

import inspect
import unittest

from transformers import MobileViTConfig
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 MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
						from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import MobileViTImageProcessor



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.config_class(**self.inputs_dict)
									self.parent.assertTrue(hasattr(lowerCAmelCase__   ,						"hidden_sizes"))
									self.parent.assertTrue(hasattr(lowerCAmelCase__   ,						"neck_hidden_sizes"))
									self.parent.assertTrue(hasattr(lowerCAmelCase__   ,						"num_attention_heads"))







class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     str   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[Any]=13   ,						lowerCAmelCase__  :     Any=32   ,						lowerCAmelCase__  :     List[Any]=2   ,						lowerCAmelCase__  :     Optional[int]=3   ,						lowerCAmelCase__  :     List[str]=640   ,						lowerCAmelCase__  :     Any=4   ,						lowerCAmelCase__  :     Optional[int]="silu"   ,						lowerCAmelCase__  :     Optional[int]=3   ,						lowerCAmelCase__  :     Union[str, Any]=32   ,						lowerCAmelCase__  :     Dict=0.1   ,						lowerCAmelCase__  :     Tuple=0.1   ,						lowerCAmelCase__  :     int=0.1   ,						lowerCAmelCase__  :     List[str]=0.02   ,						lowerCAmelCase__  :     Tuple=True   ,						lowerCAmelCase__  :     List[Any]=True   ,						lowerCAmelCase__  :     str=10   ,						lowerCAmelCase__  :     Dict=None   ,						):
									SCREAMING_SNAKE_CASE_: Any  		=       parent
									SCREAMING_SNAKE_CASE_: List[Any]  		=       batch_size
									SCREAMING_SNAKE_CASE_: Tuple  		=       image_size
									SCREAMING_SNAKE_CASE_: Tuple  		=       patch_size
									SCREAMING_SNAKE_CASE_: List[Any]  		=       num_channels
									SCREAMING_SNAKE_CASE_: str  		=       last_hidden_size
									SCREAMING_SNAKE_CASE_: List[Any]  		=       num_attention_heads
									SCREAMING_SNAKE_CASE_: Dict  		=       hidden_act
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       conv_kernel_size
									SCREAMING_SNAKE_CASE_: str  		=       output_stride
									SCREAMING_SNAKE_CASE_: Any  		=       hidden_dropout_prob
									SCREAMING_SNAKE_CASE_: Dict  		=       attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_: int  		=       classifier_dropout_prob
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       use_labels
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       is_training
									SCREAMING_SNAKE_CASE_: Any  		=       num_labels
									SCREAMING_SNAKE_CASE_: Tuple  		=       initializer_range
									SCREAMING_SNAKE_CASE_: List[str]  		=       scope



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: int  		=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

									SCREAMING_SNAKE_CASE_: List[str]  		=       None
									SCREAMING_SNAKE_CASE_: List[Any]  		=       None
									if self.use_labels:
															SCREAMING_SNAKE_CASE_: str  		=       ids_tensor([self.batch_size]   ,						self.num_labels)
															SCREAMING_SNAKE_CASE_: Tuple  		=       ids_tensor([self.batch_size, self.image_size, self.image_size]   ,						self.num_labels)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.get_config()

									return config, pixel_values, labels, pixel_labels



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									return MobileViTConfig(
									    image_size=self.image_size   ,						patch_size=self.patch_size   ,						num_channels=self.num_channels   ,						num_attention_heads=self.num_attention_heads   ,						hidden_act=self.hidden_act   ,						conv_kernel_size=self.conv_kernel_size   ,						output_stride=self.output_stride   ,						hidden_dropout_prob=self.hidden_dropout_prob   ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,						classifier_dropout_prob=self.classifier_dropout_prob   ,						initializer_range=self.initializer_range   ,						)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       MobileViTModel(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Tuple  		=       model(lowerCAmelCase__)
									self.parent.assertEqual(
									    result.last_hidden_state.shape   ,						(
									        self.batch_size,
									        self.last_hidden_size,
									        self.image_size // self.output_stride,
									        self.image_size // self.output_stride,
									    )   ,						)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.num_labels
									SCREAMING_SNAKE_CASE_: str  		=       MobileViTForImageClassification(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Dict  		=       model(lowerCAmelCase__   ,						labels=lowerCAmelCase__)
									self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels))



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Any  		=       self.num_labels
									SCREAMING_SNAKE_CASE_: List[Any]  		=       MobileViTForSemanticSegmentation(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model(lowerCAmelCase__)
									self.parent.assertEqual(
									    result.logits.shape   ,						(
									        self.batch_size,
									        self.num_labels,
									        self.image_size // self.output_stride,
									        self.image_size // self.output_stride,
									    )   ,						)
									SCREAMING_SNAKE_CASE_: List[str]  		=       model(lowerCAmelCase__   ,						labels=lowerCAmelCase__)
									self.parent.assertEqual(
									    result.logits.shape   ,						(
									        self.batch_size,
									        self.num_labels,
									        self.image_size // self.output_stride,
									        self.image_size // self.output_stride,
									    )   ,						)




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_		,    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]										=     (
			    (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			_UpperCAmelCase   :							str										=     (
			    {
			        '''feature-extraction''': MobileViTModel,
			        '''image-classification''': MobileViTForImageClassification,
			        '''image-segmentation''': MobileViTForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			_UpperCAmelCase   :							Any										=     False
			_UpperCAmelCase   :							Dict										=     False
			_UpperCAmelCase   :							Union[str, Any]										=     False
			_UpperCAmelCase   :							int										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: str  		=       MobileViTModelTester(self)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       MobileViTConfigTester(self   ,						config_class=lowerCAmelCase__   ,						has_text_modality=lowerCAmelCase__)



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



			@unittest.skip(reason="MobileViT does not use inputs_embeds")
			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									pass



			@unittest.skip(reason="MobileViT does not support input and output embeddings")
			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									pass



			@unittest.skip(reason="MobileViT does not output attentions")
			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									pass



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

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

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



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



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



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									def check_hidden_states_output(lowerCAmelCase__  :     List[Any]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     str):
															SCREAMING_SNAKE_CASE_: Dict  		=       model_class(lowerCAmelCase__)
															model.to(lowerCAmelCase__)
															model.eval()

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

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       outputs.hidden_states

															SCREAMING_SNAKE_CASE_: Any  		=       5
															self.assertEqual(len(lowerCAmelCase__)   ,						lowerCAmelCase__)

															# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
															# with the width and height being successively divided by 2.
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       2
															for i in range(len(lowerCAmelCase__)):
																					self.assertListEqual(
																					    list(hidden_states[i].shape[-2:])   ,						[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]   ,						)
																					divisor *= 2

															self.assertEqual(self.model_tester.output_stride   ,						divisor // 2)

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

									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: Dict  		=       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_: Optional[int]  		=       True

															check_hidden_states_output(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Any  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__)




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       MobileViTModel.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
						return image



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




			"""simple docstring"""







			@cached_property
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small") if is_vision_available() else None



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: int  		=       MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small").to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: int  		=       self.default_image_processor
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       prepare_img()
									SCREAMING_SNAKE_CASE_: List[str]  		=       image_processor(images=lowerCAmelCase__   ,						return_tensors="pt").to(lowerCAmelCase__)

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

									# verify the logits
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.Size((1, 1000))
									self.assertEqual(outputs.logits.shape   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Any  		=       torch.tensor([-1.9364, -1.2327, -0.4653]).to(lowerCAmelCase__)

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



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model.to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       prepare_img()
									SCREAMING_SNAKE_CASE_: Any  		=       image_processor(images=lowerCAmelCase__   ,						return_tensors="pt").to(lowerCAmelCase__)

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

									# verify the logits
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.Size((1, 21, 32, 32))
									self.assertEqual(logits.shape   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.tensor(
									    [
									        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
									        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
									        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
									    ]   ,						device=lowerCAmelCase__   ,						)

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




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: List[str]  		=       MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
									SCREAMING_SNAKE_CASE_: Dict  		=       model.to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")

									SCREAMING_SNAKE_CASE_: Dict  		=       prepare_img()
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       image_processor(images=lowerCAmelCase__   ,						return_tensors="pt").to(lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_: int  		=       outputs.logits.detach().cpu()

									SCREAMING_SNAKE_CASE_: Any  		=       image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__   ,						target_sizes=[(50, 60)])
									SCREAMING_SNAKE_CASE_: Tuple  		=       torch.Size((50, 60))
									self.assertEqual(segmentation[0].shape   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.Size((32, 32))
									self.assertEqual(segmentation[0].shape   ,						lowerCAmelCase__)









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())






