



'''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 	lowerCamelCase_							:







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

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







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size				)

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

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

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

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.get_config()

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







						def 					__lowercase    (  self   :      Dict				):

													"""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=__a				,    initializer_range=self.initializer_range				,    use_stable_embedding=__a				,    )







						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			OpenLlamaModel(config=__a				)
													model.to(__a				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Dict     					=			model(__a				,    attention_mask=__a				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			model(__a				)
													self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)				)







						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      str				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			True
													SCREAMING_SNAKE_CASE					:      Dict     					=			OpenLlamaModel(__a				)
													model.to(__a				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Dict     					=			model(
													    __a				,    attention_mask=__a				,    encoder_hidden_states=__a				,    encoder_attention_mask=__a				,    )
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			model(
													    __a				,    attention_mask=__a				,    encoder_hidden_states=__a				,    )
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			model(__a				,    attention_mask=__a				)
													self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)				)







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Optional[int]				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			OpenLlamaForCausalLM(config=__a				)
													model.to(__a				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Any     					=			model(__a				,    attention_mask=__a				,    labels=__a				)
													self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size)				)







						def 					__lowercase    (  self   :      Tuple				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      List[str]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			True
													SCREAMING_SNAKE_CASE					:      int     					=			True
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			OpenLlamaForCausalLM(config=__a				)
													model.to(__a				)
													model.eval()

													# first forward pass
													SCREAMING_SNAKE_CASE					:      Dict     					=			model(
													    __a				,    attention_mask=__a				,    encoder_hidden_states=__a				,    encoder_attention_mask=__a				,    use_cache=__a				,    )
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			outputs.past_key_values

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

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

													SCREAMING_SNAKE_CASE					:      Tuple     					=			model(
													    __a				,    attention_mask=__a				,    encoder_hidden_states=__a				,    encoder_attention_mask=__a				,    output_hidden_states=__a				,    )["hidden_states"][0]
													SCREAMING_SNAKE_CASE					:      List[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
													SCREAMING_SNAKE_CASE					:      Dict     					=			ids_tensor((1,)				,    output_from_past.shape[-1]				).item()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			output_from_no_past[:, -3:, random_slice_idx].detach()
													SCREAMING_SNAKE_CASE					:      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(__a				,    __a				,    atol=1e-3				)				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			self.prepare_config_and_inputs()
													(
													    SCREAMING_SNAKE_CASE  
													)					:      List[Any]     					=			config_and_inputs
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			{"input_ids": input_ids, "attention_mask": input_mask}
													return config, inputs_dict


@require_torch
class 	lowerCamelCase_							(					_UpperCamelCase    ,				_UpperCamelCase    ,				_UpperCamelCase    ,				unittest.TestCase			):
						_lowerCAmelCase     :   List[str]	     =				(
						    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
						)
						_lowerCAmelCase     :   Dict	     =				(OpenLlamaForCausalLM,) if is_torch_available() else ()
						_lowerCAmelCase     :   int	     =				(
						    {
						        "feature-extraction": OpenLlamaModel,
						        "text-classification": OpenLlamaForSequenceClassification,
						        "text-generation": OpenLlamaForCausalLM,
						        "zero-shot": OpenLlamaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_lowerCAmelCase     :   Dict	     =				False
						_lowerCAmelCase     :   List[str]	     =				False







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			OpenLlamaModelTester(self				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			ConfigTester(self				,    config_class=__a				,    hidden_size=37				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													self.config_tester.run_common_tests()







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__a				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																				SCREAMING_SNAKE_CASE					:      Any     					=			type
																				self.model_tester.create_and_check_model(*__a				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			3
													SCREAMING_SNAKE_CASE					:      Any     					=			input_dict["input_ids"]
													SCREAMING_SNAKE_CASE					:      Dict     					=			input_ids.ne(1				).to(__a				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			ids_tensor([self.model_tester.batch_size]				,    self.model_tester.type_sequence_label_size				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			OpenLlamaForSequenceClassification(__a				)
													model.to(__a				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      List[str]     					=			model(__a				,    attention_mask=__a				,    labels=__a				)
													self.assertEqual(result.logits.shape				,    (self.model_tester.batch_size, self.model_tester.num_labels)				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			3
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			"single_label_classification"
													SCREAMING_SNAKE_CASE					:      Any     					=			input_dict["input_ids"]
													SCREAMING_SNAKE_CASE					:      List[str]     					=			input_ids.ne(1				).to(__a				)
													SCREAMING_SNAKE_CASE					:      Any     					=			ids_tensor([self.model_tester.batch_size]				,    self.model_tester.type_sequence_label_size				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			OpenLlamaForSequenceClassification(__a				)
													model.to(__a				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      int     					=			model(__a				,    attention_mask=__a				,    labels=__a				)
													self.assertEqual(result.logits.shape				,    (self.model_tester.batch_size, self.model_tester.num_labels)				)







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Tuple     					=			3
													SCREAMING_SNAKE_CASE					:      Tuple     					=			"multi_label_classification"
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			input_dict["input_ids"]
													SCREAMING_SNAKE_CASE					:      Tuple     					=			input_ids.ne(1				).to(__a				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			ids_tensor(
													    [self.model_tester.batch_size, config.num_labels]				,    self.model_tester.type_sequence_label_size				).to(torch.float				)
													SCREAMING_SNAKE_CASE					:      int     					=			OpenLlamaForSequenceClassification(__a				)
													model.to(__a				)
													model.eval()
													SCREAMING_SNAKE_CASE					:      Any     					=			model(__a				,    attention_mask=__a				,    labels=__a				)
													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 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													pass







						@parameterized.expand([('''linear''',), ('''dynamic''',)]				)
						def 					__lowercase    (  self   :      Tuple				,    lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			ids_tensor([1, 10]				,    config.vocab_size				)
													SCREAMING_SNAKE_CASE					:      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
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			OpenLlamaModel(__a				)
													original_model.to(__a				)
													original_model.eval()
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			original_model(__a				).last_hidden_state
													SCREAMING_SNAKE_CASE					:      Dict     					=			original_model(__a				).last_hidden_state

													set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			{"type": scaling_type, "factor": 10.0}
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			OpenLlamaModel(__a				)
													scaled_model.to(__a				)
													scaled_model.eval()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			scaled_model(__a				).last_hidden_state
													SCREAMING_SNAKE_CASE					:      str     					=			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				)				)









'''simple docstring'''



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import (
							    ImageTextPipelineOutput,
							    UniDiffuserPipeline,
							)
else:
							from .modeling_text_decoder import UniDiffuserTextDecoder
							from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
							from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline




