"""simple docstring"""





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A	=					logging.get_logger(__name__)

__A	=					{
    '''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json''',
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}
class 			_snake_case  (       _lowercase		):
						snake_case__      	=      '''altclip_text_model'''






						def __init__(							self   :      Tuple				,       UpperCAmelCase   :      List[str]=250002				,       UpperCAmelCase   :      Tuple=1024				,       UpperCAmelCase   :      Dict=24				,       UpperCAmelCase   :      Union[str, Any]=16				,       UpperCAmelCase   :      str=4096				,       UpperCAmelCase   :      str="gelu"				,       UpperCAmelCase   :      List[str]=0.1				,       UpperCAmelCase   :      Optional[int]=0.1				,       UpperCAmelCase   :      Tuple=514				,       UpperCAmelCase   :      int=1				,       UpperCAmelCase   :      Union[str, Any]=0.0_2				,       UpperCAmelCase   :      Dict=0.0_2				,       UpperCAmelCase   :      Dict=1E-05				,       UpperCAmelCase   :      int=1				,       UpperCAmelCase   :      List[Any]=0				,       UpperCAmelCase   :      Optional[Any]=2				,       UpperCAmelCase   :      int="absolute"				,       UpperCAmelCase   :      Optional[int]=True				,       UpperCAmelCase   :      Dict=768				,       **UpperCAmelCase   :      Tuple				,       ):
													super().__init__(pad_token_id=A_				,       bos_token_id=A_				,       eos_token_id=A_				,       **A_     )

													__lowerCamelCase       :		str		 =  vocab_size
													__lowerCamelCase       :		List[Any]		 =  hidden_size
													__lowerCamelCase       :		Optional[int]		 =  num_hidden_layers
													__lowerCamelCase       :		List[Any]		 =  num_attention_heads
													__lowerCamelCase       :		Union[str, Any]		 =  hidden_act
													__lowerCamelCase       :		List[str]		 =  intermediate_size
													__lowerCamelCase       :		List[Any]		 =  hidden_dropout_prob
													__lowerCamelCase       :		int		 =  attention_probs_dropout_prob
													__lowerCamelCase       :		Union[str, Any]		 =  max_position_embeddings
													__lowerCamelCase       :		List[Any]		 =  type_vocab_size
													__lowerCamelCase       :		List[Any]		 =  initializer_range
													__lowerCamelCase       :		List[str]		 =  initializer_factor
													__lowerCamelCase       :		Dict		 =  layer_norm_eps
													__lowerCamelCase       :		List[str]		 =  position_embedding_type
													__lowerCamelCase       :		Dict		 =  use_cache
													__lowerCamelCase       :		Optional[Any]		 =  project_dim

class 			_snake_case  (       _lowercase		):
						snake_case__      	=      '''altclip_vision_model'''






						def __init__(							self   :      int				,       UpperCAmelCase   :      Any=768				,       UpperCAmelCase   :      List[str]=3072				,       UpperCAmelCase   :      str=512				,       UpperCAmelCase   :      Optional[Any]=12				,       UpperCAmelCase   :      str=12				,       UpperCAmelCase   :      str=3				,       UpperCAmelCase   :      Dict=224				,       UpperCAmelCase   :      Optional[int]=32				,       UpperCAmelCase   :      Optional[Any]="quick_gelu"				,       UpperCAmelCase   :      int=1E-5				,       UpperCAmelCase   :      Union[str, Any]=0.0				,       UpperCAmelCase   :      Union[str, Any]=0.0_2				,       UpperCAmelCase   :      int=1.0				,       **UpperCAmelCase   :      List[Any]				,       ):
													super().__init__(**A_     )

													__lowerCamelCase       :		Optional[Any]		 =  hidden_size
													__lowerCamelCase       :		Union[str, Any]		 =  intermediate_size
													__lowerCamelCase       :		List[Any]		 =  projection_dim
													__lowerCamelCase       :		str		 =  num_hidden_layers
													__lowerCamelCase       :		List[str]		 =  num_attention_heads
													__lowerCamelCase       :		int		 =  num_channels
													__lowerCamelCase       :		Optional[Any]		 =  patch_size
													__lowerCamelCase       :		Tuple		 =  image_size
													__lowerCamelCase       :		Union[str, Any]		 =  initializer_range
													__lowerCamelCase       :		str		 =  initializer_factor
													__lowerCamelCase       :		Union[str, Any]		 =  attention_dropout
													__lowerCamelCase       :		Dict		 =  layer_norm_eps
													__lowerCamelCase       :		List[Any]		 =  hidden_act







						@classmethod
						def    lowerCamelCase__  (							cls   :      Optional[Any]				,       UpperCAmelCase   :      Union[str, os.PathLike]				,       **UpperCAmelCase   :      str     ):
													cls._set_token_in_kwargs(A_     )

													__lowerCamelCase ,	__lowerCamelCase       :		int		 =  cls.get_config_dict(A_				,       **A_     )

													# get the vision config dict if we are loading from AltCLIPConfig
													if config_dict.get("model_type"     ) == "altclip":
																				__lowerCamelCase       :		Dict		 =  config_dict["vision_config"]

													if "model_type" in config_dict and hasattr(cls				,       "model_type"     ) and config_dict["model_type"] != cls.model_type:
																				logger.warning(
																				    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																				    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

													return cls.from_dict(A_				,       **A_     )

class 			_snake_case  (       _lowercase		):
						snake_case__      	=      '''altclip'''
						snake_case__      	=      True






						def __init__(							self   :      Optional[Any]				,       UpperCAmelCase   :      str=None				,       UpperCAmelCase   :      Tuple=None				,       UpperCAmelCase   :      str=768				,       UpperCAmelCase   :      Any=2.6_5_9_2				,       **UpperCAmelCase   :      int     ):
													# If `_config_dict` exist, we use them for the backward compatibility.
													# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
													# of confusion!).
													__lowerCamelCase       :		str		 =  kwargs.pop("text_config_dict"				,       A_     )
													__lowerCamelCase       :		Any		 =  kwargs.pop("vision_config_dict"				,       A_     )

													super().__init__(**A_     )

													# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
													# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
													# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
													if text_config_dict is not None:
																				if text_config is None:
																											__lowerCamelCase       :		Any		 =  {}

																				# This is the complete result when using `text_config_dict`.
																				__lowerCamelCase       :		List[Any]		 =  AltCLIPTextConfig(**A_     ).to_dict()

																				# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
																				for key, value in _text_config_dict.items():
																											if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
																																		# If specified in `text_config_dict`
																																		if key in text_config_dict:
																																									__lowerCamelCase       :		List[Any]		 =  (
																																									    F"""`{key}` is found in both `text_config_dict` and `text_config` but with different values. """
																																									    F"""The value `text_config_dict[\"{key}\"]` will be used instead."""
																																									)
																																		# If inferred from default argument values (just to be super careful)
																																		else:
																																									__lowerCamelCase       :		Optional[Any]		 =  (
																																									    F"""`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The """
																																									    F"""value `text_config[\"{key}\"]` will be overriden."""
																																									)
																																		logger.warning(A_     )

            # Update all values in `text_config` with the ones in `_text_config_dict`.
																				text_config.update(_text_config_dict     )

													if vision_config_dict is not None:
																				if vision_config is None:
																											__lowerCamelCase       :		Any		 =  {}

																				# This is the complete result when using `vision_config_dict`.
																				__lowerCamelCase       :		List[Any]		 =  AltCLIPVisionConfig(**A_     ).to_dict()
																				# convert keys to string instead of integer
																				if "id2label" in _vision_config_dict:
																											__lowerCamelCase       :		Union[str, Any]		 =  {
																											    str(A_     ): value for key, value in _vision_config_dict["id2label"].items()
																											}

																				# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
																				for key, value in _vision_config_dict.items():
																											if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
																																		# If specified in `vision_config_dict`
																																		if key in vision_config_dict:
																																									__lowerCamelCase       :		List[Any]		 =  (
																																									    F"""`{key}` is found in both `vision_config_dict` and `vision_config` but with different """
																																									    F"""values. The value `vision_config_dict[\"{key}\"]` will be used instead."""
																																									)
																																		# If inferred from default argument values (just to be super careful)
																																		else:
																																									__lowerCamelCase       :		Optional[int]		 =  (
																																									    F"""`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. """
																																									    F"""The value `vision_config[\"{key}\"]` will be overriden."""
																																									)
																																		logger.warning(A_     )

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
																				vision_config.update(_vision_config_dict     )

													if text_config is None:
																				__lowerCamelCase       :		Optional[Any]		 =  {}
																				logger.info("`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values."     )

													if vision_config is None:
																				__lowerCamelCase       :		Optional[int]		 =  {}
																				logger.info("`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values."     )

													__lowerCamelCase       :		List[Any]		 =  AltCLIPTextConfig(**A_     )
													__lowerCamelCase       :		int		 =  AltCLIPVisionConfig(**A_     )

													__lowerCamelCase       :		str		 =  projection_dim
													__lowerCamelCase       :		Union[str, Any]		 =  logit_scale_init_value
													__lowerCamelCase       :		Optional[int]		 =  1.0






						@classmethod
						def    lowerCamelCase__  (							cls   :      Tuple				,       UpperCAmelCase   :      AltCLIPTextConfig				,       UpperCAmelCase   :      AltCLIPVisionConfig				,       **UpperCAmelCase   :      List[Any]     ):
													return cls(text_config=text_config.to_dict()				,       vision_config=vision_config.to_dict()				,       **A_     )







						def    lowerCamelCase__  (							self   :      int     ):
													__lowerCamelCase       :		Optional[Any]		 =  copy.deepcopy(self.__dict__     )
													__lowerCamelCase       :		int		 =  self.text_config.to_dict()
													__lowerCamelCase       :		Dict		 =  self.vision_config.to_dict()
													__lowerCamelCase       :		Dict		 =  self.__class__.model_type
													return output

"""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_   )