"""simple docstring"""





import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

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 (
				    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DistilBertForMaskedLM,
				    DistilBertForMultipleChoice,
				    DistilBertForQuestionAnswering,
				    DistilBertForSequenceClassification,
				    DistilBertForTokenClassification,
				    DistilBertModel,
				)
class 			_snake_case  (       _lowercase		):






						def __init__(							self   :      Union[str, Any]				,       UpperCAmelCase   :      List[Any]				,       UpperCAmelCase   :      List[str]=13				,       UpperCAmelCase   :      Union[str, Any]=7				,       UpperCAmelCase   :      Dict=True				,       UpperCAmelCase   :      List[str]=True				,       UpperCAmelCase   :      str=False				,       UpperCAmelCase   :      Any=True				,       UpperCAmelCase   :      Any=99				,       UpperCAmelCase   :      List[Any]=32				,       UpperCAmelCase   :      int=5				,       UpperCAmelCase   :      Tuple=4				,       UpperCAmelCase   :      Optional[Any]=37				,       UpperCAmelCase   :      Optional[Any]="gelu"				,       UpperCAmelCase   :      str=0.1				,       UpperCAmelCase   :      Optional[Any]=0.1				,       UpperCAmelCase   :      Union[str, Any]=512				,       UpperCAmelCase   :      Any=16				,       UpperCAmelCase   :      Dict=2				,       UpperCAmelCase   :      str=0.0_2				,       UpperCAmelCase   :      Union[str, Any]=3				,       UpperCAmelCase   :      Tuple=4				,       UpperCAmelCase   :      Any=None				,       ):
													__lowerCamelCase       :		List[Any]		 =  parent
													__lowerCamelCase       :		Union[str, Any]		 =  batch_size
													__lowerCamelCase       :		Any		 =  seq_length
													__lowerCamelCase       :		Dict		 =  is_training
													__lowerCamelCase       :		List[Any]		 =  use_input_mask
													__lowerCamelCase       :		int		 =  use_token_type_ids
													__lowerCamelCase       :		List[str]		 =  use_labels
													__lowerCamelCase       :		Optional[int]		 =  vocab_size
													__lowerCamelCase       :		Dict		 =  hidden_size
													__lowerCamelCase       :		Optional[Any]		 =  num_hidden_layers
													__lowerCamelCase       :		Optional[int]		 =  num_attention_heads
													__lowerCamelCase       :		List[str]		 =  intermediate_size
													__lowerCamelCase       :		Optional[Any]		 =  hidden_act
													__lowerCamelCase       :		List[str]		 =  hidden_dropout_prob
													__lowerCamelCase       :		str		 =  attention_probs_dropout_prob
													__lowerCamelCase       :		Dict		 =  max_position_embeddings
													__lowerCamelCase       :		Dict		 =  type_vocab_size
													__lowerCamelCase       :		Dict		 =  type_sequence_label_size
													__lowerCamelCase       :		Optional[int]		 =  initializer_range
													__lowerCamelCase       :		Union[str, Any]		 =  num_labels
													__lowerCamelCase       :		Union[str, Any]		 =  num_choices
													__lowerCamelCase       :		str		 =  scope






						def    lowerCamelCase__  (							self   :      Any     ):
													__lowerCamelCase       :		Any		 =  ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size     )

													__lowerCamelCase       :		Tuple		 =  None
													if self.use_input_mask:
																				__lowerCamelCase       :		Optional[int]		 =  random_attention_mask([self.batch_size, self.seq_length]     )

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

													__lowerCamelCase       :		int		 =  self.get_config()

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






						def    lowerCamelCase__  (							self   :      int     ):
													return DistilBertConfig(
													    vocab_size=self.vocab_size				,       dim=self.hidden_size				,       n_layers=self.num_hidden_layers				,       n_heads=self.num_attention_heads				,       hidden_dim=self.intermediate_size				,       hidden_act=self.hidden_act				,       dropout=self.hidden_dropout_prob				,       attention_dropout=self.attention_probs_dropout_prob				,       max_position_embeddings=self.max_position_embeddings				,       initializer_range=self.initializer_range				,       )






						def    lowerCamelCase__  (							self   :      Optional[int]				,       UpperCAmelCase   :      Dict				,       UpperCAmelCase   :      Optional[int]				,       UpperCAmelCase   :      Union[str, Any]				,       UpperCAmelCase   :      Union[str, Any]				,       UpperCAmelCase   :      str				,       UpperCAmelCase   :      Optional[Any]     ):
													__lowerCamelCase       :		Optional[Any]		 =  DistilBertModel(config=A_     )
													model.to(A_     )
													model.eval()
													__lowerCamelCase       :		int		 =  model(A_				,       A_     )
													__lowerCamelCase       :		List[str]		 =  model(A_     )
													self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)     )






						def    lowerCamelCase__  (							self   :      int				,       UpperCAmelCase   :      Tuple				,       UpperCAmelCase   :      Optional[int]				,       UpperCAmelCase   :      Dict				,       UpperCAmelCase   :      int				,       UpperCAmelCase   :      List[Any]				,       UpperCAmelCase   :      int     ):
													__lowerCamelCase       :		Optional[Any]		 =  DistilBertForMaskedLM(config=A_     )
													model.to(A_     )
													model.eval()
													__lowerCamelCase       :		List[str]		 =  model(A_				,       attention_mask=A_				,       labels=A_     )
													self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)     )






						def    lowerCamelCase__  (							self   :      Optional[Any]				,       UpperCAmelCase   :      Optional[int]				,       UpperCAmelCase   :      Dict				,       UpperCAmelCase   :      int				,       UpperCAmelCase   :      int				,       UpperCAmelCase   :      str				,       UpperCAmelCase   :      Any     ):
													__lowerCamelCase       :		int		 =  DistilBertForQuestionAnswering(config=A_     )
													model.to(A_     )
													model.eval()
													__lowerCamelCase       :		Optional[Any]		 =  model(
													    A_				,       attention_mask=A_				,       start_positions=A_				,       end_positions=A_     )
													self.parent.assertEqual(result.start_logits.shape				,       (self.batch_size, self.seq_length)     )
													self.parent.assertEqual(result.end_logits.shape				,       (self.batch_size, self.seq_length)     )






						def    lowerCamelCase__  (							self   :      int				,       UpperCAmelCase   :      Union[str, Any]				,       UpperCAmelCase   :      List[Any]				,       UpperCAmelCase   :      Optional[Any]				,       UpperCAmelCase   :      int				,       UpperCAmelCase   :      List[Any]				,       UpperCAmelCase   :      Optional[Any]     ):
													__lowerCamelCase       :		Optional[int]		 =  self.num_labels
													__lowerCamelCase       :		Optional[int]		 =  DistilBertForSequenceClassification(A_     )
													model.to(A_     )
													model.eval()
													__lowerCamelCase       :		Optional[int]		 =  model(A_				,       attention_mask=A_				,       labels=A_     )
													self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)     )






						def    lowerCamelCase__  (							self   :      Dict				,       UpperCAmelCase   :      List[str]				,       UpperCAmelCase   :      Tuple				,       UpperCAmelCase   :      List[str]				,       UpperCAmelCase   :      str				,       UpperCAmelCase   :      Union[str, Any]				,       UpperCAmelCase   :      List[str]     ):
													__lowerCamelCase       :		str		 =  self.num_labels
													__lowerCamelCase       :		Dict		 =  DistilBertForTokenClassification(config=A_     )
													model.to(A_     )
													model.eval()

													__lowerCamelCase       :		Union[str, Any]		 =  model(A_				,       attention_mask=A_				,       labels=A_     )
													self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)     )






						def    lowerCamelCase__  (							self   :      int				,       UpperCAmelCase   :      Union[str, Any]				,       UpperCAmelCase   :      List[Any]				,       UpperCAmelCase   :      Union[str, Any]				,       UpperCAmelCase   :      int				,       UpperCAmelCase   :      List[str]				,       UpperCAmelCase   :      Tuple     ):
													__lowerCamelCase       :		List[Any]		 =  self.num_choices
													__lowerCamelCase       :		List[Any]		 =  DistilBertForMultipleChoice(config=A_     )
													model.to(A_     )
													model.eval()
													__lowerCamelCase       :		Optional[int]		 =  input_ids.unsqueeze(1     ).expand(-1				,       self.num_choices				,       -1     ).contiguous()
													__lowerCamelCase       :		str		 =  input_mask.unsqueeze(1     ).expand(-1				,       self.num_choices				,       -1     ).contiguous()
													__lowerCamelCase       :		Union[str, Any]		 =  model(
													    A_				,       attention_mask=A_				,       labels=A_				,       )
													self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_choices)     )







						def    lowerCamelCase__  (							self   :      str     ):
													__lowerCamelCase       :		Union[str, Any]		 =  self.prepare_config_and_inputs()
													((__lowerCamelCase) ,	(__lowerCamelCase) ,	(__lowerCamelCase) ,	(__lowerCamelCase) ,	(__lowerCamelCase) ,	(__lowerCamelCase))       :		Optional[Any]		 =  config_and_inputs
													__lowerCamelCase       :		List[str]		 =  {"input_ids": input_ids, "attention_mask": input_mask}
													return config, inputs_dict

@require_torch
class 			_snake_case  (       _lowercase					,			_lowercase					,			unittest.TestCase		):
						snake_case__      	=      (
						    (
						        DistilBertModel,
						        DistilBertForMaskedLM,
						        DistilBertForMultipleChoice,
						        DistilBertForQuestionAnswering,
						        DistilBertForSequenceClassification,
						        DistilBertForTokenClassification,
						    )
						    if is_torch_available()
						    else None
						)
						snake_case__      	=      (
						    {
						        '''feature-extraction''': DistilBertModel,
						        '''fill-mask''': DistilBertForMaskedLM,
						        '''question-answering''': DistilBertForQuestionAnswering,
						        '''text-classification''': DistilBertForSequenceClassification,
						        '''token-classification''': DistilBertForTokenClassification,
						        '''zero-shot''': DistilBertForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						snake_case__      	=      True
						snake_case__      	=      True
						snake_case__      	=      True
						snake_case__      	=      True






						def    lowerCamelCase__  (							self   :      List[str]     ):
													__lowerCamelCase       :		Union[str, Any]		 =  DistilBertModelTester(self     )
													__lowerCamelCase       :		Dict		 =  ConfigTester(self				,       config_class=A_				,       dim=37     )






						def    lowerCamelCase__  (							self   :      Optional[int]     ):
													self.config_tester.run_common_tests()






						def    lowerCamelCase__  (							self   :      Tuple     ):
													__lowerCamelCase       :		str		 =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_distilbert_model(*A_     )






						def    lowerCamelCase__  (							self   :      Optional[Any]     ):
													__lowerCamelCase       :		Dict		 =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_distilbert_for_masked_lm(*A_     )






						def    lowerCamelCase__  (							self   :      List[str]     ):
													__lowerCamelCase       :		str		 =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_distilbert_for_question_answering(*A_     )






						def    lowerCamelCase__  (							self   :      str     ):
													__lowerCamelCase       :		str		 =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_distilbert_for_sequence_classification(*A_     )






						def    lowerCamelCase__  (							self   :      Optional[Any]     ):
													__lowerCamelCase       :		Dict		 =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_distilbert_for_token_classification(*A_     )






						def    lowerCamelCase__  (							self   :      List[str]     ):
													__lowerCamelCase       :		Optional[Any]		 =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_distilbert_for_multiple_choice(*A_     )






						@slow
						def    lowerCamelCase__  (							self   :      List[Any]     ):
													for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__lowerCamelCase       :		Tuple		 =  DistilBertModel.from_pretrained(A_     )
																				self.assertIsNotNone(A_     )







						@slow
						@require_torch_gpu
						def    lowerCamelCase__  (							self   :      List[Any]     ):
													__lowerCamelCase ,	__lowerCamelCase       :		Dict		 =  self.model_tester.prepare_config_and_inputs_for_common()
													for model_class in self.all_model_classes:
																				# BertForMultipleChoice behaves incorrectly in JIT environments.
																				if model_class == DistilBertForMultipleChoice:
																											return

																				__lowerCamelCase       :		Optional[Any]		 =  True
																				__lowerCamelCase       :		Dict		 =  model_class(config=A_     )

																				__lowerCamelCase       :		Tuple		 =  self._prepare_for_class(A_				,       A_     )
																				__lowerCamelCase       :		Tuple		 =  torch.jit.trace(
																				    A_				,       (inputs_dict["input_ids"].to("cpu"     ), inputs_dict["attention_mask"].to("cpu"     ))     )

																				with tempfile.TemporaryDirectory() as tmp:
																											torch.jit.save(A_				,       os.path.join(A_				,       "traced_model.pt"     )     )
																											__lowerCamelCase       :		Dict		 =  torch.jit.load(os.path.join(A_				,       "traced_model.pt"     )				,       map_location=A_     )
																											loaded(inputs_dict["input_ids"].to(A_     )				,       inputs_dict["attention_mask"].to(A_     )     )

@require_torch
class 			_snake_case  (       unittest.TestCase		):






						@slow
						def    lowerCamelCase__  (							self   :      Optional[int]     ):
													__lowerCamelCase       :		List[Any]		 =  DistilBertModel.from_pretrained("distilbert-base-uncased"     )
													__lowerCamelCase       :		List[str]		 =  torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]     )
													__lowerCamelCase       :		List[str]		 =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
													with torch.no_grad():
																				__lowerCamelCase       :		Optional[Any]		 =  model(A_				,       attention_mask=A_     )[0]
													__lowerCamelCase       :		Optional[int]		 =  torch.Size((1, 11, 768)     )
													self.assertEqual(output.shape				,       A_     )
													__lowerCamelCase       :		Tuple		 =  torch.tensor(
													    [[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]     )

													self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,       A_				,       atol=1E-4     )     )

"""simple docstring"""


import unittest

from parameterized import parameterized

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

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


if is_torch_available():
		import torch

		from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class 					lowerCAmelCase_					:



						'''simple docstring'''


						def __init__(			self  :   Tuple	,A_  :   Any	,A_  :   int=13	,A_  :   str=7	,A_  :   Tuple=True	,A_  :   str=True	,A_  :   str=False	,A_  :   List[str]=True	,A_  :   str=99	,A_  :   str=32	,A_  :   Optional[int]=5	,A_  :   Optional[Any]=4	,A_  :   str=37	,A_  :   Optional[Any]="gelu"	,A_  :   Union[str, Any]=0.1	,A_  :   Any=0.1	,A_  :   Optional[Any]=512	,A_  :   str=16	,A_  :   int=2	,A_  :   Optional[Any]=0.02	,A_  :   str=3	,A_  :   str=4	,A_  :   List[str]=None	,)  -> str:
							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     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							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     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Any:
							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=A_	,initializer_range=self.initializer_range	,)


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   Dict	,A_  :   Optional[int]	,A_  :   Any	,A_  :   Optional[Any]	,A_  :   Any	,A_  :   Union[str, Any]	,A_  :   Tuple   )  -> List[Any]:
							A     = LlamaModel(config=A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,attention_mask=A_   )
							A     = model(A_   )
							self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   int	,A_  :   Optional[Any]	,A_  :   Optional[int]	,A_  :   Any	,A_  :   Union[str, Any]	,A_  :   Any	,A_  :   Tuple	,A_  :   Union[str, Any]	,A_  :   Dict	,)  -> List[str]:
							A     = True
							A     = LlamaModel(A_   )
							model.to(A_   )
							model.eval()
							A     = model(
							    A_	,attention_mask=A_	,encoder_hidden_states=A_	,encoder_attention_mask=A_	,)
							A     = model(
							    A_	,attention_mask=A_	,encoder_hidden_states=A_	,)
							A     = model(A_	,attention_mask=A_   )
							self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   List[str]	,A_  :   Optional[int]	,A_  :   Any	,A_  :   str	,A_  :   Dict	,A_  :   Dict	,A_  :   Tuple	,A_  :   Tuple	,A_  :   Dict	,)  -> Union[str, Any]:
							A     = LlamaForCausalLM(config=A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,attention_mask=A_	,labels=A_   )
							self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.vocab_size)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   Dict	,A_  :   Any	,A_  :   int	,A_  :   List[str]	,A_  :   Tuple	,A_  :   Any	,A_  :   Union[str, Any]	,A_  :   Any	,A_  :   int	,)  -> List[Any]:
							A     = True
							A     = True
							A     = LlamaForCausalLM(config=A_   )
							model.to(A_   )
							model.eval()

							# first forward pass
							A     = model(
							    A_	,attention_mask=A_	,encoder_hidden_states=A_	,encoder_attention_mask=A_	,use_cache=A_	,)
							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(
							    A_	,attention_mask=A_	,encoder_hidden_states=A_	,encoder_attention_mask=A_	,output_hidden_states=A_	,)['hidden_states'][0]
							A     = model(
							    A_	,attention_mask=A_	,encoder_hidden_states=A_	,encoder_attention_mask=A_	,past_key_values=A_	,output_hidden_states=A_	,)['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(A_	,A_	,atol=1e-3   )   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Optional[int]:
							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 					lowerCAmelCase_					(   _lowercase		,       _lowercase		,       _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Dict   =     (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
						_lowerCamelCase:     List[Any]   =     (LlamaForCausalLM,) if is_torch_available() else ()
						_lowerCamelCase:     Any   =     (
						    {
						        '''feature-extraction''': LlamaModel,
						        '''text-classification''': LlamaForSequenceClassification,
						        '''text-generation''': LlamaForCausalLM,
						        '''zero-shot''': LlamaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_lowerCamelCase:     int   =     False
						_lowerCamelCase:     List[str]   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Dict:
							A     = LlamaModelTester(self   )
							A     = ConfigTester(self	,config_class=A_	,hidden_size=37   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Any:
							self.config_tester.run_common_tests()


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Tuple:
							A     = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Optional[int]:
							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(*A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> List[Any]:
							A ,   A     = self.model_tester.prepare_config_and_inputs_for_common()
							A     = 3
							A     = input_dict['input_ids']
							A     = input_ids.ne(1   ).to(A_   )
							A     = ids_tensor([self.model_tester.batch_size]	,self.model_tester.type_sequence_label_size   )
							A     = LlamaForSequenceClassification(A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,attention_mask=A_	,labels=A_   )
							self.assertEqual(result.logits.shape	,(self.model_tester.batch_size, self.model_tester.num_labels)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Optional[Any]:
							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(A_   )
							A     = ids_tensor([self.model_tester.batch_size]	,self.model_tester.type_sequence_label_size   )
							A     = LlamaForSequenceClassification(A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,attention_mask=A_	,labels=A_   )
							self.assertEqual(result.logits.shape	,(self.model_tester.batch_size, self.model_tester.num_labels)   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Dict:
							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(A_   )
							A     = ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]	,self.model_tester.type_sequence_label_size   ).to(torch.float   )
							A     = LlamaForSequenceClassification(A_   )
							model.to(A_   )
							model.eval()
							A     = model(A_	,attention_mask=A_	,labels=A_   )
							self.assertEqual(result.logits.shape	,(self.model_tester.batch_size, self.model_tester.num_labels)   )


						@unittest.skip('LLaMA buffers include complex numbers, which breaks this test'   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Optional[Any]:
							pass




						@parameterized.expand([('linear',), ('dynamic',)]   )
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   Any   )  -> str:
							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(A_   )
							original_model.to(A_   )
							original_model.eval()
							A     = original_model(A_   ).last_hidden_state
							A     = original_model(A_   ).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(A_   )
							scaled_model.to(A_   )
							scaled_model.eval()
							A     = scaled_model(A_   ).last_hidden_state
							A     = scaled_model(A_   ).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(A_	,A_	,atol=1e-5   )   )
							else:
								self.assertFalse(torch.allclose(A_	,A_	,atol=1e-5   )   )

							# The output should be different for long inputs
							self.assertFalse(torch.allclose(A_	,A_	,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     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[Any]:
							A     = [1, 306, 4658, 278, 6593, 310, 2834, 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([[-6.65_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   )	,A_	,atol=1e-2	,rtol=1e-2   )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							A     = 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]	,A_	,atol=1e-5	,rtol=1e-5   )


						@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'   )
						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> int:
							A     = [1, 306, 4658, 278, 6593, 310, 2834, 338]
							A     = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'	,device_map='auto'   )
							A     = model(torch.tensor(A_   )   )
							# Expected mean on dim = -1
							A     = 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   )	,A_	,atol=1e-2	,rtol=1e-2   )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							A     = 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]	,A_	,atol=1e-5	,rtol=1e-5   )


						@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'   )
						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> str:
							A     = [1, 306, 4658, 278, 6593, 310, 2834, 338]
							A     = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'	,device_map='auto'   )
							A     = model(torch.tensor(A_   )   )
							# Expected mean on dim = -1
							A     = 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   )	,A_	,atol=1e-2	,rtol=1e-2   )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							A     = 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   )	,A_	,atol=1e-2	,rtol=1e-2   )


						@unittest.skip(
						    'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'   )
						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> str:
							A     = [1, 306, 4658, 278, 6593, 310, 2834, 338]
							A     = LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'	,device_map='auto'   )
							A     = model(torch.tensor(A_   )   )

							A     = 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   )	,A_	,atol=1e-2	,rtol=1e-2   )
							# fmt: off
							A     = 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]	,A_	,atol=1e-5	,rtol=1e-5   )




						@unittest.skip('Model is curently gated'   )
						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Tuple:
							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(A_	,return_tensors='pt'   )
							A     = LlamaForCausalLM.from_pretrained(
							    'meta-llama/Llama-2-13b-chat-hf'	,device_map='sequential'	,use_safetensors=A_   )

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