





'''simple docstring'''






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.
A__    :       Optional[int]              ={'''LayoutLMv2Config''', '''LayoutLMv3Config'''}




@is_pipeline_test
class 			UpperCAmelCase					(      unittest.TestCase       ):
      _lowercase:      Tuple											=       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      _lowercase:      Union[str, Any]											=       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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




      def     lowercase__   (      self      :			Dict      ,      __snake_case      :			str      ,      __snake_case      :			Dict      ,      __snake_case      :			Union[str, Any]  )    ->				List[Any]:
            _lowerCAmelCase												=							ZeroShotClassificationPipeline(
                model=_UpperCAmelCase      ,      tokenizer=_UpperCAmelCase      ,      candidate_labels=["""polics""", """health"""]  )
            return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]




      def     lowercase__   (      self      :			int      ,      __snake_case      :			Union[str, Any]      ,      __snake_case      :			Any  )    ->				List[str]:
            _lowerCAmelCase												=							classifier("""Who are you voting for in 2020?"""      ,      candidate_labels="""politics"""  )
            self.assertEqual(_UpperCAmelCase      ,      {"""sequence""": ANY(_UpperCAmelCase  ), """labels""": [ANY(_UpperCAmelCase  )], """scores""": [ANY(_UpperCAmelCase  )]}  )

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

            _lowerCAmelCase												=							classifier("""Who are you voting for in 2020?"""      ,      candidate_labels=["""politics"""]  )
            self.assertEqual(_UpperCAmelCase      ,      {"""sequence""": ANY(_UpperCAmelCase  ), """labels""": [ANY(_UpperCAmelCase  )], """scores""": [ANY(_UpperCAmelCase  )]}  )

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

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

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

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

            with self.assertRaises(_UpperCAmelCase  ):
                  classifier(""""""      ,      candidate_labels="""politics"""  )

            with self.assertRaises(_UpperCAmelCase  ):
                  classifier(_UpperCAmelCase      ,      candidate_labels="""politics"""  )

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

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

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

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

            self.run_entailment_id(_UpperCAmelCase  )




      def     lowercase__   (      self      :			Optional[int]      ,      __snake_case      :			Dict  )    ->				Any:
            _lowerCAmelCase												=							zero_shot_classifier.model.config
            _lowerCAmelCase												=							config.labelaid
            _lowerCAmelCase												=							zero_shot_classifier.entailment_id

            _lowerCAmelCase												=							{'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
            self.assertEqual(zero_shot_classifier.entailment_id      ,      -1  )

            _lowerCAmelCase												=							{'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
            self.assertEqual(zero_shot_classifier.entailment_id      ,      0  )

            _lowerCAmelCase												=							{'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
            self.assertEqual(zero_shot_classifier.entailment_id      ,      0  )

            _lowerCAmelCase												=							{'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
            self.assertEqual(zero_shot_classifier.entailment_id      ,      2  )

            _lowerCAmelCase												=							original_labelaid
            self.assertEqual(_UpperCAmelCase      ,      zero_shot_classifier.entailment_id  )




      @require_torch
      def     lowercase__   (      self      :			Optional[Any]  )    ->				List[Any]:
            _lowerCAmelCase												=							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?""" * 1_00      ,      candidate_labels=["""politics""", """public health""", """science"""]  )




      @require_torch
      def     lowercase__   (      self      :			Any  )    ->				Tuple:
            _lowerCAmelCase												=							pipeline(
                """zero-shot-classification"""      ,      model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""      ,      framework="""pt"""      ,      )
            _lowerCAmelCase												=							zero_shot_classifier(
                """Who are you voting for in 2020?"""      ,      candidate_labels=["""politics""", """public health""", """science"""]  )

            self.assertEqual(
                nested_simplify(_UpperCAmelCase  )      ,      {
                    """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      :			List[Any]  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							pipeline(
                """zero-shot-classification"""      ,      model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""      ,      framework="""tf"""      ,      )
            _lowerCAmelCase												=							zero_shot_classifier(
                """Who are you voting for in 2020?"""      ,      candidate_labels=["""politics""", """public health""", """science"""]  )

            self.assertEqual(
                nested_simplify(_UpperCAmelCase  )      ,      {
                    """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      :			int  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							pipeline("""zero-shot-classification"""      ,      model="""roberta-large-mnli"""      ,      framework="""pt"""  )
            _lowerCAmelCase												=							zero_shot_classifier(
                """Who are you voting for in 2020?"""      ,      candidate_labels=["""politics""", """public health""", """science"""]  )

            self.assertEqual(
                nested_simplify(_UpperCAmelCase  )      ,      {
                    """sequence""": """Who are you voting for in 2020?""",
                    """labels""": ["""politics""", """public health""", """science"""],
                    """scores""": [0.9_76, 0.0_15, 0.0_09],
                }      ,      )
            _lowerCAmelCase												=							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=_UpperCAmelCase      ,      )
            self.assertEqual(
                nested_simplify(_UpperCAmelCase  )      ,      {
                    """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      :			int  )    ->				Optional[Any]:
            _lowerCAmelCase												=							pipeline("""zero-shot-classification"""      ,      model="""roberta-large-mnli"""      ,      framework="""tf"""  )
            _lowerCAmelCase												=							zero_shot_classifier(
                """Who are you voting for in 2020?"""      ,      candidate_labels=["""politics""", """public health""", """science"""]  )

            self.assertEqual(
                nested_simplify(_UpperCAmelCase  )      ,      {
                    """sequence""": """Who are you voting for in 2020?""",
                    """labels""": ["""politics""", """public health""", """science"""],
                    """scores""": [0.9_76, 0.0_15, 0.0_09],
                }      ,      )
            _lowerCAmelCase												=							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=_UpperCAmelCase      ,      )
            self.assertEqual(
                nested_simplify(_UpperCAmelCase  )      ,      {
                    """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],
                }      ,      )













'''simple docstring'''






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 CLIPSegProcessor, ViTImageProcessor




@require_vision
class 			UpperCAmelCase					(      unittest.TestCase       ):




      def     lowercase__   (      self      :			List[str]  )    ->				Dict:
            _lowerCAmelCase												=							tempfile.mkdtemp()

            # fmt: off
            _lowerCAmelCase												=							["""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												=							dict(zip(__snake_case      ,      range(len(__snake_case  )  )  )  )
            _lowerCAmelCase												=							["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
            _lowerCAmelCase												=							{"""unk_token""": """<unk>"""}

            _lowerCAmelCase												=							os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["""vocab_file"""]  )
            _lowerCAmelCase												=							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(__snake_case  ) + """\n"""  )
            with open(self.merges_file      ,      """w"""      ,      encoding="""utf-8"""  ) as fp:
                  fp.write("""\n""".join(__snake_case  )  )

            _lowerCAmelCase												=							{
                """do_resize""": True,
                """size""": 20,
                """do_center_crop""": True,
                """crop_size""": 18,
                """do_normalize""": True,
                """image_mean""": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
                """image_std""": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
            }
            _lowerCAmelCase												=							os.path.join(self.tmpdirname      ,      __snake_case  )
            with open(self.image_processor_file      ,      """w"""      ,      encoding="""utf-8"""  ) as fp:
                  json.dump(__snake_case      ,      __snake_case  )




      def     lowercase__   (      self      :			Optional[int]      ,      **__snake_case      :			Dict  )    ->				List[str]:
            return CLIPTokenizer.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			List[str]      ,      **__snake_case      :			Any  )    ->				List[Any]:
            return CLIPTokenizerFast.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			Tuple      ,      **__snake_case      :			List[str]  )    ->				int:
            return ViTImageProcessor.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			str  )    ->				Optional[Any]:
            shutil.rmtree(self.tmpdirname  )




      def     lowercase__   (      self      :			str  )    ->				List[Any]:
            _lowerCAmelCase												=							[np.random.randint(2_55      ,      size=(3, 30, 4_00)      ,      dtype=np.uinta  )]

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

            return image_inputs




      def     lowercase__   (      self      :			Optional[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							self.get_tokenizer()
            _lowerCAmelCase												=							self.get_rust_tokenizer()
            _lowerCAmelCase												=							self.get_image_processor()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )
            processor_slow.save_pretrained(self.tmpdirname  )
            _lowerCAmelCase												=							CLIPSegProcessor.from_pretrained(self.tmpdirname      ,      use_fast=__snake_case  )

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )
            processor_fast.save_pretrained(self.tmpdirname  )
            _lowerCAmelCase												=							CLIPSegProcessor.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      ,      __snake_case  )
            self.assertIsInstance(processor_fast.tokenizer      ,      __snake_case  )

            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      ,      __snake_case  )
            self.assertIsInstance(processor_fast.image_processor      ,      __snake_case  )




      def     lowercase__   (      self      :			Optional[int]  )    ->				Dict:
            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()  )
            processor.save_pretrained(self.tmpdirname  )

            _lowerCAmelCase												=							self.get_tokenizer(bos_token="""(BOS)"""      ,      eos_token="""(EOS)"""  )
            _lowerCAmelCase												=							self.get_image_processor(do_normalize=__snake_case      ,      padding_value=1.0  )

            _lowerCAmelCase												=							CLIPSegProcessor.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  )




      def     lowercase__   (      self      :			Tuple  )    ->				List[Any]:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							image_processor(__snake_case      ,      return_tensors="""np"""  )
            _lowerCAmelCase												=							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     lowercase__   (      self      :			Any  )    ->				str:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							"""lower newer"""

            _lowerCAmelCase												=							processor(text=__snake_case  )

            _lowerCAmelCase												=							tokenizer(__snake_case  )

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




      def     lowercase__   (      self      :			str  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							"""lower newer"""
            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							processor(text=__snake_case      ,      images=__snake_case  )

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

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




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Dict:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							self.prepare_image_inputs()
            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							processor(images=__snake_case      ,      visual_prompt=__snake_case  )

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

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




      def     lowercase__   (      self      :			int  )    ->				Tuple:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

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

            _lowerCAmelCase												=							processor.batch_decode(__snake_case  )
            _lowerCAmelCase												=							tokenizer.batch_decode(__snake_case  )

            self.assertListEqual(__snake_case      ,      __snake_case  )






