





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 							__SCREAMING_SNAKE_CASE       :






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=13	,			__lowerCAmelCase=7	,			__lowerCAmelCase=True	,			__lowerCAmelCase=True	,			__lowerCAmelCase=False	,			__lowerCAmelCase=True	,			__lowerCAmelCase=99	,			__lowerCAmelCase=32	,			__lowerCAmelCase=5	,			__lowerCAmelCase=4	,			__lowerCAmelCase=37	,			__lowerCAmelCase="gelu"	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=0.1	,			__lowerCAmelCase=512	,			__lowerCAmelCase=16	,			__lowerCAmelCase=2	,			__lowerCAmelCase=0.02	,			__lowerCAmelCase=3	,			__lowerCAmelCase=4	,			__lowerCAmelCase=None	,			):
														UpperCamelCase__					  =      parent
														UpperCamelCase__					  =      batch_size
														UpperCamelCase__					  =      seq_length
														UpperCamelCase__					  =      is_training
														UpperCamelCase__					  =      use_input_mask
														UpperCamelCase__					  =      use_token_type_ids
														UpperCamelCase__					  =      use_labels
														UpperCamelCase__					  =      vocab_size
														UpperCamelCase__					  =      hidden_size
														UpperCamelCase__					  =      num_hidden_layers
														UpperCamelCase__					  =      num_attention_heads
														UpperCamelCase__					  =      intermediate_size
														UpperCamelCase__					  =      hidden_act
														UpperCamelCase__					  =      hidden_dropout_prob
														UpperCamelCase__					  =      attention_probs_dropout_prob
														UpperCamelCase__					  =      max_position_embeddings
														UpperCamelCase__					  =      type_vocab_size
														UpperCamelCase__					  =      type_sequence_label_size
														UpperCamelCase__					  =      initializer_range
														UpperCamelCase__					  =      num_labels
														UpperCamelCase__					  =      num_choices
														UpperCamelCase__					  =      scope






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      ids_tensor([self.batch_size, self.seq_length]	,			self.vocab_size	)

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

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

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

														UpperCamelCase__					  =      self.get_config()

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






							def 					_lowerCamelCase		(    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=a_	,			initializer_range=self.initializer_range	,			)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      LlamaModel(config=a_	)
														model.to(a_	)
														model.eval()
														UpperCamelCase__					  =      model(a_	,			attention_mask=a_	)
														UpperCamelCase__					  =      model(a_	)
														self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, self.seq_length, self.hidden_size)	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			):
														UpperCamelCase__					  =      True
														UpperCamelCase__					  =      LlamaModel(a_	)
														model.to(a_	)
														model.eval()
														UpperCamelCase__					  =      model(
														    a_	,			attention_mask=a_	,			encoder_hidden_states=a_	,			encoder_attention_mask=a_	,			)
														UpperCamelCase__					  =      model(
														    a_	,			attention_mask=a_	,			encoder_hidden_states=a_	,			)
														UpperCamelCase__					  =      model(a_	,			attention_mask=a_	)
														self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, self.seq_length, self.hidden_size)	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			):
														UpperCamelCase__					  =      LlamaForCausalLM(config=a_	)
														model.to(a_	)
														model.eval()
														UpperCamelCase__					  =      model(a_	,			attention_mask=a_	,			labels=a_	)
														self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.seq_length, self.vocab_size)	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			):
														UpperCamelCase__					  =      True
														UpperCamelCase__					  =      True
														UpperCamelCase__					  =      LlamaForCausalLM(config=a_	)
														model.to(a_	)
														model.eval()

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

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

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

														UpperCamelCase__					  =      model(
														    a_	,			attention_mask=a_	,			encoder_hidden_states=a_	,			encoder_attention_mask=a_	,			output_hidden_states=a_	,			)["hidden_states"][0]
														UpperCamelCase__					  =      model(
														    a_	,			attention_mask=a_	,			encoder_hidden_states=a_	,			encoder_attention_mask=a_	,			past_key_values=a_	,			output_hidden_states=a_	,			)["hidden_states"][0]

														# select random slice
														UpperCamelCase__					  =      ids_tensor((1,)	,			output_from_past.shape[-1]	).item()
														UpperCamelCase__					  =      output_from_no_past[:, -3:, random_slice_idx].detach()
														UpperCamelCase__					  =      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(a_	,			a_	,			atol=1E-3	)	)







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.prepare_config_and_inputs()
														(
														    UpperCamelCase__		
														)					  =      config_and_inputs
														UpperCamelCase__					  =      {"input_ids": input_ids, "attention_mask": input_mask}
														return config, inputs_dict






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






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      LlamaModelTester(self	)
														UpperCamelCase__					  =      ConfigTester(self	,			config_class=a_	,			hidden_size=37	)






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






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*a_	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs()
														for type in ["absolute", "relative_key", "relative_key_query"]:
																					UpperCamelCase__					  =      type
																					self.model_tester.create_and_check_model(*a_	)






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






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






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_common()
														UpperCamelCase__					  =      3
														UpperCamelCase__					  =      "multi_label_classification"
														UpperCamelCase__					  =      input_dict["input_ids"]
														UpperCamelCase__					  =      input_ids.ne(1	).to(a_	)
														UpperCamelCase__					  =      ids_tensor(
														    [self.model_tester.batch_size, config.num_labels]	,			self.model_tester.type_sequence_label_size	).to(torch.float	)
														UpperCamelCase__					  =      LlamaForSequenceClassification(a_	)
														model.to(a_	)
														model.eval()
														UpperCamelCase__					  =      model(a_	,			attention_mask=a_	,			labels=a_	)
														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	):
														pass







							@parameterized.expand([("""linear""",), ("""dynamic""",)]	)
							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      self.model_tester.prepare_config_and_inputs_for_common()
														UpperCamelCase__					  =      ids_tensor([1, 10]	,			config.vocab_size	)
														UpperCamelCase__					  =      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
														UpperCamelCase__					  =      LlamaModel(a_	)
														original_model.to(a_	)
														original_model.eval()
														UpperCamelCase__					  =      original_model(a_	).last_hidden_state
														UpperCamelCase__					  =      original_model(a_	).last_hidden_state

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

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






@require_torch
class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):






							@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""	)
							@slow
							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      [1, 306, 4658, 278, 6593, 310, 2834, 338]
														UpperCamelCase__					  =      LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""	,			device_map="""auto"""	)
														UpperCamelCase__					  =      model(torch.tensor([input_ids]	)	)
														# Expected mean on dim = -1
														UpperCamelCase__					  =      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	)	,			a_	,			atol=1E-2	,			rtol=1E-2	)
														# slicing logits[0, 0, 0:30]
														# fmt: off
														UpperCamelCase__					  =      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]	,			a_	,			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	):
														UpperCamelCase__					  =      [1, 306, 4658, 278, 6593, 310, 2834, 338]
														UpperCamelCase__					  =      LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""	,			device_map="""auto"""	)
														UpperCamelCase__					  =      model(torch.tensor(a_	)	)
														# Expected mean on dim = -1
														UpperCamelCase__					  =      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	)	,			a_	,			atol=1E-2	,			rtol=1E-2	)
														# slicing logits[0, 0, 0:30]
														# fmt: off
														UpperCamelCase__					  =      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]	,			a_	,			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	):
														UpperCamelCase__					  =      [1, 306, 4658, 278, 6593, 310, 2834, 338]
														UpperCamelCase__					  =      LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""	,			device_map="""auto"""	)
														UpperCamelCase__					  =      model(torch.tensor(a_	)	)
														# Expected mean on dim = -1
														UpperCamelCase__					  =      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	)	,			a_	,			atol=1E-2	,			rtol=1E-2	)
														# slicing logits[0, 0, 0:30]
														# fmt: off
														UpperCamelCase__					  =      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	)	,			a_	,			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	):
														UpperCamelCase__					  =      [1, 306, 4658, 278, 6593, 310, 2834, 338]
														UpperCamelCase__					  =      LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""	,			device_map="""auto"""	)
														UpperCamelCase__					  =      model(torch.tensor(a_	)	)

														UpperCamelCase__					  =      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	)	,			a_	,			atol=1E-2	,			rtol=1E-2	)
														# fmt: off
														UpperCamelCase__					  =      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]	,			a_	,			atol=1E-5	,			rtol=1E-5	)







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

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








'''simple docstring'''
def 	_A  (       A							)  ->				list:
	lowercase						:		str   					=		int(A							)
	if n_element < 1:
		lowercase						:		List[Any]   					=		ValueError("a should be a positive number"							)
		raise my_error

	lowercase						:		List[Any]   					=		[1]
	lowercase					,		lowercase					,		lowercase						:		Tuple   					=		(0, 0, 0)
	lowercase						:		Dict   					=		1
	while index < n_element:
		while hamming_list[i] * 2 <= hamming_list[-1]:
			i += 1
		while hamming_list[j] * 3 <= hamming_list[-1]:
			j += 1
		while hamming_list[k] * 5 <= hamming_list[-1]:
			k += 1
		hamming_list.append(
		    min(hamming_list[i] * 2							,hamming_list[j] * 3							,hamming_list[k] * 5							)							)
		index += 1
	return hamming_list


if __name__ == "__main__":
					lowerCAmelCase   : Union[str, Any]             =				input("""Enter the last number (nth term) of the Hamming Number Series: """)
					print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
					lowerCAmelCase   : Tuple             =				hamming(int(n))
					print("""-----------------------------------------------------""")
					print(F'''The list with nth numbers is: {hamming_numbers}''')
					print("""-----------------------------------------------------""")


