





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
lowercase      :       List[Any]									=  {"""LayoutLMv2Config""", """LayoutLMv3Config"""}





@is_pipeline_test
class  A__    (							unittest.TestCase	):




   """simple docstring"""



   __A   :				Optional[int]         =  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
   __A   :				Optional[int]         =  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

   if model_mapping is not None:
      __A   :				List[Any]         =  {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
   if tf_model_mapping is not None:
      __A   :				List[Any]         =  {
          config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
      }

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Tuple:







          '''simple docstring'''

          a__						:	List[str] =				ZeroShotClassificationPipeline(
              model=__A					,  tokenizer=__A					,  candidate_labels=['polics', 'health'])
          return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						int:







          '''simple docstring'''

          a__						:	str =				classifier('Who are you voting for in 2020?'					,  candidate_labels='politics')
          self.assertEqual(__A					,  {'sequence': ANY(__A), 'labels': [ANY(__A)], 'scores': [ANY(__A)]})

          # No kwarg
          a__						:	Any =				classifier('Who are you voting for in 2020?'					,  ['politics'])
          self.assertEqual(__A					,  {'sequence': ANY(__A), 'labels': [ANY(__A)], 'scores': [ANY(__A)]})

          a__						:	Optional[int] =				classifier('Who are you voting for in 2020?'					,  candidate_labels=['politics'])
          self.assertEqual(__A					,  {'sequence': ANY(__A), 'labels': [ANY(__A)], 'scores': [ANY(__A)]})

          a__						:	Any =				classifier('Who are you voting for in 2020?'					,  candidate_labels='politics, public health')
          self.assertEqual(
              __A					,  {'sequence': ANY(__A), 'labels': [ANY(__A), ANY(__A)], 'scores': [ANY(__A), ANY(__A)]})
          self.assertAlmostEqual(sum(nested_simplify(outputs['scores']))					,  1.0)

          a__						:	List[str] =				classifier('Who are you voting for in 2020?'					,  candidate_labels=['politics', 'public health'])
          self.assertEqual(
              __A					,  {'sequence': ANY(__A), 'labels': [ANY(__A), ANY(__A)], 'scores': [ANY(__A), ANY(__A)]})
          self.assertAlmostEqual(sum(nested_simplify(outputs['scores']))					,  1.0)

          a__						:	List[Any] =				classifier(
              'Who are you voting for in 2020?'					,  candidate_labels='politics'					,  hypothesis_template='This text is about {}')
          self.assertEqual(__A					,  {'sequence': ANY(__A), 'labels': [ANY(__A)], 'scores': [ANY(__A)]})

          # https://github.com/huggingface/transformers/issues/13846
          a__						:	str =				classifier(['I am happy']					,  ['positive', 'negative'])
          self.assertEqual(
              __A					,  [
                  {'sequence': ANY(__A), 'labels': [ANY(__A), ANY(__A)], 'scores': [ANY(__A), ANY(__A)]}
                  for i in range(1)
              ]					,  )
          a__						:	str =				classifier(['I am happy', 'I am sad']					,  ['positive', 'negative'])
          self.assertEqual(
              __A					,  [
                  {'sequence': ANY(__A), 'labels': [ANY(__A), ANY(__A)], 'scores': [ANY(__A), ANY(__A)]}
                  for i in range(2)
              ]					,  )

          with self.assertRaises(__A):
                 classifier(''					,  candidate_labels='politics')

          with self.assertRaises(__A):
                 classifier(__A					,  candidate_labels='politics')

          with self.assertRaises(__A):
                 classifier('Who are you voting for in 2020?'					,  candidate_labels='')

          with self.assertRaises(__A):
                 classifier('Who are you voting for in 2020?'					,  candidate_labels=__A)

          with self.assertRaises(__A):
                 classifier(
                     'Who are you voting for in 2020?'					,  candidate_labels='politics'					,  hypothesis_template='Not formatting template'					,  )

          with self.assertRaises(__A):
                 classifier(
                     'Who are you voting for in 2020?'					,  candidate_labels='politics'					,  hypothesis_template=__A					,  )

          self.run_entailment_id(__A)

   def 							__lowercase		(					self					,  lowercase)			->						str:







          '''simple docstring'''

          a__						:	Tuple =				zero_shot_classifier.model.config
          a__						:	List[Any] =				config.labelaid
          a__						:	Optional[int] =				zero_shot_classifier.entailment_id

          a__						:	Union[str, Any] =				{"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
          self.assertEqual(zero_shot_classifier.entailment_id					,  -1)

          a__						:	List[Any] =				{"entailment": 0, "neutral": 1, "contradiction": 2}
          self.assertEqual(zero_shot_classifier.entailment_id					,  0)

          a__						:	Dict =				{"ENTAIL": 0, "NON-ENTAIL": 1}
          self.assertEqual(zero_shot_classifier.entailment_id					,  0)

          a__						:	Optional[Any] =				{"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
          self.assertEqual(zero_shot_classifier.entailment_id					,  2)

          a__						:	Optional[Any] =				original_labelaid
          self.assertEqual(__A					,  zero_shot_classifier.entailment_id)

   @require_torch
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Union[str, Any] =				pipeline(
              'zero-shot-classification'					,  model='sshleifer/tiny-distilbert-base-cased-distilled-squad'					,  framework='pt'					,  )
          # There was a regression in 4.10 for this
          # Adding a test so we don't make the mistake again.
          # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
          zero_shot_classifier(
              'Who are you voting for in 2020?' * 100					,  candidate_labels=['politics', 'public health', 'science'])

   @require_torch
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Optional[Any] =				pipeline(
              'zero-shot-classification'					,  model='sshleifer/tiny-distilbert-base-cased-distilled-squad'					,  framework='pt'					,  )
          a__						:	Union[str, Any] =				zero_shot_classifier(
              'Who are you voting for in 2020?'					,  candidate_labels=['politics', 'public health', 'science'])

          self.assertEqual(
              nested_simplify(__A)					,  {
                  'sequence': 'Who are you voting for in 2020?',
                  'labels': ['science', 'public health', 'politics'],
                  'scores': [0.3_33, 0.3_33, 0.3_33],
              }					,  )

   @require_tf
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	int =				pipeline(
              'zero-shot-classification'					,  model='sshleifer/tiny-distilbert-base-cased-distilled-squad'					,  framework='tf'					,  )
          a__						:	List[str] =				zero_shot_classifier(
              'Who are you voting for in 2020?'					,  candidate_labels=['politics', 'public health', 'science'])

          self.assertEqual(
              nested_simplify(__A)					,  {
                  'sequence': 'Who are you voting for in 2020?',
                  'labels': ['science', 'public health', 'politics'],
                  'scores': [0.3_33, 0.3_33, 0.3_33],
              }					,  )

   @slow
   @require_torch
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Optional[int] =				pipeline('zero-shot-classification'					,  model='roberta-large-mnli'					,  framework='pt')
          a__						:	str =				zero_shot_classifier(
              'Who are you voting for in 2020?'					,  candidate_labels=['politics', 'public health', 'science'])

          self.assertEqual(
              nested_simplify(__A)					,  {
                  'sequence': 'Who are you voting for in 2020?',
                  'labels': ['politics', 'public health', 'science'],
                  'scores': [0.9_76, 0.0_15, 0.0_09],
              }					,  )
          a__						:	int =				zero_shot_classifier(
              'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
              ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
              ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
              ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
              ' machine translation tasks show these models to be superior in quality while being more parallelizable'
              ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
              ' English-to-German translation task, improving over the existing best results, including ensembles by'
              ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
              ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
              ' fraction of the training costs of the best models from the literature. We show that the Transformer'
              ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
              ' large and limited training data.'					,  candidate_labels=['machine learning', 'statistics', 'translation', 'vision']					,  multi_label=__A					,  )
          self.assertEqual(
              nested_simplify(__A)					,  {
                  'sequence': (
                      'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
                      ' networks in an encoder-decoder configuration. The best performing models also connect the'
                      ' encoder and decoder through an attention mechanism. We propose a new simple network'
                      ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
                      ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
                      ' superior in quality while being more parallelizable and requiring significantly less time to'
                      ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
                      ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
                      ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
                      ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
                      ' costs of the best models from the literature. We show that the Transformer generalizes well to'
                      ' other tasks by applying it successfully to English constituency parsing both with large and'
                      ' limited training data.'
                  ),
                  'labels': ['translation', 'machine learning', 'vision', 'statistics'],
                  'scores': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
              }					,  )

   @slow
   @require_tf
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	Tuple =				pipeline('zero-shot-classification'					,  model='roberta-large-mnli'					,  framework='tf')
          a__						:	Any =				zero_shot_classifier(
              'Who are you voting for in 2020?'					,  candidate_labels=['politics', 'public health', 'science'])

          self.assertEqual(
              nested_simplify(__A)					,  {
                  'sequence': 'Who are you voting for in 2020?',
                  'labels': ['politics', 'public health', 'science'],
                  'scores': [0.9_76, 0.0_15, 0.0_09],
              }					,  )
          a__						:	Tuple =				zero_shot_classifier(
              'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
              ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
              ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
              ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
              ' machine translation tasks show these models to be superior in quality while being more parallelizable'
              ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
              ' English-to-German translation task, improving over the existing best results, including ensembles by'
              ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
              ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
              ' fraction of the training costs of the best models from the literature. We show that the Transformer'
              ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
              ' large and limited training data.'					,  candidate_labels=['machine learning', 'statistics', 'translation', 'vision']					,  multi_label=__A					,  )
          self.assertEqual(
              nested_simplify(__A)					,  {
                  'sequence': (
                      'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
                      ' networks in an encoder-decoder configuration. The best performing models also connect the'
                      ' encoder and decoder through an attention mechanism. We propose a new simple network'
                      ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
                      ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
                      ' superior in quality while being more parallelizable and requiring significantly less time to'
                      ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
                      ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
                      ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
                      ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
                      ' costs of the best models from the literature. We show that the Transformer generalizes well to'
                      ' other tasks by applying it successfully to English constituency parsing both with large and'
                      ' limited training data.'
                  ),
                  'labels': ['translation', 'machine learning', 'vision', 'statistics'],
                  'scores': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
              }					,  )






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import OwlViTImageProcessor, OwlViTProcessor
@require_vision
class 	UpperCAmelCase_					(					unittest.TestCase    ):

   '''simple docstring'''




   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					str    =				tempfile.mkdtemp()

     # fmt: off
     lowerCamelCase :					Any    =				["", "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
     # fmt: on
     lowerCamelCase :					List[Any]    =				dict(zip(__A						,				range(len(__A ) ) ) )
     lowerCamelCase :					List[Any]    =				["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
     lowerCamelCase :					Optional[Any]    =				{"unk_token": "<unk>"}

     lowerCamelCase :					int    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["vocab_file"] )
     lowerCamelCase :					List[Any]    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["merges_file"] )
     with open(self.vocab_file						,				"w"						,				encoding="utf-8" ) as fp:
       fp.write(json.dumps(__A ) + "\n" )
     with open(self.merges_file						,				"w"						,				encoding="utf-8" ) as fp:
       fp.write("\n".join(__A ) )

     lowerCamelCase :					str    =				{
         "do_resize": True,
         "size": 20,
         "do_center_crop": True,
         "crop_size": 18,
         "do_normalize": True,
         "image_mean": [0.48145466, 0.4578275, 0.40821073],
         "image_std": [0.26862954, 0.26130258, 0.27577711],
     }
     lowerCamelCase :					str    =				os.path.join(self.tmpdirname						,				__A )
     with open(self.image_processor_file						,				"w"						,				encoding="utf-8" ) as fp:
       json.dump(__A						,				__A )






   def _snake_case    (		self						,				**__A ):






     """simple docstring"""





     return CLIPTokenizer.from_pretrained(self.tmpdirname						,				pad_token="!"						,				**__A )






   def _snake_case    (		self						,				**__A ):






     """simple docstring"""





     return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,				pad_token="!"						,				**__A )






   def _snake_case    (		self						,				**__A ):






     """simple docstring"""





     return OwlViTImageProcessor.from_pretrained(self.tmpdirname						,				**__A )






   def _snake_case    (		self ):






     """simple docstring"""





     shutil.rmtree(self.tmpdirname )






   def _snake_case    (		self ):






     """simple docstring"""





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

     lowerCamelCase :					Tuple    =				[Image.fromarray(np.moveaxis(__A						,				0						,				-1 ) ) for x in image_inputs]

     return image_inputs






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				self.get_tokenizer()
     lowerCamelCase :					Optional[Any]    =				self.get_rust_tokenizer()
     lowerCamelCase :					Tuple    =				self.get_image_processor()

     lowerCamelCase :					List[Any]    =				OwlViTProcessor(tokenizer=__A						,				image_processor=__A )
     processor_slow.save_pretrained(self.tmpdirname )
     lowerCamelCase :					Optional[Any]    =				OwlViTProcessor.from_pretrained(self.tmpdirname						,				use_fast=__A )

     lowerCamelCase :					Optional[int]    =				OwlViTProcessor(tokenizer=__A						,				image_processor=__A )
     processor_fast.save_pretrained(self.tmpdirname )
     lowerCamelCase :					Tuple    =				OwlViTProcessor.from_pretrained(self.tmpdirname )

     self.assertEqual(processor_slow.tokenizer.get_vocab()						,				tokenizer_slow.get_vocab() )
     self.assertEqual(processor_fast.tokenizer.get_vocab()						,				tokenizer_fast.get_vocab() )
     self.assertEqual(tokenizer_slow.get_vocab()						,				tokenizer_fast.get_vocab() )
     self.assertIsInstance(processor_slow.tokenizer						,				__A )
     self.assertIsInstance(processor_fast.tokenizer						,				__A )

     self.assertEqual(processor_slow.image_processor.to_json_string()						,				image_processor.to_json_string() )
     self.assertEqual(processor_fast.image_processor.to_json_string()						,				image_processor.to_json_string() )
     self.assertIsInstance(processor_slow.image_processor						,				__A )
     self.assertIsInstance(processor_fast.image_processor						,				__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				OwlViTProcessor(tokenizer=self.get_tokenizer()						,				image_processor=self.get_image_processor() )
     processor.save_pretrained(self.tmpdirname )

     lowerCamelCase :					int    =				self.get_tokenizer(bos_token="(BOS)"						,				eos_token="(EOS)" )
     lowerCamelCase :					List[str]    =				self.get_image_processor(do_normalize=__A )

     lowerCamelCase :					Optional[int]    =				OwlViTProcessor.from_pretrained(
         self.tmpdirname						,				bos_token="(BOS)"						,				eos_token="(EOS)"						,				do_normalize=__A )

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

     self.assertEqual(processor.image_processor.to_json_string()						,				image_processor_add_kwargs.to_json_string() )
     self.assertIsInstance(processor.image_processor						,				__A )






   def _snake_case    (		self ):






     """simple docstring"""





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

     lowerCamelCase :					Dict    =				OwlViTProcessor(tokenizer=__A						,				image_processor=__A )

     lowerCamelCase :					Tuple    =				self.prepare_image_inputs()

     lowerCamelCase :					int    =				image_processor(__A						,				return_tensors="np" )
     lowerCamelCase :					Union[str, Any]    =				processor(images=__A						,				return_tensors="np" )

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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				self.get_image_processor()
     lowerCamelCase :					Dict    =				self.get_tokenizer()

     lowerCamelCase :					Union[str, Any]    =				OwlViTProcessor(tokenizer=__A						,				image_processor=__A )

     lowerCamelCase :					Tuple    =				"lower newer"

     lowerCamelCase :					Union[str, Any]    =				processor(text=__A						,				return_tensors="np" )

     lowerCamelCase :					List[Any]    =				tokenizer(__A						,				return_tensors="np" )

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key][0].tolist()						,				encoded_processor[key][0].tolist() )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.get_image_processor()
     lowerCamelCase :					Any    =				self.get_tokenizer()

     lowerCamelCase :					int    =				OwlViTProcessor(tokenizer=__A						,				image_processor=__A )

     lowerCamelCase :					Optional[Any]    =				"lower newer"
     lowerCamelCase :					Dict    =				self.prepare_image_inputs()

     lowerCamelCase :					Any    =				processor(text=__A						,				images=__A )

     self.assertListEqual(list(inputs.keys() )						,				["input_ids", "attention_mask", "pixel_values"] )

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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Any    =				"google/owlvit-base-patch32"
     lowerCamelCase :					List[Any]    =				OwlViTProcessor.from_pretrained(__A )

     lowerCamelCase :					Tuple    =				["cat", "nasa badge"]
     lowerCamelCase :					str    =				processor(text=__A )

     lowerCamelCase :					Union[str, Any]    =				16
     self.assertListEqual(list(inputs.keys() )						,				["input_ids", "attention_mask"] )
     self.assertEqual(inputs["input_ids"].shape						,				(2, seq_length) )

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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					str    =				"google/owlvit-base-patch32"
     lowerCamelCase :					Optional[int]    =				OwlViTProcessor.from_pretrained(__A )

     lowerCamelCase :					Dict    =				[["cat", "nasa badge"], ["person"]]
     lowerCamelCase :					int    =				processor(text=__A )

     lowerCamelCase :					Tuple    =				16
     lowerCamelCase :					Any    =				len(__A )
     lowerCamelCase :					Optional[Any]    =				max([len(__A ) for texts in input_texts] )

     self.assertListEqual(list(inputs.keys() )						,				["input_ids", "attention_mask"] )
     self.assertEqual(inputs["input_ids"].shape						,				(batch_size * num_max_text_queries, seq_length) )

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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				"google/owlvit-base-patch32"
     lowerCamelCase :					Tuple    =				OwlViTProcessor.from_pretrained(__A )

     lowerCamelCase :					List[Any]    =				["cat", "nasa badge"]
     lowerCamelCase :					Optional[Any]    =				processor(text=__A )

     lowerCamelCase :					int    =				16
     lowerCamelCase :					List[str]    =				inputs["input_ids"]
     lowerCamelCase :					int    =				[
         [4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [4_9406, 6841, 1_1301, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
     ]

     self.assertListEqual(list(inputs.keys() )						,				["input_ids", "attention_mask"] )
     self.assertEqual(inputs["input_ids"].shape						,				(2, seq_length) )
     self.assertListEqual(list(input_ids[0] )						,				predicted_ids[0] )
     self.assertListEqual(list(input_ids[1] )						,				predicted_ids[1] )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.get_image_processor()
     lowerCamelCase :					List[str]    =				self.get_tokenizer()

     lowerCamelCase :					str    =				OwlViTProcessor(tokenizer=__A						,				image_processor=__A )

     lowerCamelCase :					Dict    =				self.prepare_image_inputs()
     lowerCamelCase :					Union[str, Any]    =				self.prepare_image_inputs()

     lowerCamelCase :					Any    =				processor(images=__A						,				query_images=__A )

     self.assertListEqual(list(inputs.keys() )						,				["query_pixel_values", "pixel_values"] )

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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				self.get_image_processor()
     lowerCamelCase :					Optional[int]    =				self.get_tokenizer()

     lowerCamelCase :					Dict    =				OwlViTProcessor(tokenizer=__A						,				image_processor=__A )

     lowerCamelCase :					Optional[int]    =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase :					List[Any]    =				processor.batch_decode(__A )
     lowerCamelCase :					Union[str, Any]    =				tokenizer.batch_decode(__A )

     self.assertListEqual(__A						,				__A )



