





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class 					__A   (  unittest.TestCase			):


				def _snake_case				(						self							):
								lowerCamelCase		       =tempfile.mkdtemp()

								lowerCamelCase		       =CLIPImageProcessor()
								lowerCamelCase		       =BertTokenizer.from_pretrained(
								    """hf-internal-testing/tiny-random-BertModel"""      ,       model_input_names=["""input_ids""", """attention_mask"""]							)

								lowerCamelCase		       =GitProcessor(UpperCAmelCase_      ,       UpperCAmelCase_							)

								processor.save_pretrained(self.tmpdirname							)


				def _snake_case				(						self      ,       **UpperCAmelCase_							):
								return AutoProcessor.from_pretrained(self.tmpdirname      ,       **UpperCAmelCase_							).tokenizer


				def _snake_case				(						self      ,       **UpperCAmelCase_							):
								return AutoProcessor.from_pretrained(self.tmpdirname      ,       **UpperCAmelCase_							).image_processor


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


				def _snake_case				(						self							):
								lowerCamelCase		       =[np.random.randint(255      ,       size=(3, 30, 400)      ,       dtype=np.uinta							)]

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

								return image_inputs


				def _snake_case				(						self							):
								lowerCamelCase		       =GitProcessor(tokenizer=self.get_tokenizer()      ,       image_processor=self.get_image_processor()							)
								processor.save_pretrained(self.tmpdirname							)

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

								lowerCamelCase		       =GitProcessor.from_pretrained(
								    self.tmpdirname      ,       bos_token="""(BOS)"""      ,       eos_token="""(EOS)"""      ,       do_normalize=UpperCAmelCase_      ,       padding_value=1.0							)

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

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


				def _snake_case				(						self							):
								lowerCamelCase		       =self.get_image_processor()
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =GitProcessor(tokenizer=UpperCAmelCase_      ,       image_processor=UpperCAmelCase_							)

								lowerCamelCase		       =self.prepare_image_inputs()

								lowerCamelCase		       =image_processor(UpperCAmelCase_      ,       return_tensors="""np"""							)
								lowerCamelCase		       =processor(images=UpperCAmelCase_      ,       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 _snake_case				(						self							):
								lowerCamelCase		       =self.get_image_processor()
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =GitProcessor(tokenizer=UpperCAmelCase_      ,       image_processor=UpperCAmelCase_							)

								lowerCamelCase		       ="""lower newer"""

								lowerCamelCase		       =processor(text=UpperCAmelCase_							)

								lowerCamelCase		       =tokenizer(UpperCAmelCase_      ,       return_token_type_ids=UpperCAmelCase_							)

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


				def _snake_case				(						self							):
								lowerCamelCase		       =self.get_image_processor()
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =GitProcessor(tokenizer=UpperCAmelCase_      ,       image_processor=UpperCAmelCase_							)

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

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

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

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


				def _snake_case				(						self							):
								lowerCamelCase		       =self.get_image_processor()
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =GitProcessor(tokenizer=UpperCAmelCase_      ,       image_processor=UpperCAmelCase_							)

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

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

								self.assertListEqual(UpperCAmelCase_      ,       UpperCAmelCase_							)


				def _snake_case				(						self							):
								lowerCamelCase		       =self.get_image_processor()
								lowerCamelCase		       =self.get_tokenizer()

								lowerCamelCase		       =GitProcessor(tokenizer=UpperCAmelCase_      ,       image_processor=UpperCAmelCase_							)

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

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

								# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
								self.assertListEqual(list(inputs.keys()							)      ,       ["""input_ids""", """attention_mask""", """pixel_values"""]							)








from collections import OrderedDict
from typing import Mapping

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


_UpperCamelCase			       =			logging.get_logger(__name__)

_UpperCamelCase			       =			{
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}



class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""rembert"""




 def __init__(self       ,       __a=250300       ,       __a=1152       ,       __a=32       ,       __a=18       ,       __a=256       ,       __a=1664       ,       __a=4608       ,       __a="gelu"       ,       __a=0.0       ,       __a=0.0       ,       __a=0.1       ,       __a=512       ,       __a=2       ,       __a=0.02       ,       __a=1E-1_2       ,       __a=True       ,       __a=0       ,       __a=312       ,       __a=313       ,       **__a       ,       ) ->					str:






  """simple docstring"""





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

  UpperCAmelCase__							=				vocab_size
  UpperCAmelCase__							=				input_embedding_size
  UpperCAmelCase__							=				output_embedding_size
  UpperCAmelCase__							=				max_position_embeddings
  UpperCAmelCase__							=				hidden_size
  UpperCAmelCase__							=				num_hidden_layers
  UpperCAmelCase__							=				num_attention_heads
  UpperCAmelCase__							=				intermediate_size
  UpperCAmelCase__							=				hidden_act
  UpperCAmelCase__							=				hidden_dropout_prob
  UpperCAmelCase__							=				attention_probs_dropout_prob
  UpperCAmelCase__							=				classifier_dropout_prob
  UpperCAmelCase__							=				initializer_range
  UpperCAmelCase__							=				type_vocab_size
  UpperCAmelCase__							=				layer_norm_eps
  UpperCAmelCase__							=				use_cache
  UpperCAmelCase__							=				False







class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 @property
 def   UpperCamelCase__							(self ) ->					Mapping[str, Mapping[int, str]]:






  """simple docstring"""





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




 @property
 def   UpperCamelCase__							(self ) ->					float:






  """simple docstring"""





  return 1E-4



