






import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
		import transformers



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]    )						->      str:





				"""simple docstring"""

				random.seed(__UpperCamelCase    )
				np.random.seed(__UpperCamelCase    )
				torch.manual_seed(__UpperCamelCase    )
				torch.cuda.manual_seed_all(__UpperCamelCase    )
				# ^^ safe to call this function even if cuda is not available






class       __lowercase      :







					'''simple docstring'''




					def __init__(				self       :			Any ,	UpperCamelCase_       :			Iterable[torch.nn.Parameter] ,	UpperCamelCase_       :			float = 0.9999 ,	UpperCamelCase_       :			float = 0.0 ,	UpperCamelCase_       :			int = 0 ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			Union[float, int] = 1.0 ,	UpperCamelCase_       :			Union[float, int] = 2 / 3 ,	UpperCamelCase_       :			Optional[Any] = None ,	UpperCamelCase_       :			Dict[str, Any] = None ,	**UpperCamelCase_       :			List[Any] ,	):


									"""simple docstring"""



									if isinstance(_snake_case ,	torch.nn.Module				):
													__A         =	(
													    """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
													    """Please pass the parameters of the module instead."""
													)
													deprecate(
													    """passing a `torch.nn.Module` to `ExponentialMovingAverage`""" ,	"""1.0.0""" ,	_snake_case ,	standard_warn=_snake_case ,	)
													__A         =	parameters.parameters()

													# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
													__A         =	True

									if kwargs.get("""max_value""" ,	_snake_case				) is not None:
													__A         =	"""The `max_value` argument is deprecated. Please use `decay` instead."""
													deprecate("""max_value""" ,	"""1.0.0""" ,	_snake_case ,	standard_warn=_snake_case				)
													__A         =	kwargs["""max_value"""]

									if kwargs.get("""min_value""" ,	_snake_case				) is not None:
													__A         =	"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
													deprecate("""min_value""" ,	"""1.0.0""" ,	_snake_case ,	standard_warn=_snake_case				)
													__A         =	kwargs["""min_value"""]

									__A         =	list(_snake_case				)
									__A         =	[p.clone().detach() for p in parameters]

									if kwargs.get("""device""" ,	_snake_case				) is not None:
													__A         =	"""The `device` argument is deprecated. Please use `to` instead."""
													deprecate("""device""" ,	"""1.0.0""" ,	_snake_case ,	standard_warn=_snake_case				)
													self.to(device=kwargs["""device"""]				)

									__A         =	None

									__A         =	decay
									__A         =	min_decay
									__A         =	update_after_step
									__A         =	use_ema_warmup
									__A         =	inv_gamma
									__A         =	power
									__A         =	0
									__A         =	None  # set in `step()`

									__A         =	model_cls
									__A         =	model_config




					@classmethod
					def 		lowerCAmelCase_     (				cls       :			Optional[Any] ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			str				):


									"""simple docstring"""



									__A    ,	__A         =	model_cls.load_config(_snake_case ,	return_unused_kwargs=_snake_case				)
									__A         =	model_cls.from_pretrained(_snake_case				)

									__A         =	cls(model.parameters() ,	model_cls=_snake_case ,	model_config=model.config				)

									ema_model.load_state_dict(_snake_case				)
									return ema_model




					def 		lowerCAmelCase_     (				self       :			str ,	UpperCamelCase_       :			Dict				):


									"""simple docstring"""



									if self.model_cls is None:
													raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""				)

									if self.model_config is None:
													raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""				)

									__A         =	self.model_cls.from_config(self.model_config				)
									__A         =	self.state_dict()
									state_dict.pop("""shadow_params""" ,	_snake_case				)

									model.register_to_config(**_snake_case				)
									self.copy_to(model.parameters()				)
									model.save_pretrained(_snake_case				)




					def 		lowerCAmelCase_     (				self       :			List[Any] ,	UpperCamelCase_       :			int				):


									"""simple docstring"""



									__A         =	max(0 ,	optimization_step - self.update_after_step - 1				)

									if step <= 0:
													return 0.0

									if self.use_ema_warmup:
													__A         =	1 - (1 + step / self.inv_gamma) ** -self.power
									else:
													__A         =	(1 + step) / (10 + step)

									__A         =	min(_snake_case ,	self.decay				)
									# make sure decay is not smaller than min_decay
									__A         =	max(_snake_case ,	self.min_decay				)
									return cur_decay_value




					@torch.no_grad()
					def 		lowerCAmelCase_     (				self       :			int ,	UpperCamelCase_       :			Iterable[torch.nn.Parameter]				):


									"""simple docstring"""



									if isinstance(_snake_case ,	torch.nn.Module				):
													__A         =	(
													    """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
													    """Please pass the parameters of the module instead."""
													)
													deprecate(
													    """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`""" ,	"""1.0.0""" ,	_snake_case ,	standard_warn=_snake_case ,	)
													__A         =	parameters.parameters()

									__A         =	list(_snake_case				)

									self.optimization_step += 1

									# Compute the decay factor for the exponential moving average.
									__A         =	self.get_decay(self.optimization_step				)
									__A         =	decay
									__A         =	1 - decay

									__A         =	contextlib.nullcontext
									if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
													import deepspeed

									for s_param, param in zip(self.shadow_params ,	_snake_case				):
													if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																	__A         =	deepspeed.zero.GatheredParameters(_snake_case ,	modifier_rank=_snake_case				)

													with context_manager():
																	if param.requires_grad:
																					s_param.sub_(one_minus_decay * (s_param - param)				)
																	else:
																					s_param.copy_(_snake_case				)




					def 		lowerCAmelCase_     (				self       :			Dict ,	UpperCamelCase_       :			Iterable[torch.nn.Parameter]				):


									"""simple docstring"""



									__A         =	list(_snake_case				)
									for s_param, param in zip(self.shadow_params ,	_snake_case				):
													param.data.copy_(s_param.to(param.device				).data				)




					def 		lowerCAmelCase_     (				self       :			Optional[int] ,	UpperCamelCase_       :			Optional[int]=None ,	UpperCamelCase_       :			List[str]=None				):


									"""simple docstring"""



									__A         =	[
									    p.to(device=_snake_case ,	dtype=_snake_case				) if p.is_floating_point() else p.to(device=_snake_case				)
									    for p in self.shadow_params
									]




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


									"""simple docstring"""



									return {
									    "decay": self.decay,
									    "min_decay": self.min_decay,
									    "optimization_step": self.optimization_step,
									    "update_after_step": self.update_after_step,
									    "use_ema_warmup": self.use_ema_warmup,
									    "inv_gamma": self.inv_gamma,
									    "power": self.power,
									    "shadow_params": self.shadow_params,
									}




					def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			Iterable[torch.nn.Parameter]				):


									"""simple docstring"""



									__A         =	[param.detach().cpu().clone() for param in parameters]




					def 		lowerCAmelCase_     (				self       :			Tuple ,	UpperCamelCase_       :			Iterable[torch.nn.Parameter]				):


									"""simple docstring"""



									if self.temp_stored_params is None:
													raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""				)
									for c_param, param in zip(self.temp_stored_params ,	_snake_case				):
													param.data.copy_(c_param.data				)

									# Better memory-wise.
									__A         =	None





					def 		lowerCAmelCase_     (				self       :			Optional[int] ,	UpperCamelCase_       :			dict				):


									"""simple docstring"""



									__A         =	copy.deepcopy(_snake_case				)

									__A         =	state_dict.get("""decay""" ,	self.decay				)
									if self.decay < 0.0 or self.decay > 1.0:
													raise ValueError("""Decay must be between 0 and 1"""				)

									__A         =	state_dict.get("""min_decay""" ,	self.min_decay				)
									if not isinstance(self.min_decay ,	_snake_case				):
													raise ValueError("""Invalid min_decay"""				)

									__A         =	state_dict.get("""optimization_step""" ,	self.optimization_step				)
									if not isinstance(self.optimization_step ,	_snake_case				):
													raise ValueError("""Invalid optimization_step"""				)

									__A         =	state_dict.get("""update_after_step""" ,	self.update_after_step				)
									if not isinstance(self.update_after_step ,	_snake_case				):
													raise ValueError("""Invalid update_after_step"""				)

									__A         =	state_dict.get("""use_ema_warmup""" ,	self.use_ema_warmup				)
									if not isinstance(self.use_ema_warmup ,	_snake_case				):
													raise ValueError("""Invalid use_ema_warmup"""				)

									__A         =	state_dict.get("""inv_gamma""" ,	self.inv_gamma				)
									if not isinstance(self.inv_gamma ,	(float, int)				):
													raise ValueError("""Invalid inv_gamma"""				)

									__A         =	state_dict.get("""power""" ,	self.power				)
									if not isinstance(self.power ,	(float, int)				):
													raise ValueError("""Invalid power"""				)

									__A         =	state_dict.get("""shadow_params""" ,	_snake_case				)
									if shadow_params is not None:
													__A         =	shadow_params
													if not isinstance(self.shadow_params ,	_snake_case				):
																	raise ValueError("""shadow_params must be a list"""				)
													if not all(isinstance(_snake_case ,	torch.Tensor				) for p in self.shadow_params				):
																	raise ValueError("""shadow_params must all be Tensors"""				)












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       __lowercase      :







     '''simple docstring'''




     def __init__(				self       :			Dict ,	UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			Any=13 ,	UpperCamelCase_       :			int=7 ,	UpperCamelCase_       :			List[str]=True ,	UpperCamelCase_       :			Optional[int]=True ,	UpperCamelCase_       :			Optional[int]=False ,	UpperCamelCase_       :			Dict=True ,	UpperCamelCase_       :			Any=99 ,	UpperCamelCase_       :			Dict=32 ,	UpperCamelCase_       :			str=5 ,	UpperCamelCase_       :			Dict=4 ,	UpperCamelCase_       :			str=37 ,	UpperCamelCase_       :			List[Any]="gelu" ,	UpperCamelCase_       :			Dict=0.1 ,	UpperCamelCase_       :			List[str]=0.1 ,	UpperCamelCase_       :			List[Any]=512 ,	UpperCamelCase_       :			Optional[Any]=16 ,	UpperCamelCase_       :			Dict=2 ,	UpperCamelCase_       :			Any=0.02 ,	UpperCamelCase_       :			Tuple=3 ,	UpperCamelCase_       :			Optional[int]=4 ,	UpperCamelCase_       :			str=None ,	):


         """simple docstring"""



         __A         =	parent
         __A         =	batch_size
         __A         =	seq_length
         __A         =	is_training
         __A         =	use_input_mask
         __A         =	use_token_type_ids
         __A         =	use_labels
         __A         =	vocab_size
         __A         =	hidden_size
         __A         =	num_hidden_layers
         __A         =	num_attention_heads
         __A         =	intermediate_size
         __A         =	hidden_act
         __A         =	hidden_dropout_prob
         __A         =	attention_probs_dropout_prob
         __A         =	max_position_embeddings
         __A         =	type_vocab_size
         __A         =	type_sequence_label_size
         __A         =	initializer_range
         __A         =	num_labels
         __A         =	num_choices
         __A         =	scope




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A         =	ids_tensor([self.batch_size, self.seq_length] ,	self.vocab_size				)

         __A         =	None
         if self.use_input_mask:
             __A         =	random_attention_mask([self.batch_size, self.seq_length]				)

         __A         =	None
         if self.use_token_type_ids:
             __A         =	ids_tensor([self.batch_size, self.seq_length] ,	self.type_vocab_size				)

         __A         =	None
         __A         =	None
         __A         =	None
         if self.use_labels:
             __A         =	ids_tensor([self.batch_size] ,	self.type_sequence_label_size				)
             __A         =	ids_tensor([self.batch_size, self.seq_length] ,	self.num_labels				)
             __A         =	ids_tensor([self.batch_size] ,	self.num_choices				)

         __A         =	self.get_config()

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




     def 		lowerCAmelCase_     (				self       :			Dict				):


         """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=UpperCamelCase_ ,	initializer_range=self.initializer_range ,	)




     def 		lowerCAmelCase_     (				self       :			int ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Any				):


         """simple docstring"""



         __A         =	LlamaModel(config=UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_				)
         __A         =	model(UpperCamelCase_				)
         self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, self.seq_length, self.hidden_size)				)




     def 		lowerCAmelCase_     (				self       :			Dict ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Tuple ,	):


         """simple docstring"""



         __A         =	True
         __A         =	LlamaModel(UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(
             UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	encoder_hidden_states=UpperCamelCase_ ,	encoder_attention_mask=UpperCamelCase_ ,	)
         __A         =	model(
             UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	encoder_hidden_states=UpperCamelCase_ ,	)
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_				)
         self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, self.seq_length, self.hidden_size)				)




     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			str ,	):


         """simple docstring"""



         __A         =	LlamaForCausalLM(config=UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	labels=UpperCamelCase_				)
         self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.seq_length, self.vocab_size)				)




     def 		lowerCAmelCase_     (				self       :			Dict ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Union[str, Any] ,	):


         """simple docstring"""



         __A         =	True
         __A         =	True
         __A         =	LlamaForCausalLM(config=UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()

         # first forward pass
         __A         =	model(
             UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	encoder_hidden_states=UpperCamelCase_ ,	encoder_attention_mask=UpperCamelCase_ ,	use_cache=UpperCamelCase_ ,	)
         __A         =	outputs.past_key_values

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

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

         __A         =	model(
             UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	encoder_hidden_states=UpperCamelCase_ ,	encoder_attention_mask=UpperCamelCase_ ,	output_hidden_states=UpperCamelCase_ ,	)["""hidden_states"""][0]
         __A         =	model(
             UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	encoder_hidden_states=UpperCamelCase_ ,	encoder_attention_mask=UpperCamelCase_ ,	past_key_values=UpperCamelCase_ ,	output_hidden_states=UpperCamelCase_ ,	)["""hidden_states"""][0]

         # select random slice
         __A         =	ids_tensor((1,) ,	output_from_past.shape[-1]				).item()
         __A         =	output_from_no_past[:, -3:, random_slice_idx].detach()
         __A         =	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(UpperCamelCase_ ,	UpperCamelCase_ ,	atol=1e-3				)				)





     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A         =	self.prepare_config_and_inputs()
         (
             (
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	(
             __A
         )    ,	
         )         =	config_and_inputs
         __A         =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
         return config, inputs_dict


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







     '''simple docstring'''
     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 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A         =	LlamaModelTester(self				)
         __A         =	ConfigTester(self ,	config_class=UpperCamelCase_ ,	hidden_size=37				)




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         self.config_tester.run_common_tests()




     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A         =	self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             __A         =	type
             self.model_tester.create_and_check_model(*UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()
         __A         =	3
         __A         =	input_dict["""input_ids"""]
         __A         =	input_ids.ne(1				).to(UpperCamelCase_				)
         __A         =	ids_tensor([self.model_tester.batch_size] ,	self.model_tester.type_sequence_label_size				)
         __A         =	LlamaForSequenceClassification(UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	labels=UpperCamelCase_				)
         self.assertEqual(result.logits.shape ,	(self.model_tester.batch_size, self.model_tester.num_labels)				)




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()
         __A         =	3
         __A         =	"""single_label_classification"""
         __A         =	input_dict["""input_ids"""]
         __A         =	input_ids.ne(1				).to(UpperCamelCase_				)
         __A         =	ids_tensor([self.model_tester.batch_size] ,	self.model_tester.type_sequence_label_size				)
         __A         =	LlamaForSequenceClassification(UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	labels=UpperCamelCase_				)
         self.assertEqual(result.logits.shape ,	(self.model_tester.batch_size, self.model_tester.num_labels)				)




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()
         __A         =	3
         __A         =	"""multi_label_classification"""
         __A         =	input_dict["""input_ids"""]
         __A         =	input_ids.ne(1				).to(UpperCamelCase_				)
         __A         =	ids_tensor(
             [self.model_tester.batch_size, config.num_labels] ,	self.model_tester.type_sequence_label_size				).to(torch.float				)
         __A         =	LlamaForSequenceClassification(UpperCamelCase_				)
         model.to(UpperCamelCase_				)
         model.eval()
         __A         =	model(UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	labels=UpperCamelCase_				)
         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 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         pass





     @parameterized.expand([("""linear""",), ("""dynamic""",)]				)
     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			Optional[Any]				):


         """simple docstring"""



         __A    ,	__A         =	self.model_tester.prepare_config_and_inputs_for_common()
         __A         =	ids_tensor([1, 10] ,	config.vocab_size				)
         __A         =	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
         __A         =	LlamaModel(UpperCamelCase_				)
         original_model.to(UpperCamelCase_				)
         original_model.eval()
         __A         =	original_model(UpperCamelCase_				).last_hidden_state
         __A         =	original_model(UpperCamelCase_				).last_hidden_state

         set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
         __A         =	{"""type""": scaling_type, """factor""": 10.0}
         __A         =	LlamaModel(UpperCamelCase_				)
         scaled_model.to(UpperCamelCase_				)
         scaled_model.eval()
         __A         =	scaled_model(UpperCamelCase_				).last_hidden_state
         __A         =	scaled_model(UpperCamelCase_				).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(UpperCamelCase_ ,	UpperCamelCase_ ,	atol=1e-5				)				)
         else:
             self.assertFalse(torch.allclose(UpperCamelCase_ ,	UpperCamelCase_ ,	atol=1e-5				)				)

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


@require_torch
class       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




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


         """simple docstring"""



         __A         =	[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A         =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf""" ,	device_map="""auto"""				)
         __A         =	model(torch.tensor([input_ids]				)				)
         # Expected mean on dim = -1
         __A         =	torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]				)
         torch.testing.assert_close(out.mean(-1				) ,	UpperCamelCase_ ,	atol=1e-2 ,	rtol=1e-2				)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __A         =	torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]				)
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30] ,	UpperCamelCase_ ,	atol=1e-5 ,	rtol=1e-5				)




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


         """simple docstring"""



         __A         =	[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A         =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf""" ,	device_map="""auto"""				)
         __A         =	model(torch.tensor(UpperCamelCase_				)				)
         # Expected mean on dim = -1
         __A         =	torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]				)
         torch.testing.assert_close(out.mean(-1				) ,	UpperCamelCase_ ,	atol=1e-2 ,	rtol=1e-2				)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __A         =	torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]				)
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30] ,	UpperCamelCase_ ,	atol=1e-5 ,	rtol=1e-5				)




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


         """simple docstring"""



         __A         =	[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A         =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf""" ,	device_map="""auto"""				)
         __A         =	model(torch.tensor(UpperCamelCase_				)				)
         # Expected mean on dim = -1
         __A         =	torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]				)
         torch.testing.assert_close(out.mean(-1				) ,	UpperCamelCase_ ,	atol=1e-2 ,	rtol=1e-2				)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __A         =	torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]				)
         # fmt: on
         torch.testing.assert_close(out.mean(-1				) ,	UpperCamelCase_ ,	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 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A         =	[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A         =	LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf""" ,	device_map="""auto"""				)
         __A         =	model(torch.tensor(UpperCamelCase_				)				)

         __A         =	torch.tensor(
             [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]] ,	dtype=torch.floataa				)
         torch.testing.assert_close(out.mean(-1				) ,	UpperCamelCase_ ,	atol=1e-2 ,	rtol=1e-2				)
         # fmt: off
         __A         =	torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]				)
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30] ,	UpperCamelCase_ ,	atol=1e-5 ,	rtol=1e-5				)





     @unittest.skip("""Model is curently gated"""				)
     @slow
     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         __A         =	"""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"""
         __A         =	"""Simply put, the theory of relativity states that """
         __A         =	LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""				)
         __A         =	tokenizer.encode(UpperCamelCase_ ,	return_tensors="""pt"""				)
         __A         =	LlamaForCausalLM.from_pretrained(
             """meta-llama/Llama-2-13b-chat-hf""" ,	device_map="""sequential""" ,	use_safetensors=UpperCamelCase_				)

         # greedy generation outputs
         __A         =	model.generate(UpperCamelCase_ ,	max_new_tokens=64 ,	top_p=UpperCamelCase_ ,	temperature=1 ,	do_sample=UpperCamelCase_				)
         __A         =	tokenizer.decode(generated_ids[0] ,	skip_special_tokens=UpperCamelCase_				)
         self.assertEqual(UpperCamelCase_ ,	UpperCamelCase_				)




