



'''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  UpperCamelCase__:




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

										"""simple docstring"""




										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   a__(	self    :				List[Any]   )->  Any:

										"""simple docstring"""




										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   a__(	self    :				Union[str, Any]   )->  Any:

										"""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=lowerCAmelCase   ,					initializer_range=self.initializer_range   ,					)




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Optional[Any]   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			LlamaModel(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   )
										UpperCAmelCase         =			model(lowerCAmelCase   )
										self.parent.assertEqual(result.last_hidden_state.shape   ,					(self.batch_size, self.seq_length, self.hidden_size)   )




				def   a__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Optional[Any]   ,					)->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			True
										UpperCAmelCase         =			LlamaModel(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(
										    lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					encoder_hidden_states=lowerCAmelCase   ,					encoder_attention_mask=lowerCAmelCase   ,					)
										UpperCAmelCase         =			model(
										    lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					encoder_hidden_states=lowerCAmelCase   ,					)
										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   )
										self.parent.assertEqual(result.last_hidden_state.shape   ,					(self.batch_size, self.seq_length, self.hidden_size)   )




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				Optional[int]   ,					)->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			LlamaForCausalLM(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					labels=lowerCAmelCase   )
										self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.seq_length, self.vocab_size)   )




				def   a__(	self    :				str   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Dict   ,					)->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			True
										UpperCAmelCase         =			True
										UpperCAmelCase         =			LlamaForCausalLM(config=lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()

										# first forward pass
										UpperCAmelCase         =			model(
										    lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					encoder_hidden_states=lowerCAmelCase   ,					encoder_attention_mask=lowerCAmelCase   ,					use_cache=lowerCAmelCase   ,					)
										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(
										    lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					encoder_hidden_states=lowerCAmelCase   ,					encoder_attention_mask=lowerCAmelCase   ,					output_hidden_states=lowerCAmelCase   ,					)["hidden_states"][0]
										UpperCAmelCase         =			model(
										    lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					encoder_hidden_states=lowerCAmelCase   ,					encoder_attention_mask=lowerCAmelCase   ,					past_key_values=lowerCAmelCase   ,					output_hidden_states=lowerCAmelCase   ,					)["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(lowerCAmelCase   ,					lowerCAmelCase   ,					atol=1E-3   )   )





				def   a__(	self    :				List[str]   )->  Tuple:

										"""simple docstring"""




										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  UpperCamelCase__(     lowercase__		,       lowercase__		,       lowercase__		,       unittest.TestCase ):
				__magic_name__       :   Optional[int]							= (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
				__magic_name__       :   Optional[Any]							= (LlamaForCausalLM,) if is_torch_available() else ()
				__magic_name__       :   Tuple							= (
				    {
				        "feature-extraction": LlamaModel,
				        "text-classification": LlamaForSequenceClassification,
				        "text-generation": LlamaForCausalLM,
				        "zero-shot": LlamaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__magic_name__       :   List[Any]							= False
				__magic_name__       :   Optional[Any]							= False




				def   a__(	self    :				Union[str, Any]   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			LlamaModelTester(self   )
										UpperCAmelCase         =			ConfigTester(self   ,					config_class=lowerCAmelCase   ,					hidden_size=37   )




				def   a__(	self    :				Union[str, Any]   )->  List[str]:

										"""simple docstring"""




										self.config_tester.run_common_tests()




				def   a__(	self    :				Union[str, Any]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase   )




				def   a__(	self    :				List[Any]   )->  List[Any]:

										"""simple docstring"""




										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(*lowerCAmelCase   )




				def   a__(	self    :				List[str]   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase         =			3
										UpperCAmelCase         =			input_dict["input_ids"]
										UpperCAmelCase         =			input_ids.ne(1   ).to(lowerCAmelCase   )
										UpperCAmelCase         =			ids_tensor([self.model_tester.batch_size]   ,					self.model_tester.type_sequence_label_size   )
										UpperCAmelCase         =			LlamaForSequenceClassification(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					labels=lowerCAmelCase   )
										self.assertEqual(result.logits.shape   ,					(self.model_tester.batch_size, self.model_tester.num_labels)   )




				def   a__(	self    :				str   )->  int:

										"""simple docstring"""




										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(lowerCAmelCase   )
										UpperCAmelCase         =			ids_tensor([self.model_tester.batch_size]   ,					self.model_tester.type_sequence_label_size   )
										UpperCAmelCase         =			LlamaForSequenceClassification(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					labels=lowerCAmelCase   )
										self.assertEqual(result.logits.shape   ,					(self.model_tester.batch_size, self.model_tester.num_labels)   )




				def   a__(	self    :				Tuple   )->  Optional[Any]:

										"""simple docstring"""




										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(lowerCAmelCase   )
										UpperCAmelCase         =			ids_tensor(
										    [self.model_tester.batch_size, config.num_labels]   ,					self.model_tester.type_sequence_label_size   ).to(torch.float   )
										UpperCAmelCase         =			LlamaForSequenceClassification(lowerCAmelCase   )
										model.to(lowerCAmelCase   )
										model.eval()
										UpperCAmelCase         =			model(lowerCAmelCase   ,					attention_mask=lowerCAmelCase   ,					labels=lowerCAmelCase   )
										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    :				Union[str, Any]   )->  Dict:

										"""simple docstring"""




										pass





				@parameterized.expand([('''linear''',), ('''dynamic''',)]   )
				def   a__(	self    :				str   ,					lowerCAmelCase    :				Optional[int]   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase         =			ids_tensor([1, 10]   ,					config.vocab_size   )
										UpperCAmelCase         =			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
										UpperCAmelCase         =			LlamaModel(lowerCAmelCase   )
										original_model.to(lowerCAmelCase   )
										original_model.eval()
										UpperCAmelCase         =			original_model(lowerCAmelCase   ).last_hidden_state
										UpperCAmelCase         =			original_model(lowerCAmelCase   ).last_hidden_state

										set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
										UpperCAmelCase         =			{"type": scaling_type, "factor": 10.0}
										UpperCAmelCase         =			LlamaModel(lowerCAmelCase   )
										scaled_model.to(lowerCAmelCase   )
										scaled_model.eval()
										UpperCAmelCase         =			scaled_model(lowerCAmelCase   ).last_hidden_state
										UpperCAmelCase         =			scaled_model(lowerCAmelCase   ).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(lowerCAmelCase   ,					lowerCAmelCase   ,					atol=1E-5   )   )
										else:
																self.assertFalse(torch.allclose(lowerCAmelCase   ,					lowerCAmelCase   ,					atol=1E-5   )   )

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







@require_torch
class  UpperCamelCase__(     unittest.TestCase ):




				@unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''   )
				@slow
				def   a__(	self    :				List[Any]   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			[1, 306, 4658, 278, 6593, 310, 2834, 338]
										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([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]   )
										torch.testing.assert_close(out.mean(-1   )   ,					lowerCAmelCase   ,					atol=1E-2   ,					rtol=1E-2   )
										# slicing logits[0, 0, 0:30]
										# fmt: off
										UpperCAmelCase         =			torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]   )
										# fmt: on
										torch.testing.assert_close(out[0, 0, :30]   ,					lowerCAmelCase   ,					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    :				Optional[int]   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			[1, 306, 4658, 278, 6593, 310, 2834, 338]
										UpperCAmelCase         =			LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''   ,					device_map='''auto'''   )
										UpperCAmelCase         =			model(torch.tensor(lowerCAmelCase   )   )
										# Expected mean on dim = -1
										UpperCAmelCase         =			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   )   ,					lowerCAmelCase   ,					atol=1E-2   ,					rtol=1E-2   )
										# slicing logits[0, 0, 0:30]
										# fmt: off
										UpperCAmelCase         =			torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]   )
										# fmt: on
										torch.testing.assert_close(out[0, 0, :30]   ,					lowerCAmelCase   ,					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    :				str   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			[1, 306, 4658, 278, 6593, 310, 2834, 338]
										UpperCAmelCase         =			LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''   ,					device_map='''auto'''   )
										UpperCAmelCase         =			model(torch.tensor(lowerCAmelCase   )   )
										# Expected mean on dim = -1
										UpperCAmelCase         =			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   )   ,					lowerCAmelCase   ,					atol=1E-2   ,					rtol=1E-2   )
										# slicing logits[0, 0, 0:30]
										# fmt: off
										UpperCAmelCase         =			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   )   ,					lowerCAmelCase   ,					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    :				Tuple   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			[1, 306, 4658, 278, 6593, 310, 2834, 338]
										UpperCAmelCase         =			LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''   ,					device_map='''auto'''   )
										UpperCAmelCase         =			model(torch.tensor(lowerCAmelCase   )   )

										UpperCAmelCase         =			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   )   ,					lowerCAmelCase   ,					atol=1E-2   ,					rtol=1E-2   )
										# fmt: off
										UpperCAmelCase         =			torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]   )
										# fmt: on
										torch.testing.assert_close(out[0, 0, :30]   ,					lowerCAmelCase   ,					atol=1E-5   ,					rtol=1E-5   )





				@unittest.skip('''Model is curently gated'''   )
				@slow
				def   a__(	self    :				List[str]   )->  int:

										"""simple docstring"""




										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(lowerCAmelCase   ,					return_tensors='''pt'''   )
										UpperCAmelCase         =			LlamaForCausalLM.from_pretrained(
										    '''meta-llama/Llama-2-13b-chat-hf'''   ,					device_map='''sequential'''   ,					use_safetensors=lowerCAmelCase   )

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









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowercase    :    Any			     =  {
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Optional[int]			     =  [
	    """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTBigCodeForSequenceClassification""",
	    """GPTBigCodeForTokenClassification""",
	    """GPTBigCodeForCausalLM""",
	    """GPTBigCodeModel""",
	    """GPTBigCodePreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_bigcode import (
		    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTBigCodeForCausalLM,
		    GPTBigCodeForSequenceClassification,
		    GPTBigCodeForTokenClassification,
		    GPTBigCodeModel,
		    GPTBigCodePreTrainedModel,
		)


else:
	import sys

	_lowercase    :    Tuple			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





