




'''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


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

lowercase      :   Union[str, Any]      =       {
    """salesforce/blip2-opt-2.7b""": """https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json""",
}







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     List[Any]          =					"""blip_2_vision_model"""







     def __init__(				self							,__a=1_408							,__a=6_144							,__a=39							,__a=16							,__a=224							,__a=14							,__a="gelu"							,__a=0.00_001							,__a=0.0							,__a=1E-10							,__a=True							,**__a							,)      ->				Union[str, Any]:
          super().__init__(**__a							)

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







     @classmethod
     def 			snake_case_      (				cls							,__a							,**__a							)      ->				"PretrainedConfig":
          cls._set_token_in_kwargs(__a							)

          snake_case ,		snake_case			:      Dict        =			cls.get_config_dict(__a							,**__a							)

          # get the vision config dict if we are loading from Blip2Config
          if config_dict.get("""model_type"""							) == "blip-2":
               snake_case			:      str        =			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(__a							,**__a							)







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					"""blip_2_qformer"""







     def __init__(				self							,__a=30_522							,__a=768							,__a=12							,__a=12							,__a=3_072							,__a="gelu"							,__a=0.1							,__a=0.1							,__a=512							,__a=0.02							,__a=1E-12							,__a=0							,__a="absolute"							,__a=2							,__a=1_408							,**__a							,)      ->				Union[str, Any]:
          super().__init__(pad_token_id=__a							,**__a							)

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







     @classmethod
     def 			snake_case_      (				cls							,__a							,**__a							)      ->				"PretrainedConfig":
          cls._set_token_in_kwargs(__a							)

          snake_case ,		snake_case			:      int        =			cls.get_config_dict(__a							,**__a							)

          # get the qformer config dict if we are loading from Blip2Config
          if config_dict.get("""model_type"""							) == "blip-2":
               snake_case			:      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(__a							,**__a							)







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     List[Any]          =					"""blip-2"""
     lowerCAmelCase_  :     str          =					True







     def __init__(				self							,__a=None							,__a=None							,__a=None							,__a=32							,**__a							)      ->				Optional[int]:
          super().__init__(**__a							)

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

          if qformer_config is None:
               snake_case			:      Tuple        =			{}
               logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values."""							)

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

          snake_case			:      str        =			BlipaVisionConfig(**__a							)
          snake_case			:      Any        =			BlipaQFormerConfig(**__a							)
          snake_case			:      List[str]        =			text_config["""model_type"""] if """model_type""" in text_config else """opt"""
          snake_case			:      str        =			CONFIG_MAPPING[text_model_type](**__a							)

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

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







     @classmethod
     def 			snake_case_      (				cls							,__a							,__a							,__a							,**__a							,)      ->				int:
          return cls(
              vision_config=vision_config.to_dict()							,qformer_config=qformer_config.to_dict()							,text_config=text_config.to_dict()							,**__a							,)







     def 			snake_case_      (				self							)      ->				Optional[int]:
          snake_case			:      Union[str, Any]        =			copy.deepcopy(self.__dict__							)
          snake_case			:      Optional[Any]        =			self.vision_config.to_dict()
          snake_case			:      Tuple        =			self.qformer_config.to_dict()
          snake_case			:      List[str]        =			self.text_config.to_dict()
          snake_case			:      Tuple        =			self.__class__.model_type
          return output









'''simple docstring'''







import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout



def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      List[Any]        =			FileLock(str(tmpdir / """foo.lock"""				)				)
     snake_case			:      str        =			FileLock(str(tmpdir / """foo.lock"""				)				)
     snake_case			:      Any        =			0.01
     with locka.acquire():
          with pytest.raises(__lowercase				):
               snake_case			:      Union[str, Any]        =			time.time()
               locka.acquire(__lowercase				)
               assert time.time() - _start > timeout







def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      List[Any]        =			"""a""" * 1_000 + """.lock"""
     snake_case			:      List[str]        =			FileLock(str(tmpdir / filename				)				)
     assert locka._lock_file.endswith(""".lock"""				)
     assert not locka._lock_file.endswith(__lowercase				)
     assert len(os.path.basename(locka._lock_file				)				) <= 255
     snake_case			:      Any        =			FileLock(tmpdir / filename				)
     with locka.acquire():
          with pytest.raises(__lowercase				):
               locka.acquire(0				)



