



"""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      UpperCAmelCase__		:



		def __init__( self  :    Optional[int]      ,							snake_case  :    List[Any]      ,							snake_case  :    Any=13      ,							snake_case  :    Optional[Any]=7      ,							snake_case  :    Tuple=True      ,							snake_case  :    Optional[Any]=True      ,							snake_case  :    Optional[int]=False      ,							snake_case  :    Dict=True      ,							snake_case  :    List[str]=99      ,							snake_case  :    List[Any]=32      ,							snake_case  :    Tuple=5      ,							snake_case  :    Tuple=4      ,							snake_case  :    Optional[int]=37      ,							snake_case  :    Union[str, Any]="gelu"      ,							snake_case  :    Tuple=0.1      ,							snake_case  :    Union[str, Any]=0.1      ,							snake_case  :    Optional[Any]=512      ,							snake_case  :    Tuple=16      ,							snake_case  :    List[str]=2      ,							snake_case  :    Union[str, Any]=0.02      ,							snake_case  :    Optional[Any]=3      ,							snake_case  :    List[str]=4      ,							snake_case  :    str=None      ,							)			->						Optional[Any]:






									'''simple docstring'''

									A         =					parent
									A         =					batch_size
									A         =					seq_length
									A         =					is_training
									A         =					use_input_mask
									A         =					use_token_type_ids
									A         =					use_labels
									A         =					vocab_size
									A         =					hidden_size
									A         =					num_hidden_layers
									A         =					num_attention_heads
									A         =					intermediate_size
									A         =					hidden_act
									A         =					hidden_dropout_prob
									A         =					attention_probs_dropout_prob
									A         =					max_position_embeddings
									A         =					type_vocab_size
									A         =					type_sequence_label_size
									A         =					initializer_range
									A         =					num_labels
									A         =					num_choices
									A         =					scope



		def 					A_		( self  :    int				)			->						Dict:






									'''simple docstring'''

									A         =					ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size				)

									A         =					None
									if self.use_input_mask:
																A         =					random_attention_mask([self.batch_size, self.seq_length]				)

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

									A         =					None
									A         =					None
									A         =					None
									if self.use_labels:
																A         =					ids_tensor([self.batch_size]      ,							self.type_sequence_label_size				)
																A         =					ids_tensor([self.batch_size, self.seq_length]      ,							self.num_labels				)
																A         =					ids_tensor([self.batch_size]      ,							self.num_choices				)

									A         =					self.get_config()

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



		def 					A_		( self  :    Tuple				)			->						List[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=snake_case      ,							initializer_range=self.initializer_range      ,							)



		def 					A_		( self  :    Tuple      ,							snake_case  :    List[Any]      ,							snake_case  :    Any      ,							snake_case  :    Optional[int]      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Any      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Union[str, Any]				)			->						Dict:






									'''simple docstring'''

									A         =					LlamaModel(config=snake_case				)
									model.to(snake_case				)
									model.eval()
									A         =					model(snake_case      ,							attention_mask=snake_case				)
									A         =					model(snake_case				)
									self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.seq_length, self.hidden_size)				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    int      ,							snake_case  :    List[Any]      ,							snake_case  :    Any      ,							snake_case  :    Tuple      ,							snake_case  :    Optional[Any]      ,							snake_case  :    List[str]      ,							snake_case  :    Dict      ,							snake_case  :    List[Any]      ,							snake_case  :    Optional[Any]      ,							)			->						List[str]:






									'''simple docstring'''

									A         =					True
									A         =					LlamaModel(snake_case				)
									model.to(snake_case				)
									model.eval()
									A         =					model(
									    snake_case      ,							attention_mask=snake_case      ,							encoder_hidden_states=snake_case      ,							encoder_attention_mask=snake_case      ,							)
									A         =					model(
									    snake_case      ,							attention_mask=snake_case      ,							encoder_hidden_states=snake_case      ,							)
									A         =					model(snake_case      ,							attention_mask=snake_case				)
									self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.seq_length, self.hidden_size)				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    Optional[int]      ,							snake_case  :    Dict      ,							snake_case  :    Dict      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    int      ,							snake_case  :    str      ,							snake_case  :    Tuple      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Tuple      ,							)			->						Any:






									'''simple docstring'''

									A         =					LlamaForCausalLM(config=snake_case				)
									model.to(snake_case				)
									model.eval()
									A         =					model(snake_case      ,							attention_mask=snake_case      ,							labels=snake_case				)
									self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.vocab_size)				)



		def 					A_		( self  :    int      ,							snake_case  :    Dict      ,							snake_case  :    Tuple      ,							snake_case  :    Dict      ,							snake_case  :    Optional[int]      ,							snake_case  :    List[str]      ,							snake_case  :    List[str]      ,							snake_case  :    str      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Any      ,							)			->						int:






									'''simple docstring'''

									A         =					True
									A         =					True
									A         =					LlamaForCausalLM(config=snake_case				)
									model.to(snake_case				)
									model.eval()

									# first forward pass
									A         =					model(
									    snake_case      ,							attention_mask=snake_case      ,							encoder_hidden_states=snake_case      ,							encoder_attention_mask=snake_case      ,							use_cache=snake_case      ,							)
									A         =					outputs.past_key_values

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

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

									A         =					model(
									    snake_case      ,							attention_mask=snake_case      ,							encoder_hidden_states=snake_case      ,							encoder_attention_mask=snake_case      ,							output_hidden_states=snake_case      ,							)['hidden_states'][0]
									A         =					model(
									    snake_case      ,							attention_mask=snake_case      ,							encoder_hidden_states=snake_case      ,							encoder_attention_mask=snake_case      ,							past_key_values=snake_case      ,							output_hidden_states=snake_case      ,							)['hidden_states'][0]

									# select random slice
									A         =					ids_tensor((1,)      ,							output_from_past.shape[-1]				).item()
									A         =					output_from_no_past[:, -3:, random_slice_idx].detach()
									A         =					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(snake_case      ,							snake_case      ,							atol=1E-3				)				)



		def 					A_		( self  :    Dict				)			->						List[str]:






									'''simple docstring'''

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






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



		def 					A_		( self  :    int				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					LlamaModelTester(self				)
									A         =					ConfigTester(self      ,							config_class=snake_case      ,							hidden_size=37				)



		def 					A_		( self  :    Optional[int]				)			->						Tuple:






									'''simple docstring'''

									self.config_tester.run_common_tests()



		def 					A_		( self  :    Dict				)			->						str:






									'''simple docstring'''

									A         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case				)



		def 					A_		( self  :    Union[str, Any]				)			->						List[Any]:






									'''simple docstring'''

									A         =					self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
																A         =					type
																self.model_tester.create_and_check_model(*snake_case				)



		def 					A_		( self  :    List[Any]				)			->						List[Any]:






									'''simple docstring'''

									A      ,  A         =					self.model_tester.prepare_config_and_inputs_for_common()
									A         =					3
									A         =					input_dict['input_ids']
									A         =					input_ids.ne(1				).to(snake_case				)
									A         =					ids_tensor([self.model_tester.batch_size]      ,							self.model_tester.type_sequence_label_size				)
									A         =					LlamaForSequenceClassification(snake_case				)
									model.to(snake_case				)
									model.eval()
									A         =					model(snake_case      ,							attention_mask=snake_case      ,							labels=snake_case				)
									self.assertEqual(result.logits.shape      ,							(self.model_tester.batch_size, self.model_tester.num_labels)				)



		def 					A_		( self  :    int				)			->						Optional[Any]:






									'''simple docstring'''

									A      ,  A         =					self.model_tester.prepare_config_and_inputs_for_common()
									A         =					3
									A         =					'single_label_classification'
									A         =					input_dict['input_ids']
									A         =					input_ids.ne(1				).to(snake_case				)
									A         =					ids_tensor([self.model_tester.batch_size]      ,							self.model_tester.type_sequence_label_size				)
									A         =					LlamaForSequenceClassification(snake_case				)
									model.to(snake_case				)
									model.eval()
									A         =					model(snake_case      ,							attention_mask=snake_case      ,							labels=snake_case				)
									self.assertEqual(result.logits.shape      ,							(self.model_tester.batch_size, self.model_tester.num_labels)				)



		def 					A_		( self  :    str				)			->						Tuple:






									'''simple docstring'''

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






									'''simple docstring'''

									pass



		@parameterized.expand([('linear',), ('dynamic',)]				)
		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    str				)			->						str:






									'''simple docstring'''

									A      ,  A         =					self.model_tester.prepare_config_and_inputs_for_common()
									A         =					ids_tensor([1, 10]      ,							config.vocab_size				)
									A         =					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         =					LlamaModel(snake_case				)
									original_model.to(snake_case				)
									original_model.eval()
									A         =					original_model(snake_case				).last_hidden_state
									A         =					original_model(snake_case				).last_hidden_state

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

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






@require_torch
class      UpperCAmelCase__		(      unittest.TestCase     ):



		@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'				)
		@slow
		def 					A_		( self  :    Union[str, Any]				)			->						Any:






									'''simple docstring'''

									A         =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
									A         =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'      ,							device_map='auto'				)
									A         =					model(torch.tensor([input_ids]				)				)
									# Expected mean on dim = -1
									A         =					torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]				)
									torch.testing.assert_close(out.mean(-1				)      ,							snake_case      ,							atol=1E-2      ,							rtol=1E-2				)
									# slicing logits[0, 0, 0:30]
									# fmt: off
									A         =					torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]				)
									# fmt: on
									torch.testing.assert_close(out[0, 0, :30]      ,							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 					A_		( self  :    List[str]				)			->						Dict:






									'''simple docstring'''

									A         =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
									A         =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'      ,							device_map='auto'				)
									A         =					model(torch.tensor(snake_case				)				)
									# Expected mean on dim = -1
									A         =					torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]				)
									torch.testing.assert_close(out.mean(-1				)      ,							snake_case      ,							atol=1E-2      ,							rtol=1E-2				)
									# slicing logits[0, 0, 0:30]
									# fmt: off
									A         =					torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]				)
									# fmt: on
									torch.testing.assert_close(out[0, 0, :30]      ,							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 					A_		( self  :    Dict				)			->						Optional[int]:






									'''simple docstring'''

									A         =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
									A         =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'      ,							device_map='auto'				)
									A         =					model(torch.tensor(snake_case				)				)
									# Expected mean on dim = -1
									A         =					torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]				)
									torch.testing.assert_close(out.mean(-1				)      ,							snake_case      ,							atol=1E-2      ,							rtol=1E-2				)
									# slicing logits[0, 0, 0:30]
									# fmt: off
									A         =					torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]				)
									# fmt: on
									torch.testing.assert_close(out.mean(-1				)      ,							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 					A_		( self  :    Dict				)			->						List[Any]:






									'''simple docstring'''

									A         =					[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
									A         =					LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'      ,							device_map='auto'				)
									A         =					model(torch.tensor(snake_case				)				)

									A         =					torch.tensor(
									    [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]      ,							dtype=torch.floataa				)
									torch.testing.assert_close(out.mean(-1				)      ,							snake_case      ,							atol=1E-2      ,							rtol=1E-2				)
									# fmt: off
									A         =					torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]				)
									# fmt: on
									torch.testing.assert_close(out[0, 0, :30]      ,							snake_case      ,							atol=1E-5      ,							rtol=1E-5				)



		@unittest.skip('Model is curently gated'				)
		@slow
		def 					A_		( self  :    Optional[Any]				)			->						List[str]:






									'''simple docstring'''

									A         =					'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         =					'Simply put, the theory of relativity states that '
									A         =					LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'				)
									A         =					tokenizer.encode(snake_case      ,							return_tensors='pt'				)
									A         =					LlamaForCausalLM.from_pretrained(
									    'meta-llama/Llama-2-13b-chat-hf'      ,							device_map='sequential'      ,							use_safetensors=snake_case				)

									# greedy generation outputs
									A         =					model.generate(snake_case      ,							max_new_tokens=64      ,							top_p=snake_case      ,							temperature=1      ,							do_sample=snake_case				)
									A         =					tokenizer.decode(generated_ids[0]      ,							skip_special_tokens=snake_case				)
									self.assertEqual(snake_case      ,							snake_case				)











"""simple docstring"""







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import (
							    AutoProcessor,
							    BertTokenizerFast,
							    BlipImageProcessor,
							    GPTaTokenizer,
							    InstructBlipProcessor,
							    PreTrainedTokenizerFast,
							)






@require_vision
class      UpperCAmelCase__		(      unittest.TestCase     ):



		def 					A_		( self  :    Dict				)			->						Dict:






									'''simple docstring'''

									A         =					tempfile.mkdtemp()

									A         =					BlipImageProcessor()
									A         =					GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'				)
									A         =					BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert'				)

									A         =					InstructBlipProcessor(snake_case      ,							snake_case      ,							snake_case				)

									processor.save_pretrained(self.tmpdirname				)



		def 					A_		( self  :    List[str]      ,							**snake_case  :    str				)			->						Dict:






									'''simple docstring'''

									return AutoProcessor.from_pretrained(self.tmpdirname      ,							**snake_case				).tokenizer



		def 					A_		( self  :    int      ,							**snake_case  :    Optional[Any]				)			->						Any:






									'''simple docstring'''

									return AutoProcessor.from_pretrained(self.tmpdirname      ,							**snake_case				).image_processor



		def 					A_		( self  :    Any      ,							**snake_case  :    Union[str, Any]				)			->						Any:






									'''simple docstring'''

									return AutoProcessor.from_pretrained(self.tmpdirname      ,							**snake_case				).qformer_tokenizer



		def 					A_		( self  :    int				)			->						Union[str, Any]:






									'''simple docstring'''

									shutil.rmtree(self.tmpdirname				)



		def 					A_		( self  :    List[Any]				)			->						Tuple:






									'''simple docstring'''

									A         =					[np.random.randint(255      ,							size=(3, 30, 400)      ,							dtype=np.uinta				)]

									A         =					[Image.fromarray(np.moveaxis(snake_case      ,							0      ,							-1				)				) for x in image_inputs]

									return image_inputs



		def 					A_		( self  :    List[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					InstructBlipProcessor(
									    tokenizer=self.get_tokenizer()      ,							image_processor=self.get_image_processor()      ,							qformer_tokenizer=self.get_qformer_tokenizer()      ,							)
									processor.save_pretrained(self.tmpdirname				)

									A         =					self.get_tokenizer(bos_token='(BOS)'      ,							eos_token='(EOS)'				)
									A         =					self.get_image_processor(do_normalize=snake_case      ,							padding_value=1.0				)

									A         =					InstructBlipProcessor.from_pretrained(
									    self.tmpdirname      ,							bos_token='(BOS)'      ,							eos_token='(EOS)'      ,							do_normalize=snake_case      ,							padding_value=1.0				)

									self.assertEqual(processor.tokenizer.get_vocab()      ,							tokenizer_add_kwargs.get_vocab()				)
									self.assertIsInstance(processor.tokenizer      ,							snake_case				)

									self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor_add_kwargs.to_json_string()				)
									self.assertIsInstance(processor.image_processor      ,							snake_case				)
									self.assertIsInstance(processor.qformer_tokenizer      ,							snake_case				)



		def 					A_		( self  :    Optional[Any]				)			->						Dict:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					self.prepare_image_inputs()

									A         =					image_processor(snake_case      ,							return_tensors='np'				)
									A         =					processor(images=snake_case      ,							return_tensors='np'				)

									for key in input_feat_extract.keys():
																self.assertAlmostEqual(input_feat_extract[key].sum()      ,							input_processor[key].sum()      ,							delta=1E-2				)



		def 					A_		( self  :    Tuple				)			->						List[str]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					'lower newer'

									A         =					processor(text=snake_case				)

									A         =					tokenizer(snake_case      ,							return_token_type_ids=snake_case				)
									A         =					qformer_tokenizer(snake_case      ,							return_token_type_ids=snake_case				)

									for key in encoded_tokens.keys():
																self.assertListEqual(encoded_tokens[key]      ,							encoded_processor[key]				)

									for key in encoded_tokens_qformer.keys():
																self.assertListEqual(encoded_tokens_qformer[key]      ,							encoded_processor['qformer_' + key]				)



		def 					A_		( self  :    List[Any]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					'lower newer'
									A         =					self.prepare_image_inputs()

									A         =					processor(text=snake_case      ,							images=snake_case				)

									self.assertListEqual(
									    list(inputs.keys()				)      ,							['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values']      ,							)

									# test if it raises when no input is passed
									with pytest.raises(snake_case				):
																processor()



		def 					A_		( self  :    Optional[Any]				)			->						List[str]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									A         =					processor.batch_decode(snake_case				)
									A         =					tokenizer.batch_decode(snake_case				)

									self.assertListEqual(snake_case      ,							snake_case				)



		def 					A_		( self  :    Tuple				)			->						List[Any]:






									'''simple docstring'''

									A         =					self.get_image_processor()
									A         =					self.get_tokenizer()
									A         =					self.get_qformer_tokenizer()

									A         =					InstructBlipProcessor(
									    tokenizer=snake_case      ,							image_processor=snake_case      ,							qformer_tokenizer=snake_case				)

									A         =					'lower newer'
									A         =					self.prepare_image_inputs()

									A         =					processor(text=snake_case      ,							images=snake_case				)

									self.assertListEqual(
									    list(inputs.keys()				)      ,							['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values']      ,							)



