
"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							=     {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''xmod'''



     def __init__(							self :List[Any]      ,  _lowerCamelCase :Optional[Any]=30_522      ,  _lowerCamelCase :Union[str, Any]=768      ,  _lowerCamelCase :str=12      ,  _lowerCamelCase :str=12      ,  _lowerCamelCase :List[str]=3_072      ,  _lowerCamelCase :Union[str, Any]="gelu"      ,  _lowerCamelCase :Optional[int]=0.1      ,  _lowerCamelCase :Tuple=0.1      ,  _lowerCamelCase :str=512      ,  _lowerCamelCase :Dict=2      ,  _lowerCamelCase :Any=0.02      ,  _lowerCamelCase :Tuple=1E-1_2      ,  _lowerCamelCase :str=1      ,  _lowerCamelCase :Optional[Any]=0      ,  _lowerCamelCase :Optional[Any]=2      ,  _lowerCamelCase :Optional[int]="absolute"      ,  _lowerCamelCase :str=True      ,  _lowerCamelCase :str=None      ,  _lowerCamelCase :int=False      ,  _lowerCamelCase :Tuple=2      ,  _lowerCamelCase :Tuple=False      ,  _lowerCamelCase :List[Any]=True      ,  _lowerCamelCase :Optional[Any]=True      ,  _lowerCamelCase :int=("en_XX",)      ,  _lowerCamelCase :Dict=None      ,  **_lowerCamelCase :Tuple      ,  ):







      '''simple docstring'''



      super().__init__(pad_token_id=snake_case__      ,  bos_token_id=snake_case__      ,  eos_token_id=snake_case__      ,  **snake_case__   )

      UpperCamelCase_     :					str   	=vocab_size
      UpperCamelCase_     :					Optional[int]   	=hidden_size
      UpperCamelCase_     :					int   	=num_hidden_layers
      UpperCamelCase_     :					Optional[Any]   	=num_attention_heads
      UpperCamelCase_     :					int   	=hidden_act
      UpperCamelCase_     :					List[str]   	=intermediate_size
      UpperCamelCase_     :					Optional[int]   	=hidden_dropout_prob
      UpperCamelCase_     :					str   	=attention_probs_dropout_prob
      UpperCamelCase_     :					List[str]   	=max_position_embeddings
      UpperCamelCase_     :					Union[str, Any]   	=type_vocab_size
      UpperCamelCase_     :					int   	=initializer_range
      UpperCamelCase_     :					int   	=layer_norm_eps
      UpperCamelCase_     :					Optional[Any]   	=position_embedding_type
      UpperCamelCase_     :					str   	=use_cache
      UpperCamelCase_     :					Optional[int]   	=classifier_dropout
      UpperCamelCase_     :					Dict   	=pre_norm
      UpperCamelCase_     :					Dict   	=adapter_reduction_factor
      UpperCamelCase_     :					int   	=adapter_layer_norm
      UpperCamelCase_     :					Dict   	=adapter_reuse_layer_norm
      UpperCamelCase_     :					Union[str, Any]   	=ln_before_adapter
      UpperCamelCase_     :					Optional[Any]   	=list(snake_case__   )
      UpperCamelCase_     :					int   	=default_language





class 				a__					(						A__       ):



     @property
     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      if self.task == "multiple-choice":
       UpperCamelCase_     :					Union[str, Any]   	={0: "batch", 1: "choice", 2: "sequence"}
      else:
       UpperCamelCase_     :					Tuple   	={0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]   )








"""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__			:
   def __init__(		self      :	Any							,			snake_case__      :	List[str]							,			snake_case__      :	Union[str, Any]=1_3							,			snake_case__      :	Dict=7							,			snake_case__      :	Optional[int]=True							,			snake_case__      :	Optional[int]=True							,			snake_case__      :	int=False							,			snake_case__      :	Optional[Any]=True							,			snake_case__      :	Optional[int]=9_9							,			snake_case__      :	List[Any]=3_2							,			snake_case__      :	Optional[Any]=5							,			snake_case__      :	Union[str, Any]=4							,			snake_case__      :	List[Any]=3_7							,			snake_case__      :	List[str]="gelu"							,			snake_case__      :	List[Any]=0.1							,			snake_case__      :	Tuple=0.1							,			snake_case__      :	Optional[Any]=5_1_2							,			snake_case__      :	Tuple=1_6							,			snake_case__      :	str=2							,			snake_case__      :	Optional[int]=0.02							,			snake_case__      :	int=3							,			snake_case__      :	Union[str, Any]=4							,			snake_case__      :	Optional[int]=None							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							parent
        UpperCAmelCase__						:    Optional[Any]							=							batch_size
        UpperCAmelCase__						:    Any							=							seq_length
        UpperCAmelCase__						:    int							=							is_training
        UpperCAmelCase__						:    Dict							=							use_input_mask
        UpperCAmelCase__						:    List[Any]							=							use_token_type_ids
        UpperCAmelCase__						:    Union[str, Any]							=							use_labels
        UpperCAmelCase__						:    Union[str, Any]							=							vocab_size
        UpperCAmelCase__						:    Union[str, Any]							=							hidden_size
        UpperCAmelCase__						:    Union[str, Any]							=							num_hidden_layers
        UpperCAmelCase__						:    str							=							num_attention_heads
        UpperCAmelCase__						:    int							=							intermediate_size
        UpperCAmelCase__						:    int							=							hidden_act
        UpperCAmelCase__						:    str							=							hidden_dropout_prob
        UpperCAmelCase__						:    Optional[int]							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    List[Any]							=							max_position_embeddings
        UpperCAmelCase__						:    str							=							type_vocab_size
        UpperCAmelCase__						:    Union[str, Any]							=							type_sequence_label_size
        UpperCAmelCase__						:    str							=							initializer_range
        UpperCAmelCase__						:    Tuple							=							num_labels
        UpperCAmelCase__						:    Union[str, Any]							=							num_choices
        UpperCAmelCase__						:    Tuple							=							scope
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size							)

        UpperCAmelCase__						:    Optional[int]							=							None
        if self.use_input_mask:
             UpperCAmelCase__						:    Union[str, Any]							=							random_attention_mask([self.batch_size, self.seq_length]							)

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

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

        UpperCAmelCase__						:    Dict							=							self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def       __a							(		self      :	Tuple							):


        '''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=snake_case__							,			initializer_range=self.initializer_range							,			)
   def       __a							(		self      :	Optional[int]							,			snake_case__      :	Optional[int]							,			snake_case__      :	Optional[int]							,			snake_case__      :	Tuple							,			snake_case__      :	Dict							,			snake_case__      :	Tuple							,			snake_case__      :	List[str]							,			snake_case__      :	Union[str, Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							LlamaModel(config=snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    List[str]							=							model(snake_case__							,			attention_mask=snake_case__							)
        UpperCAmelCase__						:    Any							=							model(snake_case__							)
        self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)							)
   def       __a							(		self      :	Any							,			snake_case__      :	Union[str, Any]							,			snake_case__      :	Optional[Any]							,			snake_case__      :	List[Any]							,			snake_case__      :	Dict							,			snake_case__      :	Optional[int]							,			snake_case__      :	Tuple							,			snake_case__      :	Tuple							,			snake_case__      :	str							,			snake_case__      :	int							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							True
        UpperCAmelCase__						:    str							=							LlamaModel(snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    Optional[Any]							=							model(
            snake_case__							,			attention_mask=snake_case__							,			encoder_hidden_states=snake_case__							,			encoder_attention_mask=snake_case__							,			)
        UpperCAmelCase__						:    str							=							model(
            snake_case__							,			attention_mask=snake_case__							,			encoder_hidden_states=snake_case__							,			)
        UpperCAmelCase__						:    List[str]							=							model(snake_case__							,			attention_mask=snake_case__							)
        self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)							)
   def       __a							(		self      :	List[str]							,			snake_case__      :	Dict							,			snake_case__      :	Optional[int]							,			snake_case__      :	int							,			snake_case__      :	Optional[int]							,			snake_case__      :	List[str]							,			snake_case__      :	Optional[int]							,			snake_case__      :	List[Any]							,			snake_case__      :	List[str]							,			snake_case__      :	Union[str, Any]							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							LlamaForCausalLM(config=snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    Optional[Any]							=							model(snake_case__							,			attention_mask=snake_case__							,			labels=snake_case__							)
        self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.vocab_size)							)
   def       __a							(		self      :	List[str]							,			snake_case__      :	Union[str, Any]							,			snake_case__      :	List[Any]							,			snake_case__      :	int							,			snake_case__      :	Optional[int]							,			snake_case__      :	List[Any]							,			snake_case__      :	int							,			snake_case__      :	Optional[Any]							,			snake_case__      :	Dict							,			snake_case__      :	Optional[int]							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							True
        UpperCAmelCase__						:    str							=							True
        UpperCAmelCase__						:    Union[str, Any]							=							LlamaForCausalLM(config=snake_case__							)
        model.to(snake_case__							)
        model.eval()

        # first forward pass
        UpperCAmelCase__						:    List[str]							=							model(
            snake_case__							,			attention_mask=snake_case__							,			encoder_hidden_states=snake_case__							,			encoder_attention_mask=snake_case__							,			use_cache=snake_case__							,			)
        UpperCAmelCase__						:    Optional[Any]							=							outputs.past_key_values

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

        # append to next input_ids and
        UpperCAmelCase__						:    str							=							torch.cat([input_ids, next_tokens]							,			dim=-1							)
        UpperCAmelCase__						:    List[Any]							=							torch.cat([input_mask, next_mask]							,			dim=-1							)

        UpperCAmelCase__						:    str							=							model(
            snake_case__							,			attention_mask=snake_case__							,			encoder_hidden_states=snake_case__							,			encoder_attention_mask=snake_case__							,			output_hidden_states=snake_case__							,			)["hidden_states"][0]
        UpperCAmelCase__						:    str							=							model(
            snake_case__							,			attention_mask=snake_case__							,			encoder_hidden_states=snake_case__							,			encoder_attention_mask=snake_case__							,			past_key_values=snake_case__							,			output_hidden_states=snake_case__							,			)["hidden_states"][0]

        # select random slice
        UpperCAmelCase__						:    Dict							=							ids_tensor((1,)							,			output_from_past.shape[-1]							).item()
        UpperCAmelCase__						:    str							=							output_from_no_past[:, -3:, random_slice_idx].detach()
        UpperCAmelCase__						:    Any							=							output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(snake_case__							,			snake_case__							,			atol=1e-3							)							)




   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    int							=							self.prepare_config_and_inputs()
        (
            (
            UpperCAmelCase__
        )							,				(
            UpperCAmelCase__
        )							,				(
            UpperCAmelCase__
        )							,				(
            UpperCAmelCase__
        )							,				(
            UpperCAmelCase__
        )							,				(
            UpperCAmelCase__
        )							,				(
            UpperCAmelCase__
        )							,				
        )						:    Optional[Any]							=							config_and_inputs
        UpperCAmelCase__						:    Union[str, Any]							=							{"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict




@require_torch
class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						__magic_name__							,						unittest.TestCase   ):
   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       __a							(		self      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							LlamaModelTester(self							)
        UpperCAmelCase__						:    str							=							ConfigTester(self							,			config_class=snake_case__							,			hidden_size=3_7							)
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        self.config_tester.run_common_tests()
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case__							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
             UpperCAmelCase__						:    List[Any]							=							type
             self.model_tester.create_and_check_model(*snake_case__							)
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    Tuple							=							self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase__						:    str							=							3
        UpperCAmelCase__						:    Any							=							input_dict["input_ids"]
        UpperCAmelCase__						:    Any							=							input_ids.ne(1							).to(snake_case__							)
        UpperCAmelCase__						:    str							=							ids_tensor([self.model_tester.batch_size]							,			self.model_tester.type_sequence_label_size							)
        UpperCAmelCase__						:    Optional[Any]							=							LlamaForSequenceClassification(snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    Optional[int]							=							model(snake_case__							,			attention_mask=snake_case__							,			labels=snake_case__							)
        self.assertEqual(result.logits.shape							,			(self.model_tester.batch_size, self.model_tester.num_labels)							)
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[Any]							=							self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase__						:    List[Any]							=							3
        UpperCAmelCase__						:    Any							=							"single_label_classification"
        UpperCAmelCase__						:    Tuple							=							input_dict["input_ids"]
        UpperCAmelCase__						:    str							=							input_ids.ne(1							).to(snake_case__							)
        UpperCAmelCase__						:    List[Any]							=							ids_tensor([self.model_tester.batch_size]							,			self.model_tester.type_sequence_label_size							)
        UpperCAmelCase__						:    Any							=							LlamaForSequenceClassification(snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    List[str]							=							model(snake_case__							,			attention_mask=snake_case__							,			labels=snake_case__							)
        self.assertEqual(result.logits.shape							,			(self.model_tester.batch_size, self.model_tester.num_labels)							)
   def       __a							(		self      :	Union[str, Any]							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase__						:    Dict							=							3
        UpperCAmelCase__						:    Tuple							=							"multi_label_classification"
        UpperCAmelCase__						:    Any							=							input_dict["input_ids"]
        UpperCAmelCase__						:    str							=							input_ids.ne(1							).to(snake_case__							)
        UpperCAmelCase__						:    Any							=							ids_tensor(
            [self.model_tester.batch_size, config.num_labels]							,			self.model_tester.type_sequence_label_size							).to(torch.float							)
        UpperCAmelCase__						:    Dict							=							LlamaForSequenceClassification(snake_case__							)
        model.to(snake_case__							)
        model.eval()
        UpperCAmelCase__						:    int							=							model(snake_case__							,			attention_mask=snake_case__							,			labels=snake_case__							)
        self.assertEqual(result.logits.shape							,			(self.model_tester.batch_size, self.model_tester.num_labels)							)
   @unittest.skip("LLaMA buffers include complex numbers, which breaks this test"							)
   def       __a							(		self      :	str							):


        '''simple docstring'''
        pass




   @parameterized.expand([("linear",), ("dynamic",)]							)
   def       __a							(		self      :	List[str]							,			snake_case__      :	int							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase__						:    List[str]							=							ids_tensor([1, 1_0]							,			config.vocab_size							)
        UpperCAmelCase__						:    int							=							ids_tensor([1, int(config.max_position_embeddings * 1.5							)]							,			config.vocab_size							)

        set_seed(4_2							)  # Fixed seed at init time so the two models get the same random weights
        UpperCAmelCase__						:    str							=							LlamaModel(snake_case__							)
        original_model.to(snake_case__							)
        original_model.eval()
        UpperCAmelCase__						:    List[Any]							=							original_model(snake_case__							).last_hidden_state
        UpperCAmelCase__						:    Tuple							=							original_model(snake_case__							).last_hidden_state

        set_seed(4_2							)  # Fixed seed at init time so the two models get the same random weights
        UpperCAmelCase__						:    str							=							{"type": scaling_type, "factor": 10.0}
        UpperCAmelCase__						:    Optional[int]							=							LlamaModel(snake_case__							)
        scaled_model.to(snake_case__							)
        scaled_model.eval()
        UpperCAmelCase__						:    Tuple							=							scaled_model(snake_case__							).last_hidden_state
        UpperCAmelCase__						:    Union[str, Any]							=							scaled_model(snake_case__							).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(snake_case__							,			snake_case__							,			atol=1e-5							)							)
        else:
             self.assertFalse(torch.allclose(snake_case__							,			snake_case__							,			atol=1e-5							)							)

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




@require_torch
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"							)
   @slow
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
        UpperCAmelCase__						:    Any							=							LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf"							,			device_map="auto"							)
        UpperCAmelCase__						:    Any							=							model(torch.tensor([input_ids]							)							)
        # Expected mean on dim = -1
        UpperCAmelCase__						:    Optional[Any]							=							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							)							,			snake_case__							,			atol=1e-2							,			rtol=1e-2							)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        UpperCAmelCase__						:    Optional[int]							=							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, :3_0]							,			snake_case__							,			atol=1e-5							,			rtol=1e-5							)
   @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"							)
   @slow
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
        UpperCAmelCase__						:    Dict							=							LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"							,			device_map="auto"							)
        UpperCAmelCase__						:    List[str]							=							model(torch.tensor(snake_case__							)							)
        # Expected mean on dim = -1
        UpperCAmelCase__						:    str							=							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							)							,			snake_case__							,			atol=1e-2							,			rtol=1e-2							)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        UpperCAmelCase__						:    str							=							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, :3_0]							,			snake_case__							,			atol=1e-5							,			rtol=1e-5							)
   @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"							)
   @slow
   def       __a							(		self      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
        UpperCAmelCase__						:    Optional[Any]							=							LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"							,			device_map="auto"							)
        UpperCAmelCase__						:    Dict							=							model(torch.tensor(snake_case__							)							)
        # Expected mean on dim = -1
        UpperCAmelCase__						:    List[str]							=							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							)							,			snake_case__							,			atol=1e-2							,			rtol=1e-2							)
        # slicing logits[0, 0, 0:30]
        # fmt: off
        UpperCAmelCase__						:    str							=							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							)							,			snake_case__							,			atol=1e-2							,			rtol=1e-2							)
   @unittest.skip(
       "Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"							)
   @slow
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							[1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
        UpperCAmelCase__						:    Optional[int]							=							LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"							,			device_map="auto"							)
        UpperCAmelCase__						:    List[Any]							=							model(torch.tensor(snake_case__							)							)

        UpperCAmelCase__						:    str							=							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							)							,			snake_case__							,			atol=1e-2							,			rtol=1e-2							)
        # fmt: off
        UpperCAmelCase__						:    Any							=							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, :3_0]							,			snake_case__							,			atol=1e-5							,			rtol=1e-5							)




   @unittest.skip("Model is curently gated"							)
   @slow
   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							"Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"
        UpperCAmelCase__						:    Any							=							"Simply put, the theory of relativity states that "
        UpperCAmelCase__						:    int							=							LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf"							)
        UpperCAmelCase__						:    List[Any]							=							tokenizer.encode(snake_case__							,			return_tensors="pt"							)
        UpperCAmelCase__						:    Union[str, Any]							=							LlamaForCausalLM.from_pretrained(
            "meta-llama/Llama-2-13b-chat-hf"							,			device_map="sequential"							,			use_safetensors=snake_case__							)

        # greedy generation outputs
        UpperCAmelCase__						:    Optional[int]							=							model.generate(snake_case__							,			max_new_tokens=6_4							,			top_p=snake_case__							,			temperature=1							,			do_sample=snake_case__							)
        UpperCAmelCase__						:    Tuple							=							tokenizer.decode(generated_ids[0]							,			skip_special_tokens=snake_case__							)
        self.assertEqual(snake_case__							,			snake_case__							)


