




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 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  Any      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Tuple=13      ,   __lowerCamelCase       :  Optional[int]=7      ,   __lowerCamelCase       :  Union[str, Any]=True      ,   __lowerCamelCase       :  Union[str, Any]=True      ,   __lowerCamelCase       :  List[Any]=False      ,   __lowerCamelCase       :  Optional[int]=True      ,   __lowerCamelCase       :  str=99      ,   __lowerCamelCase       :  Tuple=32      ,   __lowerCamelCase       :  List[str]=5      ,   __lowerCamelCase       :  Optional[int]=4      ,   __lowerCamelCase       :  List[str]=37      ,   __lowerCamelCase       :  Optional[Any]="gelu"      ,   __lowerCamelCase       :  Tuple=0.1      ,   __lowerCamelCase       :  Union[str, Any]=0.1      ,   __lowerCamelCase       :  List[Any]=5_12      ,   __lowerCamelCase       :  int=16      ,   __lowerCamelCase       :  str=2      ,   __lowerCamelCase       :  Any=0.02      ,   __lowerCamelCase       :  int=3      ,   __lowerCamelCase       :  Optional[Any]=4      ,   __lowerCamelCase       :  Dict=None      ,   )      ->  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 							__UpperCAmelCase							(   self       :  Tuple							)      ->  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 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  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=__lowerCamelCase      ,   initializer_range=self.initializer_range      ,   )





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  str							)      ->  Optional[Any]:
							a					=    LlamaModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase							)
							a					=    model(__lowerCamelCase							)
							self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Tuple      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Dict      ,   )      ->  Tuple:
							a					=    True
							a					=    LlamaModel(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(
							    __lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   encoder_hidden_states=__lowerCamelCase      ,   encoder_attention_mask=__lowerCamelCase      ,   )
							a					=    model(
							    __lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   encoder_hidden_states=__lowerCamelCase      ,   )
							a					=    model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase							)
							self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  List[Any]      ,   )      ->  str:
							a					=    LlamaForCausalLM(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size)							)





				def 							__UpperCAmelCase							(   self       :  Any      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  Tuple      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Tuple      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Dict      ,   )      ->  Optional[Any]:
							a					=    True
							a					=    True
							a					=    LlamaForCausalLM(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()

							# first forward pass
							a					=    model(
							    __lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   encoder_hidden_states=__lowerCamelCase      ,   encoder_attention_mask=__lowerCamelCase      ,   use_cache=__lowerCamelCase      ,   )
							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(
							    __lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   encoder_hidden_states=__lowerCamelCase      ,   encoder_attention_mask=__lowerCamelCase      ,   output_hidden_states=__lowerCamelCase      ,   )["hidden_states"][0]
							a					=    model(
							    __lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   encoder_hidden_states=__lowerCamelCase      ,   encoder_attention_mask=__lowerCamelCase      ,   past_key_values=__lowerCamelCase      ,   output_hidden_states=__lowerCamelCase      ,   )["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(__lowerCamelCase      ,   __lowerCamelCase      ,   atol=1e-3							)							)





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  str:
							a					=    self.prepare_config_and_inputs()
							(
							    (
							    a
							)					,	(
							    a
							)					,	(
							    a
							)					,	(
							    a
							)					,	(
							    a
							)					,	(
							    a
							)					,	(
							    a
							)					,	
							)					=    config_and_inputs
							a					=    {"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict




@require_torch
class 					snake_case__							(_UpperCamelCase					, _UpperCamelCase					, _UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



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





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Union[str, Any]:
							a					=    LlamaModelTester(self							)
							a					=    ConfigTester(self      ,   config_class=__lowerCamelCase      ,   hidden_size=37							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  List[Any]:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(   self       :  int							)      ->  List[str]:
							a					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  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(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  List[Any]:
							a					,	a					=    self.model_tester.prepare_config_and_inputs_for_common()
							a					=    3
							a					=    input_dict["input_ids"]
							a					=    input_ids.ne(1							).to(__lowerCamelCase							)
							a					=    ids_tensor([self.model_tester.batch_size]      ,   self.model_tester.type_sequence_label_size							)
							a					=    LlamaForSequenceClassification(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   labels=__lowerCamelCase							)
							self.assertEqual(result.logits.shape      ,   (self.model_tester.batch_size, self.model_tester.num_labels)							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  List[str]:
							a					,	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(__lowerCamelCase							)
							a					=    ids_tensor([self.model_tester.batch_size]      ,   self.model_tester.type_sequence_label_size							)
							a					=    LlamaForSequenceClassification(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   labels=__lowerCamelCase							)
							self.assertEqual(result.logits.shape      ,   (self.model_tester.batch_size, self.model_tester.num_labels)							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  Tuple:
							a					,	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(__lowerCamelCase							)
							a					=    ids_tensor(
							    [self.model_tester.batch_size, config.num_labels]      ,   self.model_tester.type_sequence_label_size							).to(torch.float							)
							a					=    LlamaForSequenceClassification(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   labels=__lowerCamelCase							)
							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 							__UpperCAmelCase							(   self       :  Dict							)      ->  Any:
							pass





				@parameterized.expand([("linear",), ("dynamic",)]							)
				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  Tuple							)      ->  Union[str, Any]:
							a					,	a					=    self.model_tester.prepare_config_and_inputs_for_common()
							a					=    ids_tensor([1, 10]      ,   config.vocab_size							)
							a					=    ids_tensor([1, int(config.max_position_embeddings * 1.5							)]      ,   config.vocab_size							)

							set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
							a					=    LlamaModel(__lowerCamelCase							)
							original_model.to(__lowerCamelCase							)
							original_model.eval()
							a					=    original_model(__lowerCamelCase							).last_hidden_state
							a					=    original_model(__lowerCamelCase							).last_hidden_state

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

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




@require_torch
class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"							)
				@slow
				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  int:
							a					=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
							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							)      ,   __lowerCamelCase      ,   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, :30]      ,   __lowerCamelCase      ,   atol=1e-5      ,   rtol=1e-5							)





				@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"							)
				@slow
				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  List[str]:
							a					=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
							a					=    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf"      ,   device_map="auto"							)
							a					=    model(torch.tensor(__lowerCamelCase							)							)
							# 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							)      ,   __lowerCamelCase      ,   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, :30]      ,   __lowerCamelCase      ,   atol=1e-5      ,   rtol=1e-5							)





				@unittest.skip("Logits are not exactly the same, once we fix the instabalities somehow, will update!"							)
				@slow
				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Tuple:
							a					=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
							a					=    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-13b-chat-hf"      ,   device_map="auto"							)
							a					=    model(torch.tensor(__lowerCamelCase							)							)
							# 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							)      ,   __lowerCamelCase      ,   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							)      ,   __lowerCamelCase      ,   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 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  List[Any]:
							a					=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
							a					=    LlamaForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf"      ,   device_map="auto"							)
							a					=    model(torch.tensor(__lowerCamelCase							)							)

							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							)      ,   __lowerCamelCase      ,   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, :30]      ,   __lowerCamelCase      ,   atol=1e-5      ,   rtol=1e-5							)





				@unittest.skip("Model is curently gated"							)
				@slow
				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  Optional[Any]:
							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(__lowerCamelCase      ,   return_tensors="pt"							)
							a					=    LlamaForCausalLM.from_pretrained(
							    "meta-llama/Llama-2-13b-chat-hf"      ,   device_map="sequential"      ,   use_safetensors=__lowerCamelCase							)

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












from __future__ import annotations

import time

import numpy as np

__lowerCAmelCase	:       List[str]     =	[8, 5, 9, 7]
__lowerCAmelCase	:       str     =	[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__lowerCAmelCase	:       Optional[Any]     =	[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]




class 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  Any      ,   __lowerCamelCase       :  list[int]      ,   __lowerCamelCase       :  list[list[int]]      ,   __lowerCamelCase       :  list[list[int]]      ,   )      ->  None:
							a					=    claim_vector
							a					=    allocated_resources_table
							a					=    maximum_claim_table





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  list[int]:
							return [
							    sum(p_item[i] for p_item in self.__allocated_resources_table							)
							    for i in range(len(self.__allocated_resources_table[0]							)							)
							]





				def 							__UpperCAmelCase							(   self       :  str							)      ->  list[int]:
							return np.array(self.__claim_vector							) - np.array(
							    self.__processes_resource_summation()							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  list[list[int]]:
							return [
							    list(np.array(self.__maximum_claim_table[i]							) - np.array(__lowerCamelCase							)							)
							    for i, allocated_resource in enumerate(self.__allocated_resources_table							)
							]





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  dict[int, list[int]]:
							return {self.__need().index(__lowerCamelCase							): i for i in self.__need()}





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   **__lowerCamelCase       :  Any							)      ->  None:
							a					=    self.__need()
							a					=    self.__allocated_resources_table
							a					=    self.__available_resources()
							a					=    self.__need_index_manager()
							for kw, val in kwargs.items():
										if kw and val is True:
													self.__pretty_data()
							print("_" * 50 + "\n"							)
							while need_list:
										a					=    False
										for each_need in need_list:
													a					=    True
													for index, need in enumerate(__lowerCamelCase							):
																if need > available_resources[index]:
																			a					=    False
																			break
													if execution:
																a					=    True
																# get the original index of the process from ind_ctrl db
																for original_need_index, need_clone in need_index_manager.items():
																			if each_need == need_clone:
																						a					=    original_need_index
																print(f"""Process {process_number + 1} is executing."""							)
																# remove the process run from stack
																need_list.remove(__lowerCamelCase							)
																# update available/freed resources stack
																a					=    np.array(__lowerCamelCase							) + np.array(
																    alloc_resources_table[process_number]							)
																print(
																    "Updated available resource stack for processes: "
																    + " ".join([str(__lowerCamelCase							) for x in available_resources]							)							)
																break
										if safe:
													print("The process is in a safe state.\n"							)
										else:
													print("System in unsafe state. Aborting...\n"							)
													break





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  str:
							print(" " * 9 + "Allocated Resource Table"							)
							for item in self.__allocated_resources_table:
										print(
										    f"""P{self.__allocated_resources_table.index(__lowerCamelCase							) + 1}"""
										    + " ".join(f"""{it:>8}""" for it in item							)
										    + "\n"							)
							print(" " * 9 + "System Resource Table"							)
							for item in self.__maximum_claim_table:
										print(
										    f"""P{self.__maximum_claim_table.index(__lowerCamelCase							) + 1}"""
										    + " ".join(f"""{it:>8}""" for it in item							)
										    + "\n"							)
							print(
							    "Current Usage by Active Processes: "
							    + " ".join(str(__lowerCamelCase							) for x in self.__claim_vector							)							)
							print(
							    "Initial Available Resources:       "
							    + " ".join(str(__lowerCamelCase							) for x in self.__available_resources()							)							)
							time.sleep(1							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






