

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   __lowercase				:




			"""simple docstring"""







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

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

									# use this variable to define bool_masked_pos
									SCREAMING_SNAKE_CASE_: Dict  		=       int(mask_ratio * self.seq_length)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       floats_tensor(
									    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size])

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       None
									if self.use_labels:
															SCREAMING_SNAKE_CASE_: List[str]  		=       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size)

									SCREAMING_SNAKE_CASE_: Tuple  		=       self.get_config()

									return config, pixel_values, labels



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									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=lowerCAmelCase__   ,						initializer_range=self.initializer_range   ,						)



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       VideoMAEModel(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model(lowerCAmelCase__)
									self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size))



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       VideoMAEForPreTraining(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									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
									SCREAMING_SNAKE_CASE_: Dict  		=       torch.ones((self.num_masks,))
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.cat([mask, torch.zeros(self.seq_length - mask.size(0))])
									SCREAMING_SNAKE_CASE_: List[str]  		=       mask.expand(self.batch_size   ,						-1).bool()

									SCREAMING_SNAKE_CASE_: List[Any]  		=       model(lowerCAmelCase__   ,						lowerCAmelCase__)
									# model only returns predictions for masked patches
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       mask.sum().item()
									SCREAMING_SNAKE_CASE_: str  		=       3 * self.tubelet_size * self.patch_size**2
									self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, num_masked_patches, decoder_num_labels))




			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: Dict  		=       self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       config_and_inputs
									SCREAMING_SNAKE_CASE_: int  		=       {"pixel_values": pixel_values}
									return config, inputs_dict







@require_torch
class   __lowercase				( UpperCAmelCase_		,       UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							Union[str, Any]										=     (
			    (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
			)
			_UpperCAmelCase   :							Dict										=     (
			    {'''feature-extraction''': VideoMAEModel, '''video-classification''': VideoMAEForVideoClassification}
			    if is_torch_available()
			    else {}
			)

			_UpperCAmelCase   :							Optional[int]										=     False
			_UpperCAmelCase   :							Optional[int]										=     False
			_UpperCAmelCase   :							List[Any]										=     False
			_UpperCAmelCase   :							List[str]										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       VideoMAEModelTester(self)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       ConfigTester(self   ,						config_class=lowerCAmelCase__   ,						has_text_modality=lowerCAmelCase__   ,						hidden_size=37)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     Dict=False):
									SCREAMING_SNAKE_CASE_: Any  		=       copy.deepcopy(lowerCAmelCase__)

									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
															SCREAMING_SNAKE_CASE_: List[str]  		=       torch.ones((self.model_tester.num_masks,))
															SCREAMING_SNAKE_CASE_: List[str]  		=       torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0))])
															SCREAMING_SNAKE_CASE_: Dict  		=       mask.expand(self.model_tester.batch_size   ,						-1).bool()
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       bool_masked_pos.to(lowerCAmelCase__)

									if return_labels:
															if model_class in [
															    *get_values(lowerCAmelCase__),
															]:
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       torch.zeros(
																					    self.model_tester.batch_size   ,						dtype=torch.long   ,						device=lowerCAmelCase__)

									return inputs_dict



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									self.config_tester.run_common_tests()



			@unittest.skip(reason="VideoMAE does not use inputs_embeds")
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: Tuple  		=       model_class(lowerCAmelCase__)
															self.assertIsInstance(model.get_input_embeddings()   ,						(nn.Module))
															SCREAMING_SNAKE_CASE_: Tuple  		=       model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(lowerCAmelCase__   ,						nn.Linear))



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       self.model_tester.prepare_config_and_inputs_for_common()

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

															SCREAMING_SNAKE_CASE_: int  		=       ["pixel_values"]
															self.assertListEqual(arg_names[:1]   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: Dict  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase__)



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															SCREAMING_SNAKE_CASE_: Dict  		=       VideoMAEModel.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									if not self.has_attentions:
															pass

									else:
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       self.model_tester.prepare_config_and_inputs_for_common()
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       True

															for model_class in self.all_model_classes:
																					SCREAMING_SNAKE_CASE_: int  		=       self.model_tester.seq_length - self.model_tester.num_masks
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       (
																					    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
																					)

																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       True
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       False
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       True
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       model_class(lowerCAmelCase__)
																					model.to(lowerCAmelCase__)
																					model.eval()
																					with torch.no_grad():
																											SCREAMING_SNAKE_CASE_: Any  		=       model(**self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__))
																					SCREAMING_SNAKE_CASE_: List[str]  		=       outputs.attentions
																					self.assertEqual(len(lowerCAmelCase__)   ,						self.model_tester.num_hidden_layers)

																					# check that output_attentions also work using config
																					del inputs_dict["output_attentions"]
																					SCREAMING_SNAKE_CASE_: Dict  		=       True
																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model_class(lowerCAmelCase__)
																					model.to(lowerCAmelCase__)
																					model.eval()
																					with torch.no_grad():
																											SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model(**self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__))
																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       outputs.attentions
																					self.assertEqual(len(lowerCAmelCase__)   ,						self.model_tester.num_hidden_layers)

																					self.assertListEqual(
																					    list(attentions[0].shape[-3:])   ,						[self.model_tester.num_attention_heads, seq_len, seq_len]   ,						)
																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       len(lowerCAmelCase__)

																					# Check attention is always last and order is fine
																					SCREAMING_SNAKE_CASE_: Any  		=       True
																					SCREAMING_SNAKE_CASE_: Tuple  		=       True
																					SCREAMING_SNAKE_CASE_: List[str]  		=       model_class(lowerCAmelCase__)
																					model.to(lowerCAmelCase__)
																					model.eval()
																					with torch.no_grad():
																											SCREAMING_SNAKE_CASE_: Dict  		=       model(**self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__))

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

																					SCREAMING_SNAKE_CASE_: str  		=       outputs.attentions

																					self.assertEqual(len(lowerCAmelCase__)   ,						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        _SCREAMING_SNAKE_CASE			( self  :     Any):
									def check_hidden_states_output(lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     Any):
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model_class(lowerCAmelCase__)
															model.to(lowerCAmelCase__)
															model.eval()

															with torch.no_grad():
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model(**self._prepare_for_class(lowerCAmelCase__   ,						lowerCAmelCase__))

															SCREAMING_SNAKE_CASE_: Dict  		=       outputs.hidden_states
															SCREAMING_SNAKE_CASE_: Dict  		=       self.model_tester.num_hidden_layers + 1
															self.assertEqual(len(lowerCAmelCase__)   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Tuple  		=       self.model_tester.seq_length - self.model_tester.num_masks
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       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]   ,						)

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       self.model_tester.prepare_config_and_inputs_for_common()

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

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															SCREAMING_SNAKE_CASE_: Dict  		=       True

															check_hidden_states_output(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)




			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									pass







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: Dict  		=       hf_hub_download(
						    repo_id="hf-internal-testing/spaghetti-video"				,	filename="eating_spaghetti.npy"				,	repo_type="dataset"    )
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       np.load(_UpperCAmelCase    )
						return list(_UpperCAmelCase    )



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




			"""simple docstring"""







			@cached_property
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									# 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        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics").to(
									    lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.default_image_processor
									SCREAMING_SNAKE_CASE_: int  		=       prepare_video()
									SCREAMING_SNAKE_CASE_: Tuple  		=       image_processor(lowerCAmelCase__   ,						return_tensors="pt").to(lowerCAmelCase__)

									# forward pass
									with torch.no_grad():
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model(**lowerCAmelCase__)

									# verify the logits
									SCREAMING_SNAKE_CASE_: str  		=       torch.Size((1, 400))
									self.assertEqual(outputs.logits.shape   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       torch.tensor([0.3669, -0.0688, -0.2421]).to(lowerCAmelCase__)

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




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short").to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Any  		=       self.default_image_processor
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       prepare_video()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       image_processor(lowerCAmelCase__   ,						return_tensors="pt").to(lowerCAmelCase__)

									# add boolean mask, indicating which patches to mask
									SCREAMING_SNAKE_CASE_: List[Any]  		=       hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"   ,						filename="bool_masked_pos.pt")
									SCREAMING_SNAKE_CASE_: str  		=       torch.load(lowerCAmelCase__)

									# forward pass
									with torch.no_grad():
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model(**lowerCAmelCase__)

									# verify the logits
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       torch.Size([1, 1408, 1536])
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       torch.tensor(
									    [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]   ,						device=lowerCAmelCase__)
									self.assertEqual(outputs.logits.shape   ,						lowerCAmelCase__)
									self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]   ,						lowerCAmelCase__   ,						atol=1E-4))

									# verify the loss (`config.norm_pix_loss` = `True`)
									SCREAMING_SNAKE_CASE_: str  		=       torch.tensor([0.5142]   ,						device=lowerCAmelCase__)
									self.assertTrue(torch.allclose(outputs.loss   ,						lowerCAmelCase__   ,						atol=1E-4))

									# verify the loss (`config.norm_pix_loss` = `False`)
									SCREAMING_SNAKE_CASE_: int  		=       VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"   ,						norm_pix_loss=lowerCAmelCase__).to(
									    lowerCAmelCase__)

									with torch.no_grad():
															SCREAMING_SNAKE_CASE_: Any  		=       model(**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.tensor(torch.tensor([0.6469])   ,						device=lowerCAmelCase__)
									self.assertTrue(torch.allclose(outputs.loss   ,						lowerCAmelCase__   ,						atol=1E-4))









import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.generation import (
						    FlaxForcedBOSTokenLogitsProcessor,
						    FlaxForcedEOSTokenLogitsProcessor,
						    FlaxLogitsProcessorList,
						    FlaxMinLengthLogitsProcessor,
						    FlaxTemperatureLogitsWarper,
						    FlaxTopKLogitsWarper,
						    FlaxTopPLogitsWarper,
						)



@require_flax
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: str  		=       jnp.ones((batch_size, length)) / length
									return scores



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Dict  		=       None
									SCREAMING_SNAKE_CASE_: str  		=       20

									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._get_uniform_logits(batch_size=2   ,						length=lowerCAmelCase__)

									# tweak scores to not be uniform anymore
									SCREAMING_SNAKE_CASE_: List[str]  		=       scores.at[1, 5].set((1 / length) + 0.1)  # peak, 1st batch
									SCREAMING_SNAKE_CASE_: Any  		=       scores.at[1, 10].set((1 / length) - 0.4)  # valley, 1st batch

									# compute softmax
									SCREAMING_SNAKE_CASE_: Dict  		=       jax.nn.softmax(lowerCAmelCase__   ,						axis=-1)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       FlaxTemperatureLogitsWarper(temperature=0.5)
									SCREAMING_SNAKE_CASE_: List[str]  		=       FlaxTemperatureLogitsWarper(temperature=1.3)

									SCREAMING_SNAKE_CASE_: str  		=       jax.nn.softmax(temp_dist_warper_sharper(lowerCAmelCase__   ,						scores.copy()   ,						cur_len=lowerCAmelCase__)   ,						axis=-1)
									SCREAMING_SNAKE_CASE_: int  		=       jax.nn.softmax(temp_dist_warper_smoother(lowerCAmelCase__   ,						scores.copy()   ,						cur_len=lowerCAmelCase__)   ,						axis=-1)

									# uniform distribution stays uniform
									self.assertTrue(jnp.allclose(probs[0, :]   ,						warped_prob_sharp[0, :]   ,						atol=1E-3))
									self.assertTrue(jnp.allclose(probs[0, :]   ,						warped_prob_smooth[0, :]   ,						atol=1E-3))

									# sharp peaks get higher, valleys get lower
									self.assertLess(probs[1, :].max()   ,						warped_prob_sharp[1, :].max())
									self.assertGreater(probs[1, :].min()   ,						warped_prob_sharp[1, :].min())

									# smooth peaks get lower, valleys get higher
									self.assertGreater(probs[1, :].max()   ,						warped_prob_smooth[1, :].max())
									self.assertLess(probs[1, :].min()   ,						warped_prob_smooth[1, :].min())



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       None
									SCREAMING_SNAKE_CASE_: str  		=       10
									SCREAMING_SNAKE_CASE_: Tuple  		=       2

									# create ramp distribution
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       np.broadcast_to(np.arange(lowerCAmelCase__)[None, :]   ,						(batch_size, vocab_size)).copy()
									SCREAMING_SNAKE_CASE_: Dict  		=       ramp_logits[1:, : vocab_size // 2] + vocab_size

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       FlaxTopKLogitsWarper(3)

									SCREAMING_SNAKE_CASE_: Dict  		=       top_k_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)

									# check that correct tokens are filtered
									self.assertListEqual(jnp.isinf(scores[0]).tolist()   ,						7 * [True] + 3 * [False])
									self.assertListEqual(jnp.isinf(scores[1]).tolist()   ,						2 * [True] + 3 * [False] + 5 * [True])

									# check special case
									SCREAMING_SNAKE_CASE_: Any  		=       5
									SCREAMING_SNAKE_CASE_: str  		=       FlaxTopKLogitsWarper(top_k=1   ,						filter_value=0.0   ,						min_tokens_to_keep=3)

									SCREAMING_SNAKE_CASE_: Any  		=       np.broadcast_to(np.arange(lowerCAmelCase__)[None, :]   ,						(batch_size, length)).copy()
									SCREAMING_SNAKE_CASE_: Any  		=       top_k_warp_safety_check(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)

									# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
									self.assertListEqual((scores == 0.0).sum(axis=-1).tolist()   ,						[2, 2])



			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Tuple  		=       None
									SCREAMING_SNAKE_CASE_: Dict  		=       10
									SCREAMING_SNAKE_CASE_: Dict  		=       2

									# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
									SCREAMING_SNAKE_CASE_: Tuple  		=       np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]))

									SCREAMING_SNAKE_CASE_: int  		=       FlaxTopPLogitsWarper(0.8)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       np.exp(top_p_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__))

									# dist should be filtered to keep min num values so that sum is >= top_p
									# exp (-inf) => 0
									SCREAMING_SNAKE_CASE_: Dict  		=       np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]])
									self.assertTrue(np.allclose(lowerCAmelCase__   ,						lowerCAmelCase__   ,						atol=1E-3))

									# check edge cases with negative and extreme logits
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       np.broadcast_to(np.arange(lowerCAmelCase__)[None, :]   ,						(batch_size, vocab_size)).copy() - (
									    vocab_size // 2
									)

									# make ramp_logits more extreme
									SCREAMING_SNAKE_CASE_: Dict  		=       ramp_logits[1] * 100.0

									# make sure at least 2 tokens are kept
									SCREAMING_SNAKE_CASE_: str  		=       FlaxTopPLogitsWarper(0.9   ,						min_tokens_to_keep=2   ,						filter_value=0.0)
									SCREAMING_SNAKE_CASE_: Any  		=       top_p_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)

									# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
									self.assertListEqual((filtered_dist != 0.0).sum(axis=-1).tolist()   ,						[3, 2])



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Tuple  		=       20
									SCREAMING_SNAKE_CASE_: List[str]  		=       4
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       0

									SCREAMING_SNAKE_CASE_: str  		=       FlaxMinLengthLogitsProcessor(min_length=10   ,						eos_token_id=lowerCAmelCase__)

									# check that min length is applied at length 5
									SCREAMING_SNAKE_CASE_: str  		=       ids_tensor((batch_size, 20)   ,						vocab_size=20)
									SCREAMING_SNAKE_CASE_: int  		=       5
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: str  		=       min_dist_processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()   ,						4 * [-float("inf")])

									# check that min length is not applied anymore at length 15
									SCREAMING_SNAKE_CASE_: List[str]  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       15
									SCREAMING_SNAKE_CASE_: Any  		=       min_dist_processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									self.assertFalse(jnp.isinf(lowerCAmelCase__).any())



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: int  		=       20
									SCREAMING_SNAKE_CASE_: str  		=       4
									SCREAMING_SNAKE_CASE_: List[Any]  		=       0

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase__)

									# check that all scores are -inf except the bos_token_id score
									SCREAMING_SNAKE_CASE_: int  		=       ids_tensor((batch_size, 1)   ,						vocab_size=20)
									SCREAMING_SNAKE_CASE_: List[str]  		=       1
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       logits_processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]).all())
									self.assertListEqual(scores[:, bos_token_id].tolist()   ,						4 * [0])  # score for bos_token_id shold be zero

									# check that bos_token_id is not forced if current length is greater than 1
									SCREAMING_SNAKE_CASE_: List[Any]  		=       3
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       logits_processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									self.assertFalse(jnp.isinf(lowerCAmelCase__).any())



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: Any  		=       20
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       4
									SCREAMING_SNAKE_CASE_: Dict  		=       0
									SCREAMING_SNAKE_CASE_: List[Any]  		=       5

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase__   ,						eos_token_id=lowerCAmelCase__)

									# check that all scores are -inf except the eos_token_id when max_length is reached
									SCREAMING_SNAKE_CASE_: List[Any]  		=       ids_tensor((batch_size, 4)   ,						vocab_size=20)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       4
									SCREAMING_SNAKE_CASE_: Dict  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       logits_processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]).all())
									self.assertListEqual(scores[:, eos_token_id].tolist()   ,						4 * [0])  # score for eos_token_id should be zero

									# check that eos_token_id is not forced if max_length is not reached
									SCREAMING_SNAKE_CASE_: List[str]  		=       3
									SCREAMING_SNAKE_CASE_: str  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       logits_processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									self.assertFalse(jnp.isinf(lowerCAmelCase__).any())



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: int  		=       4
									SCREAMING_SNAKE_CASE_: List[Any]  		=       10
									SCREAMING_SNAKE_CASE_: int  		=       15
									SCREAMING_SNAKE_CASE_: Dict  		=       2
									SCREAMING_SNAKE_CASE_: int  		=       1
									SCREAMING_SNAKE_CASE_: List[Any]  		=       15

									# dummy input_ids and scores
									SCREAMING_SNAKE_CASE_: int  		=       ids_tensor((batch_size, sequence_length)   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: str  		=       input_ids.copy()

									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       scores.copy()

									# instantiate all dist processors
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       FlaxTemperatureLogitsWarper(temperature=0.5)
									SCREAMING_SNAKE_CASE_: Tuple  		=       FlaxTopKLogitsWarper(3)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       FlaxTopPLogitsWarper(0.8)

									# instantiate all logits processors
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       FlaxMinLengthLogitsProcessor(min_length=10   ,						eos_token_id=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase__   ,						eos_token_id=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: str  		=       10

									# no processor list
									SCREAMING_SNAKE_CASE_: Dict  		=       temp_dist_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Tuple  		=       top_k_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Any  		=       top_p_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       min_dist_proc(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       bos_dist_proc(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Tuple  		=       eos_dist_proc(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)

									# with processor list
									SCREAMING_SNAKE_CASE_: str  		=       FlaxLogitsProcessorList(
									    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
									SCREAMING_SNAKE_CASE_: Tuple  		=       processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)

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

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




			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       4
									SCREAMING_SNAKE_CASE_: int  		=       10
									SCREAMING_SNAKE_CASE_: List[str]  		=       15
									SCREAMING_SNAKE_CASE_: List[Any]  		=       2
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       1
									SCREAMING_SNAKE_CASE_: str  		=       15

									# dummy input_ids and scores
									SCREAMING_SNAKE_CASE_: Tuple  		=       ids_tensor((batch_size, sequence_length)   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       input_ids.copy()

									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._get_uniform_logits(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       scores.copy()

									# instantiate all dist processors
									SCREAMING_SNAKE_CASE_: Dict  		=       FlaxTemperatureLogitsWarper(temperature=0.5)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       FlaxTopKLogitsWarper(3)
									SCREAMING_SNAKE_CASE_: Dict  		=       FlaxTopPLogitsWarper(0.8)

									# instantiate all logits processors
									SCREAMING_SNAKE_CASE_: int  		=       FlaxMinLengthLogitsProcessor(min_length=10   ,						eos_token_id=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       FlaxForcedEOSTokenLogitsProcessor(max_length=lowerCAmelCase__   ,						eos_token_id=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: str  		=       10

									# no processor list
									def run_no_processor_list(lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     Dict):
															SCREAMING_SNAKE_CASE_: Any  		=       temp_dist_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       top_k_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       top_p_warp(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: List[Any]  		=       min_dist_proc(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       bos_dist_proc(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       eos_dist_proc(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
															return scores

									# with processor list
									def run_processor_list(lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     Union[str, Any]):
															SCREAMING_SNAKE_CASE_: List[str]  		=       FlaxLogitsProcessorList(
															    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
															SCREAMING_SNAKE_CASE_: Dict  		=       processor(lowerCAmelCase__   ,						lowerCAmelCase__   ,						cur_len=lowerCAmelCase__)
															return scores

									SCREAMING_SNAKE_CASE_: str  		=       jax.jit(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       jax.jit(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Dict  		=       jitted_run_no_processor_list(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       jitted_run_processor_list(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)

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

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






