


import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
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 torch import nn

						from transformers import (
						    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
						    VideoMAEForPreTraining,
						    VideoMAEForVideoClassification,
						    VideoMAEModel,
						)
						from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from transformers import VideoMAEImageProcessor







class 	snake_case_       :
					def __init__(				self       :int      ,__snake_case       :Optional[int]      ,__snake_case       :Any=13      ,__snake_case       :Union[str, Any]=10      ,__snake_case       :Dict=3      ,__snake_case       :int=2      ,__snake_case       :Optional[int]=2      ,__snake_case       :Any=2      ,__snake_case       :Optional[int]=True      ,__snake_case       :Optional[Any]=True      ,__snake_case       :List[str]=32      ,__snake_case       :int=5      ,__snake_case       :List[Any]=4      ,__snake_case       :Tuple=37      ,__snake_case       :Dict="gelu"      ,__snake_case       :Any=0.1      ,__snake_case       :Optional[Any]=0.1      ,__snake_case       :int=10      ,__snake_case       :Optional[Any]=0.02      ,__snake_case       :Optional[Any]=0.9      ,__snake_case       :Dict=None      ,) ->   Dict:
												a__					=					parent
												a__					=					batch_size
												a__					=					image_size
												a__					=					num_channels
												a__					=					patch_size
												a__					=					tubelet_size
												a__					=					num_frames
												a__					=					is_training
												a__					=					use_labels
												a__					=					hidden_size
												a__					=					num_hidden_layers
												a__					=					num_attention_heads
												a__					=					intermediate_size
												a__					=					hidden_act
												a__					=					hidden_dropout_prob
												a__					=					attention_probs_dropout_prob
												a__					=					type_sequence_label_size
												a__					=					initializer_range
												a__					=					mask_ratio
												a__					=					scope

												# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
												a__					=					(image_size // patch_size) ** 2
												a__					=					(num_frames // tubelet_size) * self.num_patches_per_frame

												# use this variable to define bool_masked_pos
												a__					=					int(mask_ratio * self.seq_length				)
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Optional[Any]:
												a__					=					floats_tensor(
												    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]				)

												a__					=					None
												if self.use_labels:
																			a__					=					ids_tensor([self.batch_size]      ,self.type_sequence_label_size				)

												a__					=					self.get_config()

												return config, pixel_values, labels
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   List[str]:
												return VideoMAEConfig(
												    image_size=self.image_size      ,patch_size=self.patch_size      ,num_channels=self.num_channels      ,num_frames=self.num_frames      ,tubelet_size=self.tubelet_size      ,hidden_size=self.hidden_size      ,num_hidden_layers=self.num_hidden_layers      ,num_attention_heads=self.num_attention_heads      ,intermediate_size=self.intermediate_size      ,hidden_act=self.hidden_act      ,hidden_dropout_prob=self.hidden_dropout_prob      ,attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,is_decoder=__snake_case      ,initializer_range=self.initializer_range      ,)
					def 			lowerCamelCase__(				self       :List[str]      ,__snake_case       :List[str]      ,__snake_case       :List[str]      ,__snake_case       :Optional[Any]				) ->   Union[str, Any]:
												a__					=					VideoMAEModel(config=__snake_case				)
												model.to(__snake_case				)
												model.eval()
												a__					=					model(__snake_case				)
												self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)				)
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :List[str]      ,__snake_case       :int      ,__snake_case       :Union[str, Any]				) ->   Union[str, Any]:
												a__					=					VideoMAEForPreTraining(__snake_case				)
												model.to(__snake_case				)
												model.eval()
												# important: each video needs to have the same number of masked patches
												# hence we define a single mask, which we then repeat for each example in the batch
												a__					=					torch.ones((self.num_masks,)				)
												a__					=					torch.cat([mask, torch.zeros(self.seq_length - mask.size(0				)				)]				)
												a__					=					mask.expand(self.batch_size      ,-1				).bool()

												a__					=					model(__snake_case      ,__snake_case				)
												# model only returns predictions for masked patches
												a__					=					mask.sum().item()
												a__					=					3 * self.tubelet_size * self.patch_size**2
												self.parent.assertEqual(result.logits.shape      ,(self.batch_size, num_masked_patches, decoder_num_labels)				)



					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Union[str, Any]:
												a__					=					self.prepare_config_and_inputs()
												a__				, a__				, a__					=					config_and_inputs
												a__					=					{'pixel_values': pixel_values}
												return config, inputs_dict







@require_torch
class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   List[Any]   				=      (
					    (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
					)
					UpperCAmelCase__ :   Any   				=      (
					    {'''feature-extraction''': VideoMAEModel, '''video-classification''': VideoMAEForVideoClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCAmelCase__ :   int   				=      False
					UpperCAmelCase__ :   Any   				=      False
					UpperCAmelCase__ :   str   				=      False
					UpperCAmelCase__ :   str   				=      False
					def 			lowerCamelCase__(				self       :int				) ->   int:
												a__					=					VideoMAEModelTester(self				)
												a__					=					ConfigTester(self      ,config_class=__snake_case      ,has_text_modality=__snake_case      ,hidden_size=37				)
					def 			lowerCamelCase__(				self       :List[str]      ,__snake_case       :Dict      ,__snake_case       :Optional[Any]      ,__snake_case       :Optional[Any]=False				) ->   List[str]:
												a__					=					copy.deepcopy(__snake_case				)

												if model_class == VideoMAEForPreTraining:
																			# important: each video needs to have the same number of masked patches
																			# hence we define a single mask, which we then repeat for each example in the batch
																			a__					=					torch.ones((self.model_tester.num_masks,)				)
																			a__					=					torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0				)				)]				)
																			a__					=					mask.expand(self.model_tester.batch_size      ,-1				).bool()
																			a__					=					bool_masked_pos.to(__snake_case				)

												if return_labels:
																			if model_class in [
																			    *get_values(__snake_case				),
																			]:
																										a__					=					torch.zeros(
																										    self.model_tester.batch_size      ,dtype=torch.long      ,device=__snake_case				)

												return inputs_dict
					def 			lowerCamelCase__(				self       :Any				) ->   List[str]:
												self.config_tester.run_common_tests()
					@unittest.skip(reason='VideoMAE does not use inputs_embeds'				)
					def 			lowerCamelCase__(				self       :int				) ->   Dict:
												pass
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Dict:
												a__				, a__					=					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			a__					=					model_class(__snake_case				)
																			self.assertIsInstance(model.get_input_embeddings()      ,(nn.Module)				)
																			a__					=					model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__snake_case      ,nn.Linear				)				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   int:
												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 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[Any]:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__snake_case				)
					def 			lowerCamelCase__(				self       :Dict				) ->   Optional[Any]:
												a__					=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_pretraining(*__snake_case				)
					@slow
					def 			lowerCamelCase__(				self       :int				) ->   Tuple:
												for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			a__					=					VideoMAEModel.from_pretrained(__snake_case				)
																			self.assertIsNotNone(__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Any:
												if not self.has_attentions:
																			pass

												else:
																			a__				, a__					=					self.model_tester.prepare_config_and_inputs_for_common()
																			a__					=					True

																			for model_class in self.all_model_classes:
																										a__					=					self.model_tester.seq_length - self.model_tester.num_masks
																										a__					=					(
																										    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
																										)

																										a__					=					True
																										a__					=					False
																										a__					=					True
																										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.attentions
																										self.assertEqual(len(__snake_case				)      ,self.model_tester.num_hidden_layers				)

																										# check that output_attentions also work using config
																										del inputs_dict["output_attentions"]
																										a__					=					True
																										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.attentions
																										self.assertEqual(len(__snake_case				)      ,self.model_tester.num_hidden_layers				)

																										self.assertListEqual(
																										    list(attentions[0].shape[-3:]				)      ,[self.model_tester.num_attention_heads, seq_len, seq_len]      ,)
																										a__					=					len(__snake_case				)

																										# Check attention is always last and order is fine
																										a__					=					True
																										a__					=					True
																										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				)				)

																										self.assertEqual(out_len + 1      ,len(__snake_case				)				)

																										a__					=					outputs.attentions

																										self.assertEqual(len(__snake_case				)      ,self.model_tester.num_hidden_layers				)
																										self.assertListEqual(
																										    list(self_attentions[0].shape[-3:]				)      ,[self.model_tester.num_attention_heads, seq_len, seq_len]      ,)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Optional[int]:
												def check_hidden_states_output(__snake_case       :Dict      ,__snake_case       :Optional[int]      ,__snake_case       :Union[str, 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.hidden_states
																			a__					=					self.model_tester.num_hidden_layers + 1
																			self.assertEqual(len(__snake_case				)      ,__snake_case				)

																			a__					=					self.model_tester.seq_length - self.model_tester.num_masks
																			a__					=					num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

																			self.assertListEqual(
																			    list(hidden_states[0].shape[-2:]				)      ,[seq_length, self.model_tester.hidden_size]      ,)

												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				)



					@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
					def 			lowerCamelCase__(				self       :Tuple				) ->   Dict:
												pass






def 							__lowercase       (			):
							a__					=					hf_hub_download(
							    repo_id='hf-internal-testing/spaghetti-video' ,   filename='eating_spaghetti.npy' ,   repo_type='dataset'       )
							a__					=					np.load(__lowerCAmelCase       )
							return list(__lowerCAmelCase       )







@require_torch
@require_vision
class 	snake_case_       (unittest.TestCase				):
					@cached_property
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[str]:
												# logits were tested with a different mean and std, so we use the same here
												return (
												    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]      ,image_std=[0.5, 0.5, 0.5]				)
												    if is_vision_available()
												    else None
												)
					@slow
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   Tuple:
												a__					=					VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics'				).to(
												    __snake_case				)

												a__					=					self.default_image_processor
												a__					=					prepare_video()
												a__					=					image_processor(__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, 4_00)				)
												self.assertEqual(outputs.logits.shape      ,__snake_case				)

												a__					=					torch.tensor([0.36_69, -0.06_88, -0.24_21]				).to(__snake_case				)

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



					@slow
					def 			lowerCamelCase__(				self       :List[Any]				) ->   str:
												a__					=					VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short'				).to(__snake_case				)

												a__					=					self.default_image_processor
												a__					=					prepare_video()
												a__					=					image_processor(__snake_case      ,return_tensors='pt'				).to(__snake_case				)

												# add boolean mask, indicating which patches to mask
												a__					=					hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'      ,filename='bool_masked_pos.pt'				)
												a__					=					torch.load(__snake_case				)

												# forward pass
												with torch.no_grad():
																			a__					=					model(**__snake_case				)

												# verify the logits
												a__					=					torch.Size([1, 14_08, 15_36]				)
												a__					=					torch.tensor(
												    [[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]]      ,device=__snake_case				)
												self.assertEqual(outputs.logits.shape      ,__snake_case				)
												self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]      ,__snake_case      ,atol=1E-4				)				)

												# verify the loss (`config.norm_pix_loss` = `True`)
												a__					=					torch.tensor([0.51_42]      ,device=__snake_case				)
												self.assertTrue(torch.allclose(outputs.loss      ,__snake_case      ,atol=1E-4				)				)

												# verify the loss (`config.norm_pix_loss` = `False`)
												a__					=					VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short'      ,norm_pix_loss=__snake_case				).to(
												    __snake_case				)

												with torch.no_grad():
																			a__					=					model(**__snake_case				)

												a__					=					torch.tensor(torch.tensor([0.64_69]				)      ,device=__snake_case				)
												self.assertTrue(torch.allclose(outputs.loss      ,__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


snake_case							:	Dict					      =				get_logger(__name__)


snake_case							:	str					      =				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 	snake_case_       :
					@add_start_docstrings(__snake_case				)
					def __call__(				self       :Dict      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray				) ->   jnp.ndarray:
												raise NotImplementedError(
												    F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'				)







class 	snake_case_       :
					@add_start_docstrings(__snake_case				)
					def __call__(				self       :List[str]      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray				) ->   jnp.ndarray:
												raise NotImplementedError(
												    F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'				)







class 	snake_case_       (lowerCamelCase_				):
					@add_start_docstrings(__snake_case				)
					def __call__(				self       :Dict      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int      ,**__snake_case       :Any				) ->   jnp.ndarray:
												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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :str      ,__snake_case       :float				) ->   Tuple:
												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       :Optional[int]      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int				) ->   jnp.ndarray:
												a__					=					scores / self.temperature
												return scores







class 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Any      ,__snake_case       :float      ,__snake_case       :float = -float('Inf'				)      ,__snake_case       :int = 1				) ->   Dict:
												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       :Optional[int]      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int				) ->   jnp.ndarray:
												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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :List[str]      ,__snake_case       :int      ,__snake_case       :float = -float('Inf'				)      ,__snake_case       :int = 1				) ->   Any:
												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       :int      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int				) ->   jnp.ndarray:
												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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :int      ,__snake_case       :int				) ->   str:
												a__					=					bos_token_id



					def __call__(				self       :List[Any]      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int				) ->   jnp.ndarray:
												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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Union[str, Any]      ,__snake_case       :int      ,__snake_case       :int				) ->   List[Any]:
												a__					=					max_length
												a__					=					eos_token_id



					def __call__(				self       :int      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int				) ->   jnp.ndarray:
												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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :str      ,__snake_case       :int      ,__snake_case       :int				) ->   List[str]:
												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       :Any      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int				) ->   jnp.ndarray:
												# create boolean flag to decide if min length penalty should be applied
												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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Optional[int]      ,__snake_case       :List[str]      ,__snake_case       :Optional[int]				) ->   Tuple:
												a__					=					list(__snake_case				)
												a__					=					begin_index



					def __call__(				self       :str      ,__snake_case       :List[str]      ,__snake_case       :str      ,__snake_case       :int				) ->   str:
												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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :List[str]      ,__snake_case       :list				) ->   List[Any]:
												a__					=					list(__snake_case				)



					def __call__(				self       :Dict      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int				) ->   jnp.ndarray:
												a__					=					scores.at[..., self.suppress_tokens].set(-float('inf'				)				)

												return scores







class 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Dict      ,__snake_case       :Optional[int]				) ->   Union[str, Any]:
												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       :Optional[int]      ,__snake_case       :jnp.ndarray      ,__snake_case       :jnp.ndarray      ,__snake_case       :int				) ->   jnp.ndarray:
												def _force_token(__snake_case       :Optional[Any]				):
																			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 	snake_case_       (lowerCamelCase_				):
					def __init__(				self       :Any      ,__snake_case       :List[str]      ,__snake_case       :str      ,__snake_case       :List[Any]				) ->   Optional[int]:
												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       :Any      ,__snake_case       :List[Any]      ,__snake_case       :Optional[int]      ,__snake_case       :Optional[Any]				) ->   Tuple:
												# suppress <|notimestamps|> which is handled by without_timestamps
												a__					=					scores.at[:, self.no_timestamps_token_id].set(-float('inf'				)				)

												def handle_pairs(__snake_case       :List[str]      ,__snake_case       :Union[str, Any]				):
																			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       :Dict      ,__snake_case       :List[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




