


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 CLIPImageProcessor, CLIPProcessor


@require_vision
class   a__	(						unittest.TestCase    ):

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     int										=    tempfile.mkdtemp()

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

            a_    :     Union[str, Any]										=    os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES["""vocab_file"""]    )
            a_    :     Optional[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(UpperCAmelCase_    ) + """\n"""    )
            with open(self.merges_file      ,							"""w"""      ,							encoding="""utf-8"""    ) as fp:
                 fp.write("""\n""".join(UpperCAmelCase_    )    )

            a_    :     Dict										=    {
                'do_resize': True,
                'size': 2_0,
                'do_center_crop': True,
                'crop_size': 1_8,
                '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],
            }
            a_    :     Any										=    os.path.join(self.tmpdirname      ,							UpperCAmelCase_    )
            with open(self.image_processor_file      ,							"""w"""      ,							encoding="""utf-8"""    ) as fp:
                 json.dump(UpperCAmelCase_      ,							UpperCAmelCase_    )

       def 						UpperCAmelCase(   self      :     Dict      ,							**lowerCamelCase_      :     Union[str, Any]    ):
            return CLIPTokenizer.from_pretrained(self.tmpdirname      ,							**UpperCAmelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[int]      ,							**lowerCamelCase_      :     str    ):
            return CLIPTokenizerFast.from_pretrained(self.tmpdirname      ,							**UpperCAmelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[Any]      ,							**lowerCamelCase_      :     Union[str, Any]    ):
            return CLIPImageProcessor.from_pretrained(self.tmpdirname      ,							**UpperCAmelCase_    )

       def 						UpperCAmelCase(   self      :     str    ):
            shutil.rmtree(self.tmpdirname    )

       def 						UpperCAmelCase(   self      :     Tuple    ):
            a_    :     List[str]										=    [np.random.randint(2_5_5      ,							size=(3, 3_0, 4_0_0)      ,							dtype=np.uinta    )]

            a_    :     List[str]										=    [Image.fromarray(np.moveaxis(UpperCAmelCase_      ,							0      ,							-1    )    ) for x in image_inputs]

            return image_inputs

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Union[str, Any]										=    self.get_tokenizer()
            a_    :     Optional[Any]										=    self.get_rust_tokenizer()
            a_    :     Any										=    self.get_image_processor()

            a_    :     str										=    CLIPProcessor(tokenizer=UpperCAmelCase_      ,							image_processor=UpperCAmelCase_    )
            processor_slow.save_pretrained(self.tmpdirname    )
            a_    :     Any										=    CLIPProcessor.from_pretrained(self.tmpdirname      ,							use_fast=UpperCAmelCase_    )

            a_    :     str										=    CLIPProcessor(tokenizer=UpperCAmelCase_      ,							image_processor=UpperCAmelCase_    )
            processor_fast.save_pretrained(self.tmpdirname    )
            a_    :     Optional[int]										=    CLIPProcessor.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      ,							UpperCAmelCase_    )
            self.assertIsInstance(processor_fast.tokenizer      ,							UpperCAmelCase_    )

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

       def 						UpperCAmelCase(   self      :     List[str]    ):
            a_    :     Union[str, Any]										=    CLIPProcessor(tokenizer=self.get_tokenizer()      ,							image_processor=self.get_image_processor()    )
            processor.save_pretrained(self.tmpdirname    )

            a_    :     List[str]										=    self.get_tokenizer(bos_token="""(BOS)"""      ,							eos_token="""(EOS)"""    )
            a_    :     Tuple										=    self.get_image_processor(do_normalize=UpperCAmelCase_      ,							padding_value=1.0    )

            a_    :     Dict										=    CLIPProcessor.from_pretrained(
                self.tmpdirname      ,							bos_token="""(BOS)"""      ,							eos_token="""(EOS)"""      ,							do_normalize=UpperCAmelCase_      ,							padding_value=1.0    )

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

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

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Optional[Any]										=    self.get_image_processor()
            a_    :     int										=    self.get_tokenizer()

            a_    :     List[Any]										=    CLIPProcessor(tokenizer=UpperCAmelCase_      ,							image_processor=UpperCAmelCase_    )

            a_    :     int										=    self.prepare_image_inputs()

            a_    :     int										=    image_processor(UpperCAmelCase_      ,							return_tensors="""np"""    )
            a_    :     Optional[int]										=    processor(images=UpperCAmelCase_      ,							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 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Optional[Any]										=    self.get_image_processor()
            a_    :     Dict										=    self.get_tokenizer()

            a_    :     List[Any]										=    CLIPProcessor(tokenizer=UpperCAmelCase_      ,							image_processor=UpperCAmelCase_    )

            a_    :     Any										=    'lower newer'

            a_    :     Union[str, Any]										=    processor(text=UpperCAmelCase_    )

            a_    :     Optional[Any]										=    tokenizer(UpperCAmelCase_    )

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

       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     Optional[int]										=    self.get_image_processor()
            a_    :     List[str]										=    self.get_tokenizer()

            a_    :     List[Any]										=    CLIPProcessor(tokenizer=UpperCAmelCase_      ,							image_processor=UpperCAmelCase_    )

            a_    :     Optional[Any]										=    'lower newer'
            a_    :     List[Any]										=    self.prepare_image_inputs()

            a_    :     str										=    processor(text=UpperCAmelCase_      ,							images=UpperCAmelCase_    )

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

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

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Any										=    self.get_image_processor()
            a_    :     Dict										=    self.get_tokenizer()

            a_    :     Optional[Any]										=    CLIPProcessor(tokenizer=UpperCAmelCase_      ,							image_processor=UpperCAmelCase_    )

            a_    :     Optional[Any]										=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            a_    :     List[Any]										=    processor.batch_decode(UpperCAmelCase_    )
            a_    :     Optional[int]										=    tokenizer.batch_decode(UpperCAmelCase_    )

            self.assertListEqual(UpperCAmelCase_      ,							UpperCAmelCase_    )

       def 						UpperCAmelCase(   self      :     str    ):
            a_    :     Union[str, Any]										=    self.get_image_processor()
            a_    :     List[str]										=    self.get_tokenizer()

            a_    :     Optional[Any]										=    CLIPProcessor(tokenizer=UpperCAmelCase_      ,							image_processor=UpperCAmelCase_    )

            a_    :     List[Any]										=    'lower newer'
            a_    :     Optional[int]										=    self.prepare_image_inputs()

            a_    :     List[str]										=    processor(text=UpperCAmelCase_      ,							images=UpperCAmelCase_    )

            self.assertListEqual(list(inputs.keys()    )      ,							processor.model_input_names    )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCamelCase        =     {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     ['''ViTFeatureExtractor''']
       __lowerCamelCase        =     ['''ViTImageProcessor''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ViTForImageClassification''',
           '''ViTForMaskedImageModeling''',
           '''ViTModel''',
           '''ViTPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''TFViTForImageClassification''',
           '''TFViTModel''',
           '''TFViTPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''FlaxViTForImageClassification''',
           '''FlaxViTModel''',
           '''FlaxViTPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_vit import ViTFeatureExtractor
              from .image_processing_vit import ViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vit import (
                  VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViTForImageClassification,
                  ViTForMaskedImageModeling,
                  ViTModel,
                  ViTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
       import sys

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



