


'''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
            )			,     (
                _lowerCAmelCase
            )			,     (
                _lowerCAmelCase
            )			,     (
                _lowerCAmelCase
            )			,     (
                _lowerCAmelCase
            )			,     (
                _lowerCAmelCase
            )			,     (
                _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			,     _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			,     _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			,     _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			,     _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'''







from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCAmelCase    :     Optional[Any]					=						logging.get_logger(__name__)

lowerCAmelCase    :     str					=						{
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "gptj"
     __magic_name__    			=    {
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }







     def __init__( self      ,	snake_case__=5_0400      ,	snake_case__=2048      ,	snake_case__=4096      ,	snake_case__=28      ,	snake_case__=16      ,	snake_case__=64      ,	snake_case__=None      ,	snake_case__="gelu_new"      ,	snake_case__=0.0      ,	snake_case__=0.0      ,	snake_case__=0.0      ,	snake_case__=1E-5      ,	snake_case__=0.02      ,	snake_case__=True      ,	snake_case__=5_0256      ,	snake_case__=5_0256      ,	snake_case__=False      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       vocab_size
            _lowerCAmelCase    :							Optional[int]   			=       n_positions
            _lowerCAmelCase    :							Optional[int]   			=       n_embd
            _lowerCAmelCase    :							Optional[int]   			=       n_layer
            _lowerCAmelCase    :							str   			=       n_head
            _lowerCAmelCase    :							Tuple   			=       n_inner
            _lowerCAmelCase    :							Tuple   			=       rotary_dim
            _lowerCAmelCase    :							Optional[int]   			=       activation_function
            _lowerCAmelCase    :							Any   			=       resid_pdrop
            _lowerCAmelCase    :							List[str]   			=       embd_pdrop
            _lowerCAmelCase    :							int   			=       attn_pdrop
            _lowerCAmelCase    :							Any   			=       layer_norm_epsilon
            _lowerCAmelCase    :							Optional[int]   			=       initializer_range
            _lowerCAmelCase    :							List[str]   			=       use_cache

            _lowerCAmelCase    :							Dict   			=       bos_token_id
            _lowerCAmelCase    :							Any   			=       eos_token_id

            super().__init__(
                bos_token_id=snake_case__      ,	eos_token_id=snake_case__      ,	tie_word_embeddings=snake_case__      ,	**snake_case__   )
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__ = "default"      ,	snake_case__ = None      ,	snake_case__ = False      ,	):

            '''simple docstring'''

            super().__init__(snake_case__      ,	task=snake_case__      ,	patching_specs=snake_case__      ,	use_past=snake_case__   )
            if not getattr(self._config      ,	'pad_token_id'      ,	snake_case__   ):
                   # TODO: how to do that better?
                   _lowerCAmelCase    :							Any   			=       0







     @property
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
            if self.use_past:
                   self.fill_with_past_key_values_(snake_case__      ,	direction='inputs'   )
                   _lowerCAmelCase    :							int   			=       {0: 'batch', 1: 'past_sequence + sequence'}
            else:
                   _lowerCAmelCase    :							int   			=       {0: 'batch', 1: 'sequence'}

            return common_inputs







     @property
     def a	( self   ):

            '''simple docstring'''

            return self._config.n_layer







     @property
     def a	( self   ):

            '''simple docstring'''

            return self._config.n_head







     def a	( self      ,	snake_case__      ,	snake_case__ = -1      ,	snake_case__ = -1      ,	snake_case__ = False      ,	snake_case__ = None      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       super(snake_case__      ,	self   ).generate_dummy_inputs(
                snake_case__      ,	batch_size=snake_case__      ,	seq_length=snake_case__      ,	is_pair=snake_case__      ,	framework=snake_case__   )

            # We need to order the input in the way they appears in the forward()
            _lowerCAmelCase    :							Any   			=       OrderedDict({'input_ids': common_inputs['input_ids']}   )

            # Need to add the past_keys
            if self.use_past:
                   if not is_torch_available():
                          raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
                   else:
                          import torch

                          _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[Any]   			=       common_inputs['input_ids'].shape
                          # Not using the same length for past_key_values
                          _lowerCAmelCase    :							Any   			=       seqlen + 2
                          _lowerCAmelCase    :							Optional[int]   			=       (
                              batch,
                              self.num_attention_heads,
                              past_key_values_length,
                              self._config.hidden_size // self.num_attention_heads,
                          )
                          _lowerCAmelCase    :							Tuple   			=       [
                              (torch.zeros(snake_case__   ), torch.zeros(snake_case__   )) for _ in range(self.num_layers   )
                          ]

            _lowerCAmelCase    :							Tuple   			=       common_inputs['attention_mask']
            if self.use_past:
                   _lowerCAmelCase    :							Any   			=       ordered_inputs['attention_mask'].dtype
                   _lowerCAmelCase    :							Union[str, Any]   			=       torch.cat(
                       [ordered_inputs['attention_mask'], torch.ones(snake_case__      ,	snake_case__      ,	dtype=snake_case__   )]      ,	dim=1   )

            return ordered_inputs







     @property
     def a	( self   ):

            '''simple docstring'''

            return 13

