






import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


__a      :       int				  =   logging.getLogger(__name__)


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''




     def 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Any=None ,	UpperCamelCase_       :			Tuple=None				):


         """simple docstring"""



         __A         =	self.layer[current_layer](UpperCamelCase_ ,	UpperCamelCase_ ,	head_mask[current_layer]				)

         __A         =	layer_outputs[0]

         return hidden_states


@add_start_docstrings(
    "The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top."			,      lowercase_			,      )
class       __lowercase      (	lowercase_  ):







     '''simple docstring'''




     def __init__(				self       :			List[Any] ,	UpperCamelCase_       :			str				):


         """simple docstring"""



         super().__init__(UpperCamelCase_				)

         __A         =	BertEncoderWithPabee(UpperCamelCase_				)

         self.init_weights()
         __A         =	0
         __A         =	0
         __A         =	0

         __A         =	0




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


         """simple docstring"""



         __A         =	threshold




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


         """simple docstring"""



         __A         =	patience




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	0
         __A         =	0




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


         """simple docstring"""



         __A         =	self.inference_layers_num / self.inference_instances_num
         __A         =	(
             F"*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="
             F" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"
         )
         print(UpperCamelCase_				)





     @add_start_docstrings_to_model_forward(UpperCamelCase_				)
     def 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			Dict=None ,	UpperCamelCase_       :			int=None ,	UpperCamelCase_       :			Dict=None ,	UpperCamelCase_       :			List[Any]=None ,	UpperCamelCase_       :			int=None ,	UpperCamelCase_       :			List[str]=None ,	UpperCamelCase_       :			List[str]=None ,	UpperCamelCase_       :			Any=None ,	UpperCamelCase_       :			Optional[int]=None ,	UpperCamelCase_       :			List[str]=None ,	UpperCamelCase_       :			str=False ,	):


         """simple docstring"""



         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""				)
         elif input_ids is not None:
             __A         =	input_ids.size()
         elif inputs_embeds is not None:
             __A         =	inputs_embeds.size()[:-1]
         else:
             raise ValueError("""You have to specify either input_ids or inputs_embeds"""				)

         __A         =	input_ids.device if input_ids is not None else inputs_embeds.device

         if attention_mask is None:
             __A         =	torch.ones(UpperCamelCase_ ,	device=UpperCamelCase_				)
         if token_type_ids is None:
             __A         =	torch.zeros(UpperCamelCase_ ,	dtype=torch.long ,	device=UpperCamelCase_				)

         # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
         # ourselves in which case we just need to make it broadcastable to all heads.
         __A         =	self.get_extended_attention_mask(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				)

         # If a 2D ou 3D attention mask is provided for the cross-attention
         # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
         if self.config.is_decoder and encoder_hidden_states is not None:
             __A    ,	__A    ,	__A         =	encoder_hidden_states.size()
             __A         =	(encoder_batch_size, encoder_sequence_length)
             if encoder_attention_mask is None:
                 __A         =	torch.ones(UpperCamelCase_ ,	device=UpperCamelCase_				)
             __A         =	self.invert_attention_mask(UpperCamelCase_				)
         else:
             __A         =	None

         # Prepare head mask if needed
         # 1.0 in head_mask indicate we keep the head
         # attention_probs has shape bsz x n_heads x N x N
         # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
         # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
         __A         =	self.get_head_mask(UpperCamelCase_ ,	self.config.num_hidden_layers				)

         __A         =	self.embeddings(
             input_ids=UpperCamelCase_ ,	position_ids=UpperCamelCase_ ,	token_type_ids=UpperCamelCase_ ,	inputs_embeds=UpperCamelCase_				)
         __A         =	embedding_output

         if self.training:
             __A         =	[]
             for i in range(self.config.num_hidden_layers				):
                 __A         =	self.encoder.adaptive_forward(
                     UpperCamelCase_ ,	current_layer=UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	head_mask=UpperCamelCase_				)

                 __A         =	self.pooler(UpperCamelCase_				)
                 __A         =	output_layers[i](output_dropout(UpperCamelCase_				)				)
                 res.append(UpperCamelCase_				)
         elif self.patience == 0:  # Use all layers for inference
             __A         =	self.encoder(
                 UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	head_mask=UpperCamelCase_ ,	encoder_hidden_states=UpperCamelCase_ ,	encoder_attention_mask=UpperCamelCase_ ,	)
             __A         =	self.pooler(encoder_outputs[0]				)
             __A         =	[output_layers[self.config.num_hidden_layers - 1](UpperCamelCase_				)]
         else:
             __A         =	0
             __A         =	None
             __A         =	0
             for i in range(self.config.num_hidden_layers				):
                 calculated_layer_num += 1
                 __A         =	self.encoder.adaptive_forward(
                     UpperCamelCase_ ,	current_layer=UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	head_mask=UpperCamelCase_				)

                 __A         =	self.pooler(UpperCamelCase_				)
                 __A         =	output_layers[i](UpperCamelCase_				)
                 if regression:
                     __A         =	logits.detach()
                     if patient_result is not None:
                         __A         =	patient_result.detach()
                     if (patient_result is not None) and torch.abs(patient_result - labels				) < self.regression_threshold:
                         patient_counter += 1
                     else:
                         __A         =	0
                 else:
                     __A         =	logits.detach().argmax(dim=1				)
                     if patient_result is not None:
                         __A         =	patient_result.detach().argmax(dim=1				)
                     if (patient_result is not None) and torch.all(labels.eq(UpperCamelCase_				)				):
                         patient_counter += 1
                     else:
                         __A         =	0

                 __A         =	logits
                 if patient_counter == self.patience:
                     break
             __A         =	[patient_result]
             self.inference_layers_num += calculated_layer_num
             self.inference_instances_num += 1

         return res


@add_start_docstrings(
    "Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. "			,      lowercase_			,      )
class       __lowercase      (	lowercase_  ):







     '''simple docstring'''




     def __init__(				self       :			Optional[int] ,	UpperCamelCase_       :			Optional[Any]				):


         """simple docstring"""



         super().__init__(UpperCamelCase_				)
         __A         =	config.num_labels

         __A         =	BertModelWithPabee(UpperCamelCase_				)
         __A         =	nn.Dropout(config.hidden_dropout_prob				)
         __A         =	nn.ModuleList(
             [nn.Linear(config.hidden_size ,	self.config.num_labels				) for _ in range(config.num_hidden_layers				)]				)

         self.init_weights()





     @add_start_docstrings_to_model_forward(UpperCamelCase_				)
     def 		lowerCAmelCase_     (				self       :			Dict ,	UpperCamelCase_       :			List[str]=None ,	UpperCamelCase_       :			Tuple=None ,	UpperCamelCase_       :			Dict=None ,	UpperCamelCase_       :			Tuple=None ,	UpperCamelCase_       :			Dict=None ,	UpperCamelCase_       :			Any=None ,	UpperCamelCase_       :			List[Any]=None ,	):


         """simple docstring"""



         __A         =	self.bert(
             input_ids=UpperCamelCase_ ,	attention_mask=UpperCamelCase_ ,	token_type_ids=UpperCamelCase_ ,	position_ids=UpperCamelCase_ ,	head_mask=UpperCamelCase_ ,	inputs_embeds=UpperCamelCase_ ,	output_dropout=self.dropout ,	output_layers=self.classifiers ,	regression=self.num_labels == 1 ,	)

         __A         =	(logits[-1],)

         if labels is not None:
             __A         =	None
             __A         =	0
             for ix, logits_item in enumerate(UpperCamelCase_				):
                 if self.num_labels == 1:
                     #  We are doing regression
                     __A         =	MSELoss()
                     __A         =	loss_fct(logits_item.view(-1				) ,	labels.view(-1				)				)
                 else:
                     __A         =	CrossEntropyLoss()
                     __A         =	loss_fct(logits_item.view(-1 ,	self.num_labels				) ,	labels.view(-1				)				)
                 if total_loss is None:
                     __A         =	loss
                 else:
                     total_loss += loss * (ix + 1)
                 total_weights += ix + 1
             __A         =	(total_loss / total_weights,) + outputs

         return outputs












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_				)




