

'''simple docstring'''






import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, 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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class       SCREAMING_SNAKE_CASE__     :




    def __init__(   self							,	lowercase__							,	lowercase__=13							,	lowercase__=7							,	lowercase__=True							,	lowercase__=True							,	lowercase__=False							,	lowercase__=True							,	lowercase__=99							,	lowercase__=32							,	lowercase__=5							,	lowercase__=4							,	lowercase__=37							,	lowercase__="gelu"							,	lowercase__=0.1							,	lowercase__=0.1							,	lowercase__=512							,	lowercase__=16							,	lowercase__=2							,	lowercase__=0.02							,	lowercase__=3							,	lowercase__=4							,	lowercase__=None							,	):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_ :     Any	=			parent
          SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			batch_size
          SCREAMING_SNAKE_CASE_ :     Any	=			seq_length
          SCREAMING_SNAKE_CASE_ :     Dict	=			is_training
          SCREAMING_SNAKE_CASE_ :     List[str]	=			use_input_mask
          SCREAMING_SNAKE_CASE_ :     Any	=			use_token_type_ids
          SCREAMING_SNAKE_CASE_ :     Optional[int]	=			use_labels
          SCREAMING_SNAKE_CASE_ :     List[Any]	=			vocab_size
          SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			hidden_size
          SCREAMING_SNAKE_CASE_ :     Tuple	=			num_hidden_layers
          SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			num_attention_heads
          SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			intermediate_size
          SCREAMING_SNAKE_CASE_ :     str	=			hidden_act
          SCREAMING_SNAKE_CASE_ :     int	=			hidden_dropout_prob
          SCREAMING_SNAKE_CASE_ :     str	=			attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_ :     str	=			max_position_embeddings
          SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			type_vocab_size
          SCREAMING_SNAKE_CASE_ :     List[Any]	=			type_sequence_label_size
          SCREAMING_SNAKE_CASE_ :     Dict	=			initializer_range
          SCREAMING_SNAKE_CASE_ :     str	=			num_labels
          SCREAMING_SNAKE_CASE_ :     Optional[int]	=			num_choices
          SCREAMING_SNAKE_CASE_ :     int	=			scope




    def     __lowerCamelCase (   self     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_ :     Dict	=			ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size     )

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

          SCREAMING_SNAKE_CASE_ :     List[Any]	=			None
          if self.use_token_type_ids:
                SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			ids_tensor([self.batch_size, self.seq_length]							,	self.type_vocab_size     )

          SCREAMING_SNAKE_CASE_ :     Any	=			None
          SCREAMING_SNAKE_CASE_ :     List[str]	=			None
          SCREAMING_SNAKE_CASE_ :     Optional[int]	=			None
          if self.use_labels:
                SCREAMING_SNAKE_CASE_ :     int	=			ids_tensor([self.batch_size]							,	self.type_sequence_label_size     )
                SCREAMING_SNAKE_CASE_ :     List[str]	=			ids_tensor([self.batch_size, self.seq_length]							,	self.num_labels     )
                SCREAMING_SNAKE_CASE_ :     List[Any]	=			ids_tensor([self.batch_size]							,	self.num_choices     )

          SCREAMING_SNAKE_CASE_ :     str	=			self.get_config()

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




    def     __lowerCamelCase (   self     ):






          """simple docstring"""






          return OpenLlamaConfig(
              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							,	use_stable_embedding=__lowerCamelCase							,	)




    def     __lowerCamelCase (   self							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_ :     int	=			OpenLlamaModel(config=__lowerCamelCase     )
          model.to(__lowerCamelCase     )
          model.eval()
          SCREAMING_SNAKE_CASE_ :     str	=			model(__lowerCamelCase							,	attention_mask=__lowerCamelCase     )
          SCREAMING_SNAKE_CASE_ :     List[Any]	=			model(__lowerCamelCase     )
          self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size)     )




    def     __lowerCamelCase (   self							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_ :     List[Any]	=			True
          SCREAMING_SNAKE_CASE_ :     Tuple	=			OpenLlamaModel(__lowerCamelCase     )
          model.to(__lowerCamelCase     )
          model.eval()
          SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			model(
              __lowerCamelCase							,	attention_mask=__lowerCamelCase							,	encoder_hidden_states=__lowerCamelCase							,	encoder_attention_mask=__lowerCamelCase							,	)
          SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			model(
              __lowerCamelCase							,	attention_mask=__lowerCamelCase							,	encoder_hidden_states=__lowerCamelCase							,	)
          SCREAMING_SNAKE_CASE_ :     Dict	=			model(__lowerCamelCase							,	attention_mask=__lowerCamelCase     )
          self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size)     )




    def     __lowerCamelCase (   self							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_ :     List[str]	=			OpenLlamaForCausalLM(config=__lowerCamelCase     )
          model.to(__lowerCamelCase     )
          model.eval()
          SCREAMING_SNAKE_CASE_ :     int	=			model(__lowerCamelCase							,	attention_mask=__lowerCamelCase							,	labels=__lowerCamelCase     )
          self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.seq_length, self.vocab_size)     )




    def     __lowerCamelCase (   self							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	lowercase__							,	):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_ :     List[str]	=			True
          SCREAMING_SNAKE_CASE_ :     Tuple	=			True
          SCREAMING_SNAKE_CASE_ :     str	=			OpenLlamaForCausalLM(config=__lowerCamelCase     )
          model.to(__lowerCamelCase     )
          model.eval()

          # first forward pass
          SCREAMING_SNAKE_CASE_ :     Tuple	=			model(
              __lowerCamelCase							,	attention_mask=__lowerCamelCase							,	encoder_hidden_states=__lowerCamelCase							,	encoder_attention_mask=__lowerCamelCase							,	use_cache=__lowerCamelCase							,	)
          SCREAMING_SNAKE_CASE_ :     int	=			outputs.past_key_values

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

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

          SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			model(
              __lowerCamelCase							,	attention_mask=__lowerCamelCase							,	encoder_hidden_states=__lowerCamelCase							,	encoder_attention_mask=__lowerCamelCase							,	output_hidden_states=__lowerCamelCase							,	)['hidden_states'][0]
          SCREAMING_SNAKE_CASE_ :     int	=			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
          SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			ids_tensor((1,)							,	output_from_past.shape[-1]     ).item()
          SCREAMING_SNAKE_CASE_ :     Optional[int]	=			output_from_no_past[:, -3:, random_slice_idx].detach()
          SCREAMING_SNAKE_CASE_ :     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(__lowerCamelCase							,	__lowerCamelCase							,	atol=1e-3     )     )







    def     __lowerCamelCase (   self     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_ :     Optional[int]	=			self.prepare_config_and_inputs()
          (
              SCREAMING_SNAKE_CASE_							
          ) :     List[Any]	=			config_and_inputs
          SCREAMING_SNAKE_CASE_ :     Tuple	=			{'input_ids': input_ids, 'attention_mask': input_mask}
          return config, inputs_dict






@require_torch
class       SCREAMING_SNAKE_CASE__     (							lowerCAmelCase_,lowerCAmelCase_,lowerCAmelCase_,unittest.TestCase ):
    _A            =				(
        (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
    )
    _A            =				(OpenLlamaForCausalLM,) if is_torch_available() else ()
    _A            =				(
        {
            "feature-extraction": OpenLlamaModel,
            "text-classification": OpenLlamaForSequenceClassification,
            "text-generation": OpenLlamaForCausalLM,
            "zero-shot": OpenLlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    _A            =				False
    _A            =				False




    def     __lowerCamelCase (   self     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_ :     Any	=			OpenLlamaModelTester(self     )
          SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			ConfigTester(self							,	config_class=__lowerCamelCase							,	hidden_size=37     )




    def     __lowerCamelCase (   self     ):






          """simple docstring"""






          self.config_tester.run_common_tests()




    def     __lowerCamelCase (   self     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_ :     Optional[int]	=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__lowerCamelCase     )




    def     __lowerCamelCase (   self     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_ :     Dict	=			self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			type
                self.model_tester.create_and_check_model(*__lowerCamelCase     )




    def     __lowerCamelCase (   self     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_ :     Any	=			self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			3
          SCREAMING_SNAKE_CASE_ :     List[str]	=			input_dict['input_ids']
          SCREAMING_SNAKE_CASE_ :     Tuple	=			input_ids.ne(1     ).to(__lowerCamelCase     )
          SCREAMING_SNAKE_CASE_ :     Dict	=			ids_tensor([self.model_tester.batch_size]							,	self.model_tester.type_sequence_label_size     )
          SCREAMING_SNAKE_CASE_ :     Tuple	=			OpenLlamaForSequenceClassification(__lowerCamelCase     )
          model.to(__lowerCamelCase     )
          model.eval()
          SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			model(__lowerCamelCase							,	attention_mask=__lowerCamelCase							,	labels=__lowerCamelCase     )
          self.assertEqual(result.logits.shape							,	(self.model_tester.batch_size, self.model_tester.num_labels)     )




    def     __lowerCamelCase (   self     ):






          """simple docstring"""






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




    def     __lowerCamelCase (   self     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_ :     Any	=			self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_ :     Any	=			3
          SCREAMING_SNAKE_CASE_ :     str	=			'multi_label_classification'
          SCREAMING_SNAKE_CASE_ :     int	=			input_dict['input_ids']
          SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			input_ids.ne(1     ).to(__lowerCamelCase     )
          SCREAMING_SNAKE_CASE_ :     str	=			ids_tensor(
              [self.model_tester.batch_size, config.num_labels]							,	self.model_tester.type_sequence_label_size     ).to(torch.float     )
          SCREAMING_SNAKE_CASE_ :     str	=			OpenLlamaForSequenceClassification(__lowerCamelCase     )
          model.to(__lowerCamelCase     )
          model.eval()
          SCREAMING_SNAKE_CASE_ :     Tuple	=			model(__lowerCamelCase							,	attention_mask=__lowerCamelCase							,	labels=__lowerCamelCase     )
          self.assertEqual(result.logits.shape							,	(self.model_tester.batch_size, self.model_tester.num_labels)     )




    @unittest.skip("Open-Llama buffers include complex numbers, which breaks this test"     )
    def     __lowerCamelCase (   self     ):






          """simple docstring"""






          pass







    @parameterized.expand([("linear",), ("dynamic",)]     )
    def     __lowerCamelCase (   self							,	lowercase__     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_ :     Tuple	=			self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_ :     List[Any]	=			ids_tensor([1, 10]							,	config.vocab_size     )
          SCREAMING_SNAKE_CASE_ :     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
          SCREAMING_SNAKE_CASE_ :     int	=			OpenLlamaModel(__lowerCamelCase     )
          original_model.to(__lowerCamelCase     )
          original_model.eval()
          SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			original_model(__lowerCamelCase     ).last_hidden_state
          SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			original_model(__lowerCamelCase     ).last_hidden_state

          set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
          SCREAMING_SNAKE_CASE_ :     int	=			{'type': scaling_type, 'factor': 10.0}
          SCREAMING_SNAKE_CASE_ :     Optional[int]	=			OpenLlamaModel(__lowerCamelCase     )
          scaled_model.to(__lowerCamelCase     )
          scaled_model.eval()
          SCREAMING_SNAKE_CASE_ :     List[Any]	=			scaled_model(__lowerCamelCase     ).last_hidden_state
          SCREAMING_SNAKE_CASE_ :     Tuple	=			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     )     )







'''simple docstring'''






from math import pi






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   float:
      return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
      print(arc_length(90, 10))
