
"""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 CLIPImageProcessor, CLIPProcessor




@require_vision
class      __A(    unittest.TestCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    tempfile.mkdtemp()

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

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

       UpperCamelCase__				=    {
           '''do_resize''': True,
           '''size''': 20,
           '''do_center_crop''': True,
           '''crop_size''': 18,
           '''do_normalize''': True,
           '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
           '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
       }
       UpperCamelCase__				=    os.path.join(self.tmpdirname	,      _lowerCamelCase						)
       with open(self.image_processor_file	,      """w"""	,      encoding="""utf-8"""						) as fp:
            json.dump(_lowerCamelCase	,      _lowerCamelCase						)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return CLIPTokenizer.from_pretrained(self.tmpdirname	,      **_lowerCamelCase						)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,      **_lowerCamelCase						)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return CLIPImageProcessor.from_pretrained(self.tmpdirname	,      **_lowerCamelCase						)







  def 	UpperCAmelCase_ (self						):
       shutil.rmtree(self.tmpdirname						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    [np.random.randint(2_55	,      size=(3, 30, 4_00)	,      dtype=np.uinta						)]

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

       return image_inputs







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_rust_tokenizer()
       UpperCamelCase__				=    self.get_image_processor()

       UpperCamelCase__				=    CLIPProcessor(tokenizer=_lowerCamelCase	,      image_processor=_lowerCamelCase						)
       processor_slow.save_pretrained(self.tmpdirname						)
       UpperCamelCase__				=    CLIPProcessor.from_pretrained(self.tmpdirname	,      use_fast=_lowerCamelCase						)

       UpperCamelCase__				=    CLIPProcessor(tokenizer=_lowerCamelCase	,      image_processor=_lowerCamelCase						)
       processor_fast.save_pretrained(self.tmpdirname						)
       UpperCamelCase__				=    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	,      _lowerCamelCase						)
       self.assertIsInstance(processor_fast.tokenizer	,      _lowerCamelCase						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    CLIPProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()						)
       processor.save_pretrained(self.tmpdirname						)

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

       UpperCamelCase__				=    CLIPProcessor.from_pretrained(
           self.tmpdirname	,      bos_token="""(BOS)"""	,      eos_token="""(EOS)"""	,      do_normalize=_lowerCamelCase	,      padding_value=1.0						)

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

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()

       UpperCamelCase__				=    CLIPProcessor(tokenizer=_lowerCamelCase	,      image_processor=_lowerCamelCase						)

       UpperCamelCase__				=    self.prepare_image_inputs()

       UpperCamelCase__				=    image_processor(_lowerCamelCase	,      return_tensors="""np"""						)
       UpperCamelCase__				=    processor(images=_lowerCamelCase	,      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						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()

       UpperCamelCase__				=    CLIPProcessor(tokenizer=_lowerCamelCase	,      image_processor=_lowerCamelCase						)

       UpperCamelCase__				=    '''lower newer'''

       UpperCamelCase__				=    processor(text=_lowerCamelCase						)

       UpperCamelCase__				=    tokenizer(_lowerCamelCase						)

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()

       UpperCamelCase__				=    CLIPProcessor(tokenizer=_lowerCamelCase	,      image_processor=_lowerCamelCase						)

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

       UpperCamelCase__				=    processor(text=_lowerCamelCase	,      images=_lowerCamelCase						)

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

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()

       UpperCamelCase__				=    CLIPProcessor(tokenizer=_lowerCamelCase	,      image_processor=_lowerCamelCase						)

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

       UpperCamelCase__				=    processor.batch_decode(_lowerCamelCase						)
       UpperCamelCase__				=    tokenizer.batch_decode(_lowerCamelCase						)

       self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()

       UpperCamelCase__				=    CLIPProcessor(tokenizer=_lowerCamelCase	,      image_processor=_lowerCamelCase						)

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

       UpperCamelCase__				=    processor(text=_lowerCamelCase	,      images=_lowerCamelCase						)

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase_						     =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					[
        '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''CanineForMultipleChoice''',
        '''CanineForQuestionAnswering''',
        '''CanineForSequenceClassification''',
        '''CanineForTokenClassification''',
        '''CanineLayer''',
        '''CanineModel''',
        '''CaninePreTrainedModel''',
        '''load_tf_weights_in_canine''',
    ]


if TYPE_CHECKING:
    from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
    from .tokenization_canine import CanineTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_canine import (
            CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
            CanineForMultipleChoice,
            CanineForQuestionAnswering,
            CanineForSequenceClassification,
            CanineForTokenClassification,
            CanineLayer,
            CanineModel,
            CaninePreTrainedModel,
            load_tf_weights_in_canine,
        )


else:
    import sys

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





