



"""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 			_a :
			"""simple docstring"""



			def __init__(		self     :    Tuple				,   __UpperCamelCase     :    Optional[int]				,   __UpperCamelCase     :    Union[str, Any]=1_3				,   __UpperCamelCase     :    List[Any]=7				,   __UpperCamelCase     :    str=True				,   __UpperCamelCase     :    str=True				,   __UpperCamelCase     :    Dict=False				,   __UpperCamelCase     :    List[Any]=True				,   __UpperCamelCase     :    Dict=9_9				,   __UpperCamelCase     :    List[Any]=3_2				,   __UpperCamelCase     :    Any=5				,   __UpperCamelCase     :    int=4				,   __UpperCamelCase     :    int=3_7				,   __UpperCamelCase     :    List[Any]="gelu"				,   __UpperCamelCase     :    List[str]=0.1				,   __UpperCamelCase     :    Any=0.1				,   __UpperCamelCase     :    List[str]=5_1_2				,   __UpperCamelCase     :    str=1_6				,   __UpperCamelCase     :    Any=2				,   __UpperCamelCase     :    Union[str, Any]=0.0_2				,   __UpperCamelCase     :    List[Any]=3				,   __UpperCamelCase     :    str=4				,   __UpperCamelCase     :    List[Any]=None				,   )->Any:
							_UpperCAmelCase					=    parent
							_UpperCAmelCase					=    batch_size
							_UpperCAmelCase					=    seq_length
							_UpperCAmelCase					=    is_training
							_UpperCAmelCase					=    use_input_mask
							_UpperCAmelCase					=    use_token_type_ids
							_UpperCAmelCase					=    use_labels
							_UpperCAmelCase					=    vocab_size
							_UpperCAmelCase					=    hidden_size
							_UpperCAmelCase					=    num_hidden_layers
							_UpperCAmelCase					=    num_attention_heads
							_UpperCAmelCase					=    intermediate_size
							_UpperCAmelCase					=    hidden_act
							_UpperCAmelCase					=    hidden_dropout_prob
							_UpperCAmelCase					=    attention_probs_dropout_prob
							_UpperCAmelCase					=    max_position_embeddings
							_UpperCAmelCase					=    type_vocab_size
							_UpperCAmelCase					=    type_sequence_label_size
							_UpperCAmelCase					=    initializer_range
							_UpperCAmelCase					=    num_labels
							_UpperCAmelCase					=    num_choices
							_UpperCAmelCase					=    scope



			def  lowercase__						(		self     :    List[str]   )->int:
							_UpperCAmelCase					=    ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size   )

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

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

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

							_UpperCAmelCase					=    self.get_config()

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



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



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Any				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    Dict				,   __UpperCamelCase     :    Tuple				,   )->Tuple:
							_UpperCAmelCase					=    True
							_UpperCAmelCase					=    LlamaModel(_A   )
							model.to(_A   )
							model.eval()
							_UpperCAmelCase					=    model(
							    _A				,   attention_mask=_A				,   encoder_hidden_states=_A				,   encoder_attention_mask=_A				,   )
							_UpperCAmelCase					=    model(
							    _A				,   attention_mask=_A				,   encoder_hidden_states=_A				,   )
							_UpperCAmelCase					=    model(_A				,   attention_mask=_A   )
							self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.seq_length, self.hidden_size)   )



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    Dict				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    List[Any]				,   )->Optional[int]:
							_UpperCAmelCase					=    LlamaForCausalLM(config=_A   )
							model.to(_A   )
							model.eval()
							_UpperCAmelCase					=    model(_A				,   attention_mask=_A				,   labels=_A   )
							self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.seq_length, self.vocab_size)   )



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Optional[int]				,   __UpperCamelCase     :    Dict				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Optional[int]				,   __UpperCamelCase     :    List[str]				,   )->Optional[int]:
							_UpperCAmelCase					=    True
							_UpperCAmelCase					=    True
							_UpperCAmelCase					=    LlamaForCausalLM(config=_A   )
							model.to(_A   )
							model.eval()

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

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

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

							_UpperCAmelCase					=    model(
							    _A				,   attention_mask=_A				,   encoder_hidden_states=_A				,   encoder_attention_mask=_A				,   output_hidden_states=_A				,   )['hidden_states'][0]
							_UpperCAmelCase					=    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
							_UpperCAmelCase					=    ids_tensor((1,)				,   output_from_past.shape[-1]   ).item()
							_UpperCAmelCase					=    output_from_no_past[:, -3:, random_slice_idx].detach()
							_UpperCAmelCase					=    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  lowercase__						(		self     :    Union[str, Any]   )->int:
							_UpperCAmelCase					=    self.prepare_config_and_inputs()
							(
							    _UpperCAmelCase       
							)					=    config_and_inputs
							_UpperCAmelCase					=    {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict



@require_torch
class 			_a (					snake_case__			,							snake_case__			,							snake_case__			,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__			      =	(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
			UpperCamelCase__			      =	(LlamaForCausalLM,) if is_torch_available() else ()
			UpperCamelCase__			      =	(
			    {
			        '''feature-extraction''': LlamaModel,
			        '''text-classification''': LlamaForSequenceClassification,
			        '''text-generation''': LlamaForCausalLM,
			        '''zero-shot''': LlamaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCamelCase__			      =	False
			UpperCamelCase__			      =	False



			def  lowercase__						(		self     :    Tuple   )->Dict:
							_UpperCAmelCase					=    LlamaModelTester(self   )
							_UpperCAmelCase					=    ConfigTester(self				,   config_class=_A				,   hidden_size=3_7   )



			def  lowercase__						(		self     :    Dict   )->List[str]:
							self.config_tester.run_common_tests()



			def  lowercase__						(		self     :    str   )->Optional[int]:
							_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A   )



			def  lowercase__						(		self     :    Any   )->Dict:
							_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
											_UpperCAmelCase					=    type
											self.model_tester.create_and_check_model(*_A   )



			def  lowercase__						(		self     :    Union[str, Any]   )->Union[str, Any]:
							_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase					=    3
							_UpperCAmelCase					=    input_dict['input_ids']
							_UpperCAmelCase					=    input_ids.ne(1   ).to(_A   )
							_UpperCAmelCase					=    ids_tensor([self.model_tester.batch_size]				,   self.model_tester.type_sequence_label_size   )
							_UpperCAmelCase					=    LlamaForSequenceClassification(_A   )
							model.to(_A   )
							model.eval()
							_UpperCAmelCase					=    model(_A				,   attention_mask=_A				,   labels=_A   )
							self.assertEqual(result.logits.shape				,   (self.model_tester.batch_size, self.model_tester.num_labels)   )



			def  lowercase__						(		self     :    Optional[int]   )->Optional[int]:
							_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase					=    3
							_UpperCAmelCase					=    'single_label_classification'
							_UpperCAmelCase					=    input_dict['input_ids']
							_UpperCAmelCase					=    input_ids.ne(1   ).to(_A   )
							_UpperCAmelCase					=    ids_tensor([self.model_tester.batch_size]				,   self.model_tester.type_sequence_label_size   )
							_UpperCAmelCase					=    LlamaForSequenceClassification(_A   )
							model.to(_A   )
							model.eval()
							_UpperCAmelCase					=    model(_A				,   attention_mask=_A				,   labels=_A   )
							self.assertEqual(result.logits.shape				,   (self.model_tester.batch_size, self.model_tester.num_labels)   )



			def  lowercase__						(		self     :    int   )->List[str]:
							_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase					=    3
							_UpperCAmelCase					=    'multi_label_classification'
							_UpperCAmelCase					=    input_dict['input_ids']
							_UpperCAmelCase					=    input_ids.ne(1   ).to(_A   )
							_UpperCAmelCase					=    ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]				,   self.model_tester.type_sequence_label_size   ).to(torch.float   )
							_UpperCAmelCase					=    LlamaForSequenceClassification(_A   )
							model.to(_A   )
							model.eval()
							_UpperCAmelCase					=    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  lowercase__						(		self     :    int   )->List[str]:
							pass



			@parameterized.expand([('''linear''',), ('''dynamic''',)]   )
			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    str   )->List[str]:
							_UpperCAmelCase					=    self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase					=    ids_tensor([1, 1_0]				,   config.vocab_size   )
							_UpperCAmelCase					=    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					=    LlamaModel(_A   )
							original_model.to(_A   )
							original_model.eval()
							_UpperCAmelCase					=    original_model(_A   ).last_hidden_state
							_UpperCAmelCase					=    original_model(_A   ).last_hidden_state

							set_seed(4_2   )  # Fixed seed at init time so the two models get the same random weights
							_UpperCAmelCase					=    {'type': scaling_type, 'factor': 1_0.0}
							_UpperCAmelCase					=    LlamaModel(_A   )
							scaled_model.to(_A   )
							scaled_model.eval()
							_UpperCAmelCase					=    scaled_model(_A   ).last_hidden_state
							_UpperCAmelCase					=    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 			_a (					unittest.TestCase):
			"""simple docstring"""



			@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''   )
			@slow
			def  lowercase__						(		self     :    List[Any]   )->Any:
							_UpperCAmelCase					=    [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					=    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''				,   device_map='''auto'''   )
							_UpperCAmelCase					=    model(torch.tensor([input_ids]   )   )
							# Expected mean on dim = -1
							_UpperCAmelCase					=    torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]   )
							torch.testing.assert_close(out.mean(-1   )				,   _A				,   atol=1e-2				,   rtol=1e-2   )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							_UpperCAmelCase					=    torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]   )
							# fmt: on
							torch.testing.assert_close(out[0, 0, :3_0]				,   _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  lowercase__						(		self     :    Dict   )->List[Any]:
							_UpperCAmelCase					=    [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					=    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''				,   device_map='''auto'''   )
							_UpperCAmelCase					=    model(torch.tensor(_A   )   )
							# Expected mean on dim = -1
							_UpperCAmelCase					=    torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]   )
							torch.testing.assert_close(out.mean(-1   )				,   _A				,   atol=1e-2				,   rtol=1e-2   )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							_UpperCAmelCase					=    torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]   )
							# fmt: on
							torch.testing.assert_close(out[0, 0, :3_0]				,   _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  lowercase__						(		self     :    int   )->Optional[Any]:
							_UpperCAmelCase					=    [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					=    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''				,   device_map='''auto'''   )
							_UpperCAmelCase					=    model(torch.tensor(_A   )   )
							# Expected mean on dim = -1
							_UpperCAmelCase					=    torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]   )
							torch.testing.assert_close(out.mean(-1   )				,   _A				,   atol=1e-2				,   rtol=1e-2   )
							# slicing logits[0, 0, 0:30]
							# fmt: off
							_UpperCAmelCase					=    torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]   )
							# 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  lowercase__						(		self     :    Tuple   )->Any:
							_UpperCAmelCase					=    [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					=    LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''				,   device_map='''auto'''   )
							_UpperCAmelCase					=    model(torch.tensor(_A   )   )

							_UpperCAmelCase					=    torch.tensor(
							    [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]				,   dtype=torch.floataa   )
							torch.testing.assert_close(out.mean(-1   )				,   _A				,   atol=1e-2				,   rtol=1e-2   )
							# fmt: off
							_UpperCAmelCase					=    torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]   )
							# fmt: on
							torch.testing.assert_close(out[0, 0, :3_0]				,   _A				,   atol=1e-5				,   rtol=1e-5   )



			@unittest.skip('''Model is curently gated'''   )
			@slow
			def  lowercase__						(		self     :    Dict   )->Dict:
							_UpperCAmelCase					=    '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					=    'Simply put, the theory of relativity states that '
							_UpperCAmelCase					=    LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''   )
							_UpperCAmelCase					=    tokenizer.encode(_A				,   return_tensors='''pt'''   )
							_UpperCAmelCase					=    LlamaForCausalLM.from_pretrained(
							    '''meta-llama/Llama-2-13b-chat-hf'''				,   device_map='''sequential'''				,   use_safetensors=_A   )

							# greedy generation outputs
							_UpperCAmelCase					=    model.generate(_A				,   max_new_tokens=6_4				,   top_p=_A				,   temperature=1				,   do_sample=_A   )
							_UpperCAmelCase					=    tokenizer.decode(generated_ids[0]				,   skip_special_tokens=_A   )
							self.assertEqual(_A				,   _A   )








"""simple docstring"""


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				def is_in_circle(_SCREAMING_SNAKE_CASE   :						float					,     _SCREAMING_SNAKE_CASE   :						float				) -> bool:
								_UpperCAmelCase					=    sqrt((x**2) + (y**2)				)
								# Our circle has a radius of 1, so a distance
								# greater than 1 would land outside the circle.
								return distance_from_centre <= 1

				# The proportion of guesses that landed in the circle
				_UpperCAmelCase					=    mean(
				    int(is_in_circle(uniform(-1.0					,     1.0				)					,     uniform(-1.0					,     1.0				)				)				)
				    for _ in range(_SCREAMING_SNAKE_CASE				)				)
				# The ratio of the area for circle to square is pi/4.
				_UpperCAmelCase					=    proportion * 4
				print(f'The estimated value of pi is {pi_estimate}'				)
				print(f'The numpy value of pi is {pi}'				)
				print(f'The total error is {abs(pi - pi_estimate				)}'				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Callable[[float], float]					,     _SCREAMING_SNAKE_CASE   :						float = 0.0					,     _SCREAMING_SNAKE_CASE   :						float = 1.0					,     ):






				'''simple docstring'''



				return mean(
				    function_to_integrate(uniform(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)				) for _ in range(_SCREAMING_SNAKE_CASE				)				) * (max_value - min_value)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						float = 0.0					,     _SCREAMING_SNAKE_CASE   :						float = 1.0				):






				'''simple docstring'''



				def identity_function(_SCREAMING_SNAKE_CASE   :						float				) -> float:
								return x

				_UpperCAmelCase					=    area_under_curve_estimator(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    (max_value * max_value - min_value * min_value) / 2

				print('''******************'''				)
				print(f'Estimating area under y=x where x varies from {min_value} to {max_value}'				)
				print(f'Estimated value is {estimated_value}'				)
				print(f'Expected value is {expected_value}'				)
				print(f'Total error is {abs(estimated_value - expected_value				)}'				)
				print('''******************'''				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				def function_to_integrate(_SCREAMING_SNAKE_CASE   :						float				) -> float:
								return sqrt(4.0 - x * x				)

				_UpperCAmelCase					=    area_under_curve_estimator(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     0.0					,     2.0				)

				print('''******************'''				)
				print('''Estimating pi using area_under_curve_estimator'''				)
				print(f'Estimated value is {estimated_value}'				)
				print(f'Expected value is {pi}'				)
				print(f'Total error is {abs(estimated_value - pi				)}'				)
				print('''******************'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



