


'''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__					:



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__=13      ,	snake_case__=7      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=False      ,	snake_case__=True      ,	snake_case__=99      ,	snake_case__=32      ,	snake_case__=5      ,	snake_case__=4      ,	snake_case__=37      ,	snake_case__="gelu"      ,	snake_case__=0.1      ,	snake_case__=0.1      ,	snake_case__=512      ,	snake_case__=16      ,	snake_case__=2      ,	snake_case__=0.02      ,	snake_case__=3      ,	snake_case__=4      ,	snake_case__=None      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       parent
            _lowerCAmelCase    :							Tuple   			=       batch_size
            _lowerCAmelCase    :							str   			=       seq_length
            _lowerCAmelCase    :							Tuple   			=       is_training
            _lowerCAmelCase    :							Optional[int]   			=       use_input_mask
            _lowerCAmelCase    :							Any   			=       use_token_type_ids
            _lowerCAmelCase    :							List[str]   			=       use_labels
            _lowerCAmelCase    :							Optional[Any]   			=       vocab_size
            _lowerCAmelCase    :							Optional[Any]   			=       hidden_size
            _lowerCAmelCase    :							Optional[Any]   			=       num_hidden_layers
            _lowerCAmelCase    :							Tuple   			=       num_attention_heads
            _lowerCAmelCase    :							str   			=       intermediate_size
            _lowerCAmelCase    :							Tuple   			=       hidden_act
            _lowerCAmelCase    :							List[str]   			=       hidden_dropout_prob
            _lowerCAmelCase    :							str   			=       attention_probs_dropout_prob
            _lowerCAmelCase    :							Any   			=       max_position_embeddings
            _lowerCAmelCase    :							Tuple   			=       type_vocab_size
            _lowerCAmelCase    :							List[str]   			=       type_sequence_label_size
            _lowerCAmelCase    :							Union[str, Any]   			=       initializer_range
            _lowerCAmelCase    :							Union[str, Any]   			=       num_labels
            _lowerCAmelCase    :							Optional[int]   			=       num_choices
            _lowerCAmelCase    :							int   			=       scope







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       ids_tensor([self.batch_size, self.seq_length]      ,	self.vocab_size   )

            _lowerCAmelCase    :							Optional[int]   			=       None
            if self.use_input_mask:
                   _lowerCAmelCase    :							List[str]   			=       random_attention_mask([self.batch_size, self.seq_length]   )

            _lowerCAmelCase    :							Tuple   			=       None
            if self.use_token_type_ids:
                   _lowerCAmelCase    :							List[Any]   			=       ids_tensor([self.batch_size, self.seq_length]      ,	self.type_vocab_size   )

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

            _lowerCAmelCase    :							int   			=       self.get_config()

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







     def a	( self   ):

            '''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      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       LlamaModel(config=snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							Any   			=       model(snake_case__      ,	attention_mask=snake_case__   )
            _lowerCAmelCase    :							int   			=       model(snake_case__   )
            self.parent.assertEqual(result.last_hidden_state.shape      ,	(self.batch_size, self.seq_length, self.hidden_size)   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       True
            _lowerCAmelCase    :							Union[str, Any]   			=       LlamaModel(snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							List[Any]   			=       model(
                snake_case__      ,	attention_mask=snake_case__      ,	encoder_hidden_states=snake_case__      ,	encoder_attention_mask=snake_case__      ,	)
            _lowerCAmelCase    :							Dict   			=       model(
                snake_case__      ,	attention_mask=snake_case__      ,	encoder_hidden_states=snake_case__      ,	)
            _lowerCAmelCase    :							Any   			=       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      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       LlamaForCausalLM(config=snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							str   			=       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      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       True
            _lowerCAmelCase    :							Union[str, Any]   			=       True
            _lowerCAmelCase    :							Any   			=       LlamaForCausalLM(config=snake_case__   )
            model.to(snake_case__   )
            model.eval()

            # first forward pass
            _lowerCAmelCase    :							Dict   			=       model(
                snake_case__      ,	attention_mask=snake_case__      ,	encoder_hidden_states=snake_case__      ,	encoder_attention_mask=snake_case__      ,	use_cache=snake_case__      ,	)
            _lowerCAmelCase    :							Any   			=       outputs.past_key_values

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

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

            _lowerCAmelCase    :							Dict   			=       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]
            _lowerCAmelCase    :							List[Any]   			=       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
            _lowerCAmelCase    :							Optional[int]   			=       ids_tensor((1,)      ,	output_from_past.shape[-1]   ).item()
            _lowerCAmelCase    :							List[Any]   			=       output_from_no_past[:, -3:, random_slice_idx].detach()
            _lowerCAmelCase    :							Optional[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   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.prepare_config_and_inputs()
            (
                _lowerCAmelCase     
            )    :							str   			=       config_and_inputs
            _lowerCAmelCase    :							List[str]   			=       {'input_ids': input_ids, 'attention_mask': input_mask}
            return config, inputs_dict
@require_torch
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					SCREAMING_SNAKE_CASE_    ,					SCREAMING_SNAKE_CASE_    ,					unittest.TestCase	):



     """simple docstring"""






     __magic_name__    			=    (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
     __magic_name__    			=    (LlamaForCausalLM,) if is_torch_available() else ()
     __magic_name__    			=    (
         {
             "feature-extraction": LlamaModel,
             "text-classification": LlamaForSequenceClassification,
             "text-generation": LlamaForCausalLM,
             "zero-shot": LlamaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __magic_name__    			=    False
     __magic_name__    			=    False







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       LlamaModelTester(self   )
            _lowerCAmelCase    :							str   			=       ConfigTester(self      ,	config_class=snake_case__      ,	hidden_size=37   )







     def a	( self   ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                   _lowerCAmelCase    :							int   			=       type
                   self.model_tester.create_and_check_model(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       self.model_tester.prepare_config_and_inputs_for_common()
            _lowerCAmelCase    :							Union[str, Any]   			=       3
            _lowerCAmelCase    :							Optional[Any]   			=       input_dict['input_ids']
            _lowerCAmelCase    :							str   			=       input_ids.ne(1   ).to(snake_case__   )
            _lowerCAmelCase    :							List[str]   			=       ids_tensor([self.model_tester.batch_size]      ,	self.model_tester.type_sequence_label_size   )
            _lowerCAmelCase    :							Union[str, Any]   			=       LlamaForSequenceClassification(snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							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   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.model_tester.prepare_config_and_inputs_for_common()
            _lowerCAmelCase    :							Tuple   			=       3
            _lowerCAmelCase    :							Union[str, Any]   			=       'single_label_classification'
            _lowerCAmelCase    :							List[str]   			=       input_dict['input_ids']
            _lowerCAmelCase    :							Tuple   			=       input_ids.ne(1   ).to(snake_case__   )
            _lowerCAmelCase    :							int   			=       ids_tensor([self.model_tester.batch_size]      ,	self.model_tester.type_sequence_label_size   )
            _lowerCAmelCase    :							List[Any]   			=       LlamaForSequenceClassification(snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							Union[str, Any]   			=       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   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.model_tester.prepare_config_and_inputs_for_common()
            _lowerCAmelCase    :							Tuple   			=       3
            _lowerCAmelCase    :							Optional[int]   			=       'multi_label_classification'
            _lowerCAmelCase    :							List[Any]   			=       input_dict['input_ids']
            _lowerCAmelCase    :							Optional[Any]   			=       input_ids.ne(1   ).to(snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       ids_tensor(
                [self.model_tester.batch_size, config.num_labels]      ,	self.model_tester.type_sequence_label_size   ).to(torch.float   )
            _lowerCAmelCase    :							Any   			=       LlamaForSequenceClassification(snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							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)   )







     @unittest.skip('LLaMA buffers include complex numbers, which breaks this test'   )
     def a	( self   ):

            '''simple docstring'''

            pass







     @parameterized.expand([('linear',), ('dynamic',)]   )
     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       self.model_tester.prepare_config_and_inputs_for_common()
            _lowerCAmelCase    :							Union[str, Any]   			=       ids_tensor([1, 10]      ,	config.vocab_size   )
            _lowerCAmelCase    :							str   			=       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
            _lowerCAmelCase    :							List[Any]   			=       LlamaModel(snake_case__   )
            original_model.to(snake_case__   )
            original_model.eval()
            _lowerCAmelCase    :							List[str]   			=       original_model(snake_case__   ).last_hidden_state
            _lowerCAmelCase    :							List[Any]   			=       original_model(snake_case__   ).last_hidden_state

            set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
            _lowerCAmelCase    :							Tuple   			=       {'type': scaling_type, 'factor': 10.0}
            _lowerCAmelCase    :							Optional[Any]   			=       LlamaModel(snake_case__   )
            scaled_model.to(snake_case__   )
            scaled_model.eval()
            _lowerCAmelCase    :							Any   			=       scaled_model(snake_case__   ).last_hidden_state
            _lowerCAmelCase    :							Dict   			=       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 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'   )
     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       [1, 306, 4658, 278, 6593, 310, 2834, 338]
            _lowerCAmelCase    :							int   			=       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'      ,	device_map='auto'   )
            _lowerCAmelCase    :							Union[str, Any]   			=       model(torch.tensor([input_ids]   )   )
            # Expected mean on dim = -1
            _lowerCAmelCase    :							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
            _lowerCAmelCase    :							Any   			=       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]      ,	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   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       [1, 306, 4658, 278, 6593, 310, 2834, 338]
            _lowerCAmelCase    :							Dict   			=       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'      ,	device_map='auto'   )
            _lowerCAmelCase    :							str   			=       model(torch.tensor(snake_case__   )   )
            # Expected mean on dim = -1
            _lowerCAmelCase    :							Tuple   			=       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
            _lowerCAmelCase    :							Dict   			=       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]      ,	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   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       [1, 306, 4658, 278, 6593, 310, 2834, 338]
            _lowerCAmelCase    :							Dict   			=       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'      ,	device_map='auto'   )
            _lowerCAmelCase    :							Union[str, Any]   			=       model(torch.tensor(snake_case__   )   )
            # Expected mean on dim = -1
            _lowerCAmelCase    :							Any   			=       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
            _lowerCAmelCase    :							Union[str, Any]   			=       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   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       [1, 306, 4658, 278, 6593, 310, 2834, 338]
            _lowerCAmelCase    :							List[Any]   			=       LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'      ,	device_map='auto'   )
            _lowerCAmelCase    :							List[Any]   			=       model(torch.tensor(snake_case__   )   )

            _lowerCAmelCase    :							List[Any]   			=       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
            _lowerCAmelCase    :							List[str]   			=       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]      ,	snake_case__      ,	atol=1E-5      ,	rtol=1E-5   )







     @unittest.skip('Model is curently gated'   )
     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       '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'
            _lowerCAmelCase    :							Dict   			=       'Simply put, the theory of relativity states that '
            _lowerCAmelCase    :							List[str]   			=       LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'   )
            _lowerCAmelCase    :							List[Any]   			=       tokenizer.encode(snake_case__      ,	return_tensors='pt'   )
            _lowerCAmelCase    :							str   			=       LlamaForCausalLM.from_pretrained(
                'meta-llama/Llama-2-13b-chat-hf'      ,	device_map='sequential'      ,	use_safetensors=snake_case__   )

            # greedy generation outputs
            _lowerCAmelCase    :							List[Any]   			=       model.generate(snake_case__      ,	max_new_tokens=64      ,	top_p=snake_case__      ,	temperature=1      ,	do_sample=snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       tokenizer.decode(generated_ids[0]      ,	skip_special_tokens=snake_case__   )
            self.assertEqual(snake_case__      ,	snake_case__   )







'''simple docstring'''







import socket






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Tuple   			=       socket.socket(socket.AF_INET   ,						socket.SOCK_STREAM					)
       _lowerCAmelCase    :							Optional[int]   			=       socket.gethostname()
       _lowerCAmelCase    :							Any   			=       1_2_3_1_2

       sock.connect((host, port)					)
       sock.send(B'Hello server!'					)

       with open('Received_file'   ,						'wb'					) as out_file:
              print('File opened'					)
              print('Receiving data...'					)
              while True:
                     _lowerCAmelCase    :							Union[str, Any]   			=       sock.recv(1_0_2_4					)
                     if not data:
                            break
                     out_file.write(_A					)

       print('Successfully received the file'					)
       sock.close()
       print('Connection closed'					)


if __name__ == "__main__":
       main()

