

'''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        A  :





						def __init__(	self	,    snake_case_	,    snake_case_=1_3	,    snake_case_=7	,    snake_case_=True	,    snake_case_=True	,    snake_case_=False	,    snake_case_=True	,    snake_case_=9_9	,    snake_case_=3_2	,    snake_case_=5	,    snake_case_=4	,    snake_case_=3_7	,    snake_case_="gelu"	,    snake_case_=0.1	,    snake_case_=0.1	,    snake_case_=5_1_2	,    snake_case_=1_6	,    snake_case_=2	,    snake_case_=0.02	,    snake_case_=3	,    snake_case_=4	,    snake_case_=None	,    )			->							Optional[Any]:
										_a        =   parent
										_a        =   batch_size
										_a        =   seq_length
										_a        =   is_training
										_a        =   use_input_mask
										_a        =   use_token_type_ids
										_a        =   use_labels
										_a        =   vocab_size
										_a        =   hidden_size
										_a        =   num_hidden_layers
										_a        =   num_attention_heads
										_a        =   intermediate_size
										_a        =   hidden_act
										_a        =   hidden_dropout_prob
										_a        =   attention_probs_dropout_prob
										_a        =   max_position_embeddings
										_a        =   type_vocab_size
										_a        =   type_sequence_label_size
										_a        =   initializer_range
										_a        =   num_labels
										_a        =   num_choices
										_a        =   scope





						def __lowerCAmelCase   (	self			)			->							Optional[int]:
										_a        =   ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size			)

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

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

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

										_a        =   self.get_config()

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





						def __lowerCAmelCase   (	self			)			->							List[str]:
										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 __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_			)			->							Optional[int]:
										_a        =   LlamaModel(config=snake_case__			)
										model.to(snake_case__			)
										model.eval()
										_a        =   model(snake_case__	,    attention_mask=snake_case__			)
										_a        =   model(snake_case__			)
										self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)			)





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    )			->							List[Any]:
										_a        =   True
										_a        =   LlamaModel(snake_case__			)
										model.to(snake_case__			)
										model.eval()
										_a        =   model(
										    snake_case__	,    attention_mask=snake_case__	,    encoder_hidden_states=snake_case__	,    encoder_attention_mask=snake_case__	,    )
										_a        =   model(
										    snake_case__	,    attention_mask=snake_case__	,    encoder_hidden_states=snake_case__	,    )
										_a        =   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 __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    )			->							List[Any]:
										_a        =   LlamaForCausalLM(config=snake_case__			)
										model.to(snake_case__			)
										model.eval()
										_a        =   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 __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    )			->							Optional[int]:
										_a        =   True
										_a        =   True
										_a        =   LlamaForCausalLM(config=snake_case__			)
										model.to(snake_case__			)
										model.eval()

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

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

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

										_a        =   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]
										_a        =   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
										_a        =   ids_tensor((1,)	,    output_from_past.shape[-1]			).item()
										_a        =   output_from_no_past[:, -3:, random_slice_idx].detach()
										_a        =   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 __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										_a        =   self.prepare_config_and_inputs()
										(
										    _a				
										)        =   config_and_inputs
										_a        =   {"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict



@require_torch
class        A  (	a  ,    a  ,    a  ,    unittest.TestCase ):
						__UpperCAmelCase				:	str   =   (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
						__UpperCAmelCase				:	Any   =   (LlamaForCausalLM,) if is_torch_available() else ()
						__UpperCAmelCase				:	str   =   (
						    {
						        """feature-extraction""": LlamaModel,
						        """text-classification""": LlamaForSequenceClassification,
						        """text-generation""": LlamaForCausalLM,
						        """zero-shot""": LlamaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						__UpperCAmelCase				:	List[str]   =   False
						__UpperCAmelCase				:	List[Any]   =   False





						def __lowerCAmelCase   (	self			)			->							List[str]:
										_a        =   LlamaModelTester(self			)
										_a        =   ConfigTester(self	,    config_class=snake_case__	,    hidden_size=3_7			)





						def __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										self.config_tester.run_common_tests()





						def __lowerCAmelCase   (	self			)			->							Optional[Any]:
										_a        =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case__			)





						def __lowerCAmelCase   (	self			)			->							List[str]:
										_a        =   self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
														_a        =   type
														self.model_tester.create_and_check_model(*snake_case__			)





						def __lowerCAmelCase   (	self			)			->							List[str]:
										_a        =   self.model_tester.prepare_config_and_inputs_for_common()
										_a        =   3
										_a        =   input_dict["input_ids"]
										_a        =   input_ids.ne(1			).to(snake_case__			)
										_a        =   ids_tensor([self.model_tester.batch_size]	,    self.model_tester.type_sequence_label_size			)
										_a        =   LlamaForSequenceClassification(snake_case__			)
										model.to(snake_case__			)
										model.eval()
										_a        =   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 __lowerCAmelCase   (	self			)			->							Optional[int]:
										_a        =   self.model_tester.prepare_config_and_inputs_for_common()
										_a        =   3
										_a        =   "single_label_classification"
										_a        =   input_dict["input_ids"]
										_a        =   input_ids.ne(1			).to(snake_case__			)
										_a        =   ids_tensor([self.model_tester.batch_size]	,    self.model_tester.type_sequence_label_size			)
										_a        =   LlamaForSequenceClassification(snake_case__			)
										model.to(snake_case__			)
										model.eval()
										_a        =   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 __lowerCAmelCase   (	self			)			->							Any:
										_a        =   self.model_tester.prepare_config_and_inputs_for_common()
										_a        =   3
										_a        =   "multi_label_classification"
										_a        =   input_dict["input_ids"]
										_a        =   input_ids.ne(1			).to(snake_case__			)
										_a        =   ids_tensor(
										    [self.model_tester.batch_size, config.num_labels]	,    self.model_tester.type_sequence_label_size			).to(torch.float			)
										_a        =   LlamaForSequenceClassification(snake_case__			)
										model.to(snake_case__			)
										model.eval()
										_a        =   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 __lowerCAmelCase   (	self			)			->							int:
										pass





						@parameterized.expand([("linear",), ("dynamic",)]			)
						def __lowerCAmelCase   (	self	,    snake_case_			)			->							List[Any]:
										_a        =   self.model_tester.prepare_config_and_inputs_for_common()
										_a        =   ids_tensor([1, 1_0]	,    config.vocab_size			)
										_a        =   ids_tensor([1, int(config.max_position_embeddings * 1.5			)]	,    config.vocab_size			)

										set_seed(4_2			)  # Fixed seed at init time so the two models get the same random weights
										_a        =   LlamaModel(snake_case__			)
										original_model.to(snake_case__			)
										original_model.eval()
										_a        =   original_model(snake_case__			).last_hidden_state
										_a        =   original_model(snake_case__			).last_hidden_state

										set_seed(4_2			)  # Fixed seed at init time so the two models get the same random weights
										_a        =   {"type": scaling_type, "factor": 10.0}
										_a        =   LlamaModel(snake_case__			)
										scaled_model.to(snake_case__			)
										scaled_model.eval()
										_a        =   scaled_model(snake_case__			).last_hidden_state
										_a        =   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        A  (	unittest.TestCase ):





						@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"			)
						@slow
						def __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										_a        =   [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
										_a        =   LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf"	,    device_map="auto"			)
										_a        =   model(torch.tensor([input_ids]			)			)
										# Expected mean on dim = -1
										_a        =   torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]]			)
										torch.testing.assert_close(out.mean(-1			)	,    snake_case__	,    atol=1E-2	,    rtol=1E-2			)
										# slicing logits[0, 0, 0:30]
										# fmt: off
										_a        =   torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,]			)
										# fmt: on
										torch.testing.assert_close(out[0, 0, :3_0]	,    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 __lowerCAmelCase   (	self			)			->							List[Any]:
										_a        =   [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
										_a        =   LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"	,    device_map="auto"			)
										_a        =   model(torch.tensor(snake_case__			)			)
										# Expected mean on dim = -1
										_a        =   torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]]			)
										torch.testing.assert_close(out.mean(-1			)	,    snake_case__	,    atol=1E-2	,    rtol=1E-2			)
										# slicing logits[0, 0, 0:30]
										# fmt: off
										_a        =   torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273]			)
										# fmt: on
										torch.testing.assert_close(out[0, 0, :3_0]	,    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 __lowerCAmelCase   (	self			)			->							Optional[Any]:
										_a        =   [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
										_a        =   LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"	,    device_map="auto"			)
										_a        =   model(torch.tensor(snake_case__			)			)
										# Expected mean on dim = -1
										_a        =   torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]]			)
										torch.testing.assert_close(out.mean(-1			)	,    snake_case__	,    atol=1E-2	,    rtol=1E-2			)
										# slicing logits[0, 0, 0:30]
										# fmt: off
										_a        =   torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513]			)
										# 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 __lowerCAmelCase   (	self			)			->							List[Any]:
										_a        =   [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
										_a        =   LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"	,    device_map="auto"			)
										_a        =   model(torch.tensor(snake_case__			)			)

										_a        =   torch.tensor(
										    [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]	,    dtype=torch.floataa			)
										torch.testing.assert_close(out.mean(-1			)	,    snake_case__	,    atol=1E-2	,    rtol=1E-2			)
										# fmt: off
										_a        =   torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312]			)
										# fmt: on
										torch.testing.assert_close(out[0, 0, :3_0]	,    snake_case__	,    atol=1E-5	,    rtol=1E-5			)





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

										# greedy generation outputs
										_a        =   model.generate(snake_case__	,    max_new_tokens=6_4	,    top_p=snake_case__	,    temperature=1	,    do_sample=snake_case__			)
										_a        =   tokenizer.decode(generated_ids[0]	,    skip_special_tokens=snake_case__			)
										self.assertEqual(snake_case__	,    snake_case__			)













"""simple docstring"""



import os


def 	SCREAMING_SNAKE_CASE__       (  )->		Optional[Any]:






     '''simple docstring'''



     with open(os.path.dirname(snake_case				) + "/p022_names.txt"				) as file:
          UpperCAmelCase__						:    Tuple							=							str(file.readlines()[0]				)
          UpperCAmelCase__						:    str							=							names.replace("\""			,					""				).split(","				)

     names.sort()

     UpperCAmelCase__						:    Optional[int]							=							0
     UpperCAmelCase__						:    List[str]							=							0

     for i, name in enumerate(snake_case				):
          for letter in name:
               name_score += ord(snake_case				) - 64

          total_score += (i + 1) * name_score
          UpperCAmelCase__						:    Optional[int]							=							0
     return total_score


if __name__ == "__main__":
 print(solution())


