





'''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   _lowercase						:


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=13    ,   _UpperCAmelCase=7    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=99    ,   _UpperCAmelCase=32    ,   _UpperCAmelCase=5    ,   _UpperCAmelCase=4    ,   _UpperCAmelCase=37    ,   _UpperCAmelCase="gelu"    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=512    ,   _UpperCAmelCase=16    ,   _UpperCAmelCase=2    ,   _UpperCAmelCase=0.02    ,   _UpperCAmelCase=3    ,   _UpperCAmelCase=4    ,   _UpperCAmelCase=None    ,   ):
       A       :       Union[str, Any]							=						parent
       A       :       Dict							=						batch_size
       A       :       List[str]							=						seq_length
       A       :       str							=						is_training
       A       :       Union[str, Any]							=						use_input_mask
       A       :       List[Any]							=						use_token_type_ids
       A       :       Optional[int]							=						use_labels
       A       :       Tuple							=						vocab_size
       A       :       List[Any]							=						hidden_size
       A       :       Any							=						num_hidden_layers
       A       :       Any							=						num_attention_heads
       A       :       str							=						intermediate_size
       A       :       Any							=						hidden_act
       A       :       Any							=						hidden_dropout_prob
       A       :       List[str]							=						attention_probs_dropout_prob
       A       :       Union[str, Any]							=						max_position_embeddings
       A       :       int							=						type_vocab_size
       A       :       Optional[Any]							=						type_sequence_label_size
       A       :       Union[str, Any]							=						initializer_range
       A       :       Any							=						num_labels
       A       :       Optional[int]							=						num_choices
       A       :       Tuple							=						scope


   def 					snake_case				( self					):
       A       :       Optional[int]							=						ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size					)

       A       :       List[Any]							=						None
       if self.use_input_mask:
           A       :       Optional[Any]							=						random_attention_mask([self.batch_size, self.seq_length]					)

       A       :       Any							=						None
       if self.use_token_type_ids:
           A       :       Union[str, Any]							=						ids_tensor([self.batch_size, self.seq_length]    ,   self.type_vocab_size					)

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

       A       :       Optional[int]							=						self.get_config()

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


   def 					snake_case				( self					):
       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=_UpperCAmelCase    ,   initializer_range=self.initializer_range    ,   )


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       List[Any]							=						LlamaModel(config=_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       A       :       Optional[Any]							=						model(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					)
       A       :       int							=						model(_UpperCAmelCase					)
       self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   ):
       A       :       Optional[int]							=						True
       A       :       List[str]							=						LlamaModel(_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       A       :       str							=						model(
           _UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   encoder_hidden_states=_UpperCAmelCase    ,   encoder_attention_mask=_UpperCAmelCase    ,   )
       A       :       List[str]							=						model(
           _UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   encoder_hidden_states=_UpperCAmelCase    ,   )
       A       :       List[Any]							=						model(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase					)
       self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   ):
       A       :       Dict							=						LlamaForCausalLM(config=_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       A       :       Union[str, Any]							=						model(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   labels=_UpperCAmelCase					)
       self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.vocab_size)					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   ):
       A       :       Tuple							=						True
       A       :       Union[str, Any]							=						True
       A       :       Tuple							=						LlamaForCausalLM(config=_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()

       # first forward pass
       A       :       List[str]							=						model(
           _UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   encoder_hidden_states=_UpperCAmelCase    ,   encoder_attention_mask=_UpperCAmelCase    ,   use_cache=_UpperCAmelCase    ,   )
       A       :       Optional[int]							=						outputs.past_key_values

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

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

       A       :       int							=						model(
           _UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   encoder_hidden_states=_UpperCAmelCase    ,   encoder_attention_mask=_UpperCAmelCase    ,   output_hidden_states=_UpperCAmelCase    ,   )['''hidden_states'''][0]
       A       :       Dict							=						model(
           _UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   encoder_hidden_states=_UpperCAmelCase    ,   encoder_attention_mask=_UpperCAmelCase    ,   past_key_values=_UpperCAmelCase    ,   output_hidden_states=_UpperCAmelCase    ,   )['''hidden_states'''][0]

       # select random slice
       A       :       int							=						ids_tensor((1,)    ,   output_from_past.shape[-1]					).item()
       A       :       List[str]							=						output_from_no_past[:, -3:, random_slice_idx].detach()
       A       :       Dict							=						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(_UpperCAmelCase    ,   _UpperCAmelCase    ,   atol=1E-3					)					)


   def 					snake_case				( self					):
       A       :       Dict							=						self.prepare_config_and_inputs()
       (
           A	
       )       :       Any							=						config_and_inputs
       A       :       Optional[Any]							=						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict







@require_torch
class   _lowercase						(							a		,					a		,					a		,					unittest.TestCase				):
   _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 					snake_case				( self					):
       A       :       int							=						LlamaModelTester(self					)
       A       :       Any							=						ConfigTester(self    ,   config_class=_UpperCAmelCase    ,   hidden_size=37					)


   def 					snake_case				( self					):
       self.config_tester.run_common_tests()


   def 					snake_case				( self					):
       A       :       int							=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase					)


   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
           A       :       Optional[Any]							=						type
           self.model_tester.create_and_check_model(*_UpperCAmelCase					)


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


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


   def 					snake_case				( self					):
       A       :       List[str]							=						self.model_tester.prepare_config_and_inputs_for_common()
       A       :       Dict							=						3
       A       :       str							=						'''multi_label_classification'''
       A       :       List[Any]							=						input_dict['''input_ids''']
       A       :       int							=						input_ids.ne(1					).to(_UpperCAmelCase					)
       A       :       Optional[int]							=						ids_tensor(
           [self.model_tester.batch_size, config.num_labels]    ,   self.model_tester.type_sequence_label_size					).to(torch.float					)
       A       :       Union[str, Any]							=						LlamaForSequenceClassification(_UpperCAmelCase					)
       model.to(_UpperCAmelCase					)
       model.eval()
       A       :       Dict							=						model(_UpperCAmelCase    ,   attention_mask=_UpperCAmelCase    ,   labels=_UpperCAmelCase					)
       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 					snake_case				( self					):
       pass


   @parameterized.expand([('''linear''',), ('''dynamic''',)]					)
   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       Tuple							=						self.model_tester.prepare_config_and_inputs_for_common()
       A       :       str							=						ids_tensor([1, 10]    ,   config.vocab_size					)
       A       :       int							=						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
       A       :       List[Any]							=						LlamaModel(_UpperCAmelCase					)
       original_model.to(_UpperCAmelCase					)
       original_model.eval()
       A       :       List[Any]							=						original_model(_UpperCAmelCase					).last_hidden_state
       A       :       Optional[Any]							=						original_model(_UpperCAmelCase					).last_hidden_state

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

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







@require_torch
class   _lowercase						(							unittest.TestCase				):


   @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''					)
   @slow
   def 					snake_case				( self					):
       A       :       List[str]							=						[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       A       :       Optional[int]							=						LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''    ,   device_map='''auto'''					)
       A       :       Optional[int]							=						model(torch.tensor([input_ids]					)					)
       # Expected mean on dim = -1
       A       :       Union[str, 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					)    ,   _UpperCAmelCase    ,   atol=1E-2    ,   rtol=1E-2					)
       # slicing logits[0, 0, 0:30]
       # fmt: off
       A       :       List[str]							=						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]    ,   _UpperCAmelCase    ,   atol=1E-5    ,   rtol=1E-5					)


   @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''					)
   @slow
   def 					snake_case				( self					):
       A       :       int							=						[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       A       :       Any							=						LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''    ,   device_map='''auto'''					)
       A       :       List[str]							=						model(torch.tensor(_UpperCAmelCase					)					)
       # Expected mean on dim = -1
       A       :       List[Any]							=						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					)    ,   _UpperCAmelCase    ,   atol=1E-2    ,   rtol=1E-2					)
       # slicing logits[0, 0, 0:30]
       # fmt: off
       A       :       Tuple							=						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]    ,   _UpperCAmelCase    ,   atol=1E-5    ,   rtol=1E-5					)


   @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''					)
   @slow
   def 					snake_case				( self					):
       A       :       int							=						[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       A       :       Optional[int]							=						LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''    ,   device_map='''auto'''					)
       A       :       Tuple							=						model(torch.tensor(_UpperCAmelCase					)					)
       # Expected mean on dim = -1
       A       :       Optional[int]							=						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					)    ,   _UpperCAmelCase    ,   atol=1E-2    ,   rtol=1E-2					)
       # slicing logits[0, 0, 0:30]
       # fmt: off
       A       :       Optional[int]							=						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					)    ,   _UpperCAmelCase    ,   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 					snake_case				( self					):
       A       :       str							=						[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
       A       :       Tuple							=						LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''    ,   device_map='''auto'''					)
       A       :       List[Any]							=						model(torch.tensor(_UpperCAmelCase					)					)

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


   @unittest.skip('''Model is curently gated'''					)
   @slow
   def 					snake_case				( self					):
       A       :       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'''
       A       :       Union[str, Any]							=						'''Simply put, the theory of relativity states that '''
       A       :       Tuple							=						LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''					)
       A       :       Any							=						tokenizer.encode(_UpperCAmelCase    ,   return_tensors='''pt'''					)
       A       :       List[str]							=						LlamaForCausalLM.from_pretrained(
           '''meta-llama/Llama-2-13b-chat-hf'''    ,   device_map='''sequential'''    ,   use_safetensors=_UpperCAmelCase					)

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







'''simple docstring'''






from ..utils import DummyObject, requires_backends


class   _lowercase						(							metaclass=a				):
   _UpperCamelCase       		=   ["""onnx"""]


   def __init__( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       requires_backends(self    ,   ['''onnx''']					)


   @classmethod
   def 					snake_case				( cls    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       requires_backends(cls    ,   ['''onnx''']					)


   @classmethod
   def 					snake_case				( cls    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       requires_backends(cls    ,   ['''onnx''']					)
