






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			Dict							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    tempfile.mkdtemp()

         _UpperCAmelCase							=    [
             """[UNK]""",
             """[CLS]""",
             """[SEP]""",
             """[PAD]""",
             """[MASK]""",
             """的""",
             """价""",
             """格""",
             """是""",
             """15""",
             """便""",
             """alex""",
             """##andra""",
             """，""",
             """。""",
             """-""",
             """t""",
             """shirt""",
         ]
         _UpperCAmelCase							=    os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES["""vocab_file"""]							)
         with open(self.vocab_file       ,				"""w"""       ,				encoding="""utf-8"""							) as vocab_writer:
                vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)

         _UpperCAmelCase							=    {
             """do_resize""": True,
             """size""": {"""height""": 224, """width""": 224},
             """do_center_crop""": True,
             """crop_size""": {"""height""": 18, """width""": 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],
             """do_convert_rgb""": True,
         }
         _UpperCAmelCase							=    os.path.join(self.tmpdirname       ,				lowerCamelCase							)
         with open(self.image_processor_file       ,				"""w"""       ,				encoding="""utf-8"""							) as fp:
                json.dump(lowerCamelCase       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				**lowerCamelCase		:			List[str]							)						->	Tuple:


         """simple docstring"""
         return BertTokenizer.from_pretrained(self.tmpdirname       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]       ,				**lowerCamelCase		:			List[str]							)						->	Union[str, Any]:


         """simple docstring"""
         return BertTokenizerFast.from_pretrained(self.tmpdirname       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				**lowerCamelCase		:			List[Any]							)						->	int:


         """simple docstring"""
         return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	int:


         """simple docstring"""
         shutil.rmtree(self.tmpdirname							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    [np.random.randint(255       ,				size=(3, 30, 400)       ,				dtype=np.uinta							)]

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

         return image_inputs


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_tokenizer()
         _UpperCAmelCase							=    self.get_rust_tokenizer()
         _UpperCAmelCase							=    self.get_image_processor()

         _UpperCAmelCase							=    ChineseCLIPProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)
         processor_slow.save_pretrained(self.tmpdirname							)
         _UpperCAmelCase							=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname       ,				use_fast=lowerCamelCase							)

         _UpperCAmelCase							=    ChineseCLIPProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)
         processor_fast.save_pretrained(self.tmpdirname							)
         _UpperCAmelCase							=    ChineseCLIPProcessor.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  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()       ,				image_processor=self.get_image_processor()							)
         processor.save_pretrained(self.tmpdirname							)

         _UpperCAmelCase							=    self.get_tokenizer(cls_token="""(CLS)"""       ,				sep_token="""(SEP)"""							)
         _UpperCAmelCase							=    self.get_image_processor(do_normalize=lowerCamelCase							)

         _UpperCAmelCase							=    ChineseCLIPProcessor.from_pretrained(
             self.tmpdirname       ,				cls_token="""(CLS)"""       ,				sep_token="""(SEP)"""       ,				do_normalize=lowerCamelCase							)

         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  lowerCamelCase       (      self		:			List[str]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    ChineseCLIPProcessor(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_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()       ,				input_processor[key].sum()       ,				delta=1E-2							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    ChineseCLIPProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)

         _UpperCAmelCase							=    """Alexandra，T-shirt的价格是15便士。"""

         _UpperCAmelCase							=    processor(text=lowerCamelCase							)

         _UpperCAmelCase							=    tokenizer(lowerCamelCase							)

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


  def  lowerCamelCase       (      self		:			Any							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    ChineseCLIPProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)

         _UpperCAmelCase							=    """Alexandra，T-shirt的价格是15便士。"""
         _UpperCAmelCase							=    self.prepare_image_inputs()

         _UpperCAmelCase							=    processor(text=lowerCamelCase       ,				images=lowerCamelCase							)

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

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


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    ChineseCLIPProcessor(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  lowerCamelCase       (      self		:			Optional[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    ChineseCLIPProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)

         _UpperCAmelCase							=    """Alexandra，T-shirt的价格是15便士。"""
         _UpperCAmelCase							=    self.prepare_image_inputs()

         _UpperCAmelCase							=    processor(text=lowerCamelCase       ,				images=lowerCamelCase							)

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







import os
import sys
import unittest


__a:     Tuple   					=					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__a:     Tuple   					=					os.path.join(git_repo_path, '''src''', '''transformers''')

__a:     Optional[Any]   					=					'''
{0} = None
'''

__a:     List[Any]   					=					'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


__a:     int   					=					'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			Tuple							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""							)
         self.assertIsNone(lowerCamelCase							)

         _UpperCAmelCase							=    find_backend("""    if not is_tokenizers_available():"""							)
         self.assertEqual(lowerCamelCase       ,				"""tokenizers"""							)

         _UpperCAmelCase							=    find_backend("""    if not is_tensorflow_text_available():"""							)
         self.assertEqual(lowerCamelCase       ,				"""tensorflow_text"""							)

         _UpperCAmelCase							=    find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tokenizers"""							)

         _UpperCAmelCase							=    find_backend(
             """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tensorflow_text"""							)

         _UpperCAmelCase							=    find_backend(
             """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""							)
         self.assertEqual(lowerCamelCase       ,				"""sentencepiece_and_tokenizers_and_vision"""							)


  def  lowerCamelCase       (      self		:			int							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    read_init()
         # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
         self.assertIn("""torch"""       ,				lowerCamelCase							)
         self.assertIn("""tensorflow_text"""       ,				lowerCamelCase							)
         self.assertIn("""sentencepiece_and_tokenizers"""       ,				lowerCamelCase							)

         # Likewise, we can't assert on the exact content of a key
         self.assertIn("""BertModel"""       ,				objects["""torch"""]							)
         self.assertIn("""TFBertModel"""       ,				objects["""tf"""]							)
         self.assertIn("""FlaxBertModel"""       ,				objects["""flax"""]							)
         self.assertIn("""BertModel"""       ,				objects["""torch"""]							)
         self.assertIn("""TFBertTokenizer"""       ,				objects["""tensorflow_text"""]							)
         self.assertIn("""convert_slow_tokenizer"""       ,				objects["""sentencepiece_and_tokenizers"""]							)


  def  lowerCamelCase       (      self		:			int							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    create_dummy_object("""CONSTANT"""       ,				"""'torch'"""							)
         self.assertEqual(lowerCamelCase       ,				"""\nCONSTANT = None\n"""							)

         _UpperCAmelCase							=    create_dummy_object("""function"""       ,				"""'torch'"""							)
         self.assertEqual(
             lowerCamelCase       ,				"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""							)

         _UpperCAmelCase							=    """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
         _UpperCAmelCase							=    create_dummy_object("""FakeClass"""       ,				"""'torch'"""							)
         self.assertEqual(lowerCamelCase       ,				lowerCamelCase							)





  def  lowerCamelCase       (      self		:			Dict							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
         _UpperCAmelCase							=    create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}							)
         self.assertEqual(dummy_files["""torch"""]       ,				lowerCamelCase							)