
'''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			):
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   __A		:     str												=		tempfile.mkdtemp()

   __A		:     Dict												=		[
       """[UNK]""",
       """[CLS]""",
       """[SEP]""",
       """[PAD]""",
       """[MASK]""",
       """的""",
       """价""",
       """格""",
       """是""",
       """15""",
       """便""",
       """alex""",
       """##andra""",
       """，""",
       """。""",
       """-""",
       """t""",
       """shirt""",
   ]
   __A		:     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]       )       )

   __A		:     List[str]												=		{
       """do_resize""": True,
       """size""": {"""height""": 2_24, """width""": 2_24},
       """do_center_crop""": True,
       """crop_size""": {"""height""": 18, """width""": 18},
       """do_normalize""": True,
       """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
       """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
       """do_convert_rgb""": True,
   }
   __A		:     Optional[Any]												=		os.path.join(self.tmpdirname				,   lowerCamelCase       )
   with open(self.image_processor_file				,   """w"""				,   encoding="""utf-8"""       ) as fp:
     json.dump(lowerCamelCase				,   lowerCamelCase       )
 def 		lowercase_(   self			:    Any				,   **lowerCamelCase			:    str       ):



   """simple docstring"""





   return BertTokenizer.from_pretrained(self.tmpdirname				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Optional[int]				,   **lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





   return BertTokenizerFast.from_pretrained(self.tmpdirname				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    List[Any]				,   **lowerCamelCase			:    Union[str, Any]       ):



   """simple docstring"""





   return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   shutil.rmtree(self.tmpdirname       )
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		[np.random.randint(2_55				,   size=(3, 30, 4_00)				,   dtype=np.uinta       )]

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

   return image_inputs
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





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

   __A		:     List[str]												=		ChineseCLIPProcessor(tokenizer=lowerCamelCase				,   image_processor=lowerCamelCase       )
   processor_slow.save_pretrained(self.tmpdirname       )
   __A		:     Dict												=		ChineseCLIPProcessor.from_pretrained(self.tmpdirname				,   use_fast=lowerCamelCase       )

   __A		:     int												=		ChineseCLIPProcessor(tokenizer=lowerCamelCase				,   image_processor=lowerCamelCase       )
   processor_fast.save_pretrained(self.tmpdirname       )
   __A		:     Tuple												=		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 		lowercase_(   self			:    int       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		ChineseCLIPProcessor(tokenizer=self.get_tokenizer()				,   image_processor=self.get_image_processor()       )
   processor.save_pretrained(self.tmpdirname       )

   __A		:     List[Any]												=		self.get_tokenizer(cls_token="""(CLS)"""				,   sep_token="""(SEP)"""       )
   __A		:     Dict												=		self.get_image_processor(do_normalize=lowerCamelCase       )

   __A		:     Optional[int]												=		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 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		self.get_image_processor()
   __A		:     Dict												=		self.get_tokenizer()

   __A		:     List[Any]												=		ChineseCLIPProcessor(tokenizer=lowerCamelCase				,   image_processor=lowerCamelCase       )

   __A		:     str												=		self.prepare_image_inputs()

   __A		:     List[str]												=		image_processor(lowerCamelCase				,   return_tensors="""np"""       )
   __A		:     Optional[Any]												=		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 		lowercase_(   self			:    int       ):



   """simple docstring"""





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

   __A		:     Any												=		ChineseCLIPProcessor(tokenizer=lowerCamelCase				,   image_processor=lowerCamelCase       )

   __A		:     Tuple												=		"""Alexandra，T-shirt的价格是15便士。"""

   __A		:     str												=		processor(text=lowerCamelCase       )

   __A		:     Optional[int]												=		tokenizer(lowerCamelCase       )

   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]				,   encoded_processor[key]       )
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		self.get_image_processor()
   __A		:     Union[str, Any]												=		self.get_tokenizer()

   __A		:     Any												=		ChineseCLIPProcessor(tokenizer=lowerCamelCase				,   image_processor=lowerCamelCase       )

   __A		:     List[str]												=		"""Alexandra，T-shirt的价格是15便士。"""
   __A		:     List[Any]												=		self.prepare_image_inputs()

   __A		:     Optional[int]												=		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 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





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

   __A		:     List[str]												=		ChineseCLIPProcessor(tokenizer=lowerCamelCase				,   image_processor=lowerCamelCase       )

   __A		:     int												=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   __A		:     str												=		processor.batch_decode(lowerCamelCase       )
   __A		:     Union[str, Any]												=		tokenizer.batch_decode(lowerCamelCase       )

   self.assertListEqual(lowerCamelCase				,   lowerCamelCase       )






 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





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

   __A		:     Dict												=		ChineseCLIPProcessor(tokenizer=lowerCamelCase				,   image_processor=lowerCamelCase       )

   __A		:     List[str]												=		"""Alexandra，T-shirt的价格是15便士。"""
   __A		:     List[Any]												=		self.prepare_image_inputs()

   __A		:     List[str]												=		processor(text=lowerCamelCase				,   images=lowerCamelCase       )

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

'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__   :		int 		=logging.get_logger(__name__)

A__   :		Union[str, Any] 		={
    'facebook/dpr-ctx_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-single-nq-base': (
        'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-ctx_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-multiset-base': (
        'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
    ),
}







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''dpr'''
 def __init__(   self			:    Optional[int]				,   lowerCamelCase			:    Tuple=3_05_22				,   lowerCamelCase			:    List[str]=7_68				,   lowerCamelCase			:    Union[str, Any]=12				,   lowerCamelCase			:    Tuple=12				,   lowerCamelCase			:    Dict=30_72				,   lowerCamelCase			:    List[Any]="gelu"				,   lowerCamelCase			:    List[Any]=0.1				,   lowerCamelCase			:    Union[str, Any]=0.1				,   lowerCamelCase			:    Optional[Any]=5_12				,   lowerCamelCase			:    List[Any]=2				,   lowerCamelCase			:    int=0.02				,   lowerCamelCase			:    Union[str, Any]=1e-1_2				,   lowerCamelCase			:    int=0				,   lowerCamelCase			:    List[str]="absolute"				,   lowerCamelCase			:    int = 0				,   **lowerCamelCase			:    List[Any]				,   ):



   """simple docstring"""





   super().__init__(pad_token_id=lowerCamelCase				,   **lowerCamelCase       )

   __A		:     Dict												=		vocab_size
   __A		:     int												=		hidden_size
   __A		:     str												=		num_hidden_layers
   __A		:     Union[str, Any]												=		num_attention_heads
   __A		:     Union[str, Any]												=		hidden_act
   __A		:     Optional[Any]												=		intermediate_size
   __A		:     str												=		hidden_dropout_prob
   __A		:     int												=		attention_probs_dropout_prob
   __A		:     int												=		max_position_embeddings
   __A		:     Dict												=		type_vocab_size
   __A		:     Dict												=		initializer_range
   __A		:     Union[str, Any]												=		layer_norm_eps
   __A		:     int												=		projection_dim
   __A		:     List[Any]												=		position_embedding_type



