






"""simple docstring"""







import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
			from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor


class    __UpperCAmelCase:




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__=13	,  snake_case__=32	,  snake_case__=3	,  snake_case__=4	,  snake_case__=[10, 20, 30, 40]	,  snake_case__=[2, 2, 3, 2]	,  snake_case__=True	,  snake_case__=True	,  snake_case__=37	,  snake_case__="gelu"	,  snake_case__=10	,  snake_case__=0.02	,  snake_case__=["stage2", "stage3", "stage4"]	,  snake_case__=[2, 3, 4]	,  snake_case__=None	,  ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	parent
												lowercase__    :				Union[str, Any]=	batch_size
												lowercase__    :				int=	image_size
												lowercase__    :				str=	num_channels
												lowercase__    :				List[Any]=	num_stages
												lowercase__    :				Union[str, Any]=	hidden_sizes
												lowercase__    :				List[str]=	depths
												lowercase__    :				Optional[int]=	is_training
												lowercase__    :				List[str]=	use_labels
												lowercase__    :				str=	intermediate_size
												lowercase__    :				str=	hidden_act
												lowercase__    :				Union[str, Any]=	num_labels
												lowercase__    :				List[Any]=	initializer_range
												lowercase__    :				Optional[int]=	out_features
												lowercase__    :				Dict=	out_indices
												lowercase__    :				Optional[int]=	scope




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

												lowercase__    :				str=	None
												if self.use_labels:
																		lowercase__    :				Optional[Any]=	ids_tensor([self.batch_size]	,  self.num_labels      )

												lowercase__    :				Optional[int]=	self.get_config()
												return config, pixel_values, labels




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return ConvNextConfig(
												    num_channels=self.num_channels	,  hidden_sizes=self.hidden_sizes	,  depths=self.depths	,  num_stages=self.num_stages	,  hidden_act=self.hidden_act	,  is_decoder=__UpperCAmelCase	,  initializer_range=self.initializer_range	,  out_features=self.out_features	,  out_indices=self.out_indices	,  num_labels=self.num_labels	,  )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	ConvNextModel(config=__UpperCAmelCase      )
												model.to(__UpperCAmelCase      )
												model.eval()
												lowercase__    :				Tuple=	model(__UpperCAmelCase      )
												# expected last hidden states: B, C, H // 32, W // 32
												self.parent.assertEqual(
												    result.last_hidden_state.shape	,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,  )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	ConvNextForImageClassification(__UpperCAmelCase      )
												model.to(__UpperCAmelCase      )
												model.eval()
												lowercase__    :				int=	model(__UpperCAmelCase	,  labels=__UpperCAmelCase      )
												self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels)      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	ConvNextBackbone(config=__UpperCAmelCase      )
												model.to(__UpperCAmelCase      )
												model.eval()
												lowercase__    :				Union[str, Any]=	model(__UpperCAmelCase      )

												# verify hidden states
												self.parent.assertEqual(len(result.feature_maps      )	,  len(config.out_features      )      )
												self.parent.assertListEqual(list(result.feature_maps[0].shape      )	,  [self.batch_size, self.hidden_sizes[1], 4, 4]      )

												# verify channels
												self.parent.assertEqual(len(model.channels      )	,  len(config.out_features      )      )
												self.parent.assertListEqual(model.channels	,  config.hidden_sizes[1:]      )

												# verify backbone works with out_features=None
												lowercase__    :				Dict=	None
												lowercase__    :				Dict=	ConvNextBackbone(config=__UpperCAmelCase      )
												model.to(__UpperCAmelCase      )
												model.eval()
												lowercase__    :				List[Any]=	model(__UpperCAmelCase      )

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps      )	,  1      )
												self.parent.assertListEqual(list(result.feature_maps[0].shape      )	,  [self.batch_size, self.hidden_sizes[-1], 1, 1]      )

												# verify channels
												self.parent.assertEqual(len(model.channels      )	,  1      )
												self.parent.assertListEqual(model.channels	,  [config.hidden_sizes[-1]]      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				str=	self.prepare_config_and_inputs()
												lowercase__    :				Any=	config_and_inputs
												lowercase__    :				Dict=	{"""pixel_values""": pixel_values}
												return config, inputs_dict


@require_torch
class    __UpperCAmelCase(       __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase    	= (
						    (
						        ConvNextModel,
						        ConvNextForImageClassification,
						        ConvNextBackbone,
						    )
						    if is_torch_available()
						    else ()
						)
						__lowerCamelCase    	= (
						    {"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
						    if is_torch_available()
						    else {}
						)

						__lowerCamelCase    	= True
						__lowerCamelCase    	= False
						__lowerCamelCase    	= False
						__lowerCamelCase    	= False
						__lowerCamelCase    	= False




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				str=	ConvNextModelTester(self      )
												lowercase__    :				Dict=	ConfigTester(self	,  config_class=__UpperCAmelCase	,  has_text_modality=__UpperCAmelCase	,  hidden_size=37      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												self.create_and_test_config_common_properties()
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return




						@unittest.skip(reason="ConvNext does not use inputs_embeds"      )
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												pass




						@unittest.skip(reason="ConvNext does not support input and output embeddings"      )
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												pass




						@unittest.skip(reason="ConvNext does not use feedforward chunking"      )
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		lowercase__    :				Optional[int]=	model_class(__UpperCAmelCase      )
																		lowercase__    :				int=	inspect.signature(model.forward      )
																		# signature.parameters is an OrderedDict => so arg_names order is deterministic
																		lowercase__    :				Optional[int]=	[*signature.parameters.keys()]

																		lowercase__    :				int=	["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]	,  __UpperCAmelCase      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				int=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCAmelCase      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*__UpperCAmelCase      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												def check_hidden_states_output(snake_case__	,  snake_case__	,  snake_case__      ):
																		lowercase__    :				str=	model_class(__UpperCAmelCase      )
																		model.to(__UpperCAmelCase      )
																		model.eval()

																		with torch.no_grad():
																								lowercase__    :				Any=	model(**self._prepare_for_class(__UpperCAmelCase	,  __UpperCAmelCase      )      )

																		lowercase__    :				List[str]=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		lowercase__    :				List[str]=	self.model_tester.num_stages
																		self.assertEqual(len(__UpperCAmelCase      )	,  expected_num_stages + 1      )

																		# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
																		self.assertListEqual(
																		    list(hidden_states[0].shape[-2:]      )	,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]	,  )

												lowercase__    :				Any=	self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		lowercase__    :				List[str]=	True
																		check_hidden_states_output(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase      )

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		lowercase__    :				List[str]=	True

																		check_hidden_states_output(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Any=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase      )




						@slow
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		lowercase__    :				List[Any]=	ConvNextModel.from_pretrained(__UpperCAmelCase      )
																		self.assertIsNotNone(__UpperCAmelCase      )





def 						lowercase__()    ->Optional[Any]:




						"""simple docstring"""



						lowercase__    :				Any=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
						return image


@require_torch
@require_vision
class    __UpperCAmelCase(       unittest.TestCase							):




						"""simple docstring"""




						@cached_property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224"      ) if is_vision_available() else None




						@slow
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Dict=	ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"      ).to(__UpperCAmelCase      )

												lowercase__    :				Union[str, Any]=	self.default_image_processor
												lowercase__    :				Tuple=	prepare_img()
												lowercase__    :				Optional[int]=	image_processor(images=__UpperCAmelCase	,  return_tensors="pt"      ).to(__UpperCAmelCase      )

												# forward pass
												with torch.no_grad():
																		lowercase__    :				Optional[Any]=	model(**__UpperCAmelCase      )

												# verify the logits
												lowercase__    :				Optional[Any]=	torch.Size((1, 1000)      )
												self.assertEqual(outputs.logits.shape	,  __UpperCAmelCase      )

												lowercase__    :				Dict=	torch.tensor([-0.02_60, -0.47_39, 0.19_11]      ).to(__UpperCAmelCase      )

												self.assertTrue(torch.allclose(outputs.logits[0, :3]	,  __UpperCAmelCase	,  atol=1e-4      )      )


@require_torch
class    __UpperCAmelCase(       unittest.TestCase  ,  __SCREAMING_SNAKE_CASE							):




						"""simple docstring"""




						__lowerCamelCase    	= (ConvNextBackbone,) if is_torch_available() else ()
						__lowerCamelCase    	= ConvNextConfig

						__lowerCamelCase    	= False




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	ConvNextModelTester(self      )














"""simple docstring"""







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			:    Any				 =		get_logger(__name__)


a			:    Any				 =		r"""
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

"""


class    __UpperCAmelCase:




						"""simple docstring"""




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


class    __UpperCAmelCase:




						"""simple docstring"""




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


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						@add_start_docstrings(snake_case__      )
						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  **snake_case__      ):
												'''simple docstring'''
												for processor in self:
																		lowercase__    :				Optional[Any]=	inspect.signature(processor.__call__      ).parameters
																		if len(snake_case__      ) > 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.'''      )
																								lowercase__    :				Union[str, Any]=	processor(snake_case__	,  snake_case__	,  snake_case__	,  **snake_case__      )
																		else:
																								lowercase__    :				Dict=	processor(snake_case__	,  snake_case__	,  snake_case__      )
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




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

												lowercase__    :				Any=	temperature




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				int=	scores / self.temperature
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__ = -float("Inf"      )	,  snake_case__ = 1      ):
												'''simple docstring'''
												if not isinstance(snake_case__	,  snake_case__      ) 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(snake_case__	,  snake_case__      ) 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}'''      )

												lowercase__    :				int=	top_p
												lowercase__    :				Optional[int]=	filter_value
												lowercase__    :				Tuple=	min_tokens_to_keep




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				Dict=	lax.top_k(snake_case__	,  scores.shape[-1]      )

												lowercase__    :				Optional[int]=	jnp.full_like(snake_case__	,  self.filter_value      )
												lowercase__    :				Union[str, Any]=	jax.nn.softmax(snake_case__	,  axis=-1      ).cumsum(axis=-1      )
												lowercase__    :				str=	cumulative_probs < self.top_p

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

												# min tokens to keep
												lowercase__    :				Optional[int]=	score_mask.at[:, : self.min_tokens_to_keep].set(snake_case__      )

												lowercase__    :				str=	jnp.where(snake_case__	,  snake_case__	,  snake_case__      )
												lowercase__    :				str=	jax.lax.sort_key_val(snake_case__	,  snake_case__      )[-1]

												return next_scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




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

												lowercase__    :				List[Any]=	max(snake_case__	,  snake_case__      )
												lowercase__    :				Dict=	filter_value




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				Optional[Any]=	scores.shape
												lowercase__    :				int=	jnp.full(batch_size * vocab_size	,  self.filter_value      )

												lowercase__    :				Dict=	min(self.top_k	,  scores.shape[-1]      )  # Safety check
												lowercase__,     lowercase__    :				List[Any]=	lax.top_k(snake_case__	,  snake_case__      )
												lowercase__    :				Optional[int]=	jnp.broadcast_to((jnp.arange(snake_case__      ) * vocab_size)[:, None]	,  (batch_size, topk)      ).flatten()
												lowercase__    :				str=	topk_scores.flatten()
												lowercase__    :				Any=	topk_indices.flatten() + shift

												lowercase__    :				Optional[Any]=	next_scores_flat.at[topk_indices_flat].set(snake_case__      )
												lowercase__    :				str=	next_scores_flat.reshape(snake_case__	,  snake_case__      )
												return next_scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	bos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	jnp.full(scores.shape	,  -float("inf"      )      )

												lowercase__    :				int=	1 - jnp.bool_(cur_len - 1      )

												lowercase__    :				int=	jnp.where(snake_case__	,  new_scores.at[:, self.bos_token_id].set(0      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	max_length
												lowercase__    :				str=	eos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	jnp.full(scores.shape	,  -float("inf"      )      )

												lowercase__    :				Any=	1 - jnp.bool_(cur_len - self.max_length + 1      )

												lowercase__    :				Optional[int]=	jnp.where(snake_case__	,  new_scores.at[:, self.eos_token_id].set(0      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




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

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

												lowercase__    :				List[str]=	min_length
												lowercase__    :				Dict=	eos_token_id




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												# create boolean flag to decide if min length penalty should be applied
												lowercase__    :				Tuple=	1 - jnp.clip(cur_len - self.min_length	,  0	,  1      )

												lowercase__    :				Dict=	jnp.where(snake_case__	,  scores.at[:, self.eos_token_id].set(-float("inf"      )      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	list(snake_case__      )
												lowercase__    :				List[Any]=	begin_index




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				str=	1 - jnp.bool_(cur_len - self.begin_index      )

												lowercase__    :				str=	jnp.where(snake_case__	,  scores.at[:, self.begin_suppress_tokens].set(-float("inf"      )      )	,  snake_case__      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	list(snake_case__      )




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	scores.at[..., self.suppress_tokens].set(-float("inf"      )      )

												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				int=	dict(snake_case__      )
												# 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.
												lowercase__    :				List[Any]=	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:
																								lowercase__    :				List[Any]=	force_token_array.at[index].set(snake_case__      )
												lowercase__    :				int=	jnp.intaa(snake_case__      )




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												def _force_token(snake_case__      ):
																		lowercase__    :				Dict=	scores.shape[0]
																		lowercase__    :				Any=	self.force_token_array[generation_idx]

																		lowercase__    :				List[Any]=	jnp.ones_like(snake_case__	,  dtype=scores.dtype      ) * -float("inf"      )
																		lowercase__    :				List[Any]=	jnp.zeros((batch_size, 1)	,  dtype=scores.dtype      )
																		lowercase__    :				List[str]=	lax.dynamic_update_slice(snake_case__	,  snake_case__	,  (0, current_token)      )
																		return new_scores

												lowercase__    :				Dict=	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(snake_case__      )	,  lambda: scores	,  )	,  )
												return scores


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				str=	generate_config.eos_token_id
												lowercase__    :				Optional[int]=	generate_config.no_timestamps_token_id
												lowercase__    :				Dict=	generate_config.no_timestamps_token_id + 1

												lowercase__    :				List[Any]=	decoder_input_length + 1

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




						def __call__(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												# suppress <|notimestamps|> which is handled by without_timestamps
												lowercase__    :				int=	scores.at[:, self.no_timestamps_token_id].set(-float("inf"      )      )

												def handle_pairs(snake_case__	,  snake_case__      ):
																		lowercase__    :				Union[str, Any]=	jnp.where((cur_len - self.begin_index) >= 1	,  snake_case__	,  snake_case__      )
																		lowercase__    :				Tuple=	jnp.where(
																		    input_ids_k[cur_len - 1] >= self.timestamp_begin	,  True and last_was_timestamp	,  snake_case__	,  )

																		lowercase__    :				int=	jnp.where((cur_len - self.begin_index) < 2	,  snake_case__	,  snake_case__      )
																		lowercase__    :				Optional[int]=	jnp.where(
																		    input_ids_k[cur_len - 2] >= self.timestamp_begin	,  snake_case__	,  snake_case__	,  )

																		return jnp.where(
																		    snake_case__	,  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"      )      )	,  )	,  snake_case__	,  )

												lowercase__    :				List[str]=	jax.vmap(snake_case__      )(snake_case__	,  snake_case__      )

												lowercase__    :				str=	jnp.where(cur_len == self.begin_index	,  snake_case__	,  snake_case__      )
												lowercase__    :				List[Any]=	jnp.where(
												    self.max_initial_timestamp_index is not None	,  True and apply_max_initial_timestamp	,  snake_case__	,  )

												lowercase__    :				Any=	self.timestamp_begin + self.max_initial_timestamp_index

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

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

												def handle_cumulative_probs(snake_case__	,  snake_case__      ):
																		lowercase__    :				Dict=	jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]	,  axis=-1      )
																		lowercase__    :				Union[str, Any]=	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"      )      )	,  snake_case__	,  )

												lowercase__    :				Optional[int]=	jax.vmap(snake_case__      )(snake_case__	,  snake_case__      )

												return scores






