





'''simple docstring'''






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 			lowerCamelCase					:


					def __init__(					self	:  Union[str, Any]       ,     __snake_case	:  int       ,     __snake_case	:  Tuple=13       ,     __snake_case	:  Dict=10       ,     __snake_case	:  int=3       ,     __snake_case	:  Union[str, Any]=2       ,     __snake_case	:  Any=2       ,     __snake_case	:  Optional[int]=2       ,     __snake_case	:  Any=True       ,     __snake_case	:  Optional[int]=True       ,     __snake_case	:  Dict=32       ,     __snake_case	:  int=5       ,     __snake_case	:  Union[str, Any]=4       ,     __snake_case	:  Any=37       ,     __snake_case	:  str="gelu"       ,     __snake_case	:  str=0.1       ,     __snake_case	:  Optional[Any]=0.1       ,     __snake_case	:  int=10       ,     __snake_case	:  Union[str, Any]=0.02       ,     __snake_case	:  Any=0.9       ,     __snake_case	:  Optional[Any]=None       ,     ):





											'''simple docstring'''





											_snake_case:   Dict  					=		parent
											_snake_case:   Tuple  					=		batch_size
											_snake_case:   List[str]  					=		image_size
											_snake_case:   Dict  					=		num_channels
											_snake_case:   List[Any]  					=		patch_size
											_snake_case:   Optional[int]  					=		tubelet_size
											_snake_case:   str  					=		num_frames
											_snake_case:   int  					=		is_training
											_snake_case:   Union[str, Any]  					=		use_labels
											_snake_case:   Tuple  					=		hidden_size
											_snake_case:   List[str]  					=		num_hidden_layers
											_snake_case:   Union[str, Any]  					=		num_attention_heads
											_snake_case:   Any  					=		intermediate_size
											_snake_case:   Tuple  					=		hidden_act
											_snake_case:   Union[str, Any]  					=		hidden_dropout_prob
											_snake_case:   int  					=		attention_probs_dropout_prob
											_snake_case:   Union[str, Any]  					=		type_sequence_label_size
											_snake_case:   Any  					=		initializer_range
											_snake_case:   Tuple  					=		mask_ratio
											_snake_case:   Union[str, Any]  					=		scope

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

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		floats_tensor(
											    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]       )

											_snake_case:   int  					=		None
											if self.use_labels:
																	_snake_case:   List[str]  					=		ids_tensor([self.batch_size]       ,     self.type_sequence_label_size       )

											_snake_case:   Dict  					=		self.get_config()

											return config, pixel_values, labels


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											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    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  List[str]       ,     __snake_case	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		VideoMAEModel(config=__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   str  					=		model(__snake_case       )
											self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ,     __snake_case	:  int       ,     __snake_case	:  Any       ,     __snake_case	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   Any  					=		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
											_snake_case:   Dict  					=		torch.ones((self.num_masks,)       )
											_snake_case:   int  					=		torch.cat([mask, torch.zeros(self.seq_length - mask.size(0       )       )]       )
											_snake_case:   Any  					=		mask.expand(self.batch_size       ,     -1       ).bool()

											_snake_case:   int  					=		model(__snake_case       ,     __snake_case       )
											# model only returns predictions for masked patches
											_snake_case:   Any  					=		mask.sum().item()
											_snake_case:   Any  					=		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       ):





											'''simple docstring'''





											_snake_case:   int  					=		self.prepare_config_and_inputs()
											_snake_case					,						_snake_case					,						_snake_case:   Any  					=		config_and_inputs
											_snake_case:   Optional[int]  					=		{'pixel_values': pixel_values}
											return config, inputs_dict







@require_torch
class 			lowerCamelCase					(						__UpperCAmelCase     ,							__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    (
					    (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
					)
					_SCREAMING_SNAKE_CASE							   =    (
					    {"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
					    if is_torch_available()
					    else {}
					)

					_SCREAMING_SNAKE_CASE							   =    False
					_SCREAMING_SNAKE_CASE							   =    False
					_SCREAMING_SNAKE_CASE							   =    False
					_SCREAMING_SNAKE_CASE							   =    False


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   int  					=		VideoMAEModelTester(self       )
											_snake_case:   int  					=		ConfigTester(self       ,     config_class=__snake_case       ,     has_text_modality=__snake_case       ,     hidden_size=37       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  Optional[Any]=False       ):





											'''simple docstring'''





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

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

											return inputs_dict


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											self.config_tester.run_common_tests()


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





											'''simple docstring'''





											pass


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case					,						_snake_case:   Tuple  					=		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	_snake_case:   Optional[int]  					=		model_class(__snake_case       )
																	self.assertIsInstance(model.get_input_embeddings()       ,     (nn.Module)       )
																	_snake_case:   Optional[int]  					=		model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(__snake_case       ,     nn.Linear       )       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case					,						_snake_case:   int  					=		self.model_tester.prepare_config_and_inputs_for_common()

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

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*__snake_case       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_snake_case:   Tuple  					=		VideoMAEModel.from_pretrained(__snake_case       )
																	self.assertIsNotNone(__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											if not self.has_attentions:
																	pass

											else:
																	_snake_case					,						_snake_case:   str  					=		self.model_tester.prepare_config_and_inputs_for_common()
																	_snake_case:   Optional[int]  					=		True

																	for model_class in self.all_model_classes:
																							_snake_case:   Union[str, Any]  					=		self.model_tester.seq_length - self.model_tester.num_masks
																							_snake_case:   Tuple  					=		(
																							    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
																							)

																							_snake_case:   int  					=		True
																							_snake_case:   Union[str, Any]  					=		False
																							_snake_case:   str  					=		True
																							_snake_case:   Dict  					=		model_class(__snake_case       )
																							model.to(__snake_case       )
																							model.eval()
																							with torch.no_grad():
																													_snake_case:   Dict  					=		model(**self._prepare_for_class(__snake_case       ,     __snake_case       )       )
																							_snake_case:   Dict  					=		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"]
																							_snake_case:   Optional[Any]  					=		True
																							_snake_case:   Dict  					=		model_class(__snake_case       )
																							model.to(__snake_case       )
																							model.eval()
																							with torch.no_grad():
																													_snake_case:   List[str]  					=		model(**self._prepare_for_class(__snake_case       ,     __snake_case       )       )
																							_snake_case:   Optional[int]  					=		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]       ,     )
																							_snake_case:   int  					=		len(__snake_case       )

																							# Check attention is always last and order is fine
																							_snake_case:   List[str]  					=		True
																							_snake_case:   Union[str, Any]  					=		True
																							_snake_case:   Optional[Any]  					=		model_class(__snake_case       )
																							model.to(__snake_case       )
																							model.eval()
																							with torch.no_grad():
																													_snake_case:   List[Any]  					=		model(**self._prepare_for_class(__snake_case       ,     __snake_case       )       )

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

																							_snake_case:   Optional[Any]  					=		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    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





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

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

																	_snake_case:   Union[str, Any]  					=		outputs.hidden_states
																	_snake_case:   List[Any]  					=		self.model_tester.num_hidden_layers + 1
																	self.assertEqual(len(__snake_case       )       ,     __snake_case       )

																	_snake_case:   Union[str, Any]  					=		self.model_tester.seq_length - self.model_tester.num_masks
																	_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]       ,     )

											_snake_case					,						_snake_case:   List[str]  					=		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	_snake_case:   str  					=		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"]
																	_snake_case:   Optional[int]  					=		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    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											pass





def       lowercase_      (		)   ->Tuple:
						_snake_case:   str  					=		hf_hub_download(
						    repo_id='hf-internal-testing/spaghetti-video'				,  filename='eating_spaghetti.npy'				,  repo_type='dataset'						)
						_snake_case:   Optional[int]  					=		np.load(lowercase__						)
						return list(lowercase__						)







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


					@cached_property
					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											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	:  Optional[int]       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics'       ).to(
											    __snake_case       )

											_snake_case:   Union[str, Any]  					=		self.default_image_processor
											_snake_case:   Optional[Any]  					=		prepare_video()
											_snake_case:   Dict  					=		image_processor(__snake_case       ,     return_tensors='pt'       ).to(__snake_case       )

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

											# verify the logits
											_snake_case:   Optional[int]  					=		torch.Size((1, 4_00)       )
											self.assertEqual(outputs.logits.shape       ,     __snake_case       )

											_snake_case:   Any  					=		torch.tensor([0.3_669, -0.0_688, -0.2_421]       ).to(__snake_case       )

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




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





											'''simple docstring'''





											_snake_case:   int  					=		VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short'       ).to(__snake_case       )

											_snake_case:   Optional[Any]  					=		self.default_image_processor
											_snake_case:   Optional[int]  					=		prepare_video()
											_snake_case:   str  					=		image_processor(__snake_case       ,     return_tensors='pt'       ).to(__snake_case       )

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

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

											# verify the logits
											_snake_case:   str  					=		torch.Size([1, 14_08, 15_36]       )
											_snake_case:   str  					=		torch.tensor(
											    [[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]       ,     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`)
											_snake_case:   Any  					=		torch.tensor([0.5_142]       ,     device=__snake_case       )
											self.assertTrue(torch.allclose(outputs.loss       ,     __snake_case       ,     atol=1e-4       )       )

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

											with torch.no_grad():
																	_snake_case:   List[str]  					=		model(**__snake_case       )

											_snake_case:   int  					=		torch.tensor(torch.tensor([0.6_469]       )       ,     device=__snake_case       )
											self.assertTrue(torch.allclose(outputs.loss       ,     __snake_case       ,     atol=1e-4       )       )












'''simple docstring'''






import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class 			lowerCamelCase					:


					def __init__(					self	:  Optional[int]       ,     __snake_case	:  int       ,     __snake_case	:  Tuple=13       ,     __snake_case	:  Any=7       ,     __snake_case	:  Union[str, Any]=True       ,     __snake_case	:  List[str]=True       ,     __snake_case	:  Optional[int]=False       ,     __snake_case	:  List[Any]=True       ,     __snake_case	:  str=99       ,     __snake_case	:  Optional[int]=32       ,     __snake_case	:  Any=5       ,     __snake_case	:  Tuple=4       ,     __snake_case	:  List[Any]=37       ,     __snake_case	:  Union[str, Any]="gelu"       ,     __snake_case	:  List[str]=0.1       ,     __snake_case	:  int=0.1       ,     __snake_case	:  Any=5_12       ,     __snake_case	:  Dict=16       ,     __snake_case	:  int=2       ,     __snake_case	:  Any=0.02       ,     __snake_case	:  Any=3       ,     __snake_case	:  str=4       ,     __snake_case	:  int=None       ,     ):





											'''simple docstring'''





											_snake_case:   Dict  					=		parent
											_snake_case:   Optional[int]  					=		batch_size
											_snake_case:   List[Any]  					=		seq_length
											_snake_case:   Union[str, Any]  					=		is_training
											_snake_case:   Optional[Any]  					=		use_input_mask
											_snake_case:   Dict  					=		use_token_type_ids
											_snake_case:   Any  					=		use_labels
											_snake_case:   Optional[Any]  					=		vocab_size
											_snake_case:   List[Any]  					=		hidden_size
											_snake_case:   int  					=		num_hidden_layers
											_snake_case:   List[str]  					=		num_attention_heads
											_snake_case:   List[Any]  					=		intermediate_size
											_snake_case:   Optional[Any]  					=		hidden_act
											_snake_case:   str  					=		hidden_dropout_prob
											_snake_case:   List[str]  					=		attention_probs_dropout_prob
											_snake_case:   Dict  					=		max_position_embeddings
											_snake_case:   Optional[Any]  					=		type_vocab_size
											_snake_case:   List[Any]  					=		type_sequence_label_size
											_snake_case:   List[str]  					=		initializer_range
											_snake_case:   List[str]  					=		num_labels
											_snake_case:   Tuple  					=		num_choices
											_snake_case:   Dict  					=		scope


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   str  					=		ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )

											_snake_case:   int  					=		None
											if self.use_input_mask:
																	_snake_case:   List[Any]  					=		random_attention_mask([self.batch_size, self.seq_length]       )

											_snake_case:   List[Any]  					=		None
											if self.use_token_type_ids:
																	_snake_case:   str  					=		ids_tensor([self.batch_size, self.seq_length]       ,     self.type_vocab_size       )

											_snake_case:   Optional[int]  					=		None
											_snake_case:   Tuple  					=		None
											_snake_case:   Union[str, Any]  					=		None
											if self.use_labels:
																	_snake_case:   Any  					=		ids_tensor([self.batch_size]       ,     self.type_sequence_label_size       )
																	_snake_case:   Optional[int]  					=		ids_tensor([self.batch_size, self.seq_length]       ,     self.num_labels       )
																	_snake_case:   Optional[int]  					=		ids_tensor([self.batch_size]       ,     self.num_choices       )

											_snake_case:   Union[str, Any]  					=		self.get_config()

											return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											return LlamaConfig(
											    vocab_size=self.vocab_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       ,     max_position_embeddings=self.max_position_embeddings       ,     type_vocab_size=self.type_vocab_size       ,     is_decoder=__snake_case       ,     initializer_range=self.initializer_range       ,     )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Any       ,     __snake_case	:  Any       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Tuple       ,     __snake_case	:  int       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		LlamaModel(config=__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   Optional[Any]  					=		model(__snake_case       ,     attention_mask=__snake_case       )
											_snake_case:   Tuple  					=		model(__snake_case       )
											self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ,     __snake_case	:  Any       ,     __snake_case	:  Tuple       ,     __snake_case	:  Tuple       ,     __snake_case	:  Dict       ,     __snake_case	:  str       ,     __snake_case	:  int       ,     __snake_case	:  List[str]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Any       ,     ):





											'''simple docstring'''





											_snake_case:   str  					=		True
											_snake_case:   int  					=		LlamaModel(__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   int  					=		model(
											    __snake_case       ,     attention_mask=__snake_case       ,     encoder_hidden_states=__snake_case       ,     encoder_attention_mask=__snake_case       ,     )
											_snake_case:   Optional[int]  					=		model(
											    __snake_case       ,     attention_mask=__snake_case       ,     encoder_hidden_states=__snake_case       ,     )
											_snake_case:   List[str]  					=		model(__snake_case       ,     attention_mask=__snake_case       )
											self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  List[str]       ,     __snake_case	:  Dict       ,     __snake_case	:  Dict       ,     __snake_case	:  List[str]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  int       ,     __snake_case	:  int       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Union[str, Any]       ,     ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		LlamaForCausalLM(config=__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   str  					=		model(__snake_case       ,     attention_mask=__snake_case       ,     labels=__snake_case       )
											self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  Dict       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  Any       ,     ):





											'''simple docstring'''





											_snake_case:   Any  					=		True
											_snake_case:   Optional[int]  					=		True
											_snake_case:   List[str]  					=		LlamaForCausalLM(config=__snake_case       )
											model.to(__snake_case       )
											model.eval()

											# first forward pass
											_snake_case:   Dict  					=		model(
											    __snake_case       ,     attention_mask=__snake_case       ,     encoder_hidden_states=__snake_case       ,     encoder_attention_mask=__snake_case       ,     use_cache=__snake_case       ,     )
											_snake_case:   Optional[int]  					=		outputs.past_key_values

											# create hypothetical multiple next token and extent to next_input_ids
											_snake_case:   Any  					=		ids_tensor((self.batch_size, 3)       ,     config.vocab_size       )
											_snake_case:   str  					=		ids_tensor((self.batch_size, 3)       ,     vocab_size=2       )

											# append to next input_ids and
											_snake_case:   Tuple  					=		torch.cat([input_ids, next_tokens]       ,     dim=-1       )
											_snake_case:   Tuple  					=		torch.cat([input_mask, next_mask]       ,     dim=-1       )

											_snake_case:   Tuple  					=		model(
											    __snake_case       ,     attention_mask=__snake_case       ,     encoder_hidden_states=__snake_case       ,     encoder_attention_mask=__snake_case       ,     output_hidden_states=__snake_case       ,     )['hidden_states'][0]
											_snake_case:   Dict  					=		model(
											    __snake_case       ,     attention_mask=__snake_case       ,     encoder_hidden_states=__snake_case       ,     encoder_attention_mask=__snake_case       ,     past_key_values=__snake_case       ,     output_hidden_states=__snake_case       ,     )['hidden_states'][0]

											# select random slice
											_snake_case:   List[Any]  					=		ids_tensor((1,)       ,     output_from_past.shape[-1]       ).item()
											_snake_case:   str  					=		output_from_no_past[:, -3:, random_slice_idx].detach()
											_snake_case:   Tuple  					=		output_from_past[:, :, random_slice_idx].detach()

											self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]       )

											# test that outputs are equal for slice
											self.parent.assertTrue(torch.allclose(__snake_case       ,     __snake_case       ,     atol=1e-3       )       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		self.prepare_config_and_inputs()
											(
											    (
											    _snake_case
											)					,						(
											    _snake_case
											)					,						(
											    _snake_case
											)					,						(
											    _snake_case
											)					,						(
											    _snake_case
											)					,						(
											    _snake_case
											)					,						(
											    _snake_case
											)					,						
											):   Dict  					=		config_and_inputs
											_snake_case:   Any  					=		{'input_ids': input_ids, 'attention_mask': input_mask}
											return config, inputs_dict







@require_torch
class 			lowerCamelCase					(						__UpperCAmelCase     ,							__UpperCAmelCase     ,							__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
					_SCREAMING_SNAKE_CASE							   =    (LlamaForCausalLM,) if is_torch_available() else ()
					_SCREAMING_SNAKE_CASE							   =    (
					    {
					        "feature-extraction": LlamaModel,
					        "text-classification": LlamaForSequenceClassification,
					        "text-generation": LlamaForCausalLM,
					        "zero-shot": LlamaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					_SCREAMING_SNAKE_CASE							   =    False
					_SCREAMING_SNAKE_CASE							   =    False


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		LlamaModelTester(self       )
											_snake_case:   Dict  					=		ConfigTester(self       ,     config_class=__snake_case       ,     hidden_size=37       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											self.config_tester.run_common_tests()


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																	_snake_case:   Union[str, Any]  					=		type
																	self.model_tester.create_and_check_model(*__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case					,						_snake_case:   Optional[Any]  					=		self.model_tester.prepare_config_and_inputs_for_common()
											_snake_case:   int  					=		3
											_snake_case:   Optional[Any]  					=		input_dict['input_ids']
											_snake_case:   Tuple  					=		input_ids.ne(1       ).to(__snake_case       )
											_snake_case:   Optional[Any]  					=		ids_tensor([self.model_tester.batch_size]       ,     self.model_tester.type_sequence_label_size       )
											_snake_case:   Union[str, Any]  					=		LlamaForSequenceClassification(__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   str  					=		model(__snake_case       ,     attention_mask=__snake_case       ,     labels=__snake_case       )
											self.assertEqual(result.logits.shape       ,     (self.model_tester.batch_size, self.model_tester.num_labels)       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case					,						_snake_case:   List[Any]  					=		self.model_tester.prepare_config_and_inputs_for_common()
											_snake_case:   Dict  					=		3
											_snake_case:   str  					=		'single_label_classification'
											_snake_case:   List[str]  					=		input_dict['input_ids']
											_snake_case:   Optional[int]  					=		input_ids.ne(1       ).to(__snake_case       )
											_snake_case:   List[str]  					=		ids_tensor([self.model_tester.batch_size]       ,     self.model_tester.type_sequence_label_size       )
											_snake_case:   Optional[Any]  					=		LlamaForSequenceClassification(__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   Optional[Any]  					=		model(__snake_case       ,     attention_mask=__snake_case       ,     labels=__snake_case       )
											self.assertEqual(result.logits.shape       ,     (self.model_tester.batch_size, self.model_tester.num_labels)       )


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





											'''simple docstring'''





											_snake_case					,						_snake_case:   int  					=		self.model_tester.prepare_config_and_inputs_for_common()
											_snake_case:   Any  					=		3
											_snake_case:   Optional[int]  					=		'multi_label_classification'
											_snake_case:   Tuple  					=		input_dict['input_ids']
											_snake_case:   Optional[Any]  					=		input_ids.ne(1       ).to(__snake_case       )
											_snake_case:   List[str]  					=		ids_tensor(
											    [self.model_tester.batch_size, config.num_labels]       ,     self.model_tester.type_sequence_label_size       ).to(torch.float       )
											_snake_case:   Union[str, Any]  					=		LlamaForSequenceClassification(__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   Optional[int]  					=		model(__snake_case       ,     attention_mask=__snake_case       ,     labels=__snake_case       )
											self.assertEqual(result.logits.shape       ,     (self.model_tester.batch_size, self.model_tester.num_labels)       )


					@unittest.skip('LLaMA buffers include complex numbers, which breaks this test'       )
					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											pass




					@parameterized.expand([('linear',), ('dynamic',)]       )
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case					,						_snake_case:   int  					=		self.model_tester.prepare_config_and_inputs_for_common()
											_snake_case:   Optional[int]  					=		ids_tensor([1, 10]       ,     config.vocab_size       )
											_snake_case:   Union[str, Any]  					=		ids_tensor([1, int(config.max_position_embeddings * 1.5       )]       ,     config.vocab_size       )

											set_seed(42       )  # Fixed seed at init time so the two models get the same random weights
											_snake_case:   Tuple  					=		LlamaModel(__snake_case       )
											original_model.to(__snake_case       )
											original_model.eval()
											_snake_case:   List[Any]  					=		original_model(__snake_case       ).last_hidden_state
											_snake_case:   List[str]  					=		original_model(__snake_case       ).last_hidden_state

											set_seed(42       )  # Fixed seed at init time so the two models get the same random weights
											_snake_case:   Tuple  					=		{'type': scaling_type, 'factor': 10.0}
											_snake_case:   List[Any]  					=		LlamaModel(__snake_case       )
											scaled_model.to(__snake_case       )
											scaled_model.eval()
											_snake_case:   Dict  					=		scaled_model(__snake_case       ).last_hidden_state
											_snake_case:   str  					=		scaled_model(__snake_case       ).last_hidden_state

											# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
											# maximum sequence length, so the outputs for the short input should match.
											if scaling_type == "dynamic":
																	self.assertTrue(torch.allclose(__snake_case       ,     __snake_case       ,     atol=1e-5       )       )
											else:
																	self.assertFalse(torch.allclose(__snake_case       ,     __snake_case       ,     atol=1e-5       )       )

											# The output should be different for long inputs
											self.assertFalse(torch.allclose(__snake_case       ,     __snake_case       ,     atol=1e-5       )       )







@require_torch
class 			lowerCamelCase					(						unittest.TestCase  ):


					@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'       )
					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
											_snake_case:   Optional[int]  					=		LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'       ,     device_map='auto'       )
											_snake_case:   Optional[int]  					=		model(torch.tensor([input_ids]       )       )
											# Expected mean on dim = -1
											_snake_case:   List[Any]  					=		torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]]       )
											torch.testing.assert_close(out.mean(-1       )       ,     __snake_case       ,     atol=1e-2       ,     rtol=1e-2       )
											# slicing logits[0, 0, 0:30]
											# fmt: off
											_snake_case:   Tuple  					=		torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,]       )
											# fmt: on
											torch.testing.assert_close(out[0, 0, :30]       ,     __snake_case       ,     atol=1e-5       ,     rtol=1e-5       )


					@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'       )
					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
											_snake_case:   Any  					=		LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'       ,     device_map='auto'       )
											_snake_case:   int  					=		model(torch.tensor(__snake_case       )       )
											# Expected mean on dim = -1
											_snake_case:   List[Any]  					=		torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]]       )
											torch.testing.assert_close(out.mean(-1       )       ,     __snake_case       ,     atol=1e-2       ,     rtol=1e-2       )
											# slicing logits[0, 0, 0:30]
											# fmt: off
											_snake_case:   Optional[Any]  					=		torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273]       )
											# fmt: on
											torch.testing.assert_close(out[0, 0, :30]       ,     __snake_case       ,     atol=1e-5       ,     rtol=1e-5       )


					@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'       )
					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
											_snake_case:   Any  					=		LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'       ,     device_map='auto'       )
											_snake_case:   List[str]  					=		model(torch.tensor(__snake_case       )       )
											# Expected mean on dim = -1
											_snake_case:   List[Any]  					=		torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]]       )
											torch.testing.assert_close(out.mean(-1       )       ,     __snake_case       ,     atol=1e-2       ,     rtol=1e-2       )
											# slicing logits[0, 0, 0:30]
											# fmt: off
											_snake_case:   Optional[int]  					=		torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513]       )
											# fmt: on
											torch.testing.assert_close(out.mean(-1       )       ,     __snake_case       ,     atol=1e-2       ,     rtol=1e-2       )


					@unittest.skip(
					    'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'       )
					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   int  					=		[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
											_snake_case:   Any  					=		LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'       ,     device_map='auto'       )
											_snake_case:   Dict  					=		model(torch.tensor(__snake_case       )       )

											_snake_case:   Optional[Any]  					=		torch.tensor(
											    [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]       ,     dtype=torch.floataa       )
											torch.testing.assert_close(out.mean(-1       )       ,     __snake_case       ,     atol=1e-2       ,     rtol=1e-2       )
											# fmt: off
											_snake_case:   str  					=		torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312]       )
											# fmt: on
											torch.testing.assert_close(out[0, 0, :30]       ,     __snake_case       ,     atol=1e-5       ,     rtol=1e-5       )




					@unittest.skip('Model is curently gated'       )
					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
											_snake_case:   Dict  					=		'Simply put, the theory of relativity states that '
											_snake_case:   List[str]  					=		LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'       )
											_snake_case:   Optional[Any]  					=		tokenizer.encode(__snake_case       ,     return_tensors='pt'       )
											_snake_case:   Optional[int]  					=		LlamaForCausalLM.from_pretrained(
											    'meta-llama/Llama-2-13b-chat-hf'       ,     device_map='sequential'       ,     use_safetensors=__snake_case       )

											# greedy generation outputs
											_snake_case:   List[Any]  					=		model.generate(__snake_case       ,     max_new_tokens=64       ,     top_p=__snake_case       ,     temperature=1       ,     do_sample=__snake_case       )
											_snake_case:   Dict  					=		tokenizer.decode(generated_ids[0]       ,     skip_special_tokens=__snake_case       )
											self.assertEqual(__snake_case       ,     __snake_case       )





