


"""simple docstring"""
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,
     BertTokenizerFast,
     BlipImageProcessor,
     GPTaTokenizer,
     InstructBlipProcessor,
     PreTrainedTokenizerFast,
 )




@require_vision
class 	lowercase__				(					unittest.TestCase				):





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Dict   =		tempfile.mkdtemp()

      _lowerCamelCase	:					int   =		BlipImageProcessor()
      _lowerCamelCase	:					str   =		GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model""")
      _lowerCamelCase	:					List[Any]   =		BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert""")

      _lowerCamelCase	:					str   =		InstructBlipProcessor(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)

      processor.save_pretrained(self.tmpdirname)





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  Tuple:
      return AutoProcessor.from_pretrained(self.tmpdirname      ,   **SCREAMING_SNAKE_CASE).tokenizer





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      return AutoProcessor.from_pretrained(self.tmpdirname      ,   **SCREAMING_SNAKE_CASE).image_processor





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  str:
      return AutoProcessor.from_pretrained(self.tmpdirname      ,   **SCREAMING_SNAKE_CASE).qformer_tokenizer





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      shutil.rmtree(self.tmpdirname)





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Any   =		[np.random.randint(255      ,   size=(3, 30, 400)      ,   dtype=np.uinta)]

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

      return image_inputs





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					Any   =		InstructBlipProcessor(
          tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()      ,   qformer_tokenizer=self.get_qformer_tokenizer()      ,   )
      processor.save_pretrained(self.tmpdirname)

      _lowerCamelCase	:					Dict   =		self.get_tokenizer(bos_token="""(BOS)"""      ,   eos_token="""(EOS)""")
      _lowerCamelCase	:					Dict   =		self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE      ,   padding_value=1.0)

      _lowerCamelCase	:					Union[str, Any]   =		InstructBlipProcessor.from_pretrained(
          self.tmpdirname      ,   bos_token="""(BOS)"""      ,   eos_token="""(EOS)"""      ,   do_normalize=SCREAMING_SNAKE_CASE      ,   padding_value=1.0)

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

      self.assertEqual(processor.image_processor.to_json_string()      ,   image_processor_add_kwargs.to_json_string())
      self.assertIsInstance(processor.image_processor      ,   SCREAMING_SNAKE_CASE)
      self.assertIsInstance(processor.qformer_tokenizer      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					Optional[int]   =		self.get_image_processor()
      _lowerCamelCase	:					List[Any]   =		self.get_tokenizer()
      _lowerCamelCase	:					int   =		self.get_qformer_tokenizer()

      _lowerCamelCase	:					Union[str, Any]   =		InstructBlipProcessor(
          tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE      ,   qformer_tokenizer=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		self.prepare_image_inputs()

      _lowerCamelCase	:					Optional[Any]   =		image_processor(SCREAMING_SNAKE_CASE      ,   return_tensors="""np""")
      _lowerCamelCase	:					Any   =		processor(images=SCREAMING_SNAKE_CASE      ,   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:
      _lowerCamelCase	:					Optional[Any]   =		self.get_image_processor()
      _lowerCamelCase	:					int   =		self.get_tokenizer()
      _lowerCamelCase	:					str   =		self.get_qformer_tokenizer()

      _lowerCamelCase	:					Optional[Any]   =		InstructBlipProcessor(
          tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE      ,   qformer_tokenizer=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[str]   =		"""lower newer"""

      _lowerCamelCase	:					Dict   =		processor(text=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		tokenizer(SCREAMING_SNAKE_CASE      ,   return_token_type_ids=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		qformer_tokenizer(SCREAMING_SNAKE_CASE      ,   return_token_type_ids=SCREAMING_SNAKE_CASE)

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

      for key in encoded_tokens_qformer.keys():
         self.assertListEqual(encoded_tokens_qformer[key]      ,   encoded_processor["""qformer_""" + key])





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Union[str, Any]   =		self.get_image_processor()
      _lowerCamelCase	:					List[str]   =		self.get_tokenizer()
      _lowerCamelCase	:					Optional[Any]   =		self.get_qformer_tokenizer()

      _lowerCamelCase	:					str   =		InstructBlipProcessor(
          tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE      ,   qformer_tokenizer=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		"""lower newer"""
      _lowerCamelCase	:					Union[str, Any]   =		self.prepare_image_inputs()

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

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

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





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Optional[Any]   =		self.get_image_processor()
      _lowerCamelCase	:					Union[str, Any]   =		self.get_tokenizer()
      _lowerCamelCase	:					Any   =		self.get_qformer_tokenizer()

      _lowerCamelCase	:					List[str]   =		InstructBlipProcessor(
          tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE      ,   qformer_tokenizer=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[Any]   =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      _lowerCamelCase	:					str   =		processor.batch_decode(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE)

      self.assertListEqual(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					List[Any]   =		self.get_image_processor()
      _lowerCamelCase	:					str   =		self.get_tokenizer()
      _lowerCamelCase	:					Optional[int]   =		self.get_qformer_tokenizer()

      _lowerCamelCase	:					int   =		InstructBlipProcessor(
          tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE      ,   qformer_tokenizer=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Any   =		"""lower newer"""
      _lowerCamelCase	:					str   =		self.prepare_image_inputs()

      _lowerCamelCase	:					List[str]   =		processor(text=SCREAMING_SNAKE_CASE      ,   images=SCREAMING_SNAKE_CASE)

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





"""simple docstring"""
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				=						{
    """kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
    """kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
    """kssteven/ibert-roberta-large-mnli""": (
        """https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
    ),
}




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					'''ibert'''





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=3_0522      ,   SCREAMING_SNAKE_CASE=768      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=3072      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=1e-1_2      ,   SCREAMING_SNAKE_CASE=1      ,   SCREAMING_SNAKE_CASE=0      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE="absolute"      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE="none"      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Any:
      super().__init__(pad_token_id=SCREAMING_SNAKE_CASE      ,   bos_token_id=SCREAMING_SNAKE_CASE      ,   eos_token_id=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

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







class 	lowercase__				(					A_				):





   @property
   def 		UpperCamelCase_  (							self)  ->  Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
         _lowerCamelCase	:					Dict   =		{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
         _lowerCamelCase	:					Optional[int]   =		{0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ])

