





import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
    from PIL import Image

    from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor







@require_tokenizers
@require_vision
class __a (   unittest.TestCase			):





   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       tempfile.mkdtemp()

      # fmt: off
      __SCREAMING_SNAKE_CASE      							=       ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
      # fmt: on
      __SCREAMING_SNAKE_CASE      							=       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      							=       {
          """do_resize""": True,
          """size""": {"""height""": 18, """width""": 18},
          """do_normalize""": True,
          """image_mean""": [0.5, 0.5, 0.5],
          """image_std""": [0.5, 0.5, 0.5],
      }
      __SCREAMING_SNAKE_CASE      							=       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				:			str				,**lowerCamelCase				:			Any    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Tuple				,**lowerCamelCase				:			List[Any]    ):

      '''simple docstring'''

      return ViTImageProcessor.from_pretrained(self.tmpdirname				,**lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      shutil.rmtree(self.tmpdirname    )





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

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

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

      return image_inputs





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_tokenizer()
      __SCREAMING_SNAKE_CASE      							=       self.get_image_processor()

      __SCREAMING_SNAKE_CASE      							=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase				,image_processor=lowerCamelCase    )

      processor.save_pretrained(self.tmpdirname    )
      __SCREAMING_SNAKE_CASE      							=       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname    )

      self.assertEqual(processor.tokenizer.get_vocab()				,tokenizer.get_vocab()    )
      self.assertIsInstance(processor.tokenizer				,(BertTokenizer, BertTokenizerFast)    )

      self.assertEqual(processor.image_processor.to_json_string()				,image_processor.to_json_string()    )
      self.assertIsInstance(processor.image_processor				,lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       VisionTextDualEncoderProcessor(
          tokenizer=self.get_tokenizer()				,image_processor=self.get_image_processor()    )
      processor.save_pretrained(self.tmpdirname    )

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

      __SCREAMING_SNAKE_CASE      							=       VisionTextDualEncoderProcessor.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				,(BertTokenizer, BertTokenizerFast)    )

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





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_image_processor()
      __SCREAMING_SNAKE_CASE      							=       self.get_tokenizer()

      __SCREAMING_SNAKE_CASE      							=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase				,image_processor=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE      							=       image_processor(lowerCamelCase				,return_tensors="""np"""    )
      __SCREAMING_SNAKE_CASE      							=       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      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_image_processor()
      __SCREAMING_SNAKE_CASE      							=       self.get_tokenizer()

      __SCREAMING_SNAKE_CASE      							=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase				,image_processor=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       """lower newer"""

      __SCREAMING_SNAKE_CASE      							=       processor(text=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       tokenizer(lowerCamelCase    )

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





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_image_processor()
      __SCREAMING_SNAKE_CASE      							=       self.get_tokenizer()

      __SCREAMING_SNAKE_CASE      							=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase				,image_processor=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       """lower newer"""
      __SCREAMING_SNAKE_CASE      							=       self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE      							=       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 self.assertRaises(lowerCamelCase    ):
         processor()





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_image_processor()
      __SCREAMING_SNAKE_CASE      							=       self.get_tokenizer()

      __SCREAMING_SNAKE_CASE      							=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase				,image_processor=lowerCamelCase    )

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

      __SCREAMING_SNAKE_CASE      							=       processor.batch_decode(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       tokenizer.batch_decode(lowerCamelCase    )

      self.assertListEqual(lowerCamelCase				,lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_image_processor()
      __SCREAMING_SNAKE_CASE      							=       self.get_tokenizer()

      __SCREAMING_SNAKE_CASE      							=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase				,image_processor=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       """lower newer"""
      __SCREAMING_SNAKE_CASE      							=       self.prepare_image_inputs()

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

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












'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a        =						logging.get_logger(__name__)

a        =						{
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}







class __a (   _snake_case			):
   __UpperCamelCase   : Tuple	      =	'camembert'





   def __init__(					self				:			int				,lowerCamelCase				:			List[Any]=3_0522				,lowerCamelCase				:			List[Any]=768				,lowerCamelCase				:			str=12				,lowerCamelCase				:			List[str]=12				,lowerCamelCase				:			Optional[Any]=3072				,lowerCamelCase				:			Tuple="gelu"				,lowerCamelCase				:			List[str]=0.1				,lowerCamelCase				:			Tuple=0.1				,lowerCamelCase				:			Union[str, Any]=512				,lowerCamelCase				:			Dict=2				,lowerCamelCase				:			Tuple=0.02				,lowerCamelCase				:			List[Any]=1E-1_2				,lowerCamelCase				:			Union[str, Any]=1				,lowerCamelCase				:			Optional[Any]=0				,lowerCamelCase				:			List[Any]=2				,lowerCamelCase				:			List[str]="absolute"				,lowerCamelCase				:			int=True				,lowerCamelCase				:			Any=None				,**lowerCamelCase				:			Optional[Any]				,):

      '''simple docstring'''

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

      __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      							=       position_embedding_type
      __SCREAMING_SNAKE_CASE      							=       use_cache
      __SCREAMING_SNAKE_CASE      							=       classifier_dropout







class __a (   _snake_case			):





   @property
   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      if self.task == "multiple-choice":
         __SCREAMING_SNAKE_CASE      							=       {0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
         __SCREAMING_SNAKE_CASE      							=       {0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]    )





