



'''simple docstring'''





import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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_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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
							from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor


class      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       Dict				,					_snake_case					:       Tuple				,					_snake_case					:       Optional[Any]=13				,					_snake_case					:       str=32				,					_snake_case					:       Tuple=3				,					_snake_case					:       int=4				,					_snake_case					:       Any=[10, 20, 30, 40]				,					_snake_case					:       Optional[int]=[2, 2, 3, 2]				,					_snake_case					:       Optional[int]=True				,					_snake_case					:       List[str]=True				,					_snake_case					:       Any=37				,					_snake_case					:       Tuple="gelu"				,					_snake_case					:       Optional[int]=10				,					_snake_case					:       Optional[int]=0.02				,					_snake_case					:       List[str]=["stage2", "stage3", "stage4"]				,					_snake_case					:       Union[str, Any]=[2, 3, 4]				,					_snake_case					:       List[str]=None				,					):






								"""simple docstring"""


								A__       =    parent
								A__       =    batch_size
								A__       =    image_size
								A__       =    num_channels
								A__       =    num_stages
								A__       =    hidden_sizes
								A__       =    depths
								A__       =    is_training
								A__       =    use_labels
								A__       =    intermediate_size
								A__       =    hidden_act
								A__       =    num_labels
								A__       =    initializer_range
								A__       =    out_features
								A__       =    out_indices
								A__       =    scope



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

								A__       =    None
								if self.use_labels:
											A__       =    ids_tensor([self.batch_size]				,					self.num_labels					)

								A__       =    self.get_config()

								return config, pixel_values, labels



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								return ConvNextVaConfig(
								    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=_snake_case				,					initializer_range=self.initializer_range				,					out_features=self.out_features				,					out_indices=self.out_indices				,					num_labels=self.num_labels				,					)



					def     _a							(     self					:       List[str]				,					_snake_case					:       List[str]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								A__       =    ConvNextVaModel(config=_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case					)
								# 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     _a							(     self					:       int				,					_snake_case					:       Optional[int]				,					_snake_case					:       Tuple				,					_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    ConvNextVaForImageClassification(_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case				,					labels=_snake_case					)
								self.parent.assertEqual(result.logits.shape				,					(self.batch_size, self.num_labels)					)



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       List[Any]				,					_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    ConvNextVaBackbone(config=_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case					)

								# 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
								A__       =    None
								A__       =    ConvNextVaBackbone(config=_snake_case					)
								model.to(_snake_case					)
								model.eval()
								A__       =    model(_snake_case					)

								# 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     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    self.prepare_config_and_inputs()
								A__     , A__     , A__       =    config_and_inputs
								A__       =    {'pixel_values': pixel_values}
								return config, inputs_dict



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    self.prepare_config_and_inputs()
								A__     , A__     , A__       =    config_and_inputs
								A__       =    {'pixel_values': pixel_values, 'labels': labels}
								return config, inputs_dict







@require_torch
class      __lowerCAmelCase     (		UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Dict			       =    (
					    (
					        ConvNextVaModel,
					        ConvNextVaForImageClassification,
					        ConvNextVaBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					A__				:		List[str]			       =    (
					    {"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					A__				:		Optional[int]			       =    False
					A__				:		Union[str, Any]			       =    False
					A__				:		Tuple			       =    False
					A__				:		Dict			       =    False
					A__				:		str			       =    False



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    ConvNextVaModelTester(self					)
								A__       =    ConfigTester(self				,					config_class=_snake_case				,					has_text_modality=_snake_case				,					hidden_size=37					)



					def     _a							(     self					:       List[str]					):






								"""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     _a							(     self					:       str					):






								"""simple docstring"""


								return



					@unittest.skip(reason='ConvNextV2 does not use inputs_embeds'					)
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								pass



					@unittest.skip(reason='ConvNextV2 does not support input and output embeddings'					)
					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								pass



					@unittest.skip(reason='ConvNextV2 does not use feedforward chunking'					)
					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       str					):






								"""simple docstring"""


								if not self.model_tester.is_training:
											return

								for model_class in self.all_model_classes:
											A__     , A__       =    self.model_tester.prepare_config_and_inputs_with_labels()
											A__       =    True

											if model_class.__name__ in [
											    *get_values(_snake_case					),
											    *get_values(_snake_case					),
											]:
														continue

											A__       =    model_class(_snake_case					)
											model.to(_snake_case					)
											model.train()
											A__       =    self._prepare_for_class(_snake_case				,					_snake_case				,					return_labels=_snake_case					)
											A__       =    model(**_snake_case					).loss
											loss.backward()



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								if not self.model_tester.is_training:
											return

								for model_class in self.all_model_classes:
											A__     , A__       =    self.model_tester.prepare_config_and_inputs_with_labels()
											A__       =    False
											A__       =    True

											if (
											    model_class.__name__
											    in [*get_values(_snake_case					), *get_values(_snake_case					)]
											    or not model_class.supports_gradient_checkpointing
											):
														continue

											A__       =    model_class(_snake_case					)
											model.to(_snake_case					)
											model.gradient_checkpointing_enable()
											model.train()
											A__       =    self._prepare_for_class(_snake_case				,					_snake_case				,					return_labels=_snake_case					)
											A__       =    model(**_snake_case					).loss
											loss.backward()



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

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

											A__       =    ['pixel_values']
											self.assertListEqual(arg_names[:1]				,					_snake_case					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								def check_hidden_states_output(_snake_case					:       Tuple				,					_snake_case					:       List[Any]				,					_snake_case					:       Optional[Any]					):
											A__       =    model_class(_snake_case					)
											model.to(_snake_case					)
											model.eval()

											with torch.no_grad():
														A__       =    model(**self._prepare_for_class(_snake_case				,					_snake_case					)					)

											A__       =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											A__       =    self.model_tester.num_stages
											self.assertEqual(len(_snake_case					)				,					expected_num_stages + 1					)

											# ConvNextV2'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]				,					)

								A__     , A__       =    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											A__       =    True
											check_hidden_states_output(_snake_case				,					_snake_case				,					_snake_case					)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											A__       =    True

											check_hidden_states_output(_snake_case				,					_snake_case				,					_snake_case					)



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case					)



					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__       =    ConvNextVaModel.from_pretrained(_snake_case					)
											self.assertIsNotNone(_snake_case					)







def A						(       )						->      Any:
			A__       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
			return image






@require_torch
@require_vision
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@cached_property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'					) if is_vision_available() else None



					@slow
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__       =    ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'					).to(_snake_case					)

								A__       =    self.default_image_processor
								A__       =    prepare_img()
								A__       =    preprocessor(images=_snake_case				,					return_tensors='pt'					).to(_snake_case					)

								# forward pass
								with torch.no_grad():
											A__       =    model(**_snake_case					)

								# verify the logits
								A__       =    torch.Size((1, 10_00)					)
								self.assertEqual(outputs.logits.shape				,					_snake_case					)

								A__       =    torch.tensor([0.9996, 0.1966, -0.4386]					).to(_snake_case					)
								self.assertTrue(torch.allclose(outputs.logits[0, :3]				,					_snake_case				,					atol=1E-4					)					)









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


SCREAMING_SNAKE_CASE__       =      get_logger(__name__)


SCREAMING_SNAKE_CASE__       =      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      __lowerCAmelCase     :
					"""simple docstring"""






					@add_start_docstrings(_snake_case					)
					def __call__(     self					:       Optional[int]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray					):






								"""simple docstring"""


								raise NotImplementedError(
								    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''					)







class      __lowerCAmelCase     :
					"""simple docstring"""






					@add_start_docstrings(_snake_case					)
					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray					):






								"""simple docstring"""


								raise NotImplementedError(
								    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''					)







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					@add_start_docstrings(_snake_case					)
					def __call__(     self					:       Any				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int				,					**_snake_case					:       Optional[int]					):






								"""simple docstring"""


								for processor in self:
											A__       =    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.'''					)
														A__       =    processor(_snake_case				,					_snake_case				,					_snake_case				,					**_snake_case					)
											else:
														A__       =    processor(_snake_case				,					_snake_case				,					_snake_case					)
								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Any				,					_snake_case					:       float					):






								"""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}'''					)

								A__       =    temperature



					def __call__(     self					:       str				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    scores / self.temperature
								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Optional[Any]				,					_snake_case					:       float				,					_snake_case					:       float = -float('Inf'					)				,					_snake_case					:       int = 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}'''					)

								A__       =    top_p
								A__       =    filter_value
								A__       =    min_tokens_to_keep



					def __call__(     self					:       str				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__     , A__       =    lax.top_k(_snake_case				,					scores.shape[-1]					)

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

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

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

								A__       =    jnp.where(_snake_case				,					_snake_case				,					_snake_case					)
								A__       =    jax.lax.sort_key_val(_snake_case				,					_snake_case					)[-1]

								return next_scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Union[str, Any]				,					_snake_case					:       int				,					_snake_case					:       float = -float('Inf'					)				,					_snake_case					:       int = 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}'''					)

								A__       =    max(_snake_case				,					_snake_case					)
								A__       =    filter_value



					def __call__(     self					:       Optional[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__     , A__       =    scores.shape
								A__       =    jnp.full(batch_size * vocab_size				,					self.filter_value					)

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

								A__       =    next_scores_flat.at[topk_indices_flat].set(_snake_case					)
								A__       =    next_scores_flat.reshape(_snake_case				,					_snake_case					)
								return next_scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Any				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    bos_token_id



					def __call__(     self					:       Optional[int]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    jnp.full(scores.shape				,					-float('inf'					)					)

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

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

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Any				,					_snake_case					:       int				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    max_length
								A__       =    eos_token_id



					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    jnp.full(scores.shape				,					-float('inf'					)					)

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

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

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Dict				,					_snake_case					:       int				,					_snake_case					:       int					):






								"""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}'''					)

								A__       =    min_length
								A__       =    eos_token_id



					def __call__(     self					:       int				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    1 - jnp.clip(cur_len - self.min_length				,					0				,					1					)

								A__       =    jnp.where(_snake_case				,					scores.at[:, self.eos_token_id].set(-float('inf'					)					)				,					_snake_case					)

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       int				,					_snake_case					:       Tuple				,					_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    list(_snake_case					)
								A__       =    begin_index



					def __call__(     self					:       Union[str, Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       str				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    1 - jnp.bool_(cur_len - self.begin_index					)

								A__       =    jnp.where(_snake_case				,					scores.at[:, self.begin_suppress_tokens].set(-float('inf'					)					)				,					_snake_case					)

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       int				,					_snake_case					:       list					):






								"""simple docstring"""


								A__       =    list(_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								A__       =    scores.at[..., self.suppress_tokens].set(-float('inf'					)					)

								return scores







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       List[str]				,					_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    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.
								A__       =    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:
														A__       =    force_token_array.at[index].set(_snake_case					)
								A__       =    jnp.intaa(_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       jnp.ndarray				,					_snake_case					:       int					):






								"""simple docstring"""


								def _force_token(_snake_case					:       Dict					):
											A__       =    scores.shape[0]
											A__       =    self.force_token_array[generation_idx]

											A__       =    jnp.ones_like(_snake_case				,					dtype=scores.dtype					) * -float('inf'					)
											A__       =    jnp.zeros((batch_size, 1)				,					dtype=scores.dtype					)
											A__       =    lax.dynamic_update_slice(_snake_case				,					_snake_case				,					(0, current_token)					)
											return new_scores

								A__       =    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      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def __init__(     self					:       Union[str, Any]				,					_snake_case					:       Union[str, Any]				,					_snake_case					:       Dict				,					_snake_case					:       List[Any]					):






								"""simple docstring"""


								A__       =    generate_config.eos_token_id
								A__       =    generate_config.no_timestamps_token_id
								A__       =    generate_config.no_timestamps_token_id + 1

								A__       =    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'					):
											A__       =    generate_config.max_initial_timestamp_index
								else:
											A__       =    model_config.vocab_size
								if self.max_initial_timestamp_index is None:
											A__       =    model_config.vocab_size



					def __call__(     self					:       Tuple				,					_snake_case					:       List[Any]				,					_snake_case					:       Dict				,					_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    scores.at[:, self.no_timestamps_token_id].set(-float('inf'					)					)

								def handle_pairs(_snake_case					:       Dict				,					_snake_case					:       str					):
											A__       =    jnp.where((cur_len - self.begin_index) >= 1				,					_snake_case				,					_snake_case					)
											A__       =    jnp.where(
											    input_ids_k[cur_len - 1] >= self.timestamp_begin				,					True and last_was_timestamp				,					_snake_case				,					)

											A__       =    jnp.where((cur_len - self.begin_index) < 2				,					_snake_case				,					_snake_case					)
											A__       =    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				,					)

								A__       =    jax.vmap(_snake_case					)(_snake_case				,					_snake_case					)

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

								A__       =    self.timestamp_begin + self.max_initial_timestamp_index

								A__       =    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
								A__       =    jax.nn.log_softmax(_snake_case				,					axis=-1					)

								def handle_cumulative_probs(_snake_case					:       List[Any]				,					_snake_case					:       Union[str, Any]					):
											A__       =    jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]				,					axis=-1					)
											A__       =    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				,					)

								A__       =    jax.vmap(_snake_case					)(_snake_case				,					_snake_case					)

								return scores




