


'''simple docstring'''
import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer

class 						_lowerCamelCase					:




	'''simple docstring'''


	def __init__(      self      ,      __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=0.0_2      ,      __lowercase=3      ,      __lowercase=4      ,      __lowercase=None      ,      ):

		"""simple docstring"""







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


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Any       =			ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size						)

		__A  :		str       =			None
		if self.use_input_mask:
			__A  :		Union[str, Any]       =			random_attention_mask([self.batch_size, self.seq_length]						)

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

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

		__A  :		Any       =			self.get_config()

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


	def  snake_case__   (      self						):

		"""simple docstring"""







		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=__lowercase      ,      initializer_range=self.initializer_range      ,      )


	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

		"""simple docstring"""







		__A  :		Dict       =			LlamaModel(config=__lowercase						)
		model.to(__lowercase						)
		model.eval()
		__A  :		List[Any]       =			model(__lowercase      ,      attention_mask=__lowercase						)
		__A  :		List[str]       =			model(__lowercase						)
		self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)						)


	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

		"""simple docstring"""







		__A  :		Any       =			True
		__A  :		List[Any]       =			LlamaModel(__lowercase						)
		model.to(__lowercase						)
		model.eval()
		__A  :		List[Any]       =			model(
		    __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      )
		__A  :		Tuple       =			model(
		    __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      )
		__A  :		List[Any]       =			model(__lowercase      ,      attention_mask=__lowercase						)
		self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)						)


	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

		"""simple docstring"""







		__A  :		Dict       =			LlamaForCausalLM(config=__lowercase						)
		model.to(__lowercase						)
		model.eval()
		__A  :		Optional[int]       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
		self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)						)


	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

		"""simple docstring"""







		__A  :		Optional[Any]       =			True
		__A  :		Any       =			True
		__A  :		Union[str, Any]       =			LlamaForCausalLM(config=__lowercase						)
		model.to(__lowercase						)
		model.eval()

		# first forward pass
		__A  :		Dict       =			model(
		    __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      use_cache=__lowercase      ,      )
		__A  :		Any       =			outputs.past_key_values

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

		# append to next input_ids and
		__A  :		List[Any]       =			torch.cat([input_ids, next_tokens]      ,      dim=-1						)
		__A  :		Optional[int]       =			torch.cat([input_mask, next_mask]      ,      dim=-1						)

		__A  :		Optional[Any]       =			model(
		    __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      output_hidden_states=__lowercase      ,      )['hidden_states'][0]
		__A  :		Dict       =			model(
		    __lowercase      ,      attention_mask=__lowercase      ,      encoder_hidden_states=__lowercase      ,      encoder_attention_mask=__lowercase      ,      past_key_values=__lowercase      ,      output_hidden_states=__lowercase      ,      )['hidden_states'][0]

		# select random slice
		__A  :		List[Any]       =			ids_tensor((1,)      ,      output_from_past.shape[-1]						).item()
		__A  :		List[str]       =			output_from_no_past[:, -3:, random_slice_idx].detach()
		__A  :		int       =			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(__lowercase      ,      __lowercase      ,      atol=1E-3						)						)







	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Union[str, Any]       =			self.prepare_config_and_inputs()
		(
		    __A
		)  :		Dict       =			config_and_inputs
		__A  :		int       =			{'input_ids': input_ids, 'attention_mask': input_mask}
		return config, inputs_dict


@require_torch
class 						_lowerCamelCase					(    snake_case_    ,		snake_case_    ,		snake_case_    ,		unittest.TestCase				):




	'''simple docstring'''
	__lowercase				:		str    							=						(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
	__lowercase				:		Optional[int]    							=						(LlamaForCausalLM,) if is_torch_available() else ()
	__lowercase				:		Any    							=						(
	    {
	        '''feature-extraction''': LlamaModel,
	        '''text-classification''': LlamaForSequenceClassification,
	        '''text-generation''': LlamaForCausalLM,
	        '''zero-shot''': LlamaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	__lowercase				:		List[Any]    							=						False
	__lowercase				:		List[str]    							=						False


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		List[Any]       =			LlamaModelTester(self						)
		__A  :		Any       =			ConfigTester(self      ,      config_class=__lowercase      ,      hidden_size=37						)


	def  snake_case__   (      self						):

		"""simple docstring"""







		self.config_tester.run_common_tests()


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		str       =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__lowercase						)


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		List[str]       =			self.model_tester.prepare_config_and_inputs()
		for type in ["absolute", "relative_key", "relative_key_query"]:
			__A  :		int       =			type
			self.model_tester.create_and_check_model(*__lowercase						)


	def  snake_case__   (      self						):

		"""simple docstring"""







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


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Any       =			self.model_tester.prepare_config_and_inputs_for_common()
		__A  :		Any       =			3
		__A  :		Dict       =			'single_label_classification'
		__A  :		Any       =			input_dict['input_ids']
		__A  :		Dict       =			input_ids.ne(1						).to(__lowercase						)
		__A  :		Optional[Any]       =			ids_tensor([self.model_tester.batch_size]      ,      self.model_tester.type_sequence_label_size						)
		__A  :		int       =			LlamaForSequenceClassification(__lowercase						)
		model.to(__lowercase						)
		model.eval()
		__A  :		Tuple       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
		self.assertEqual(result.logits.shape      ,      (self.model_tester.batch_size, self.model_tester.num_labels)						)


	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Optional[Any]       =			self.model_tester.prepare_config_and_inputs_for_common()
		__A  :		str       =			3
		__A  :		Optional[int]       =			'multi_label_classification'
		__A  :		int       =			input_dict['input_ids']
		__A  :		Optional[Any]       =			input_ids.ne(1						).to(__lowercase						)
		__A  :		int       =			ids_tensor(
		    [self.model_tester.batch_size, config.num_labels]      ,      self.model_tester.type_sequence_label_size						).to(torch.float						)
		__A  :		List[Any]       =			LlamaForSequenceClassification(__lowercase						)
		model.to(__lowercase						)
		model.eval()
		__A  :		Any       =			model(__lowercase      ,      attention_mask=__lowercase      ,      labels=__lowercase						)
		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						):

		"""simple docstring"""







		pass







	@parameterized.expand([('linear',), ('dynamic',)]						)
	def  snake_case__   (      self      ,      __lowercase						):

		"""simple docstring"""







		__A  :		Union[str, Any]       =			self.model_tester.prepare_config_and_inputs_for_common()
		__A  :		List[str]       =			ids_tensor([1, 10]      ,      config.vocab_size						)
		__A  :		Dict       =			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  :		Optional[int]       =			LlamaModel(__lowercase						)
		original_model.to(__lowercase						)
		original_model.eval()
		__A  :		int       =			original_model(__lowercase						).last_hidden_state
		__A  :		str       =			original_model(__lowercase						).last_hidden_state

		set_seed(42						)  # Fixed seed at init time so the two models get the same random weights
		__A  :		List[Any]       =			{'type': scaling_type, 'factor': 10.0}
		__A  :		str       =			LlamaModel(__lowercase						)
		scaled_model.to(__lowercase						)
		scaled_model.eval()
		__A  :		Union[str, Any]       =			scaled_model(__lowercase						).last_hidden_state
		__A  :		Optional[Any]       =			scaled_model(__lowercase						).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(__lowercase      ,      __lowercase      ,      atol=1E-5						)						)
		else:
			self.assertFalse(torch.allclose(__lowercase      ,      __lowercase      ,      atol=1E-5						)						)

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


@require_torch
class 						_lowerCamelCase					(    unittest.TestCase				):




	'''simple docstring'''


	@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'						)
	@slow
	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		str       =			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
		__A  :		List[str]       =			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'      ,      device_map='auto'						)
		__A  :		Optional[Any]       =			model(torch.tensor([input_ids]						)						)
		# Expected mean on dim = -1
		__A  :		int       =			torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]						)
		torch.testing.assert_close(out.mean(-1						)      ,      __lowercase      ,      atol=1E-2      ,      rtol=1E-2						)
		# slicing logits[0, 0, 0:30]
		# fmt: off
		__A  :		List[str]       =			torch.tensor([-12.8_281, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -12.8_281, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]						)
		# fmt: on
		torch.testing.assert_close(out[0, 0, :30]      ,      __lowercase      ,      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						):

		"""simple docstring"""







		__A  :		Dict       =			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
		__A  :		int       =			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'      ,      device_map='auto'						)
		__A  :		Union[str, Any]       =			model(torch.tensor(__lowercase						)						)
		# Expected mean on dim = -1
		__A  :		Tuple       =			torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]						)
		torch.testing.assert_close(out.mean(-1						)      ,      __lowercase      ,      atol=1E-2      ,      rtol=1E-2						)
		# slicing logits[0, 0, 0:30]
		# fmt: off
		__A  :		int       =			torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]						)
		# fmt: on
		torch.testing.assert_close(out[0, 0, :30]      ,      __lowercase      ,      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						):

		"""simple docstring"""







		__A  :		str       =			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
		__A  :		int       =			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'      ,      device_map='auto'						)
		__A  :		str       =			model(torch.tensor(__lowercase						)						)
		# Expected mean on dim = -1
		__A  :		Union[str, Any]       =			torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]						)
		torch.testing.assert_close(out.mean(-1						)      ,      __lowercase      ,      atol=1E-2      ,      rtol=1E-2						)
		# slicing logits[0, 0, 0:30]
		# fmt: off
		__A  :		str       =			torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]						)
		# fmt: on
		torch.testing.assert_close(out.mean(-1						)      ,      __lowercase      ,      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						):

		"""simple docstring"""







		__A  :		Dict       =			[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
		__A  :		List[str]       =			LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'      ,      device_map='auto'						)
		__A  :		Union[str, Any]       =			model(torch.tensor(__lowercase						)						)

		__A  :		List[str]       =			torch.tensor(
		    [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]      ,      dtype=torch.floataa						)
		torch.testing.assert_close(out.mean(-1						)      ,      __lowercase      ,      atol=1E-2      ,      rtol=1E-2						)
		# fmt: off
		__A  :		List[Any]       =			torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]						)
		# fmt: on
		torch.testing.assert_close(out[0, 0, :30]      ,      __lowercase      ,      atol=1E-5      ,      rtol=1E-5						)







	@unittest.skip('Model is curently gated'						)
	@slow
	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		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'
		__A  :		Any       =			'Simply put, the theory of relativity states that '
		__A  :		Tuple       =			LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'						)
		__A  :		List[Any]       =			tokenizer.encode(__lowercase      ,      return_tensors='pt'						)
		__A  :		List[Any]       =			LlamaForCausalLM.from_pretrained(
		    'meta-llama/Llama-2-13b-chat-hf'      ,      device_map='sequential'      ,      use_safetensors=__lowercase						)

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










'''simple docstring'''
from collections.abc import Sequence


def 			_lowercase       (				UpperCamelCase__       :					Sequence[float],				UpperCamelCase__       :					float			):
 return sum(c * (x**i) for i, c in enumerate(UpperCamelCase__			)			)


def 			_lowercase       (				UpperCamelCase__       :					Sequence[float],				UpperCamelCase__       :					float			):
 __A  :		Optional[Any]       =			0.0
 for coeff in reversed(UpperCamelCase__			):
  __A  :		List[str]       =			result * x + coeff
 return result


if __name__ == "__main__":
 UpperCAmelCase_ :     Any								=      (0.0, 0.0, 5.0, 9.3, 7.0)
 UpperCAmelCase_ :     Any								=      10.0
 print(evaluate_poly(poly, x))
 print(horner(poly, x))






