




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


SCREAMING_SNAKE_CASE__       :      Optional[int]  =   get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__	(lowerCAmelCase__    ,				unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	str	     =      XLMRobertaTokenizer
    lowerCamelCase_      :	str	     =      XLMRobertaTokenizerFast
    lowerCamelCase_      :	int	     =      True
    lowerCamelCase_      :	List[Any]	     =      True







    def     _lowercase     (    self     )     ->      Optional[int]:
           super().setUp()

           # We have a SentencePiece fixture for testing
           lowerCamelCase   :      Any			 =					XLMRobertaTokenizer(UpperCamelCase__     ,  keep_accents=UpperCamelCase__     )
           tokenizer.save_pretrained(self.tmpdirname     )







    def     _lowercase     (    self     )     ->      List[str]:
           lowerCamelCase   :      Optional[int]			 =					"<pad>"
           lowerCamelCase   :      Union[str, 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     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      str			 =					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     _lowercase     (    self     )     ->      Dict:
           self.assertEqual(self.get_tokenizer().vocab_size     ,  1002     )







    def     _lowercase     (    self     )     ->      Any:
           lowerCamelCase   :      List[str]			 =					XLMRobertaTokenizer(UpperCamelCase__     ,  keep_accents=UpperCamelCase__     )

           lowerCamelCase   :      str			 =					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   :      int			 =					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   :      Optional[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   :      Any			 =					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     _lowercase     (    self     )     ->      Any:
           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   :      Dict			 =					(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   :      str			 =					self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,  **UpperCamelCase__     )
                         lowerCamelCase   :      Any			 =					self.tokenizer_class.from_pretrained(UpperCamelCase__     ,  **UpperCamelCase__     )

                         lowerCamelCase   :      Optional[Any]			 =					tempfile.mkdtemp()

                         lowerCamelCase   :      Dict			 =					tokenizer_r.save_pretrained(UpperCamelCase__     )
                         lowerCamelCase   :      Tuple			 =					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   :      Any			 =					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   :      int			 =					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__     )     )
                                # 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   :      str			 =					tempfile.mkdtemp()

                         lowerCamelCase   :      int			 =					tokenizer_r.save_pretrained(UpperCamelCase__     ,  legacy_format=UpperCamelCase__     )
                         lowerCamelCase   :      List[str]			 =					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   :      Optional[int]			 =					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__     )

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

                         lowerCamelCase   :      Any			 =					tokenizer_r.save_pretrained(UpperCamelCase__     ,  legacy_format=UpperCamelCase__     )
                         lowerCamelCase   :      int			 =					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   :      Optional[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     _lowercase     (    self     )     ->      List[str]:
           return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"     )







    def     _lowercase     (    self     )     ->      List[Any]:
           with tempfile.NamedTemporaryFile() as f:
                  shutil.copyfile(UpperCamelCase__     ,  f.name     )
                  lowerCamelCase   :      Optional[Any]			 =					XLMRobertaTokenizer(f.name     ,  keep_accents=UpperCamelCase__     )
                  lowerCamelCase   :      Optional[Any]			 =					pickle.dumps(UpperCamelCase__     )
           pickle.loads(UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      int:
           if not self.test_rust_tokenizer:
                  return

           lowerCamelCase   :      Dict			 =					self.get_tokenizer()
           lowerCamelCase   :      Optional[Any]			 =					self.get_rust_tokenizer()

           lowerCamelCase   :      int			 =					"I was born in 92000, and this is falsé."

           lowerCamelCase   :      List[str]			 =					tokenizer.tokenize(UpperCamelCase__     )
           lowerCamelCase   :      List[Any]			 =					rust_tokenizer.tokenize(UpperCamelCase__     )
           self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__     )

           lowerCamelCase   :      str			 =					tokenizer.encode(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					rust_tokenizer.encode(UpperCamelCase__     ,  add_special_tokens=UpperCamelCase__     )
           self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__     )

           lowerCamelCase   :      Optional[Any]			 =					self.get_rust_tokenizer()
           lowerCamelCase   :      List[str]			 =					tokenizer.encode(UpperCamelCase__     )
           lowerCamelCase   :      int			 =					rust_tokenizer.encode(UpperCamelCase__     )
           self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__     )







    @slow
    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      str			 =					"Hello World!"
           lowerCamelCase   :      Optional[Any]			 =					[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     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      List[str]			 =					(
               "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     _lowercase     (    self     )     ->      Any:
           # fmt: off
           lowerCamelCase   :      int			 =					{"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"     ,  )












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


SCREAMING_SNAKE_CASE__       :      Optional[int]  =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :      Dict  =   {
    'salesforce/blip2-opt-2.7b': 'https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json',
}




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Union[str, Any]	     =      """blip_2_vision_model"""







    def __init__(    self     ,  UpperCamelCase__=1408     ,  UpperCamelCase__=6144     ,  UpperCamelCase__=39     ,  UpperCamelCase__=16     ,  UpperCamelCase__=224     ,  UpperCamelCase__=14     ,  UpperCamelCase__="gelu"     ,  UpperCamelCase__=0.00001     ,  UpperCamelCase__=0.0     ,  UpperCamelCase__=1e-10     ,  UpperCamelCase__=True     ,  **UpperCamelCase__     ,  )     ->      Optional[Any]:
           super().__init__(**UpperCamelCase__     )

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







    @classmethod
    def     _lowercase     (    cls     ,  UpperCamelCase__     ,  **UpperCamelCase__     )     ->      "PretrainedConfig":
           cls._set_token_in_kwargs(UpperCamelCase__     )

           lowerCamelCase ,      lowerCamelCase   :      List[str]			 =					cls.get_config_dict(UpperCamelCase__     ,  **UpperCamelCase__     )

           # get the vision config dict if we are loading from Blip2Config
           if config_dict.get("model_type"     ) == "blip-2":
                  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 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Dict	     =      """blip_2_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__=0.02     ,  UpperCamelCase__=1e-12     ,  UpperCamelCase__=0     ,  UpperCamelCase__="absolute"     ,  UpperCamelCase__=2     ,  UpperCamelCase__=1408     ,  **UpperCamelCase__     ,  )     ->      int:
           super().__init__(pad_token_id=UpperCamelCase__     ,  **UpperCamelCase__     )

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







    @classmethod
    def     _lowercase     (    cls     ,  UpperCamelCase__     ,  **UpperCamelCase__     )     ->      "PretrainedConfig":
           cls._set_token_in_kwargs(UpperCamelCase__     )

           lowerCamelCase ,      lowerCamelCase   :      str			 =					cls.get_config_dict(UpperCamelCase__     ,  **UpperCamelCase__     )

           # get the qformer config dict if we are loading from Blip2Config
           if config_dict.get("model_type"     ) == "blip-2":
                  lowerCamelCase   :      int			 =					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 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	List[str]	     =      """blip-2"""
    lowerCamelCase_      :	int	     =      True







    def __init__(    self     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  UpperCamelCase__=32     ,  **UpperCamelCase__     )     ->      str:
           super().__init__(**UpperCamelCase__     )

           if vision_config is None:
                  lowerCamelCase   :      List[Any]			 =					{}
                  logger.info("vision_config is None. initializing the Blip2VisionConfig with default values."     )

           if qformer_config is None:
                  lowerCamelCase   :      List[Any]			 =					{}
                  logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values."     )

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

           lowerCamelCase   :      Optional[int]			 =					BlipaVisionConfig(**UpperCamelCase__     )
           lowerCamelCase   :      str			 =					BlipaQFormerConfig(**UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					text_config["model_type"] if "model_type" in text_config else "opt"
           lowerCamelCase   :      str			 =					CONFIG_MAPPING[text_model_type](**UpperCamelCase__     )

           lowerCamelCase   :      Optional[Any]			 =					self.text_config.tie_word_embeddings
           lowerCamelCase   :      int			 =					self.text_config.is_encoder_decoder

           lowerCamelCase   :      Optional[Any]			 =					num_query_tokens
           lowerCamelCase   :      int			 =					self.vision_config.hidden_size
           lowerCamelCase   :      Tuple			 =					self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
           lowerCamelCase   :      Dict			 =					1.0
           lowerCamelCase   :      List[Any]			 =					0.02







    @classmethod
    def     _lowercase     (    cls     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     ,  )     ->      str:
           return cls(
               vision_config=vision_config.to_dict()     ,  qformer_config=qformer_config.to_dict()     ,  text_config=text_config.to_dict()     ,  **UpperCamelCase__     ,  )







    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      Tuple			 =					copy.deepcopy(self.__dict__     )
           lowerCamelCase   :      Tuple			 =					self.vision_config.to_dict()
           lowerCamelCase   :      int			 =					self.qformer_config.to_dict()
           lowerCamelCase   :      Optional[Any]			 =					self.text_config.to_dict()
           lowerCamelCase   :      int			 =					self.__class__.model_type
           return output






