




'''simple docstring'''



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 				_a							(       unittest.TestCase      ):





       '''simple docstring'''






       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[str]   =		tempfile.mkdtemp()

             SCREAMING_SNAKE_CASE					:	int   =		[
                 '[UNK]',
                 '[CLS]',
                 '[SEP]',
                 '[PAD]',
                 '[MASK]',
                 '的',
                 '价',
                 '格',
                 '是',
                 '15',
                 '便',
                 'alex',
                 '##andra',
                 '，',
                 '。',
                 '-',
                 't',
                 'shirt',
             ]
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		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]   )   )

             SCREAMING_SNAKE_CASE					:	Tuple   =		{
                 'do_resize': True,
                 'size': {'height': 224, 'width': 224},
                 'do_center_crop': True,
                 'crop_size': {'height': 18, 'width': 18},
                 'do_normalize': True,
                 'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
                 'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
                 'do_convert_rgb': True,
             }
             SCREAMING_SNAKE_CASE					:	List[Any]   =		os.path.join(self.tmpdirname,				A   )
             with open(self.image_processor_file,				'w',				encoding='utf-8'   ) as fp:
                   json.dump(A,				A   )







       def     UpperCamelCase_				(	self,				**A   ):







             '''simple docstring'''







             return BertTokenizer.from_pretrained(self.tmpdirname,				**A   )







       def     UpperCamelCase_				(	self,				**A   ):







             '''simple docstring'''







             return BertTokenizerFast.from_pretrained(self.tmpdirname,				**A   )







       def     UpperCamelCase_				(	self,				**A   ):







             '''simple docstring'''







             return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname,				**A   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             shutil.rmtree(self.tmpdirname   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







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

             SCREAMING_SNAKE_CASE					:	Dict   =		[Image.fromarray(np.moveaxis(A,				0,				-1   )   ) for x in image_inputs]

             return image_inputs







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[str]   =		self.get_tokenizer()
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		self.get_rust_tokenizer()
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		self.get_image_processor()

             SCREAMING_SNAKE_CASE					:	Any   =		ChineseCLIPProcessor(tokenizer=A,				image_processor=A   )
             processor_slow.save_pretrained(self.tmpdirname   )
             SCREAMING_SNAKE_CASE					:	Any   =		ChineseCLIPProcessor.from_pretrained(self.tmpdirname,				use_fast=A   )

             SCREAMING_SNAKE_CASE					:	int   =		ChineseCLIPProcessor(tokenizer=A,				image_processor=A   )
             processor_fast.save_pretrained(self.tmpdirname   )
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		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,				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     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	int   =		ChineseCLIPProcessor(tokenizer=self.get_tokenizer(),				image_processor=self.get_image_processor()   )
             processor.save_pretrained(self.tmpdirname   )

             SCREAMING_SNAKE_CASE					:	str   =		self.get_tokenizer(cls_token='(CLS)',				sep_token='(SEP)'   )
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		self.get_image_processor(do_normalize=A   )

             SCREAMING_SNAKE_CASE					:	int   =		ChineseCLIPProcessor.from_pretrained(
                 self.tmpdirname,				cls_token='(CLS)',				sep_token='(SEP)',				do_normalize=A   )

             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     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[Any]   =		self.get_image_processor()
             SCREAMING_SNAKE_CASE					:	List[Any]   =		self.get_tokenizer()

             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		ChineseCLIPProcessor(tokenizer=A,				image_processor=A   )

             SCREAMING_SNAKE_CASE					:	Tuple   =		self.prepare_image_inputs()

             SCREAMING_SNAKE_CASE					:	Tuple   =		image_processor(A,				return_tensors='np'   )
             SCREAMING_SNAKE_CASE					:	Any   =		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     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[Any]   =		self.get_image_processor()
             SCREAMING_SNAKE_CASE					:	List[str]   =		self.get_tokenizer()

             SCREAMING_SNAKE_CASE					:	Dict   =		ChineseCLIPProcessor(tokenizer=A,				image_processor=A   )

             SCREAMING_SNAKE_CASE					:	Tuple   =		'Alexandra，T-shirt的价格是15便士。'

             SCREAMING_SNAKE_CASE					:	Dict   =		processor(text=A   )

             SCREAMING_SNAKE_CASE					:	List[str]   =		tokenizer(A   )

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







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







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

             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		ChineseCLIPProcessor(tokenizer=A,				image_processor=A   )

             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		'Alexandra，T-shirt的价格是15便士。'
             SCREAMING_SNAKE_CASE					:	List[str]   =		self.prepare_image_inputs()

             SCREAMING_SNAKE_CASE					:	Any   =		processor(text=A,				images=A   )

             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(A   ):
                   processor()







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







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

             SCREAMING_SNAKE_CASE					:	int   =		ChineseCLIPProcessor(tokenizer=A,				image_processor=A   )

             SCREAMING_SNAKE_CASE					:	Tuple   =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             SCREAMING_SNAKE_CASE					:	List[str]   =		processor.batch_decode(A   )
             SCREAMING_SNAKE_CASE					:	Any   =		tokenizer.batch_decode(A   )

             self.assertListEqual(A,				A   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Any   =		self.get_image_processor()
             SCREAMING_SNAKE_CASE					:	List[Any]   =		self.get_tokenizer()

             SCREAMING_SNAKE_CASE					:	int   =		ChineseCLIPProcessor(tokenizer=A,				image_processor=A   )

             SCREAMING_SNAKE_CASE					:	Any   =		'Alexandra，T-shirt的价格是15便士。'
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		self.prepare_image_inputs()

             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		processor(text=A,				images=A   )

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



from ....configuration_utils import PretrainedConfig
from ....utils import logging


A   :     str       =				logging.get_logger(__name__)

# TODO: upload to AWS
A   :     Dict       =				{
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''retribert'''







  def __init__(							self       :				Optional[int]     ,   __magic_name__       :				Optional[Any]=30_522     ,   __magic_name__       :				int=768     ,   __magic_name__       :				Dict=8     ,   __magic_name__       :				List[Any]=12     ,   __magic_name__       :				Tuple=3_072     ,   __magic_name__       :				List[Any]="gelu"     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Tuple=512     ,   __magic_name__       :				Dict=2     ,   __magic_name__       :				int=0.02     ,   __magic_name__       :				List[Any]=1e-12     ,   __magic_name__       :				List[str]=True     ,   __magic_name__       :				Dict=128     ,   __magic_name__       :				Union[str, Any]=0     ,   **__magic_name__       :				List[Any]     ,   )     ->		Dict:
        super().__init__(pad_token_id=__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     vocab_size
        SCREAMING_SNAKE_CASE_      =     hidden_size
        SCREAMING_SNAKE_CASE_      =     num_hidden_layers
        SCREAMING_SNAKE_CASE_      =     num_attention_heads
        SCREAMING_SNAKE_CASE_      =     hidden_act
        SCREAMING_SNAKE_CASE_      =     intermediate_size
        SCREAMING_SNAKE_CASE_      =     hidden_dropout_prob
        SCREAMING_SNAKE_CASE_      =     attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_      =     max_position_embeddings
        SCREAMING_SNAKE_CASE_      =     type_vocab_size
        SCREAMING_SNAKE_CASE_      =     initializer_range
        SCREAMING_SNAKE_CASE_      =     layer_norm_eps
        SCREAMING_SNAKE_CASE_      =     share_encoders
        SCREAMING_SNAKE_CASE_      =     projection_dim


