



'''simple docstring'''





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase										=     get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
@require_tokenizers
class 	lowerCAmelCase_(    lowerCamelCase__						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				Union[str, Any]           =				XLMRobertaTokenizer
  __lowercase							:				str           =				XLMRobertaTokenizerFast
  __lowercase							:				Tuple           =				True
  __lowercase							:				Dict           =				True




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      super().setUp()

      # We have a SentencePiece fixture for testing
      lowerCAmelCase__			:  List[str]											= XLMRobertaTokenizer(__UpperCAmelCase  ,keep_accents=__UpperCAmelCase				)
      tokenizer.save_pretrained(self.tmpdirname				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      lowerCAmelCase__			:  Optional[Any]											= "<pad>"
      lowerCAmelCase__			:  Optional[Any]											= 1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase				)  ,__UpperCAmelCase				)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase				)  ,__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  Tuple											= list(self.get_tokenizer().get_vocab().keys()				)

      self.assertEqual(vocab_keys[0]  ,"""<s>"""				)
      self.assertEqual(vocab_keys[1]  ,"""<pad>"""				)
      self.assertEqual(vocab_keys[-1]  ,"""<mask>"""				)
      self.assertEqual(len(__UpperCAmelCase				)  ,1002				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      self.assertEqual(self.get_tokenizer().vocab_size  ,1002				)




  def 							UpperCAmelCase_     (   self				)   ->		int:
      lowerCAmelCase__			:  Tuple											= XLMRobertaTokenizer(__UpperCAmelCase  ,keep_accents=__UpperCAmelCase				)

      lowerCAmelCase__			:  Optional[Any]											= tokenizer.tokenize("""This is a test"""				)
      self.assertListEqual(__UpperCAmelCase  ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)  ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]  ,)

      lowerCAmelCase__			:  Any											= tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
      self.assertListEqual(
          __UpperCAmelCase  ,[
              SPIECE_UNDERLINE + """I""",
              SPIECE_UNDERLINE + """was""",
              SPIECE_UNDERLINE + """b""",
              """or""",
              """n""",
              SPIECE_UNDERLINE + """in""",
              SPIECE_UNDERLINE + """""",
              """9""",
              """2""",
              """0""",
              """0""",
              """0""",
              """,""",
              SPIECE_UNDERLINE + """and""",
              SPIECE_UNDERLINE + """this""",
              SPIECE_UNDERLINE + """is""",
              SPIECE_UNDERLINE + """f""",
              """al""",
              """s""",
              """é""",
              """.""",
          ]  ,)
      lowerCAmelCase__			:  int											= tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)
      self.assertListEqual(
          __UpperCAmelCase  ,[
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
              #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
          ]  ,)

      lowerCAmelCase__			:  str											= tokenizer.convert_ids_to_tokens(__UpperCAmelCase				)
      self.assertListEqual(
          __UpperCAmelCase  ,[
              SPIECE_UNDERLINE + """I""",
              SPIECE_UNDERLINE + """was""",
              SPIECE_UNDERLINE + """b""",
              """or""",
              """n""",
              SPIECE_UNDERLINE + """in""",
              SPIECE_UNDERLINE + """""",
              """<unk>""",
              """2""",
              """0""",
              """0""",
              """0""",
              """,""",
              SPIECE_UNDERLINE + """and""",
              SPIECE_UNDERLINE + """this""",
              SPIECE_UNDERLINE + """is""",
              SPIECE_UNDERLINE + """f""",
              """al""",
              """s""",
              """<unk>""",
              """.""",
          ]  ,)




  def 							UpperCAmelCase_     (   self				)   ->		List[str]:




      if not self.test_slow_tokenizer:
          # as we don't have a slow version, we can't compare the outputs between slow and fast versions
          return

      lowerCAmelCase__			:  List[Any]											= (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
              lowerCAmelCase__			:  List[str]											= self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase  ,**__UpperCAmelCase				)
              lowerCAmelCase__			:  Optional[int]											= self.tokenizer_class.from_pretrained(__UpperCAmelCase  ,**__UpperCAmelCase				)

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

              lowerCAmelCase__			:  Optional[Any]											= tokenizer_r.save_pretrained(__UpperCAmelCase				)
              lowerCAmelCase__			:  Any											= tokenizer_p.save_pretrained(__UpperCAmelCase				)

              # Checks it save with the same files + the tokenizer.json file for the fast one
              self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files				)				)
              lowerCAmelCase__			:  Dict											= tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f				)
              self.assertSequenceEqual(__UpperCAmelCase  ,__UpperCAmelCase				)

              # Checks everything loads correctly in the same way
              lowerCAmelCase__			:  Optional[int]											= tokenizer_r.from_pretrained(__UpperCAmelCase				)
              lowerCAmelCase__			:  Union[str, Any]											= tokenizer_p.from_pretrained(__UpperCAmelCase				)

              # Check special tokens are set accordingly on Rust and Python
              for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(__UpperCAmelCase  ,__UpperCAmelCase				)				)
                  # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                  # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

              shutil.rmtree(__UpperCAmelCase				)

              # Save tokenizer rust, legacy_format=True
              lowerCAmelCase__			:  List[str]											= tempfile.mkdtemp()

              lowerCAmelCase__			:  Dict											= tokenizer_r.save_pretrained(__UpperCAmelCase  ,legacy_format=__UpperCAmelCase				)
              lowerCAmelCase__			:  Any											= tokenizer_p.save_pretrained(__UpperCAmelCase				)

              # Checks it save with the same files
              self.assertSequenceEqual(__UpperCAmelCase  ,__UpperCAmelCase				)

              # Checks everything loads correctly in the same way
              lowerCAmelCase__			:  List[str]											= tokenizer_r.from_pretrained(__UpperCAmelCase				)
              lowerCAmelCase__			:  Dict											= tokenizer_p.from_pretrained(__UpperCAmelCase				)

              # Check special tokens are set accordingly on Rust and Python
              for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(__UpperCAmelCase  ,__UpperCAmelCase				)				)

              shutil.rmtree(__UpperCAmelCase				)

              # Save tokenizer rust, legacy_format=False
              lowerCAmelCase__			:  int											= tempfile.mkdtemp()

              lowerCAmelCase__			:  Optional[int]											= tokenizer_r.save_pretrained(__UpperCAmelCase  ,legacy_format=__UpperCAmelCase				)
              lowerCAmelCase__			:  List[Any]											= tokenizer_p.save_pretrained(__UpperCAmelCase				)

              # Checks it saved the tokenizer.json file
              self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files				)				)

              # Checks everything loads correctly in the same way
              lowerCAmelCase__			:  List[str]											= tokenizer_r.from_pretrained(__UpperCAmelCase				)
              lowerCAmelCase__			:  Any											= tokenizer_p.from_pretrained(__UpperCAmelCase				)

              # Check special tokens are set accordingly on Rust and Python
              for key in tokenizer_pp.special_tokens_map:
                  self.assertTrue(hasattr(__UpperCAmelCase  ,__UpperCAmelCase				)				)

              shutil.rmtree(__UpperCAmelCase				)




  @cached_property
  def 							UpperCAmelCase_     (   self				)   ->		int:
      return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""				)




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:




      with tempfile.NamedTemporaryFile() as f:
          shutil.copyfile(__UpperCAmelCase  ,f.name				)
          lowerCAmelCase__			:  Union[str, Any]											= XLMRobertaTokenizer(f.name  ,keep_accents=__UpperCAmelCase				)
          lowerCAmelCase__			:  int											= pickle.dumps(__UpperCAmelCase				)
      pickle.loads(__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:




      if not self.test_rust_tokenizer:
          return

      lowerCAmelCase__			:  Any											= self.get_tokenizer()
      lowerCAmelCase__			:  Any											= self.get_rust_tokenizer()

      lowerCAmelCase__			:  Dict											= "I was born in 92000, and this is falsé."

      lowerCAmelCase__			:  List[str]											= tokenizer.tokenize(__UpperCAmelCase				)
      lowerCAmelCase__			:  Optional[int]											= rust_tokenizer.tokenize(__UpperCAmelCase				)
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase				)

      lowerCAmelCase__			:  Optional[int]											= tokenizer.encode(__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase				)
      lowerCAmelCase__			:  str											= rust_tokenizer.encode(__UpperCAmelCase  ,add_special_tokens=__UpperCAmelCase				)
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase				)

      lowerCAmelCase__			:  Union[str, Any]											= self.get_rust_tokenizer()
      lowerCAmelCase__			:  Optional[Any]											= tokenizer.encode(__UpperCAmelCase				)
      lowerCAmelCase__			:  Union[str, Any]											= rust_tokenizer.encode(__UpperCAmelCase				)
      self.assertListEqual(__UpperCAmelCase  ,__UpperCAmelCase				)




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  List[str]											= "Hello World!"
      lowerCAmelCase__			:  str											= [0, 3_5378, 6661, 38, 2]
      # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
      # xlmr.eval()
      # xlmr.encode(symbols)

      self.assertListEqual(__UpperCAmelCase  ,self.big_tokenizer.encode(__UpperCAmelCase				)				)




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  Any											= (
          "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
          " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
      )
      lowerCAmelCase__			:  Union[str, Any]											= [
          0,
          3293,
          83,
          10,
          4552,
          4989,
          7986,
          678,
          10,
          5915,
          111,
          17_9459,
          12_4850,
          4,
          6044,
          237,
          12,
          6,
          5,
          6,
          4,
          6780,
          705,
          15,
          1388,
          44,
          378,
          1_0114,
          711,
          152,
          20,
          6,
          5,
          2_2376,
          642,
          1221,
          1_5190,
          3_4153,
          450,
          5608,
          959,
          1119,
          5_7702,
          136,
          186,
          47,
          1098,
          2_9367,
          47,
          # 4426, # What fairseq tokenizes from "<unk>": "_<"
          # 3678, # What fairseq tokenizes from "<unk>": "unk"
          # 2740, # What fairseq tokenizes from "<unk>": ">"
          3,  # What we tokenize from "<unk>": "<unk>"
          6,  # Residue from the tokenization: an extra sentencepiece underline
          4,
          6044,
          237,
          6284,
          5_0901,
          528,
          31,
          90,
          34,
          927,
          2,
      ]
      # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
      # xlmr.eval()
      # xlmr.encode(symbols)

      self.assertListEqual(__UpperCAmelCase  ,self.big_tokenizer.encode(__UpperCAmelCase				)				)






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  str											= {"input_ids": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=__UpperCAmelCase  ,model_name="""xlm-roberta-base"""  ,revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""  ,)








'''simple docstring'''





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCAmelCase										=     logging.get_logger(__name__)

_lowerCAmelCase										=     {
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				'''instructblip_vision_model'''




  def __init__(   self  ,__UpperCAmelCase=1408  ,__UpperCAmelCase=6144  ,__UpperCAmelCase=39  ,__UpperCAmelCase=16  ,__UpperCAmelCase=224  ,__UpperCAmelCase=14  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=1E-6  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=1E-10  ,__UpperCAmelCase=True  ,**__UpperCAmelCase  ,)   ->		List[Any]:
      super().__init__(**__UpperCAmelCase				)

      lowerCAmelCase__			:  Any											= hidden_size
      lowerCAmelCase__			:  List[str]											= intermediate_size
      lowerCAmelCase__			:  str											= num_hidden_layers
      lowerCAmelCase__			:  Tuple											= num_attention_heads
      lowerCAmelCase__			:  Union[str, Any]											= patch_size
      lowerCAmelCase__			:  int											= image_size
      lowerCAmelCase__			:  Tuple											= initializer_range
      lowerCAmelCase__			:  Optional[int]											= attention_dropout
      lowerCAmelCase__			:  List[str]											= layer_norm_eps
      lowerCAmelCase__			:  List[Any]											= hidden_act
      lowerCAmelCase__			:  int											= qkv_bias






  @classmethod
  def 							UpperCAmelCase_     (   cls  ,__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		"PretrainedConfig":
      cls._set_token_in_kwargs(__UpperCAmelCase				)

      lowerCAmelCase__				,       lowerCAmelCase__			:  Tuple											= cls.get_config_dict(__UpperCAmelCase  ,**__UpperCAmelCase				)

      # get the vision config dict if we are loading from InstructBlipConfig
      if config_dict.get("""model_type"""				) == "instructblip":
          lowerCAmelCase__			:  Optional[int]											= config_dict["""vision_config"""]

      if "model_type" in config_dict and hasattr(cls  ,"""model_type"""				) and config_dict["model_type"] != cls.model_type:
          logger.warning(
              F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
              F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""				)

      return cls.from_dict(__UpperCAmelCase  ,**__UpperCAmelCase				)







class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				int           =				'''instructblip_qformer'''




  def __init__(   self  ,__UpperCAmelCase=3_0522  ,__UpperCAmelCase=768  ,__UpperCAmelCase=12  ,__UpperCAmelCase=12  ,__UpperCAmelCase=3072  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=512  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-12  ,__UpperCAmelCase=0  ,__UpperCAmelCase="absolute"  ,__UpperCAmelCase=2  ,__UpperCAmelCase=1408  ,**__UpperCAmelCase  ,)   ->		Tuple:
      super().__init__(pad_token_id=__UpperCAmelCase  ,**__UpperCAmelCase				)

      lowerCAmelCase__			:  List[Any]											= vocab_size
      lowerCAmelCase__			:  Optional[Any]											= hidden_size
      lowerCAmelCase__			:  Any											= num_hidden_layers
      lowerCAmelCase__			:  int											= num_attention_heads
      lowerCAmelCase__			:  Tuple											= hidden_act
      lowerCAmelCase__			:  int											= intermediate_size
      lowerCAmelCase__			:  Tuple											= hidden_dropout_prob
      lowerCAmelCase__			:  Optional[Any]											= attention_probs_dropout_prob
      lowerCAmelCase__			:  Optional[Any]											= max_position_embeddings
      lowerCAmelCase__			:  Any											= initializer_range
      lowerCAmelCase__			:  Tuple											= layer_norm_eps
      lowerCAmelCase__			:  Dict											= position_embedding_type
      lowerCAmelCase__			:  int											= cross_attention_frequency
      lowerCAmelCase__			:  List[Any]											= encoder_hidden_size






  @classmethod
  def 							UpperCAmelCase_     (   cls  ,__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		"PretrainedConfig":
      cls._set_token_in_kwargs(__UpperCAmelCase				)

      lowerCAmelCase__				,       lowerCAmelCase__			:  Any											= cls.get_config_dict(__UpperCAmelCase  ,**__UpperCAmelCase				)

      # get the qformer config dict if we are loading from InstructBlipConfig
      if config_dict.get("""model_type"""				) == "instructblip":
          lowerCAmelCase__			:  Tuple											= config_dict["""qformer_config"""]

      if "model_type" in config_dict and hasattr(cls  ,"""model_type"""				) and config_dict["model_type"] != cls.model_type:
          logger.warning(
              F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
              F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""				)

      return cls.from_dict(__UpperCAmelCase  ,**__UpperCAmelCase				)







class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Any           =				'''instructblip'''
  __lowercase							:				str           =				True




  def __init__(   self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=32  ,**__UpperCAmelCase				)   ->		Any:
      super().__init__(**__UpperCAmelCase				)

      if vision_config is None:
          lowerCAmelCase__			:  Any											= {}
          logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values."""				)

      if qformer_config is None:
          lowerCAmelCase__			:  List[str]											= {}
          logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."""				)

      if text_config is None:
          lowerCAmelCase__			:  List[Any]											= {}
          logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""				)

      lowerCAmelCase__			:  Any											= InstructBlipVisionConfig(**__UpperCAmelCase				)
      lowerCAmelCase__			:  Tuple											= InstructBlipQFormerConfig(**__UpperCAmelCase				)
      lowerCAmelCase__			:  Tuple											= text_config["""model_type"""] if """model_type""" in text_config else """opt"""
      lowerCAmelCase__			:  Any											= CONFIG_MAPPING[text_model_type](**__UpperCAmelCase				)

      lowerCAmelCase__			:  Union[str, Any]											= self.text_config.tie_word_embeddings
      lowerCAmelCase__			:  Any											= self.text_config.is_encoder_decoder

      lowerCAmelCase__			:  int											= num_query_tokens
      lowerCAmelCase__			:  List[str]											= self.vision_config.hidden_size
      lowerCAmelCase__			:  Optional[int]											= self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
      lowerCAmelCase__			:  Optional[Any]											= 1.0
      lowerCAmelCase__			:  Dict											= 0.0_2




  @classmethod
  def 							UpperCAmelCase_     (   cls  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,**__UpperCAmelCase  ,)   ->		Union[str, Any]:
      return cls(
          vision_config=vision_config.to_dict()  ,qformer_config=qformer_config.to_dict()  ,text_config=text_config.to_dict()  ,**__UpperCAmelCase  ,)






  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  List[Any]											= copy.deepcopy(self.__dict__				)
      lowerCAmelCase__			:  str											= self.vision_config.to_dict()
      lowerCAmelCase__			:  Union[str, Any]											= self.qformer_config.to_dict()
      lowerCAmelCase__			:  Union[str, Any]											= self.text_config.to_dict()
      lowerCAmelCase__			:  str											= self.__class__.model_type
      return output


