





"""simple docstring"""





import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY




@is_pipeline_test
@require_torch_or_tf
class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      _A     :       Dict         =       MODEL_FOR_CAUSAL_LM_MAPPING
      _A     :       Optional[int]         =       TF_MODEL_FOR_CAUSAL_LM_MAPPING

      @require_torch
      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						pipeline(task='''text-generation'''						,				model='''sshleifer/tiny-ctrl'''						,				framework='''pt''' )
        # Using `do_sample=False` to force deterministic output
        __UpperCAmelCase    :						int       =						text_generator('''This is a test'''						,				do_sample=__lowercase )
        self.assertEqual(
            __lowercase						,				[
                {
                    '''generated_text''': (
                        '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
                        ''' oscope. FiliFili@@'''
                    )
                }
            ]						,				)

        __UpperCAmelCase    :						str       =						text_generator(['''This is a test''', '''This is a second test'''] )
        self.assertEqual(
            __lowercase						,				[
                [
                    {
                        '''generated_text''': (
                            '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
                            ''' oscope. FiliFili@@'''
                        )
                    }
                ],
                [
                    {
                        '''generated_text''': (
                            '''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
                            ''' oscope. oscope. FiliFili@@'''
                        )
                    }
                ],
            ]						,				)

        __UpperCAmelCase    :						Optional[int]       =						text_generator('''This is a test'''						,				do_sample=__lowercase						,				num_return_sequences=2						,				return_tensors=__lowercase )
        self.assertEqual(
            __lowercase						,				[
                {'''generated_token_ids''': ANY(__lowercase )},
                {'''generated_token_ids''': ANY(__lowercase )},
            ]						,				)
        __UpperCAmelCase    :						List[str]       =						text_generator.model.config.eos_token_id
        __UpperCAmelCase    :						List[Any]       =						'''<pad>'''
        __UpperCAmelCase    :						str       =						text_generator(
            ['''This is a test''', '''This is a second test''']						,				do_sample=__lowercase						,				num_return_sequences=2						,				batch_size=2						,				return_tensors=__lowercase						,				)
        self.assertEqual(
            __lowercase						,				[
                [
                    {'''generated_token_ids''': ANY(__lowercase )},
                    {'''generated_token_ids''': ANY(__lowercase )},
                ],
                [
                    {'''generated_token_ids''': ANY(__lowercase )},
                    {'''generated_token_ids''': ANY(__lowercase )},
                ],
            ]						,				)

      @require_tf
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						pipeline(task='''text-generation'''						,				model='''sshleifer/tiny-ctrl'''						,				framework='''tf''' )

        # Using `do_sample=False` to force deterministic output
        __UpperCAmelCase    :						str       =						text_generator('''This is a test'''						,				do_sample=__lowercase )
        self.assertEqual(
            __lowercase						,				[
                {
                    '''generated_text''': (
                        '''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
                        ''' please,'''
                    )
                }
            ]						,				)

        __UpperCAmelCase    :						int       =						text_generator(['''This is a test''', '''This is a second test''']						,				do_sample=__lowercase )
        self.assertEqual(
            __lowercase						,				[
                [
                    {
                        '''generated_text''': (
                            '''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
                            ''' please,'''
                        )
                    }
                ],
                [
                    {
                        '''generated_text''': (
                            '''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
                            ''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
                        )
                    }
                ],
            ]						,				)

      def       A_					(							self       :      List[Any]						,				__lowercase       :      Tuple						,				__lowercase       :      Tuple						,				__lowercase       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						TextGenerationPipeline(model=__lowercase						,				tokenizer=__lowercase )
        return text_generator, ["This is a test", "Another test"]

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						'''Hello I believe in'''
        __UpperCAmelCase    :						Union[str, Any]       =						pipeline('''text-generation'''						,				model='''hf-internal-testing/tiny-random-gpt2''' )
        __UpperCAmelCase    :						int       =						text_generator(__lowercase )
        self.assertEqual(
            __lowercase						,				[{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}]						,				)

        __UpperCAmelCase    :						Tuple       =						text_generator(__lowercase						,				stop_sequence=''' fe''' )
        self.assertEqual(__lowercase						,				[{'''generated_text''': '''Hello I believe in fe'''}] )

      def       A_					(							self       :      int						,				__lowercase       :      List[Any]						,				__lowercase       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						text_generator.model
        __UpperCAmelCase    :						Optional[int]       =						text_generator.tokenizer

        __UpperCAmelCase    :						Tuple       =						text_generator('''This is a test''' )
        self.assertEqual(__lowercase						,				[{'''generated_text''': ANY(__lowercase )}] )
        self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )

        __UpperCAmelCase    :						Tuple       =						text_generator('''This is a test'''						,				return_full_text=__lowercase )
        self.assertEqual(__lowercase						,				[{'''generated_text''': ANY(__lowercase )}] )
        self.assertNotIn('''This is a test'''						,				outputs[0]['''generated_text'''] )

        __UpperCAmelCase    :						Dict       =						pipeline(task='''text-generation'''						,				model=__lowercase						,				tokenizer=__lowercase						,				return_full_text=__lowercase )
        __UpperCAmelCase    :						Dict       =						text_generator('''This is a test''' )
        self.assertEqual(__lowercase						,				[{'''generated_text''': ANY(__lowercase )}] )
        self.assertNotIn('''This is a test'''						,				outputs[0]['''generated_text'''] )

        __UpperCAmelCase    :						List[str]       =						text_generator('''This is a test'''						,				return_full_text=__lowercase )
        self.assertEqual(__lowercase						,				[{'''generated_text''': ANY(__lowercase )}] )
        self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )

        __UpperCAmelCase    :						List[str]       =						text_generator(['''This is great !''', '''Something else''']						,				num_return_sequences=2						,				do_sample=__lowercase )
        self.assertEqual(
            __lowercase						,				[
                [{'''generated_text''': ANY(__lowercase )}, {'''generated_text''': ANY(__lowercase )}],
                [{'''generated_text''': ANY(__lowercase )}, {'''generated_text''': ANY(__lowercase )}],
            ]						,				)

        if text_generator.tokenizer.pad_token is not None:
          __UpperCAmelCase    :						Tuple       =						text_generator(
              ['''This is great !''', '''Something else''']						,				num_return_sequences=2						,				batch_size=2						,				do_sample=__lowercase )
          self.assertEqual(
              __lowercase						,				[
                  [{'''generated_text''': ANY(__lowercase )}, {'''generated_text''': ANY(__lowercase )}],
                  [{'''generated_text''': ANY(__lowercase )}, {'''generated_text''': ANY(__lowercase )}],
              ]						,				)

        with self.assertRaises(__lowercase ):
          __UpperCAmelCase    :						Union[str, Any]       =						text_generator('''test'''						,				return_full_text=__lowercase						,				return_text=__lowercase )
        with self.assertRaises(__lowercase ):
          __UpperCAmelCase    :						Tuple       =						text_generator('''test'''						,				return_full_text=__lowercase						,				return_tensors=__lowercase )
        with self.assertRaises(__lowercase ):
          __UpperCAmelCase    :						str       =						text_generator('''test'''						,				return_text=__lowercase						,				return_tensors=__lowercase )

        # Empty prompt is slighly special
        # it requires BOS token to exist.
        # Special case for Pegasus which will always append EOS so will
        # work even without BOS.
        if (
            text_generator.tokenizer.bos_token_id is not None
            or "Pegasus" in tokenizer.__class__.__name__
            or "Git" in model.__class__.__name__
        ):
          __UpperCAmelCase    :						Union[str, Any]       =						text_generator('''''' )
          self.assertEqual(__lowercase						,				[{'''generated_text''': ANY(__lowercase )}] )
        else:
          with self.assertRaises((ValueError, AssertionError) ):
            __UpperCAmelCase    :						Union[str, Any]       =						text_generator('''''' )

        if text_generator.framework == "tf":
          # TF generation does not support max_new_tokens, and it's impossible
          # to control long generation with only max_length without
          # fancy calculation, dismissing tests for now.
          return
        # We don't care about infinite range models.
        # They already work.
        # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
        __UpperCAmelCase    :						str       =						['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
        if (
            tokenizer.model_max_length < 10_000
            and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
        ):
          # Handling of large generations
          with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
            text_generator('''This is a test''' * 500						,				max_new_tokens=20 )

          __UpperCAmelCase    :						Tuple       =						text_generator('''This is a test''' * 500						,				handle_long_generation='''hole'''						,				max_new_tokens=20 )
          # Hole strategy cannot work
          with self.assertRaises(__lowercase ):
            text_generator(
                '''This is a test''' * 500						,				handle_long_generation='''hole'''						,				max_new_tokens=tokenizer.model_max_length + 10						,				)

      @require_torch
      @require_accelerate
      @require_torch_gpu
      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        import torch

        # Classic `model_kwargs`
        __UpperCAmelCase    :						Any       =						pipeline(
            model='''hf-internal-testing/tiny-random-bloom'''						,				model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa}						,				)
        self.assertEqual(pipe.model.device						,				torch.device(0 ) )
        self.assertEqual(pipe.model.lm_head.weight.dtype						,				torch.bfloataa )
        __UpperCAmelCase    :						Union[str, Any]       =						pipe('''This is a test''' )
        self.assertEqual(
            __lowercase						,				[
                {
                    '''generated_text''': (
                        '''This is a test test test test test test test test test test test test test test test test'''
                        ''' test'''
                    )
                }
            ]						,				)

        # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
        __UpperCAmelCase    :						Tuple       =						pipeline(model='''hf-internal-testing/tiny-random-bloom'''						,				device_map='''auto'''						,				torch_dtype=torch.bfloataa )
        self.assertEqual(pipe.model.device						,				torch.device(0 ) )
        self.assertEqual(pipe.model.lm_head.weight.dtype						,				torch.bfloataa )
        __UpperCAmelCase    :						Union[str, Any]       =						pipe('''This is a test''' )
        self.assertEqual(
            __lowercase						,				[
                {
                    '''generated_text''': (
                        '''This is a test test test test test test test test test test test test test test test test'''
                        ''' test'''
                    )
                }
            ]						,				)

        # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
        __UpperCAmelCase    :						int       =						pipeline(model='''hf-internal-testing/tiny-random-bloom'''						,				device_map='''auto''' )
        self.assertEqual(pipe.model.device						,				torch.device(0 ) )
        self.assertEqual(pipe.model.lm_head.weight.dtype						,				torch.floataa )
        __UpperCAmelCase    :						int       =						pipe('''This is a test''' )
        self.assertEqual(
            __lowercase						,				[
                {
                    '''generated_text''': (
                        '''This is a test test test test test test test test test test test test test test test test'''
                        ''' test'''
                    )
                }
            ]						,				)

      @require_torch
      @require_torch_gpu
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        import torch

        __UpperCAmelCase    :						Dict       =						pipeline(model='''hf-internal-testing/tiny-random-bloom'''						,				device=0						,				torch_dtype=torch.floataa )
        pipe('''This is a test''' )

      @require_torch
      @require_accelerate
      @require_torch_gpu
      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        import torch

        __UpperCAmelCase    :						Any       =						pipeline(model='''hf-internal-testing/tiny-random-bloom'''						,				device_map='''auto'''						,				torch_dtype=torch.floataa )
        pipe('''This is a test'''						,				do_sample=__lowercase						,				top_p=0.5 )







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


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						'''Hello world'''
        __UpperCAmelCase    :						str       =						pipeline('''text-generation'''						,				model='''hf-internal-testing/tiny-random-gpt2''' )
        if text_generator.model.framework == "tf":
          __UpperCAmelCase    :						Dict       =						logging.get_logger('''transformers.generation.tf_utils''' )
        else:
          __UpperCAmelCase    :						str       =						logging.get_logger('''transformers.generation.utils''' )
        __UpperCAmelCase    :						int       =						'''Both `max_new_tokens`'''  # The beggining of the message to be checked in this test

        # Both are set by the user -> log warning
        with CaptureLogger(__lowercase ) as cl:
          __UpperCAmelCase    :						Dict       =						text_generator(__lowercase						,				max_length=10						,				max_new_tokens=1 )
        self.assertIn(__lowercase						,				cl.out )

        # The user only sets one -> no warning
        with CaptureLogger(__lowercase ) as cl:
          __UpperCAmelCase    :						int       =						text_generator(__lowercase						,				max_new_tokens=1 )
        self.assertNotIn(__lowercase						,				cl.out )

        with CaptureLogger(__lowercase ) as cl:
          __UpperCAmelCase    :						Tuple       =						text_generator(__lowercase						,				max_length=10 )
        self.assertNotIn(__lowercase						,				cl.out )






"""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 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						tempfile.mkdtemp()

        # fmt: off
        __UpperCAmelCase    :						Optional[int]       =						['''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
        __UpperCAmelCase    :						List[str]       =						dict(zip(__lowercase						,				range(len(__lowercase ) ) ) )
        __UpperCAmelCase    :						Any       =						['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
        __UpperCAmelCase    :						Dict       =						{'''unk_token''': '''<unk>'''}

        __UpperCAmelCase    :						Optional[int]       =						os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['''vocab_file'''] )
        __UpperCAmelCase    :						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(__lowercase ) + '''\n''' )
        with open(self.merges_file						,				'''w'''						,				encoding='''utf-8''' ) as fp:
          fp.write('''\n'''.join(__lowercase ) )

        __UpperCAmelCase    :						List[str]       =						{
            '''do_resize''': True,
            '''size''': 20,
            '''do_center_crop''': True,
            '''crop_size''': 18,
            '''do_normalize''': True,
            '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
            '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
        }
        __UpperCAmelCase    :						Any       =						os.path.join(self.tmpdirname						,				__lowercase )
        with open(self.image_processor_file						,				'''w'''						,				encoding='''utf-8''' ) as fp:
          json.dump(__lowercase						,				__lowercase )

      def       A_					(							self       :      Optional[Any]						,				**__lowercase       :      Tuple ):


        '''simple docstring'''




        return CLIPTokenizer.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      Any						,				**__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      Dict						,				**__lowercase       :      str ):


        '''simple docstring'''




        return ViTImageProcessor.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        shutil.rmtree(self.tmpdirname )

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


        '''simple docstring'''




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

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

        return image_inputs

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.get_tokenizer()
        __UpperCAmelCase    :						Dict       =						self.get_rust_tokenizer()
        __UpperCAmelCase    :						Optional[Any]       =						self.get_image_processor()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )
        processor_slow.save_pretrained(self.tmpdirname )
        __UpperCAmelCase    :						List[Any]       =						CLIPSegProcessor.from_pretrained(self.tmpdirname						,				use_fast=__lowercase )

        __UpperCAmelCase    :						Tuple       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )
        processor_fast.save_pretrained(self.tmpdirname )
        __UpperCAmelCase    :						str       =						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						,				__lowercase )
        self.assertIsInstance(processor_fast.tokenizer						,				__lowercase )

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

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=self.get_tokenizer()						,				image_processor=self.get_image_processor() )
        processor.save_pretrained(self.tmpdirname )

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

        __UpperCAmelCase    :						List[str]       =						CLIPSegProcessor.from_pretrained(
            self.tmpdirname						,				bos_token='''(BOS)'''						,				eos_token='''(EOS)'''						,				do_normalize=__lowercase						,				padding_value=1.0 )

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

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

      def       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						self.get_image_processor()
        __UpperCAmelCase    :						List[str]       =						self.get_tokenizer()

        __UpperCAmelCase    :						Optional[Any]       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						List[Any]       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						Dict       =						image_processor(__lowercase						,				return_tensors='''np''' )
        __UpperCAmelCase    :						Optional[int]       =						processor(images=__lowercase						,				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       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.get_image_processor()
        __UpperCAmelCase    :						Any       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						int       =						'''lower newer'''

        __UpperCAmelCase    :						int       =						processor(text=__lowercase )

        __UpperCAmelCase    :						Any       =						tokenizer(__lowercase )

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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.get_image_processor()
        __UpperCAmelCase    :						Union[str, Any]       =						self.get_tokenizer()

        __UpperCAmelCase    :						List[Any]       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						str       =						'''lower newer'''
        __UpperCAmelCase    :						int       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						str       =						processor(text=__lowercase						,				images=__lowercase )

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

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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						self.get_image_processor()
        __UpperCAmelCase    :						str       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						self.prepare_image_inputs()
        __UpperCAmelCase    :						Optional[Any]       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						Tuple       =						processor(images=__lowercase						,				visual_prompt=__lowercase )

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

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







      def       A_					(							self       :      str ):


        '''simple docstring'''




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

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						Dict       =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        __UpperCAmelCase    :						int       =						processor.batch_decode(__lowercase )
        __UpperCAmelCase    :						int       =						tokenizer.batch_decode(__lowercase )

        self.assertListEqual(__lowercase						,				__lowercase )