





import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any     ):
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   image.size
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		SCREAMING_SNAKE_CASE__								=   image.resize((w, h)       , resample=PIL_INTERPOLATION["""lanczos"""]     )
		SCREAMING_SNAKE_CASE__								=   np.array(UpperCamelCase__     ).astype(np.floataa     ) / 2_5_5.0
		SCREAMING_SNAKE_CASE__								=   image[None].transpose(0       , 3       , 1       , 2     )
		SCREAMING_SNAKE_CASE__								=   torch.from_numpy(UpperCamelCase__     )
		return 2.0 * image - 1.0




class 	UpperCamelCase_							(  UpperCamelCase__     ):


							def __init__(				self     :Any				,					__A     :VQModel				,					__A     :UNetaDModel				,					__A     :Union[
							        DDIMScheduler,
							        PNDMScheduler,
							        LMSDiscreteScheduler,
							        EulerDiscreteScheduler,
							        EulerAncestralDiscreteScheduler,
							        DPMSolverMultistepScheduler,
							    ]				,					) -> Tuple:



									"""simple docstring"""



									super().__init__()
									self.register_modules(vqvae=__A				,					unet=__A				,					scheduler=__A		)


							@torch.no_grad()
							def __call__(				self     :List[Any]				,					__A     :Union[torch.Tensor, PIL.Image.Image] = None				,					__A     :Optional[int] = 1				,					__A     :Optional[int] = 100				,					__A     :Optional[float] = 0.0				,					__A     :Optional[Union[torch.Generator, List[torch.Generator]]] = None				,					__A     :Optional[str] = "pil"				,					__A     :bool = True				,					) -> Union[Tuple, ImagePipelineOutput]:



									"""simple docstring"""



									if isinstance(__A				,					PIL.Image.Image		):
											SCREAMING_SNAKE_CASE__								=   1
									elif isinstance(__A				,					torch.Tensor		):
											SCREAMING_SNAKE_CASE__								=   image.shape[0]
									else:
											raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__A		)}'''		)

									if isinstance(__A				,					PIL.Image.Image		):
											SCREAMING_SNAKE_CASE__								=   preprocess(__A		)

									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   image.shape[-2:]

									# in_channels should be 6: 3 for latents, 3 for low resolution image
									SCREAMING_SNAKE_CASE__								=   (batch_size, self.unet.config.in_channels // 2, height, width)
									SCREAMING_SNAKE_CASE__								=   next(self.unet.parameters()		).dtype

									SCREAMING_SNAKE_CASE__								=   randn_tensor(__A				,					generator=__A				,					device=self.device				,					dtype=__A		)

									SCREAMING_SNAKE_CASE__								=   image.to(device=self.device				,					dtype=__A		)

									# set timesteps and move to the correct device
									self.scheduler.set_timesteps(__A				,					device=self.device		)
									SCREAMING_SNAKE_CASE__								=   self.scheduler.timesteps

									# scale the initial noise by the standard deviation required by the scheduler
									SCREAMING_SNAKE_CASE__								=   latents * self.scheduler.init_noise_sigma

									# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
									# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
									# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
									# and should be between [0, 1]
									SCREAMING_SNAKE_CASE__								=   """eta""" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
									SCREAMING_SNAKE_CASE__								=   {}
									if accepts_eta:
											SCREAMING_SNAKE_CASE__								=   eta

									for t in self.progress_bar(__A		):
											# concat latents and low resolution image in the channel dimension.
											SCREAMING_SNAKE_CASE__								=   torch.cat([latents, image]				,					dim=1		)
											SCREAMING_SNAKE_CASE__								=   self.scheduler.scale_model_input(__A				,					__A		)
											# predict the noise residual
											SCREAMING_SNAKE_CASE__								=   self.unet(__A				,					__A		).sample
											# compute the previous noisy sample x_t -> x_t-1
											SCREAMING_SNAKE_CASE__								=   self.scheduler.step(__A				,					__A				,					__A				,					**__A		).prev_sample

									# decode the image latents with the VQVAE
									SCREAMING_SNAKE_CASE__								=   self.vqvae.decode(__A		).sample
									SCREAMING_SNAKE_CASE__								=   torch.clamp(__A				,					-1.0				,					1.0		)
									SCREAMING_SNAKE_CASE__								=   image / 2 + 0.5
									SCREAMING_SNAKE_CASE__								=   image.cpu().permute(0				,					2				,					3				,					1		).numpy()

									if output_type == "pil":
											SCREAMING_SNAKE_CASE__								=   self.numpy_to_pil(__A		)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=__A		)





"""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__      :






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase=13    , lowercase=7    , lowercase=True    , lowercase=True    , lowercase=False    , lowercase=True    , lowercase=99    , lowercase=32    , lowercase=5    , lowercase=4    , lowercase=37    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=16    , lowercase=2    , lowercase=0.02    , lowercase=3    , lowercase=4    , lowercase=None    , ):
							_lowerCamelCase	:     Tuple       =       parent
							_lowerCamelCase	:     Optional[int]       =       batch_size
							_lowerCamelCase	:     str       =       seq_length
							_lowerCamelCase	:     Dict       =       is_training
							_lowerCamelCase	:     List[Any]       =       use_input_mask
							_lowerCamelCase	:     Optional[int]       =       use_token_type_ids
							_lowerCamelCase	:     str       =       use_labels
							_lowerCamelCase	:     int       =       vocab_size
							_lowerCamelCase	:     Any       =       hidden_size
							_lowerCamelCase	:     Tuple       =       num_hidden_layers
							_lowerCamelCase	:     Optional[int]       =       num_attention_heads
							_lowerCamelCase	:     Union[str, Any]       =       intermediate_size
							_lowerCamelCase	:     Dict       =       hidden_act
							_lowerCamelCase	:     Any       =       hidden_dropout_prob
							_lowerCamelCase	:     str       =       attention_probs_dropout_prob
							_lowerCamelCase	:     List[Any]       =       max_position_embeddings
							_lowerCamelCase	:     Any       =       type_vocab_size
							_lowerCamelCase	:     Dict       =       type_sequence_label_size
							_lowerCamelCase	:     List[Any]       =       initializer_range
							_lowerCamelCase	:     Any       =       num_labels
							_lowerCamelCase	:     List[str]       =       num_choices
							_lowerCamelCase	:     Tuple       =       scope
			def    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size    )

							_lowerCamelCase	:     int       =       None
							if self.use_input_mask:
											_lowerCamelCase	:     str       =       random_attention_mask([self.batch_size, self.seq_length]    )

							_lowerCamelCase	:     Tuple       =       None
							if self.use_token_type_ids:
											_lowerCamelCase	:     Optional[int]       =       ids_tensor([self.batch_size, self.seq_length]    , self.type_vocab_size    )

							_lowerCamelCase	:     List[Any]       =       None
							_lowerCamelCase	:     Optional[Any]       =       None
							_lowerCamelCase	:     Optional[Any]       =       None
							if self.use_labels:
											_lowerCamelCase	:     Any       =       ids_tensor([self.batch_size]    , self.type_sequence_label_size    )
											_lowerCamelCase	:     Tuple       =       ids_tensor([self.batch_size, self.seq_length]    , self.num_labels    )
											_lowerCamelCase	:     Optional[Any]       =       ids_tensor([self.batch_size]    , self.num_choices    )

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

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
			def    A_       (    self    ):
							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=lowercase    , initializer_range=self.initializer_range    , )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     str       =       LlamaModel(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[Any]       =       model(lowercase    , attention_mask=lowercase    )
							_lowerCamelCase	:     Any       =       model(lowercase    )
							self.parent.assertEqual(result.last_hidden_state.shape    , (self.batch_size, self.seq_length, self.hidden_size)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , ):
							_lowerCamelCase	:     str       =       True
							_lowerCamelCase	:     Dict       =       LlamaModel(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     Union[str, Any]       =       model(
							    lowercase    , attention_mask=lowercase    , encoder_hidden_states=lowercase    , encoder_attention_mask=lowercase    , )
							_lowerCamelCase	:     Tuple       =       model(
							    lowercase    , attention_mask=lowercase    , encoder_hidden_states=lowercase    , )
							_lowerCamelCase	:     Union[str, Any]       =       model(lowercase    , attention_mask=lowercase    )
							self.parent.assertEqual(result.last_hidden_state.shape    , (self.batch_size, self.seq_length, self.hidden_size)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , ):
							_lowerCamelCase	:     Optional[Any]       =       LlamaForCausalLM(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[Any]       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.vocab_size)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , ):
							_lowerCamelCase	:     int       =       True
							_lowerCamelCase	:     Optional[Any]       =       True
							_lowerCamelCase	:     str       =       LlamaForCausalLM(config=lowercase    )
							model.to(lowercase    )
							model.eval()

							# first forward pass
							_lowerCamelCase	:     Dict       =       model(
							    lowercase    , attention_mask=lowercase    , encoder_hidden_states=lowercase    , encoder_attention_mask=lowercase    , use_cache=lowercase    , )
							_lowerCamelCase	:     Union[str, Any]       =       outputs.past_key_values

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

							# append to next input_ids and
							_lowerCamelCase	:     Optional[Any]       =       torch.cat([input_ids, next_tokens]    , dim=-1    )
							_lowerCamelCase	:     int       =       torch.cat([input_mask, next_mask]    , dim=-1    )

							_lowerCamelCase	:     Any       =       model(
							    lowercase    , attention_mask=lowercase    , encoder_hidden_states=lowercase    , encoder_attention_mask=lowercase    , output_hidden_states=lowercase    , )['hidden_states'][0]
							_lowerCamelCase	:     Dict       =       model(
							    lowercase    , attention_mask=lowercase    , encoder_hidden_states=lowercase    , encoder_attention_mask=lowercase    , past_key_values=lowercase    , output_hidden_states=lowercase    , )['hidden_states'][0]

							# select random slice
							_lowerCamelCase	:     Optional[Any]       =       ids_tensor((1,)    , output_from_past.shape[-1]    ).item()
							_lowerCamelCase	:     Any       =       output_from_no_past[:, -3:, random_slice_idx].detach()
							_lowerCamelCase	:     Any       =       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(lowercase    , lowercase    , atol=1E-3    )    )






			def    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       self.prepare_config_and_inputs()
							(
							    (
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					(
							    _lowerCamelCase
							),					
							)	:     List[str]       =       config_and_inputs
							_lowerCamelCase	:     int       =       {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict






@require_torch
class lowerCAmelCase__      (	lowercase,      lowercase,      lowercase,      unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__ =     (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
			lowerCamelCase__ =     (LlamaForCausalLM,) if is_torch_available() else ()
			lowerCamelCase__ =     (
			    {
			        """feature-extraction""": LlamaModel,
			        """text-classification""": LlamaForSequenceClassification,
			        """text-generation""": LlamaForCausalLM,
			        """zero-shot""": LlamaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__ =     False
			lowerCamelCase__ =     False
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[Any]       =       LlamaModelTester(self    )
							_lowerCamelCase	:     List[Any]       =       ConfigTester(self    , config_class=lowercase    , hidden_size=37    )
			def    A_       (    self    ):
							self.config_tester.run_common_tests()
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[int]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Any       =       self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
											_lowerCamelCase	:     Any       =       type
											self.model_tester.create_and_check_model(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     Tuple       =       self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase	:     List[str]       =       3
							_lowerCamelCase	:     Dict       =       input_dict['input_ids']
							_lowerCamelCase	:     List[str]       =       input_ids.ne(1    ).to(lowercase    )
							_lowerCamelCase	:     Tuple       =       ids_tensor([self.model_tester.batch_size]    , self.model_tester.type_sequence_label_size    )
							_lowerCamelCase	:     Union[str, Any]       =       LlamaForSequenceClassification(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[str]       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							self.assertEqual(result.logits.shape    , (self.model_tester.batch_size, self.model_tester.num_labels)    )
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     Optional[Any]       =       self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase	:     Dict       =       3
							_lowerCamelCase	:     List[str]       =       'single_label_classification'
							_lowerCamelCase	:     Union[str, Any]       =       input_dict['input_ids']
							_lowerCamelCase	:     List[Any]       =       input_ids.ne(1    ).to(lowercase    )
							_lowerCamelCase	:     Optional[Any]       =       ids_tensor([self.model_tester.batch_size]    , self.model_tester.type_sequence_label_size    )
							_lowerCamelCase	:     List[Any]       =       LlamaForSequenceClassification(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     int       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							self.assertEqual(result.logits.shape    , (self.model_tester.batch_size, self.model_tester.num_labels)    )
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     List[str]       =       self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase	:     List[Any]       =       3
							_lowerCamelCase	:     Union[str, Any]       =       'multi_label_classification'
							_lowerCamelCase	:     Dict       =       input_dict['input_ids']
							_lowerCamelCase	:     Optional[Any]       =       input_ids.ne(1    ).to(lowercase    )
							_lowerCamelCase	:     List[Any]       =       ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]    , self.model_tester.type_sequence_label_size    ).to(torch.float    )
							_lowerCamelCase	:     Optional[Any]       =       LlamaForSequenceClassification(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     int       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							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    A_       (    self    ):
							pass






			@parameterized.expand([('linear',), ('dynamic',)]    )
			def    A_       (    self    , lowercase    ):
							_lowerCamelCase,					_lowerCamelCase	:     Any       =       self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase	:     List[Any]       =       ids_tensor([1, 10]    , config.vocab_size    )
							_lowerCamelCase	:     int       =       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
							_lowerCamelCase	:     Union[str, Any]       =       LlamaModel(lowercase    )
							original_model.to(lowercase    )
							original_model.eval()
							_lowerCamelCase	:     str       =       original_model(lowercase    ).last_hidden_state
							_lowerCamelCase	:     int       =       original_model(lowercase    ).last_hidden_state

							set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
							_lowerCamelCase	:     List[str]       =       {'type': scaling_type, 'factor': 10.0}
							_lowerCamelCase	:     Optional[int]       =       LlamaModel(lowercase    )
							scaled_model.to(lowercase    )
							scaled_model.eval()
							_lowerCamelCase	:     int       =       scaled_model(lowercase    ).last_hidden_state
							_lowerCamelCase	:     Union[str, Any]       =       scaled_model(lowercase    ).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(lowercase    , lowercase    , atol=1E-5    )    )
							else:
											self.assertFalse(torch.allclose(lowercase    , lowercase    , atol=1E-5    )    )

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






@require_torch
class lowerCAmelCase__      (	unittest.TestCase		):






			'''simple docstring'''



			@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'    )
			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
							_lowerCamelCase	:     Dict       =       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'    , device_map='auto'    )
							_lowerCamelCase	:     Union[str, Any]       =       model(torch.tensor([input_ids]    )    )
							# Expected mean on dim = -1
							_lowerCamelCase	:     List[str]       =       torch.tensor([[-6.65_50, -4.12_27, -4.98_59, -3.24_06, 0.82_62, -3.00_33, 1.29_64, -3.36_99]]    )
							torch.testing.assert_close(out.mean(-1    )    , lowercase    , atol=1E-2    , rtol=1E-2    )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							_lowerCamelCase	:     Any       =       torch.tensor([-12.82_81, -7.44_53, -0.46_39, -8.06_25, -7.25_00, -8.00_00, -6.48_83, -7.76_95, -7.84_38, -7.03_12, -6.21_88, -7.13_28, -1.84_96, 1.99_61, -8.62_50, -6.72_27, -12.82_81, -6.94_92, -7.07_42, -7.78_52, -7.58_20, -7.90_62, -6.93_75, -7.98_05, -8.34_38, -8.15_62, -8.04_69, -7.62_50, -7.74_22, -7.33_98,]    )
							# fmt: on
							torch.testing.assert_close(out[0, 0, :30]    , lowercase    , atol=1E-5    , rtol=1E-5    )
			@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'    )
			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
							_lowerCamelCase	:     str       =       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'    , device_map='auto'    )
							_lowerCamelCase	:     List[Any]       =       model(torch.tensor(lowercase    )    )
							# Expected mean on dim = -1
							_lowerCamelCase	:     Tuple       =       torch.tensor([[-2.06_22, -1.27_94, -1.16_38, -0.97_88, -1.46_03, -1.02_38, -1.78_93, -1.44_11]]    )
							torch.testing.assert_close(out.mean(-1    )    , lowercase    , atol=1E-2    , rtol=1E-2    )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							_lowerCamelCase	:     Dict       =       torch.tensor([-8.14_06, -8.05_47, 2.74_61, -1.23_44, -0.14_48, -1.82_62, -1.00_20, -1.81_54, -1.68_95, -1.85_16, -2.35_74, -0.92_77, 3.75_98, 6.57_42, -1.29_98, -0.11_77, -8.14_06, -2.96_88, -2.91_99, -3.16_99, -3.52_54, -2.35_55, -2.79_88, -3.41_41, -2.82_62, -4.51_95, -3.33_79, -3.31_64, -2.78_32, -3.02_73]    )
							# fmt: on
							torch.testing.assert_close(out[0, 0, :30]    , lowercase    , atol=1E-5    , rtol=1E-5    )
			@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'    )
			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
							_lowerCamelCase	:     Optional[Any]       =       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'    , device_map='auto'    )
							_lowerCamelCase	:     int       =       model(torch.tensor(lowercase    )    )
							# Expected mean on dim = -1
							_lowerCamelCase	:     str       =       torch.tensor([[-0.85_62, -1.85_20, -0.75_51, -0.41_62, -1.51_61, -1.20_38, -2.48_23, -2.32_54]]    )
							torch.testing.assert_close(out.mean(-1    )    , lowercase    , atol=1E-2    , rtol=1E-2    )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							_lowerCamelCase	:     Union[str, Any]       =       torch.tensor([-2.22_27, 4.88_28, 0.90_23, -0.45_78, -0.78_71, -0.10_33, -0.62_21, -0.57_86, -0.78_03, -1.06_74, -1.29_20, -0.15_70, 0.80_08, 2.07_23, -0.94_97, 0.27_71, -2.22_27, -0.76_12, -1.43_46, -1.20_61, -1.64_26, -0.30_00, -0.71_39, -1.19_34, -1.86_91, -1.69_73, -1.59_47, -1.27_05, -0.35_23, -0.55_13]    )
							# fmt: on
							torch.testing.assert_close(out.mean(-1    )    , lowercase    , 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    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       [1, 306, 4658, 278, 6593, 310, 2834, 338]
							_lowerCamelCase	:     int       =       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'    , device_map='auto'    )
							_lowerCamelCase	:     Union[str, Any]       =       model(torch.tensor(lowercase    )    )

							_lowerCamelCase	:     Optional[Any]       =       torch.tensor(
							    [[-4.23_27, -3.33_60, -4.66_65, -4.76_31, -1.81_80, -3.41_70, -1.42_11, -3.18_10]]    , dtype=torch.floataa    )
							torch.testing.assert_close(out.mean(-1    )    , lowercase    , atol=1E-2    , rtol=1E-2    )
							# fmt: off
							_lowerCamelCase	:     List[str]       =       torch.tensor([-9.49_22, -3.95_51, 1.79_98, -5.67_58, -5.10_55, -5.89_84, -4.83_20, -6.80_86, -6.53_91, -5.61_72, -5.58_20, -5.53_52, 1.78_81, 3.62_89, -6.51_17, -3.47_85, -9.50_00, -6.03_52, -6.81_25, -6.01_95, -6.68_36, -5.47_27, -6.28_12, -6.03_91, -7.33_98, -7.42_97, -7.48_44, -6.58_20, -5.87_89, -5.53_12]    )
							# fmt: on
							torch.testing.assert_close(out[0, 0, :30]    , lowercase    , atol=1E-5    , rtol=1E-5    )






			@unittest.skip('Model is curently gated'    )
			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       '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'
							_lowerCamelCase	:     str       =       'Simply put, the theory of relativity states that '
							_lowerCamelCase	:     List[Any]       =       LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'    )
							_lowerCamelCase	:     List[str]       =       tokenizer.encode(lowercase    , return_tensors='pt'    )
							_lowerCamelCase	:     List[str]       =       LlamaForCausalLM.from_pretrained(
							    'meta-llama/Llama-2-13b-chat-hf'    , device_map='sequential'    , use_safetensors=lowercase    )

							# greedy generation outputs
							_lowerCamelCase	:     Optional[int]       =       model.generate(lowercase    , max_new_tokens=64    , top_p=lowercase    , temperature=1    , do_sample=lowercase    )
							_lowerCamelCase	:     Optional[int]       =       tokenizer.decode(generated_ids[0]    , skip_special_tokens=lowercase    )
							self.assertEqual(lowercase    , lowercase    )