

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   ,     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   ,     ):
											snake_case_           =					parent
											snake_case_           =					batch_size
											snake_case_           =					seq_length
											snake_case_           =					is_training
											snake_case_           =					use_input_mask
											snake_case_           =					use_token_type_ids
											snake_case_           =					use_labels
											snake_case_           =					vocab_size
											snake_case_           =					hidden_size
											snake_case_           =					num_hidden_layers
											snake_case_           =					num_attention_heads
											snake_case_           =					intermediate_size
											snake_case_           =					hidden_act
											snake_case_           =					hidden_dropout_prob
											snake_case_           =					attention_probs_dropout_prob
											snake_case_           =					max_position_embeddings
											snake_case_           =					type_vocab_size
											snake_case_           =					type_sequence_label_size
											snake_case_           =					initializer_range
											snake_case_           =					num_labels
											snake_case_           =					num_choices
											snake_case_           =					scope




				def 					a							(      self ):
											snake_case_           =					ids_tensor([self.batch_size, self.seq_length]   ,     self.vocab_size )

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

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

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

											snake_case_           =					self.get_config()

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




				def 					a							(      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=snake_case   ,     initializer_range=self.initializer_range   ,     )




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_           =					LlamaModel(config=snake_case )
											model.to(snake_case )
											model.eval()
											snake_case_           =					model(snake_case   ,     attention_mask=snake_case )
											snake_case_           =					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   ,     ):
											snake_case_           =					True
											snake_case_           =					LlamaModel(snake_case )
											model.to(snake_case )
											model.eval()
											snake_case_           =					model(
											    snake_case   ,     attention_mask=snake_case   ,     encoder_hidden_states=snake_case   ,     encoder_attention_mask=snake_case   ,     )
											snake_case_           =					model(
											    snake_case   ,     attention_mask=snake_case   ,     encoder_hidden_states=snake_case   ,     )
											snake_case_           =					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   ,     ):
											snake_case_           =					LlamaForCausalLM(config=snake_case )
											model.to(snake_case )
											model.eval()
											snake_case_           =					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   ,     ):
											snake_case_           =					True
											snake_case_           =					True
											snake_case_           =					LlamaForCausalLM(config=snake_case )
											model.to(snake_case )
											model.eval()

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

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

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

											snake_case_           =					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]
											snake_case_           =					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
											snake_case_           =					ids_tensor((1,)   ,     output_from_past.shape[-1] ).item()
											snake_case_           =					output_from_no_past[:, -3:, random_slice_idx].detach()
											snake_case_           =					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 ):
											snake_case_           =					self.prepare_config_and_inputs()
											(
											    (
											    snake_case_
											)       ,					(
											    snake_case_
											)       ,					(
											    snake_case_
											)       ,					(
											    snake_case_
											)       ,					(
											    snake_case_
											)       ,					(
											    snake_case_
											)       ,					(
											    snake_case_
											)       ,					
											)           =					config_and_inputs
											snake_case_           =					{'input_ids': input_ids, 'attention_mask': input_mask}
											return config, inputs_dict







@require_torch
class 				lowercase       ( lowercase_			,						lowercase_			,						lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				List[Any]     =    (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
				__SCREAMING_SNAKE_CASE      :				Dict     =    (LlamaForCausalLM,) if is_torch_available() else ()
				__SCREAMING_SNAKE_CASE      :				str     =    (
				    {
				        '''feature-extraction''': LlamaModel,
				        '''text-classification''': LlamaForSequenceClassification,
				        '''text-generation''': LlamaForCausalLM,
				        '''zero-shot''': LlamaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__SCREAMING_SNAKE_CASE      :				List[Any]     =    False
				__SCREAMING_SNAKE_CASE      :				Dict     =    False




				def 					a							(      self ):
											snake_case_           =					LlamaModelTester(self )
											snake_case_           =					ConfigTester(self   ,     config_class=snake_case   ,     hidden_size=37 )




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




				def 					a							(      self ):
											snake_case_           =					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case )




				def 					a							(      self ):
											snake_case_           =					self.model_tester.prepare_config_and_inputs()
											for type in ["absolute", "relative_key", "relative_key_query"]:
																		snake_case_           =					type
																		self.model_tester.create_and_check_model(*snake_case )




				def 					a							(      self ):
											snake_case_       ,					snake_case_           =					self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_           =					3
											snake_case_           =					input_dict['input_ids']
											snake_case_           =					input_ids.ne(1 ).to(snake_case )
											snake_case_           =					ids_tensor([self.model_tester.batch_size]   ,     self.model_tester.type_sequence_label_size )
											snake_case_           =					LlamaForSequenceClassification(snake_case )
											model.to(snake_case )
											model.eval()
											snake_case_           =					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 ):
											snake_case_       ,					snake_case_           =					self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_           =					3
											snake_case_           =					'single_label_classification'
											snake_case_           =					input_dict['input_ids']
											snake_case_           =					input_ids.ne(1 ).to(snake_case )
											snake_case_           =					ids_tensor([self.model_tester.batch_size]   ,     self.model_tester.type_sequence_label_size )
											snake_case_           =					LlamaForSequenceClassification(snake_case )
											model.to(snake_case )
											model.eval()
											snake_case_           =					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 ):
											snake_case_       ,					snake_case_           =					self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_           =					3
											snake_case_           =					'multi_label_classification'
											snake_case_           =					input_dict['input_ids']
											snake_case_           =					input_ids.ne(1 ).to(snake_case )
											snake_case_           =					ids_tensor(
											    [self.model_tester.batch_size, config.num_labels]   ,     self.model_tester.type_sequence_label_size ).to(torch.float )
											snake_case_           =					LlamaForSequenceClassification(snake_case )
											model.to(snake_case )
											model.eval()
											snake_case_           =					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 ):
											pass




				@parameterized.expand([('linear',), ('dynamic',)] )
				def 					a							(      self   ,     snake_case ):
											snake_case_       ,					snake_case_           =					self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_           =					ids_tensor([1, 10]   ,     config.vocab_size )
											snake_case_           =					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
											snake_case_           =					LlamaModel(snake_case )
											original_model.to(snake_case )
											original_model.eval()
											snake_case_           =					original_model(snake_case ).last_hidden_state
											snake_case_           =					original_model(snake_case ).last_hidden_state

											set_seed(42 )  # Fixed seed at init time so the two models get the same random weights
											snake_case_           =					{'type': scaling_type, 'factor': 10.0}
											snake_case_           =					LlamaModel(snake_case )
											scaled_model.to(snake_case )
											scaled_model.eval()
											snake_case_           =					scaled_model(snake_case ).last_hidden_state
											snake_case_           =					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 				lowercase       ( unittest.TestCase    ):




				@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!' )
				@slow
				def 					a							(      self ):
											snake_case_           =					[1, 306, 4658, 278, 6593, 310, 2834, 338]
											snake_case_           =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'   ,     device_map='auto' )
											snake_case_           =					model(torch.tensor([input_ids] ) )
											# Expected mean on dim = -1
											snake_case_           =					torch.tensor([[-6.65_50, -4.12_27, -4.98_59, -3.24_06, 0.82_62, -3.00_33, 1.29_64, -3.36_99]] )
											torch.testing.assert_close(out.mean(-1 )   ,     snake_case   ,     atol=1e-2   ,     rtol=1e-2 )
											# slicing logits[0, 0, 0:30]
											# fmt: off
											snake_case_           =					torch.tensor([-12.82_81, -7.44_53, -0.46_39, -8.06_25, -7.25_00, -8.00_00, -6.48_83, -7.76_95, -7.84_38, -7.03_12, -6.21_88, -7.13_28, -1.84_96, 1.99_61, -8.62_50, -6.72_27, -12.82_81, -6.94_92, -7.07_42, -7.78_52, -7.58_20, -7.90_62, -6.93_75, -7.98_05, -8.34_38, -8.15_62, -8.04_69, -7.62_50, -7.74_22, -7.33_98,] )
											# 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 ):
											snake_case_           =					[1, 306, 4658, 278, 6593, 310, 2834, 338]
											snake_case_           =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'   ,     device_map='auto' )
											snake_case_           =					model(torch.tensor(snake_case ) )
											# Expected mean on dim = -1
											snake_case_           =					torch.tensor([[-2.06_22, -1.27_94, -1.16_38, -0.97_88, -1.46_03, -1.02_38, -1.78_93, -1.44_11]] )
											torch.testing.assert_close(out.mean(-1 )   ,     snake_case   ,     atol=1e-2   ,     rtol=1e-2 )
											# slicing logits[0, 0, 0:30]
											# fmt: off
											snake_case_           =					torch.tensor([-8.14_06, -8.05_47, 2.74_61, -1.23_44, -0.14_48, -1.82_62, -1.00_20, -1.81_54, -1.68_95, -1.85_16, -2.35_74, -0.92_77, 3.75_98, 6.57_42, -1.29_98, -0.11_77, -8.14_06, -2.96_88, -2.91_99, -3.16_99, -3.52_54, -2.35_55, -2.79_88, -3.41_41, -2.82_62, -4.51_95, -3.33_79, -3.31_64, -2.78_32, -3.02_73] )
											# 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 ):
											snake_case_           =					[1, 306, 4658, 278, 6593, 310, 2834, 338]
											snake_case_           =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'   ,     device_map='auto' )
											snake_case_           =					model(torch.tensor(snake_case ) )
											# Expected mean on dim = -1
											snake_case_           =					torch.tensor([[-0.85_62, -1.85_20, -0.75_51, -0.41_62, -1.51_61, -1.20_38, -2.48_23, -2.32_54]] )
											torch.testing.assert_close(out.mean(-1 )   ,     snake_case   ,     atol=1e-2   ,     rtol=1e-2 )
											# slicing logits[0, 0, 0:30]
											# fmt: off
											snake_case_           =					torch.tensor([-2.22_27, 4.88_28, 0.90_23, -0.45_78, -0.78_71, -0.10_33, -0.62_21, -0.57_86, -0.78_03, -1.06_74, -1.29_20, -0.15_70, 0.80_08, 2.07_23, -0.94_97, 0.27_71, -2.22_27, -0.76_12, -1.43_46, -1.20_61, -1.64_26, -0.30_00, -0.71_39, -1.19_34, -1.86_91, -1.69_73, -1.59_47, -1.27_05, -0.35_23, -0.55_13] )
											# 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 ):
											snake_case_           =					[1, 306, 4658, 278, 6593, 310, 2834, 338]
											snake_case_           =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'   ,     device_map='auto' )
											snake_case_           =					model(torch.tensor(snake_case ) )

											snake_case_           =					torch.tensor(
											    [[-4.23_27, -3.33_60, -4.66_65, -4.76_31, -1.81_80, -3.41_70, -1.42_11, -3.18_10]]   ,     dtype=torch.floataa )
											torch.testing.assert_close(out.mean(-1 )   ,     snake_case   ,     atol=1e-2   ,     rtol=1e-2 )
											# fmt: off
											snake_case_           =					torch.tensor([-9.49_22, -3.95_51, 1.79_98, -5.67_58, -5.10_55, -5.89_84, -4.83_20, -6.80_86, -6.53_91, -5.61_72, -5.58_20, -5.53_52, 1.78_81, 3.62_89, -6.51_17, -3.47_85, -9.50_00, -6.03_52, -6.81_25, -6.01_95, -6.68_36, -5.47_27, -6.28_12, -6.03_91, -7.33_98, -7.42_97, -7.48_44, -6.58_20, -5.87_89, -5.53_12] )
											# 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 ):
											snake_case_           =					'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'
											snake_case_           =					'Simply put, the theory of relativity states that '
											snake_case_           =					LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf' )
											snake_case_           =					tokenizer.encode(snake_case   ,     return_tensors='pt' )
											snake_case_           =					LlamaForCausalLM.from_pretrained(
											    'meta-llama/Llama-2-13b-chat-hf'   ,     device_map='sequential'   ,     use_safetensors=snake_case )

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







from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset







class 				lowercase       ( lowercase_    ):




				def 					a							(      self ):
											return [
											    {"col_1": 3, "col_2": "a"},
											    {"col_1": 2, "col_2": "b"},
											    {"col_1": 1, "col_2": "c"},
											    {"col_1": 0, "col_2": "d"},
											]




				def 					a							(      self ):
											snake_case_           =					{'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
											return Dataset.from_dict(snake_case )




				def 					a							(      self ):
											snake_case_           =					self._create_example_records()
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertListEqual(dset.column_names   ,     ['col_1', 'col_2'] )
											for i, r in enumerate(snake_case ):
																		self.assertDictEqual(snake_case   ,     example_records[i] )




				def 					a							(      self ):
											snake_case_           =					self._create_example_records()
											snake_case_           =					Dataset.from_list(snake_case )
											snake_case_           =					Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
											self.assertEqual(dset.info   ,     dset_from_dict.info )




				def 					a							(      self ):  # checks what happens with missing columns
											snake_case_           =					[{'col_1': 1}, {'col_2': 'x'}]
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertDictEqual(dset[0]   ,     {'col_1': 1} )
											self.assertDictEqual(dset[1]   ,     {'col_1': None} )  # NB: first record is used for columns




				def 					a							(      self ):  # checks if the type can be inferred from the second record
											snake_case_           =					[{'col_1': []}, {'col_1': [1, 2]}]
											snake_case_           =					Dataset.from_list(snake_case )
											self.assertEqual(dset.info.features['col_1']   ,     Sequence(Value('int64' ) ) )




				def 					a							(      self ):
											snake_case_           =					Dataset.from_list([] )
											self.assertEqual(len(snake_case )   ,     0 )
											self.assertListEqual(dset.column_names   ,     [] )




