

"""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 					_lowerCAmelCase  :







 """simple docstring"""







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


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						ids_tensor([self.batch_size, self.seq_length],					self.vocab_size	)

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

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

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

    __lowercase								=						self.get_config()

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


 def 					_lowercase (			self							:			List[Any]	):
    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 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Tuple	):
    __lowercase								=						LlamaModel(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)	)


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			List[Any],					):
    __lowercase								=						True
    __lowercase								=						LlamaModel(UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(
        UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					encoder_hidden_states=UpperCAmelCase__,					encoder_attention_mask=UpperCAmelCase__,					)
    __lowercase								=						model(
        UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					encoder_hidden_states=UpperCAmelCase__,					)
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__	)
    self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)	)


 def 					_lowercase (			self							:			Any,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Any,					):
    __lowercase								=						LlamaForCausalLM(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					labels=UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)	)


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			Tuple,					):
    __lowercase								=						True
    __lowercase								=						True
    __lowercase								=						LlamaForCausalLM(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()

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

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

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

    __lowercase								=						model(
        UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					encoder_hidden_states=UpperCAmelCase__,					encoder_attention_mask=UpperCAmelCase__,					output_hidden_states=UpperCAmelCase__,					)["hidden_states"][0]
    __lowercase								=						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
    __lowercase								=						ids_tensor((1,),					output_from_past.shape[-1]	).item()
    __lowercase								=						output_from_no_past[:, -3:, random_slice_idx].detach()
    __lowercase								=						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 					_lowercase (			self							:			Dict	):
    __lowercase								=						self.prepare_config_and_inputs()
    (
        (
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,
    )								=						config_and_inputs
    __lowercase								=						{"input_ids": input_ids, "attention_mask": input_mask}
    return config, inputs_dict







@require_torch
class 					_lowerCAmelCase  (	lowercase     ,lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Optional[Any]   		=	(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
 __UpperCAmelCase	:					str   		=	(LlamaForCausalLM,) if is_torch_available() else ()
 __UpperCAmelCase	:					Optional[Any]   		=	(
     {
         "feature-extraction": LlamaModel,
         "text-classification": LlamaForSequenceClassification,
         "text-generation": LlamaForCausalLM,
         "zero-shot": LlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __UpperCAmelCase	:					Union[str, Any]   		=	False
 __UpperCAmelCase	:					List[Any]   		=	False


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						LlamaModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					hidden_size=3_7	)


 def 					_lowercase (			self							:			Optional[Any]	):
    self.config_tester.run_common_tests()


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
       __lowercase								=						type
       self.model_tester.create_and_check_model(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()
    __lowercase								=						3
    __lowercase								=						input_dict["input_ids"]
    __lowercase								=						input_ids.ne(1	).to(UpperCAmelCase__	)
    __lowercase								=						ids_tensor([self.model_tester.batch_size],					self.model_tester.type_sequence_label_size	)
    __lowercase								=						LlamaForSequenceClassification(UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					labels=UpperCAmelCase__	)
    self.assertEqual(result.logits.shape,					(self.model_tester.batch_size, self.model_tester.num_labels)	)


 def 					_lowercase (			self							:			List[str]	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()
    __lowercase								=						3
    __lowercase								=						"single_label_classification"
    __lowercase								=						input_dict["input_ids"]
    __lowercase								=						input_ids.ne(1	).to(UpperCAmelCase__	)
    __lowercase								=						ids_tensor([self.model_tester.batch_size],					self.model_tester.type_sequence_label_size	)
    __lowercase								=						LlamaForSequenceClassification(UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					labels=UpperCAmelCase__	)
    self.assertEqual(result.logits.shape,					(self.model_tester.batch_size, self.model_tester.num_labels)	)


 def 					_lowercase (			self							:			Any	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()
    __lowercase								=						3
    __lowercase								=						"multi_label_classification"
    __lowercase								=						input_dict["input_ids"]
    __lowercase								=						input_ids.ne(1	).to(UpperCAmelCase__	)
    __lowercase								=						ids_tensor(
        [self.model_tester.batch_size, config.num_labels],					self.model_tester.type_sequence_label_size	).to(torch.float	)
    __lowercase								=						LlamaForSequenceClassification(UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						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 					_lowercase (			self							:			str	):
    pass



 @parameterized.expand([("linear",), ("dynamic",)]	)
 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			str	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()
    __lowercase								=						ids_tensor([1, 1_0],					config.vocab_size	)
    __lowercase								=						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
    __lowercase								=						LlamaModel(UpperCAmelCase__	)
    original_model.to(UpperCAmelCase__	)
    original_model.eval()
    __lowercase								=						original_model(UpperCAmelCase__	).last_hidden_state
    __lowercase								=						original_model(UpperCAmelCase__	).last_hidden_state

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







 """simple docstring"""







 @unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"	)
 @slow
 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						[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]
    __lowercase								=						LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf",					device_map="auto"	)
    __lowercase								=						model(torch.tensor([input_ids]	)	)
    # Expected mean on dim = -1
    __lowercase								=						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	),					UpperCAmelCase__,					atol=1E-2,					rtol=1E-2	)
    # slicing logits[0, 0, 0:30]
    # fmt: off
    __lowercase								=						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],					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 					_lowercase (			self							:			int	):
    __lowercase								=						[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]
    __lowercase								=						LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf",					device_map="auto"	)
    __lowercase								=						model(torch.tensor(UpperCAmelCase__	)	)
    # Expected mean on dim = -1
    __lowercase								=						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	),					UpperCAmelCase__,					atol=1E-2,					rtol=1E-2	)
    # slicing logits[0, 0, 0:30]
    # fmt: off
    __lowercase								=						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],					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 					_lowercase (			self							:			Optional[int]	):
    __lowercase								=						[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]
    __lowercase								=						LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf",					device_map="auto"	)
    __lowercase								=						model(torch.tensor(UpperCAmelCase__	)	)
    # Expected mean on dim = -1
    __lowercase								=						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	),					UpperCAmelCase__,					atol=1E-2,					rtol=1E-2	)
    # slicing logits[0, 0, 0:30]
    # fmt: off
    __lowercase								=						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	),					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 					_lowercase (			self							:			Tuple	):
    __lowercase								=						[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]
    __lowercase								=						LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf",					device_map="auto"	)
    __lowercase								=						model(torch.tensor(UpperCAmelCase__	)	)

    __lowercase								=						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	),					UpperCAmelCase__,					atol=1E-2,					rtol=1E-2	)
    # fmt: off
    __lowercase								=						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],					UpperCAmelCase__,					atol=1E-5,					rtol=1E-5	)



 @unittest.skip("Model is curently gated"	)
 @slow
 def 					_lowercase (			self							:			Any	):
    __lowercase								=						"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"
    __lowercase								=						"Simply put, the theory of relativity states that "
    __lowercase								=						LlamaTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf"	)
    __lowercase								=						tokenizer.encode(UpperCAmelCase__,					return_tensors="pt"	)
    __lowercase								=						LlamaForCausalLM.from_pretrained(
        "meta-llama/Llama-2-13b-chat-hf",					device_map="sequential",					use_safetensors=UpperCAmelCase__	)

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







"""simple docstring"""





def   _A  (  UpperCamelCase_     :       list[int])	->						float:
   '''simple docstring'''


   if not nums:  # Makes sure that the list is not empty
      raise ValueError("List is empty")

   __lowercase								=						sum(UpperCamelCase_) / len(UpperCamelCase_)  # Calculate the average
   return sum(abs(x - average) for x in nums) / len(UpperCamelCase_)


if __name__ == "__main__":
   import doctest

   doctest.testmod()




