





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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       tempfile.mkdtemp()

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

         __snake_case								=       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''vocab_file''']				)
         __snake_case								=       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__				)				)

         __snake_case								=       {
             '''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],
         }
         __snake_case								=       os.path.join(self.tmpdirname    ,      a__				)
         with open(self.image_processor_file    ,      '''w'''    ,      encoding='''utf-8'''				) as fp:
            json.dump(a__    ,      a__				)


      def        a			(self	:				str    ,      **a__	:				int				):





         """simple docstring"""



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


      def        a			(self	:				Dict    ,      **a__	:				str				):





         """simple docstring"""



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


      def        a			(self	:				Optional[Any]    ,      **a__	:				int				):





         """simple docstring"""



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


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         shutil.rmtree(self.tmpdirname				)


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





         """simple docstring"""



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

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

         return image_inputs


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.get_tokenizer()
         __snake_case								=       self.get_rust_tokenizer()
         __snake_case								=       self.get_image_processor()

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

         __snake_case								=       CLIPSegProcessor(tokenizer=a__    ,      image_processor=a__				)
         processor_fast.save_pretrained(self.tmpdirname				)
         __snake_case								=       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    ,      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        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       CLIPSegProcessor(tokenizer=self.get_tokenizer()    ,      image_processor=self.get_image_processor()				)
         processor.save_pretrained(self.tmpdirname				)

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

         __snake_case								=       CLIPSegProcessor.from_pretrained(
             self.tmpdirname    ,      bos_token='''(BOS)'''    ,      eos_token='''(EOS)'''    ,      do_normalize=a__    ,      padding_value=1.0				)

         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        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.get_image_processor()
         __snake_case								=       self.get_tokenizer()

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

         __snake_case								=       self.prepare_image_inputs()

         __snake_case								=       image_processor(a__    ,      return_tensors='''np'''				)
         __snake_case								=       processor(images=a__    ,      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	:				int				):





         """simple docstring"""



         __snake_case								=       self.get_image_processor()
         __snake_case								=       self.get_tokenizer()

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

         __snake_case								=       '''lower newer'''

         __snake_case								=       processor(text=a__				)

         __snake_case								=       tokenizer(a__				)

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


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       self.get_image_processor()
         __snake_case								=       self.get_tokenizer()

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

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

         __snake_case								=       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        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.get_image_processor()
         __snake_case								=       self.get_tokenizer()

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

         __snake_case								=       self.prepare_image_inputs()
         __snake_case								=       self.prepare_image_inputs()

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

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

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


      def        a			(self	:				Any				):





         """simple docstring"""



         __snake_case								=       self.get_image_processor()
         __snake_case								=       self.get_tokenizer()

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

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

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

         self.assertListEqual(a__    ,      a__				)








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case_           =      {
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =      ['VivitImageProcessor']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =      [
        'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'VivitModel',
        'VivitPreTrainedModel',
        'VivitForVideoClassification',
    ]


if TYPE_CHECKING:
    from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_vivit import VivitImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vivit import (
            VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            VivitForVideoClassification,
            VivitModel,
            VivitPreTrainedModel,
        )


else:
    import sys

    snake_case_           =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

