

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						,       a						,       a=13						,       a=7						,       a=True						,       a=True						,       a=False						,       a=True						,       a=99						,       a=32						,       a=5						,       a=4						,       a=37						,       a="gelu"						,       a=0.1						,       a=0.1						,       a=512						,       a=16						,       a=2						,       a=0.02						,       a=3						,       a=4						,       a=None						,       ):
         lowercase__   :       Any				=	parent
         lowercase__   :       Union[str, Any]				=	batch_size
         lowercase__   :       List[Any]				=	seq_length
         lowercase__   :       Dict				=	is_training
         lowercase__   :       Optional[Any]				=	use_input_mask
         lowercase__   :       Any				=	use_token_type_ids
         lowercase__   :       Optional[Any]				=	use_labels
         lowercase__   :       Tuple				=	vocab_size
         lowercase__   :       Optional[int]				=	hidden_size
         lowercase__   :       List[Any]				=	num_hidden_layers
         lowercase__   :       Optional[Any]				=	num_attention_heads
         lowercase__   :       Optional[int]				=	intermediate_size
         lowercase__   :       int				=	hidden_act
         lowercase__   :       Any				=	hidden_dropout_prob
         lowercase__   :       Union[str, Any]				=	attention_probs_dropout_prob
         lowercase__   :       Optional[Any]				=	max_position_embeddings
         lowercase__   :       Tuple				=	type_vocab_size
         lowercase__   :       List[Any]				=	type_sequence_label_size
         lowercase__   :       Optional[Any]				=	initializer_range
         lowercase__   :       str				=	num_labels
         lowercase__   :       List[Any]				=	num_choices
         lowercase__   :       Optional[int]				=	scope





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

         lowercase__   :       Optional[Any]				=	None
         if self.use_input_mask:
              lowercase__   :       int				=	random_attention_mask([self.batch_size, self.seq_length])

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

         lowercase__   :       List[str]				=	None
         lowercase__   :       List[Any]				=	None
         lowercase__   :       str				=	None
         if self.use_labels:
              lowercase__   :       Any				=	ids_tensor([self.batch_size]						,       self.type_sequence_label_size)
              lowercase__   :       Dict				=	ids_tensor([self.batch_size, self.seq_length]						,       self.num_labels)
              lowercase__   :       Union[str, Any]				=	ids_tensor([self.batch_size]						,       self.num_choices)

         lowercase__   :       Union[str, Any]				=	self.get_config()

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





    def 				snake_case_    (				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 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
         lowercase__   :       Any				=	LlamaModel(config=a)
         model.to(a)
         model.eval()
         lowercase__   :       str				=	model(a						,       attention_mask=a)
         lowercase__   :       Tuple				=	model(a)
         self.parent.assertEqual(result.last_hidden_state.shape						,       (self.batch_size, self.seq_length, self.hidden_size))





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





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a						,       a						,       a						,       ):
         lowercase__   :       List[str]				=	LlamaForCausalLM(config=a)
         model.to(a)
         model.eval()
         lowercase__   :       Optional[Any]				=	model(a						,       attention_mask=a						,       labels=a)
         self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.seq_length, self.vocab_size))





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a						,       a						,       a						,       ):
         lowercase__   :       Dict				=	True
         lowercase__   :       Optional[Any]				=	True
         lowercase__   :       int				=	LlamaForCausalLM(config=a)
         model.to(a)
         model.eval()

         # first forward pass
         lowercase__   :       str				=	model(
             a						,       attention_mask=a						,       encoder_hidden_states=a						,       encoder_attention_mask=a						,       use_cache=a						,       )
         lowercase__   :       Tuple				=	outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         lowercase__   :       Optional[int]				=	ids_tensor((self.batch_size, 3)						,       config.vocab_size)
         lowercase__   :       str				=	ids_tensor((self.batch_size, 3)						,       vocab_size=2)

         # append to next input_ids and
         lowercase__   :       Union[str, Any]				=	torch.cat([input_ids, next_tokens]						,       dim=-1)
         lowercase__   :       Dict				=	torch.cat([input_mask, next_mask]						,       dim=-1)

         lowercase__   :       Optional[int]				=	model(
             a						,       attention_mask=a						,       encoder_hidden_states=a						,       encoder_attention_mask=a						,       output_hidden_states=a						,       )['hidden_states'][0]
         lowercase__   :       Any				=	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
         lowercase__   :       str				=	ids_tensor((1,)						,       output_from_past.shape[-1]).item()
         lowercase__   :       Optional[Any]				=	output_from_no_past[:, -3:, random_slice_idx].detach()
         lowercase__   :       List[Any]				=	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 				snake_case_    (				self):
         lowercase__   :       Tuple				=	self.prepare_config_and_inputs()
         (
             (
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			(
             lowercase__
         )				,			
         )   :       Tuple				=	config_and_inputs
         lowercase__   :       Tuple				=	{'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict



@require_torch
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	__snake_case		,	__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	Optional[int]														=					(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
    __lowerCamelCase						:	Any														=					(LlamaForCausalLM,) if is_torch_available() else ()
    __lowerCamelCase						:	Dict														=					(
        {
            """feature-extraction""": LlamaModel,
            """text-classification""": LlamaForSequenceClassification,
            """text-generation""": LlamaForCausalLM,
            """zero-shot""": LlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    __lowerCamelCase						:	int														=					False
    __lowerCamelCase						:	Union[str, Any]														=					False





    def 				snake_case_    (				self):
         lowercase__   :       str				=	LlamaModelTester(self)
         lowercase__   :       Tuple				=	ConfigTester(self						,       config_class=a						,       hidden_size=37)





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





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a)





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





    def 				snake_case_    (				self):
         lowercase__				,			lowercase__   :       Dict				=	self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__   :       Any				=	3
         lowercase__   :       str				=	input_dict['input_ids']
         lowercase__   :       str				=	input_ids.ne(1).to(a)
         lowercase__   :       List[Any]				=	ids_tensor([self.model_tester.batch_size]						,       self.model_tester.type_sequence_label_size)
         lowercase__   :       int				=	LlamaForSequenceClassification(a)
         model.to(a)
         model.eval()
         lowercase__   :       int				=	model(a						,       attention_mask=a						,       labels=a)
         self.assertEqual(result.logits.shape						,       (self.model_tester.batch_size, self.model_tester.num_labels))





    def 				snake_case_    (				self):
         lowercase__				,			lowercase__   :       List[str]				=	self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__   :       Tuple				=	3
         lowercase__   :       Optional[int]				=	'single_label_classification'
         lowercase__   :       Union[str, Any]				=	input_dict['input_ids']
         lowercase__   :       int				=	input_ids.ne(1).to(a)
         lowercase__   :       List[str]				=	ids_tensor([self.model_tester.batch_size]						,       self.model_tester.type_sequence_label_size)
         lowercase__   :       int				=	LlamaForSequenceClassification(a)
         model.to(a)
         model.eval()
         lowercase__   :       str				=	model(a						,       attention_mask=a						,       labels=a)
         self.assertEqual(result.logits.shape						,       (self.model_tester.batch_size, self.model_tester.num_labels))





    def 				snake_case_    (				self):
         lowercase__				,			lowercase__   :       str				=	self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__   :       Any				=	3
         lowercase__   :       Any				=	'multi_label_classification'
         lowercase__   :       int				=	input_dict['input_ids']
         lowercase__   :       Optional[int]				=	input_ids.ne(1).to(a)
         lowercase__   :       Dict				=	ids_tensor(
             [self.model_tester.batch_size, config.num_labels]						,       self.model_tester.type_sequence_label_size).to(torch.float)
         lowercase__   :       Tuple				=	LlamaForSequenceClassification(a)
         model.to(a)
         model.eval()
         lowercase__   :       int				=	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 				snake_case_    (				self):
         pass





    @parameterized.expand([('linear',), ('dynamic',)])
    def 				snake_case_    (				self						,       a):
         lowercase__				,			lowercase__   :       Dict				=	self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__   :       List[Any]				=	ids_tensor([1, 10]						,       config.vocab_size)
         lowercase__   :       Union[str, Any]				=	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
         lowercase__   :       Tuple				=	LlamaModel(a)
         original_model.to(a)
         original_model.eval()
         lowercase__   :       List[str]				=	original_model(a).last_hidden_state
         lowercase__   :       int				=	original_model(a).last_hidden_state

         set_seed(42)  # Fixed seed at init time so the two models get the same random weights
         lowercase__   :       Union[str, Any]				=	{'type': scaling_type, 'factor': 10.0}
         lowercase__   :       int				=	LlamaModel(a)
         scaled_model.to(a)
         scaled_model.eval()
         lowercase__   :       int				=	scaled_model(a).last_hidden_state
         lowercase__   :       Dict				=	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 				snake_case_    (				self):
         lowercase__   :       Any				=	[1, 306, 4658, 278, 6593, 310, 2834, 338]
         lowercase__   :       List[Any]				=	LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'						,       device_map='auto')
         lowercase__   :       Optional[int]				=	model(torch.tensor([input_ids]))
         # Expected mean on dim = -1
         lowercase__   :       Tuple				=	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)						,       a						,       atol=1e-2						,       rtol=1e-2)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         lowercase__   :       List[str]				=	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]						,       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 				snake_case_    (				self):
         lowercase__   :       Dict				=	[1, 306, 4658, 278, 6593, 310, 2834, 338]
         lowercase__   :       List[Any]				=	LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'						,       device_map='auto')
         lowercase__   :       Dict				=	model(torch.tensor(a))
         # Expected mean on dim = -1
         lowercase__   :       List[str]				=	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)						,       a						,       atol=1e-2						,       rtol=1e-2)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         lowercase__   :       Union[str, Any]				=	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]						,       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 				snake_case_    (				self):
         lowercase__   :       Tuple				=	[1, 306, 4658, 278, 6593, 310, 2834, 338]
         lowercase__   :       Tuple				=	LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'						,       device_map='auto')
         lowercase__   :       Optional[int]				=	model(torch.tensor(a))
         # Expected mean on dim = -1
         lowercase__   :       Dict				=	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)						,       a						,       atol=1e-2						,       rtol=1e-2)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         lowercase__   :       Tuple				=	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)						,       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 				snake_case_    (				self):
         lowercase__   :       Union[str, Any]				=	[1, 306, 4658, 278, 6593, 310, 2834, 338]
         lowercase__   :       Optional[Any]				=	LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'						,       device_map='auto')
         lowercase__   :       str				=	model(torch.tensor(a))

         lowercase__   :       Any				=	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)						,       a						,       atol=1e-2						,       rtol=1e-2)
         # fmt: off
         lowercase__   :       Union[str, Any]				=	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]						,       a						,       atol=1e-5						,       rtol=1e-5)





    @unittest.skip('Model is curently gated')
    @slow
    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	'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__   :       Tuple				=	'Simply put, the theory of relativity states that '
         lowercase__   :       Dict				=	LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf')
         lowercase__   :       List[str]				=	tokenizer.encode(a						,       return_tensors='pt')
         lowercase__   :       Union[str, Any]				=	LlamaForCausalLM.from_pretrained(
             'meta-llama/Llama-2-13b-chat-hf'						,       device_map='sequential'						,       use_safetensors=a)

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




def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int = 50_000_000      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	set()
     lowercase__   :       Any				=	int((limit - 24) ** (1 / 2)      )

     lowercase__   :       Any				=	set(range(3     ,					prime_square_limit + 1     ,					2      )      )
     primes.add(2      )
     for p in range(3     ,					prime_square_limit + 1     ,					2      ):
          if p not in primes:
               continue
          primes.difference_update(set(range(p * p     ,					prime_square_limit + 1     ,					SCREAMING_SNAKE_CASE_      )      )      )

     for primea in primes:
          lowercase__   :       Any				=	primea * primea
          for primea in primes:
               lowercase__   :       Optional[int]				=	primea * primea * primea
               if square + cube >= limit - 16:
                    break
               for primea in primes:
                    lowercase__   :       Dict				=	primea * primea * primea * primea
                    lowercase__   :       List[Any]				=	square + cube + tetr
                    if total >= limit:
                         break
                    ret.add(SCREAMING_SNAKE_CASE_      )

     return len(SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
       print(F'''{solution() = }''')

