


'''simple docstring'''



import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

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 (
		    FlaubertForMultipleChoice,
		    FlaubertForQuestionAnswering,
		    FlaubertForQuestionAnsweringSimple,
		    FlaubertForSequenceClassification,
		    FlaubertForTokenClassification,
		    FlaubertModel,
		    FlaubertWithLMHeadModel,
		)
		from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST


class 		_snake_case       (  a__						):




					def __init__(   self				,				_lowerCamelCase				,				_lowerCamelCase=13				,				_lowerCamelCase=7				,				_lowerCamelCase=True				,				_lowerCamelCase=True				,				_lowerCamelCase=True				,				_lowerCamelCase=True				,				_lowerCamelCase=True				,				_lowerCamelCase=False				,				_lowerCamelCase=False				,				_lowerCamelCase=False				,				_lowerCamelCase=2				,				_lowerCamelCase=99				,				_lowerCamelCase=0				,				_lowerCamelCase=32				,				_lowerCamelCase=5				,				_lowerCamelCase=4				,				_lowerCamelCase=0.1				,				_lowerCamelCase=0.1				,				_lowerCamelCase=512				,				_lowerCamelCase=12				,				_lowerCamelCase=2				,				_lowerCamelCase=0.02				,				_lowerCamelCase=3				,				_lowerCamelCase=4				,				_lowerCamelCase="last"				,				_lowerCamelCase=None				,				_lowerCamelCase=None				,				):
								UpperCAmelCase__	:       Tuple   =  parent
								UpperCAmelCase__	:       Optional[Any]   =  batch_size
								UpperCAmelCase__	:       Dict   =  seq_length
								UpperCAmelCase__	:       Optional[int]   =  is_training
								UpperCAmelCase__	:       Tuple   =  use_input_lengths
								UpperCAmelCase__	:       List[str]   =  use_token_type_ids
								UpperCAmelCase__	:       str   =  use_labels
								UpperCAmelCase__	:       List[str]   =  gelu_activation
								UpperCAmelCase__	:       List[Any]   =  sinusoidal_embeddings
								UpperCAmelCase__	:       Any   =  causal
								UpperCAmelCase__	:       Union[str, Any]   =  asm
								UpperCAmelCase__	:       int   =  n_langs
								UpperCAmelCase__	:       Any   =  vocab_size
								UpperCAmelCase__	:       List[Any]   =  n_special
								UpperCAmelCase__	:       int   =  hidden_size
								UpperCAmelCase__	:       Tuple   =  num_hidden_layers
								UpperCAmelCase__	:       str   =  num_attention_heads
								UpperCAmelCase__	:       str   =  hidden_dropout_prob
								UpperCAmelCase__	:       Optional[Any]   =  attention_probs_dropout_prob
								UpperCAmelCase__	:       Dict   =  max_position_embeddings
								UpperCAmelCase__	:       Optional[int]   =  type_vocab_size
								UpperCAmelCase__	:       str   =  type_sequence_label_size
								UpperCAmelCase__	:       str   =  initializer_range
								UpperCAmelCase__	:       Optional[Any]   =  num_labels
								UpperCAmelCase__	:       List[Any]   =  num_choices
								UpperCAmelCase__	:       str   =  summary_type
								UpperCAmelCase__	:       Tuple   =  use_proj
								UpperCAmelCase__	:       Tuple   =  scope




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       int   =  ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size)
								UpperCAmelCase__	:       Union[str, Any]   =  random_attention_mask([self.batch_size, self.seq_length])

								UpperCAmelCase__	:       Union[str, Any]   =  None
								if self.use_input_lengths:
											UpperCAmelCase__	:       Any   =  (
											    ids_tensor([self.batch_size]				,				vocab_size=2) + self.seq_length - 2
											)  # small variation of seq_length

								UpperCAmelCase__	:       str   =  None
								if self.use_token_type_ids:
											UpperCAmelCase__	:       str   =  ids_tensor([self.batch_size, self.seq_length]				,				self.n_langs)

								UpperCAmelCase__	:       List[Any]   =  None
								UpperCAmelCase__	:       Tuple   =  None
								UpperCAmelCase__	:       str   =  None
								if self.use_labels:
											UpperCAmelCase__	:       Dict   =  ids_tensor([self.batch_size]				,				self.type_sequence_label_size)
											UpperCAmelCase__	:       Optional[Any]   =  ids_tensor([self.batch_size, self.seq_length]				,				self.num_labels)
											UpperCAmelCase__	:       Tuple   =  ids_tensor([self.batch_size]				,				2).float()
											UpperCAmelCase__	:       List[str]   =  ids_tensor([self.batch_size]				,				self.num_choices)

								UpperCAmelCase__	:       Dict   =  self.get_config()

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_lengths,
								    sequence_labels,
								    token_labels,
								    is_impossible_labels,
								    choice_labels,
								    input_mask,
								)




					def 					snake_case__    (   self):
								return FlaubertConfig(
								    vocab_size=self.vocab_size				,				n_special=self.n_special				,				emb_dim=self.hidden_size				,				n_layers=self.num_hidden_layers				,				n_heads=self.num_attention_heads				,				dropout=self.hidden_dropout_prob				,				attention_dropout=self.attention_probs_dropout_prob				,				gelu_activation=self.gelu_activation				,				sinusoidal_embeddings=self.sinusoidal_embeddings				,				asm=self.asm				,				causal=self.causal				,				n_langs=self.n_langs				,				max_position_embeddings=self.max_position_embeddings				,				initializer_range=self.initializer_range				,				summary_type=self.summary_type				,				use_proj=self.use_proj				,				)




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				):
								UpperCAmelCase__	:       List[str]   =  FlaubertModel(config=__UpperCAmelCase)
								model.to(__UpperCAmelCase)
								model.eval()
								UpperCAmelCase__	:       Any   =  model(__UpperCAmelCase				,				lengths=__UpperCAmelCase				,				langs=__UpperCAmelCase)
								UpperCAmelCase__	:       Dict   =  model(__UpperCAmelCase				,				langs=__UpperCAmelCase)
								UpperCAmelCase__	:       Dict   =  model(__UpperCAmelCase)
								self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size))




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				):
								UpperCAmelCase__	:       int   =  FlaubertWithLMHeadModel(__UpperCAmelCase)
								model.to(__UpperCAmelCase)
								model.eval()

								UpperCAmelCase__	:       List[Any]   =  model(__UpperCAmelCase				,				token_type_ids=__UpperCAmelCase				,				labels=__UpperCAmelCase)
								self.parent.assertEqual(result.loss.shape				,				())
								self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size))




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				):
								UpperCAmelCase__	:       str   =  FlaubertForQuestionAnsweringSimple(__UpperCAmelCase)
								model.to(__UpperCAmelCase)
								model.eval()

								UpperCAmelCase__	:       List[Any]   =  model(__UpperCAmelCase)

								UpperCAmelCase__	:       int   =  model(__UpperCAmelCase				,				start_positions=__UpperCAmelCase				,				end_positions=__UpperCAmelCase)
								self.parent.assertEqual(result.start_logits.shape				,				(self.batch_size, self.seq_length))
								self.parent.assertEqual(result.end_logits.shape				,				(self.batch_size, self.seq_length))




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				):
								UpperCAmelCase__	:       List[str]   =  FlaubertForQuestionAnswering(__UpperCAmelCase)
								model.to(__UpperCAmelCase)
								model.eval()

								UpperCAmelCase__	:       Any   =  model(__UpperCAmelCase)

								UpperCAmelCase__	:       Optional[int]   =  model(
								    __UpperCAmelCase				,				start_positions=__UpperCAmelCase				,				end_positions=__UpperCAmelCase				,				cls_index=__UpperCAmelCase				,				is_impossible=__UpperCAmelCase				,				p_mask=__UpperCAmelCase				,				)

								UpperCAmelCase__	:       Union[str, Any]   =  model(
								    __UpperCAmelCase				,				start_positions=__UpperCAmelCase				,				end_positions=__UpperCAmelCase				,				cls_index=__UpperCAmelCase				,				is_impossible=__UpperCAmelCase				,				)

								(UpperCAmelCase__							)	:       Dict   =  result_with_labels.to_tuple()

								UpperCAmelCase__	:       List[Any]   =  model(__UpperCAmelCase				,				start_positions=__UpperCAmelCase				,				end_positions=__UpperCAmelCase)

								(UpperCAmelCase__							)	:       int   =  result_with_labels.to_tuple()

								self.parent.assertEqual(result_with_labels.loss.shape				,				())
								self.parent.assertEqual(result.start_top_log_probs.shape				,				(self.batch_size, model.config.start_n_top))
								self.parent.assertEqual(result.start_top_index.shape				,				(self.batch_size, model.config.start_n_top))
								self.parent.assertEqual(
								    result.end_top_log_probs.shape				,				(self.batch_size, model.config.start_n_top * model.config.end_n_top))
								self.parent.assertEqual(
								    result.end_top_index.shape				,				(self.batch_size, model.config.start_n_top * model.config.end_n_top))
								self.parent.assertEqual(result.cls_logits.shape				,				(self.batch_size,))




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				):
								UpperCAmelCase__	:       List[str]   =  FlaubertForSequenceClassification(__UpperCAmelCase)
								model.to(__UpperCAmelCase)
								model.eval()

								UpperCAmelCase__	:       Dict   =  model(__UpperCAmelCase)
								UpperCAmelCase__	:       Optional[Any]   =  model(__UpperCAmelCase				,				labels=__UpperCAmelCase)

								self.parent.assertEqual(result.loss.shape				,				())
								self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.type_sequence_label_size))




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				):
								UpperCAmelCase__	:       int   =  self.num_labels
								UpperCAmelCase__	:       Dict   =  FlaubertForTokenClassification(__UpperCAmelCase)
								model.to(__UpperCAmelCase)
								model.eval()

								UpperCAmelCase__	:       List[str]   =  model(__UpperCAmelCase				,				attention_mask=__UpperCAmelCase				,				labels=__UpperCAmelCase)
								self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.num_labels))




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				):
								UpperCAmelCase__	:       List[str]   =  self.num_choices
								UpperCAmelCase__	:       Union[str, Any]   =  FlaubertForMultipleChoice(config=__UpperCAmelCase)
								model.to(__UpperCAmelCase)
								model.eval()
								UpperCAmelCase__	:       int   =  input_ids.unsqueeze(1).expand(-1				,				self.num_choices				,				-1).contiguous()
								UpperCAmelCase__	:       Dict   =  token_type_ids.unsqueeze(1).expand(-1				,				self.num_choices				,				-1).contiguous()
								UpperCAmelCase__	:       Optional[int]   =  input_mask.unsqueeze(1).expand(-1				,				self.num_choices				,				-1).contiguous()
								UpperCAmelCase__	:       List[Any]   =  model(
								    __UpperCAmelCase				,				attention_mask=__UpperCAmelCase				,				token_type_ids=__UpperCAmelCase				,				labels=__UpperCAmelCase				,				)
								self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_choices))




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       Optional[int]   =  self.prepare_config_and_inputs()
								(
								    UpperCAmelCase__							
								)	:       List[Any]   =  config_and_inputs
								UpperCAmelCase__	:       Union[str, Any]   =  {
								    'input_ids': input_ids,
								    'token_type_ids': token_type_ids,
								    'lengths': input_lengths,
								    'attention_mask': input_mask,
								}
								return config, inputs_dict


@require_torch
class 		_snake_case       (  a__							, a__							, unittest.TestCase						):
					lowerCAmelCase   :Optional[int]				=       (
					    (
					        FlaubertModel,
					        FlaubertWithLMHeadModel,
					        FlaubertForQuestionAnswering,
					        FlaubertForQuestionAnsweringSimple,
					        FlaubertForSequenceClassification,
					        FlaubertForTokenClassification,
					        FlaubertForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					lowerCAmelCase   :List[str]				=       (
					    {
					        """feature-extraction""": FlaubertModel,
					        """fill-mask""": FlaubertWithLMHeadModel,
					        """question-answering""": FlaubertForQuestionAnsweringSimple,
					        """text-classification""": FlaubertForSequenceClassification,
					        """token-classification""": FlaubertForTokenClassification,
					        """zero-shot""": FlaubertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase):

								if (
								    pipeline_test_casse_name == "QAPipelineTests"
								    and tokenizer_name is not None
								    and not tokenizer_name.endswith("""Fast""")
								):
											# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
											# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
											# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
											return True

								return False




					def 					snake_case__    (   self				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase=False):
								UpperCAmelCase__	:       Optional[Any]   =  super()._prepare_for_class(__UpperCAmelCase				,				__UpperCAmelCase				,				return_labels=__UpperCAmelCase)

								if return_labels:
											if model_class.__name__ == "FlaubertForQuestionAnswering":
														UpperCAmelCase__	:       List[Any]   =  torch.zeros(
														    self.model_tester.batch_size				,				dtype=torch.long				,				device=__UpperCAmelCase)
														UpperCAmelCase__	:       Any   =  torch.zeros(
														    self.model_tester.batch_size				,				dtype=torch.long				,				device=__UpperCAmelCase)

								return inputs_dict




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       str   =  FlaubertModelTester(self)
								UpperCAmelCase__	:       Dict   =  ConfigTester(self				,				config_class=__UpperCAmelCase				,				emb_dim=37)




					def 					snake_case__    (   self):
								self.config_tester.run_common_tests()




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       Union[str, Any]   =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_model(*__UpperCAmelCase)




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       List[str]   =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_lm_head(*__UpperCAmelCase)




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       Tuple   =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_simple_qa(*__UpperCAmelCase)




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       Tuple   =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_qa(*__UpperCAmelCase)




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       int   =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_sequence_classif(*__UpperCAmelCase)




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       List[Any]   =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_token_classif(*__UpperCAmelCase)




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       Optional[int]   =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_multiple_choice(*__UpperCAmelCase)




					@slow
					def 					snake_case__    (   self):

								for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCAmelCase__	:       Optional[int]   =  FlaubertModel.from_pretrained(__UpperCAmelCase)
											self.assertIsNotNone(__UpperCAmelCase)




					@slow
					@require_torch_gpu
					def 					snake_case__    (   self):
								UpperCAmelCase__	:       Optional[Any]   =  self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
											# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
											if model_class == FlaubertForMultipleChoice:
														return

											UpperCAmelCase__	:       List[str]   =  True
											UpperCAmelCase__	:       Optional[int]   =  model_class(config=__UpperCAmelCase)

											UpperCAmelCase__	:       Dict   =  self._prepare_for_class(__UpperCAmelCase				,				__UpperCAmelCase)
											UpperCAmelCase__	:       Tuple   =  torch.jit.trace(
											    __UpperCAmelCase				,				(inputs_dict["""input_ids"""].to("""cpu"""), inputs_dict["""attention_mask"""].to("""cpu""")))

											with tempfile.TemporaryDirectory() as tmp:
														torch.jit.save(__UpperCAmelCase				,				os.path.join(__UpperCAmelCase				,				"""traced_model.pt"""))
														UpperCAmelCase__	:       Optional[Any]   =  torch.jit.load(os.path.join(__UpperCAmelCase				,				"""traced_model.pt""")				,				map_location=__UpperCAmelCase)
														loaded(inputs_dict["""input_ids"""].to(__UpperCAmelCase)				,				inputs_dict["""attention_mask"""].to(__UpperCAmelCase))


@require_torch
class 		_snake_case       (  unittest.TestCase						):




					@slow
					def 					snake_case__    (   self):
								UpperCAmelCase__	:       Optional[int]   =  FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased""")
								UpperCAmelCase__	:       Optional[int]   =  torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
								with torch.no_grad():
											UpperCAmelCase__	:       Optional[int]   =  model(__UpperCAmelCase)[0]
								UpperCAmelCase__	:       Union[str, Any]   =  torch.Size((1, 11, 768))
								self.assertEqual(output.shape				,				__UpperCAmelCase)
								UpperCAmelCase__	:       Tuple   =  torch.tensor(
								    [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]])

								self.assertTrue(torch.allclose(output[:, :3, :3]				,				__UpperCAmelCase				,				atol=1e-4))






"""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    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				tempfile.mkdtemp()

         lowerCAmelCase__     :List[Any]											=				BlipImageProcessor()
         lowerCAmelCase__     :Union[str, Any]											=				GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model' )
         lowerCAmelCase__     :Union[str, Any]											=				BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert' )

         lowerCAmelCase__     :List[str]											=				InstructBlipProcessor(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

         processor.save_pretrained(self.tmpdirname )






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

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






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

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






       def 	snake_case		(       self       ,					**__UpperCAmelCase ):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         shutil.rmtree(self.tmpdirname )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				[np.random.randint(2_5_5       ,					size=(3, 3_0, 4_0_0)       ,					dtype=np.uinta )]

         lowerCAmelCase__     :Dict											=				[Image.fromarray(np.moveaxis(__UpperCAmelCase       ,					0       ,					-1 ) ) for x in image_inputs]

         return image_inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				InstructBlipProcessor(
             tokenizer=self.get_tokenizer()       ,					image_processor=self.get_image_processor()       ,					qformer_tokenizer=self.get_qformer_tokenizer()       ,					)
         processor.save_pretrained(self.tmpdirname )

         lowerCAmelCase__     :Tuple											=				self.get_tokenizer(bos_token='(BOS)'       ,					eos_token='(EOS)' )
         lowerCAmelCase__     :str											=				self.get_image_processor(do_normalize=__UpperCAmelCase       ,					padding_value=1.0 )

         lowerCAmelCase__     :Dict											=				InstructBlipProcessor.from_pretrained(
             self.tmpdirname       ,					bos_token='(BOS)'       ,					eos_token='(EOS)'       ,					do_normalize=__UpperCAmelCase       ,					padding_value=1.0 )

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

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.get_image_processor()
         lowerCAmelCase__     :List[Any]											=				self.get_tokenizer()
         lowerCAmelCase__     :Optional[int]											=				self.get_qformer_tokenizer()

         lowerCAmelCase__     :str											=				InstructBlipProcessor(
             tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase       ,					qformer_tokenizer=__UpperCAmelCase )

         lowerCAmelCase__     :Dict											=				self.prepare_image_inputs()

         lowerCAmelCase__     :List[str]											=				image_processor(__UpperCAmelCase       ,					return_tensors='np' )
         lowerCAmelCase__     :Optional[Any]											=				processor(images=__UpperCAmelCase       ,					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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				self.get_image_processor()
         lowerCAmelCase__     :Optional[int]											=				self.get_tokenizer()
         lowerCAmelCase__     :Optional[int]											=				self.get_qformer_tokenizer()

         lowerCAmelCase__     :Any											=				InstructBlipProcessor(
             tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase       ,					qformer_tokenizer=__UpperCAmelCase )

         lowerCAmelCase__     :Optional[int]											=				'lower newer'

         lowerCAmelCase__     :Dict											=				processor(text=__UpperCAmelCase )

         lowerCAmelCase__     :Union[str, Any]											=				tokenizer(__UpperCAmelCase       ,					return_token_type_ids=__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				qformer_tokenizer(__UpperCAmelCase       ,					return_token_type_ids=__UpperCAmelCase )

         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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.get_image_processor()
         lowerCAmelCase__     :Optional[int]											=				self.get_tokenizer()
         lowerCAmelCase__     :str											=				self.get_qformer_tokenizer()

         lowerCAmelCase__     :Dict											=				InstructBlipProcessor(
             tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase       ,					qformer_tokenizer=__UpperCAmelCase )

         lowerCAmelCase__     :Union[str, Any]											=				'lower newer'
         lowerCAmelCase__     :Dict											=				self.prepare_image_inputs()

         lowerCAmelCase__     :Tuple											=				processor(text=__UpperCAmelCase       ,					images=__UpperCAmelCase )

         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(__UpperCAmelCase ):
           processor()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				self.get_image_processor()
         lowerCAmelCase__     :Dict											=				self.get_tokenizer()
         lowerCAmelCase__     :Optional[int]											=				self.get_qformer_tokenizer()

         lowerCAmelCase__     :int											=				InstructBlipProcessor(
             tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase       ,					qformer_tokenizer=__UpperCAmelCase )

         lowerCAmelCase__     :List[str]											=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         lowerCAmelCase__     :Tuple											=				processor.batch_decode(__UpperCAmelCase )
         lowerCAmelCase__     :Optional[int]											=				tokenizer.batch_decode(__UpperCAmelCase )

         self.assertListEqual(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				self.get_image_processor()
         lowerCAmelCase__     :Optional[int]											=				self.get_tokenizer()
         lowerCAmelCase__     :Dict											=				self.get_qformer_tokenizer()

         lowerCAmelCase__     :Optional[Any]											=				InstructBlipProcessor(
             tokenizer=__UpperCAmelCase       ,					image_processor=__UpperCAmelCase       ,					qformer_tokenizer=__UpperCAmelCase )

         lowerCAmelCase__     :Optional[int]											=				'lower newer'
         lowerCAmelCase__     :Optional[int]											=				self.prepare_image_inputs()

         lowerCAmelCase__     :int											=				processor(text=__UpperCAmelCase       ,					images=__UpperCAmelCase )

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