(-float("inf"	)	)	,  _lowerCAmelCase	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       list(_lowerCAmelCase	)






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       scores.at[..., self.suppress_tokens].set(-float("inf"	)	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       dict(_lowerCAmelCase	)
										# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
										# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
										# Indexes without forced tokens will have a negative value.
										lowerCamelCase__       =       jnp.ones((max(force_token_map.keys()	) + 1)	,  dtype=jnp.intaa	) * -1
										for index, token in force_token_map.items():
															if token is not None:
																				lowerCamelCase__       =       force_token_array.at[index].set(_lowerCAmelCase	)
										lowerCamelCase__       =       jnp.intaa(_lowerCAmelCase	)






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										def _force_token(_lowerCAmelCase	):
															lowerCamelCase__       =       scores.shape[0]
															lowerCamelCase__       =       self.force_token_array[generation_idx]

															lowerCamelCase__       =       jnp.ones_like(_lowerCAmelCase	,  dtype=scores.dtype	) * -float("inf"	)
															lowerCamelCase__       =       jnp.zeros((batch_size, 1)	,  dtype=scores.dtype	)
															lowerCamelCase__       =       lax.dynamic_update_slice(_lowerCAmelCase	,  _lowerCAmelCase	,  (0, current_token)	)
															return new_scores

										lowerCamelCase__       =       lax.cond(
										    cur_len >= self.force_token_array.shape[0]	,  lambda: scores	,  lambda: lax.cond(
										        self.force_token_array[cur_len] >= 0	,  lambda: _force_token(_lowerCAmelCase	)	,  lambda: scores	,  )	,  )
										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       generate_config.eos_token_id
										lowerCamelCase__       =       generate_config.no_timestamps_token_id
										lowerCamelCase__       =       generate_config.no_timestamps_token_id + 1

										lowerCamelCase__       =       decoder_input_length + 1

										if generate_config.is_multilingual:
															# room for language token and task token
															self.begin_index += 2
										if hasattr(_lowerCAmelCase	,  "max_initial_timestamp_index"	):
															lowerCamelCase__       =       generate_config.max_initial_timestamp_index
										else:
															lowerCamelCase__       =       model_config.vocab_size
										if self.max_initial_timestamp_index is None:
															lowerCamelCase__       =       model_config.vocab_size






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										# suppress <|notimestamps|> which is handled by without_timestamps
										lowerCamelCase__       =       scores.at[:, self.no_timestamps_token_id].set(-float("inf"	)	)

										def handle_pairs(_lowerCAmelCase	,  _lowerCAmelCase	):
															lowerCamelCase__       =       jnp.where((cur_len - self.begin_index) >= 1	,  _lowerCAmelCase	,  _lowerCAmelCase	)
															lowerCamelCase__       =       jnp.where(
															    input_ids_k[cur_len - 1] >= self.timestamp_begin	,  True and last_was_timestamp	,  _lowerCAmelCase	,  )

															lowerCamelCase__       =       jnp.where((cur_len - self.begin_index) < 2	,  _lowerCAmelCase	,  _lowerCAmelCase	)
															lowerCamelCase__       =       jnp.where(
															    input_ids_k[cur_len - 2] >= self.timestamp_begin	,  _lowerCAmelCase	,  _lowerCAmelCase	,  )

															return jnp.where(
															    _lowerCAmelCase	,  jnp.where(
															        penultimate_was_timestamp > 0	,  scores_k.at[self.timestamp_begin :].set(-float("inf"	)	)	,  scores_k.at[: self.eos_token_id].set(-float("inf"	)	)	,  )	,  _lowerCAmelCase	,  )

										lowerCamelCase__       =       jax.vmap(_lowerCAmelCase	)(_lowerCAmelCase	,  _lowerCAmelCase	)

										lowerCamelCase__       =       jnp.where(cur_len == self.begin_index	,  _lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       jnp.where(
										    self.max_initial_timestamp_index is not None	,  True and apply_max_initial_timestamp	,  _lowerCAmelCase	,  )

										lowerCamelCase__       =       self.timestamp_begin + self.max_initial_timestamp_index

										lowerCamelCase__       =       jnp.where(
										    _lowerCAmelCase	,  scores.at[:, last_allowed + 1 :].set(-float("inf"	)	)	,  _lowerCAmelCase	,  )

										# if sum of probability over timestamps is above any other token, sample timestamp
										lowerCamelCase__       =       jax.nn.log_softmax(_lowerCAmelCase	,  axis=-1	)

										def handle_cumulative_probs(_lowerCAmelCase	,  _lowerCAmelCase	):
															lowerCamelCase__       =       jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]	,  axis=-1	)
															lowerCamelCase__       =       jnp.max(logprobs_k[: self.timestamp_begin]	)
															return jnp.where(
															    timestamp_logprob > max_text_token_logprob	,  scores_k.at[: self.timestamp_begin].set(-float("inf"	)	)	,  _lowerCAmelCase	,  )

										lowerCamelCase__       =       jax.vmap(_lowerCAmelCase	)(_lowerCAmelCase	,  _lowerCAmelCase	)

										return scores

