


"""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      A:

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=13					,				SCREAMING_SNAKE_CASE__=7					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=99					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=5					,				SCREAMING_SNAKE_CASE__=4					,				SCREAMING_SNAKE_CASE__=37					,				SCREAMING_SNAKE_CASE__="gelu"					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=5_12					,				SCREAMING_SNAKE_CASE__=16					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=4					,				SCREAMING_SNAKE_CASE__=None					,				)      ->	int:

					"""simple docstring"""



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

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size   )

					_UpperCamelCase					:int					     =	None
					if self.use_input_mask:
								_UpperCamelCase					:List[str]					     =	random_attention_mask([self.batch_size, self.seq_length]   )

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

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

					_UpperCamelCase					:Dict					     =	self.get_config()

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""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=SCREAMING_SNAKE_CASE__					,				initializer_range=self.initializer_range					,				)
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[int]:

					"""simple docstring"""



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

					"""simple docstring"""



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

					"""simple docstring"""



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

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	True
					_UpperCamelCase					:Any					     =	True
					_UpperCamelCase					:int					     =	LlamaForCausalLM(config=SCREAMING_SNAKE_CASE__   )
					model.to(SCREAMING_SNAKE_CASE__   )
					model.eval()

					# first forward pass
					_UpperCamelCase					:Any					     =	model(
					    SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__					,				encoder_hidden_states=SCREAMING_SNAKE_CASE__					,				encoder_attention_mask=SCREAMING_SNAKE_CASE__					,				use_cache=SCREAMING_SNAKE_CASE__					,				)
					_UpperCamelCase					:str					     =	outputs.past_key_values

					# create hypothetical multiple next token and extent to next_input_ids
					_UpperCamelCase					:Dict					     =	ids_tensor((self.batch_size, 3)					,				config.vocab_size   )
					_UpperCamelCase					:List[Any]					     =	ids_tensor((self.batch_size, 3)					,				vocab_size=2   )

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

					_UpperCamelCase					:str					     =	model(
					    SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__					,				encoder_hidden_states=SCREAMING_SNAKE_CASE__					,				encoder_attention_mask=SCREAMING_SNAKE_CASE__					,				output_hidden_states=SCREAMING_SNAKE_CASE__					,				)['''hidden_states'''][0]
					_UpperCamelCase					:List[Any]					     =	model(
					    SCREAMING_SNAKE_CASE__					,				attention_mask=SCREAMING_SNAKE_CASE__					,				encoder_hidden_states=SCREAMING_SNAKE_CASE__					,				encoder_attention_mask=SCREAMING_SNAKE_CASE__					,				past_key_values=SCREAMING_SNAKE_CASE__					,				output_hidden_states=SCREAMING_SNAKE_CASE__					,				)['''hidden_states'''][0]

					# select random slice
					_UpperCamelCase					:Union[str, Any]					     =	ids_tensor((1,)					,				output_from_past.shape[-1]   ).item()
					_UpperCamelCase					:Union[str, Any]					     =	output_from_no_past[:, -3:, random_slice_idx].detach()
					_UpperCamelCase					:Union[str, 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(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				atol=1E-3   )   )





		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.prepare_config_and_inputs()
					(
					    (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      (
					    _UpperCamelCase
					)     ,      
					)					:Tuple					     =	config_and_inputs
					_UpperCamelCase					:int					     =	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
					return config, inputs_dict






@require_torch
class      A(  lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
		A    =							(LlamaForCausalLM,) if is_torch_available() else ()
		A    =							(
		    {
		        "feature-extraction": LlamaModel,
		        "text-classification": LlamaForSequenceClassification,
		        "text-generation": LlamaForCausalLM,
		        "zero-shot": LlamaForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		A    =							False
		A    =							False
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	LlamaModelTester(self   )
					_UpperCamelCase					:Dict					     =	ConfigTester(self					,				config_class=SCREAMING_SNAKE_CASE__					,				hidden_size=37   )
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					self.config_tester.run_common_tests()
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
								_UpperCamelCase					:List[Any]					     =	type
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



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

					"""simple docstring"""



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

					"""simple docstring"""



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

					"""simple docstring"""



					pass





		@parameterized.expand([('''linear''',), ('''dynamic''',)]   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase     ,      _UpperCamelCase					:Tuple					     =	self.model_tester.prepare_config_and_inputs_for_common()
					_UpperCamelCase					:Dict					     =	ids_tensor([1, 10]					,				config.vocab_size   )
					_UpperCamelCase					:Tuple					     =	ids_tensor([1, int(config.max_position_embeddings * 1.5   )]					,				config.vocab_size   )

					set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
					_UpperCamelCase					:Any					     =	LlamaModel(SCREAMING_SNAKE_CASE__   )
					original_model.to(SCREAMING_SNAKE_CASE__   )
					original_model.eval()
					_UpperCamelCase					:Union[str, Any]					     =	original_model(SCREAMING_SNAKE_CASE__   ).last_hidden_state
					_UpperCamelCase					:Union[str, Any]					     =	original_model(SCREAMING_SNAKE_CASE__   ).last_hidden_state

					set_seed(42   )  # Fixed seed at init time so the two models get the same random weights
					_UpperCamelCase					:Optional[Any]					     =	{'''type''': scaling_type, '''factor''': 1_0.0}
					_UpperCamelCase					:Optional[Any]					     =	LlamaModel(SCREAMING_SNAKE_CASE__   )
					scaled_model.to(SCREAMING_SNAKE_CASE__   )
					scaled_model.eval()
					_UpperCamelCase					:Union[str, Any]					     =	scaled_model(SCREAMING_SNAKE_CASE__   ).last_hidden_state
					_UpperCamelCase					:Dict					     =	scaled_model(SCREAMING_SNAKE_CASE__   ).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(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				atol=1E-5   )   )
					else:
								self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				atol=1E-5   )   )

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






@require_torch
class      A(  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:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
					_UpperCamelCase					:List[str]					     =	LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''					,				device_map='''auto'''   )
					_UpperCamelCase					:Dict					     =	model(torch.tensor([input_ids]   )   )
					# Expected mean on dim = -1
					_UpperCamelCase					:List[str]					     =	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   )					,				SCREAMING_SNAKE_CASE__					,				atol=1E-2					,				rtol=1E-2   )
					# slicing logits[0, 0, 0:30]
					# fmt: off
					_UpperCamelCase					:List[str]					     =	torch.tensor([-1_2.8_2_8_1, -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, -1_2.8_2_8_1, -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]					,				SCREAMING_SNAKE_CASE__					,				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   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
					_UpperCamelCase					:Union[str, Any]					     =	LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''					,				device_map='''auto'''   )
					_UpperCamelCase					:Optional[Any]					     =	model(torch.tensor(SCREAMING_SNAKE_CASE__   )   )
					# Expected mean on dim = -1
					_UpperCamelCase					:Union[str, Any]					     =	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   )					,				SCREAMING_SNAKE_CASE__					,				atol=1E-2					,				rtol=1E-2   )
					# slicing logits[0, 0, 0:30]
					# fmt: off
					_UpperCamelCase					:Union[str, Any]					     =	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]					,				SCREAMING_SNAKE_CASE__					,				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   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
					_UpperCamelCase					:int					     =	LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''					,				device_map='''auto'''   )
					_UpperCamelCase					:Tuple					     =	model(torch.tensor(SCREAMING_SNAKE_CASE__   )   )
					# Expected mean on dim = -1
					_UpperCamelCase					:int					     =	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   )					,				SCREAMING_SNAKE_CASE__					,				atol=1E-2					,				rtol=1E-2   )
					# slicing logits[0, 0, 0:30]
					# fmt: off
					_UpperCamelCase					:Union[str, Any]					     =	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   )					,				SCREAMING_SNAKE_CASE__					,				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   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	[1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
					_UpperCamelCase					:List[str]					     =	LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''					,				device_map='''auto'''   )
					_UpperCamelCase					:int					     =	model(torch.tensor(SCREAMING_SNAKE_CASE__   )   )

					_UpperCamelCase					:Tuple					     =	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   )					,				SCREAMING_SNAKE_CASE__					,				atol=1E-2					,				rtol=1E-2   )
					# fmt: off
					_UpperCamelCase					:int					     =	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]					,				SCREAMING_SNAKE_CASE__					,				atol=1E-5					,				rtol=1E-5   )





		@unittest.skip('''Model is curently gated'''   )
		@slow
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	'''Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'''
					_UpperCamelCase					:int					     =	'''Simply put, the theory of relativity states that '''
					_UpperCamelCase					:List[Any]					     =	LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''   )
					_UpperCamelCase					:int					     =	tokenizer.encode(SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   )
					_UpperCamelCase					:Optional[int]					     =	LlamaForCausalLM.from_pretrained(
					    '''meta-llama/Llama-2-13b-chat-hf'''					,				device_map='''sequential'''					,				use_safetensors=SCREAMING_SNAKE_CASE__   )

					# greedy generation outputs
					_UpperCamelCase					:List[str]					     =	model.generate(SCREAMING_SNAKE_CASE__					,				max_new_tokens=64					,				top_p=SCREAMING_SNAKE_CASE__					,				temperature=1					,				do_sample=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Tuple					     =	tokenizer.decode(generated_ids[0]					,				skip_special_tokens=SCREAMING_SNAKE_CASE__   )
					self.assertEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )





"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__()

					# make sure scheduler can always be converted to DDIM
					_UpperCamelCase					:int					     =	DDIMScheduler.from_config(scheduler.config   )

					self.register_modules(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )





		@torch.no_grad()
		def __call__( self					,				SCREAMING_SNAKE_CASE__ = 1					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = 0.0					,				SCREAMING_SNAKE_CASE__ = 50					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = "pil"					,				SCREAMING_SNAKE_CASE__ = True					,				)      ->	Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""



					if isinstance(self.unet.config.sample_size					,				SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Optional[int]					     =	(
								    batch_size,
								    self.unet.config.in_channels,
								    self.unet.config.sample_size,
								    self.unet.config.sample_size,
								)
					else:
								_UpperCamelCase					:Tuple					     =	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) and len(SCREAMING_SNAKE_CASE__   ) != batch_size:
								raise ValueError(
								    f"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__   )}, but requested an effective batch"
								    f" size of {batch_size}. Make sure the batch size matches the length of the generators."   )

					_UpperCamelCase					:Optional[int]					     =	randn_tensor(SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__					,				device=self.device					,				dtype=self.unet.dtype   )

					# set step values
					self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

					for t in self.progress_bar(self.scheduler.timesteps   ):
								# 1. predict noise model_output
								_UpperCamelCase					:List[str]					     =	self.unet(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								_UpperCamelCase					:Optional[Any]					     =	self.scheduler.step(
								    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				eta=SCREAMING_SNAKE_CASE__					,				use_clipped_model_output=SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__   ).prev_sample

					_UpperCamelCase					:str					     =	(image / 2 + 0.5).clamp(0					,				1   )
					_UpperCamelCase					:int					     =	image.cpu().permute(0					,				2					,				3					,				1   ).numpy()
					if output_type == "pil":
								_UpperCamelCase					:Union[str, Any]					     =	self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

					if not return_dict:
								return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   )

