
"""simple docstring"""





import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
    pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a		       =							logging.get_logger(__name__)

_a		       =							{
    'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Dict       =		"""align_text_model"""







  def __init__(		self				,   lowercase_=3_0522				,   lowercase_=768				,   lowercase_=12				,   lowercase_=12				,   lowercase_=3072				,   lowercase_="gelu"				,   lowercase_=0.1				,   lowercase_=0.1				,   lowercase_=512				,   lowercase_=2				,   lowercase_=0.02				,   lowercase_=1E-1_2				,   lowercase_=0				,   lowercase_="absolute"				,   lowercase_=True				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(**lowercase_  )

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







  @classmethod
  def        UpperCamelCase__					(		cls				,   lowercase_				,   **lowercase_  ):

     """simple docstring"""
     cls._set_token_in_kwargs(lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    cls.get_config_dict(lowercase_				,   **lowercase_  )

     # get the text config dict if we are loading from AlignConfig
     if config_dict.get("model_type"  ) == "align":
        UpperCAmelCase_					:					Union[str, Any]			     =    config_dict["text_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(lowercase_				,   **lowercase_  )




class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						int       =		"""align_vision_model"""







  def __init__(		self				,   lowercase_ = 3				,   lowercase_ = 600				,   lowercase_ = 2.0				,   lowercase_ = 3.1				,   lowercase_ = 8				,   lowercase_ = [3, 3, 5, 3, 5, 5, 3]				,   lowercase_ = [32, 16, 24, 40, 80, 112, 192]				,   lowercase_ = [16, 24, 40, 80, 112, 192, 320]				,   lowercase_ = []				,   lowercase_ = [1, 2, 2, 2, 1, 2, 1]				,   lowercase_ = [1, 2, 2, 3, 3, 4, 1]				,   lowercase_ = [1, 6, 6, 6, 6, 6, 6]				,   lowercase_ = 0.25				,   lowercase_ = "swish"				,   lowercase_ = 2560				,   lowercase_ = "mean"				,   lowercase_ = 0.02				,   lowercase_ = 0.0_01				,   lowercase_ = 0.99				,   lowercase_ = 0.2				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(**lowercase_  )

     UpperCAmelCase_					:					List[str]			     =    num_channels
     UpperCAmelCase_					:					Optional[Any]			     =    image_size
     UpperCAmelCase_					:					Any			     =    width_coefficient
     UpperCAmelCase_					:					Dict			     =    depth_coefficient
     UpperCAmelCase_					:					Union[str, Any]			     =    depth_divisor
     UpperCAmelCase_					:					int			     =    kernel_sizes
     UpperCAmelCase_					:					Dict			     =    in_channels
     UpperCAmelCase_					:					Tuple			     =    out_channels
     UpperCAmelCase_					:					Optional[int]			     =    depthwise_padding
     UpperCAmelCase_					:					Dict			     =    strides
     UpperCAmelCase_					:					Tuple			     =    num_block_repeats
     UpperCAmelCase_					:					Tuple			     =    expand_ratios
     UpperCAmelCase_					:					Dict			     =    squeeze_expansion_ratio
     UpperCAmelCase_					:					Union[str, Any]			     =    hidden_act
     UpperCAmelCase_					:					Optional[Any]			     =    hidden_dim
     UpperCAmelCase_					:					List[Any]			     =    pooling_type
     UpperCAmelCase_					:					Union[str, Any]			     =    initializer_range
     UpperCAmelCase_					:					str			     =    batch_norm_eps
     UpperCAmelCase_					:					List[str]			     =    batch_norm_momentum
     UpperCAmelCase_					:					Any			     =    drop_connect_rate
     UpperCAmelCase_					:					str			     =    sum(lowercase_  ) * 4







  @classmethod
  def        UpperCamelCase__					(		cls				,   lowercase_				,   **lowercase_  ):

     """simple docstring"""
     cls._set_token_in_kwargs(lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    cls.get_config_dict(lowercase_				,   **lowercase_  )

     # get the vision config dict if we are loading from AlignConfig
     if config_dict.get("model_type"  ) == "align":
        UpperCAmelCase_					:					Any			     =    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(lowercase_				,   **lowercase_  )




class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						int       =		"""align"""
  SCREAMING_SNAKE_CASE__			:						str       =		True







  def __init__(		self				,   lowercase_=None				,   lowercase_=None				,   lowercase_=640				,   lowercase_=1.0				,   lowercase_=0.02				,   **lowercase_				,   ):

     """simple docstring"""
     super().__init__(**lowercase_  )

     if text_config is None:
        UpperCAmelCase_					:					Union[str, Any]			     =    {}
        logger.info("text_config is None. Initializing the AlignTextConfig with default values."  )

     if vision_config is None:
        UpperCAmelCase_					:					Tuple			     =    {}
        logger.info("vision_config is None. Initializing the AlignVisionConfig with default values."  )

     UpperCAmelCase_					:					Tuple			     =    AlignTextConfig(**lowercase_  )
     UpperCAmelCase_					:					str			     =    AlignVisionConfig(**lowercase_  )

     UpperCAmelCase_					:					List[Any]			     =    projection_dim
     UpperCAmelCase_					:					Union[str, Any]			     =    temperature_init_value
     UpperCAmelCase_					:					Union[str, Any]			     =    initializer_range







  @classmethod
  def        UpperCamelCase__					(		cls				,   lowercase_				,   lowercase_				,   **lowercase_  ):

     """simple docstring"""
     return cls(text_config=text_config.to_dict()				,   vision_config=vision_config.to_dict()				,   **lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    copy.deepcopy(self.__dict__  )
     UpperCAmelCase_					:					Dict			     =    self.text_config.to_dict()
     UpperCAmelCase_					:					str			     =    self.vision_config.to_dict()
     UpperCAmelCase_					:					Optional[Any]			     =    self.__class__.model_type
     return output




"""simple docstring"""





import argparse
from collections import defaultdict







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    f"""{file}_{class_name}_{test_name}"""
   done_test[_id] += 1

   with open(__lowerCamelCase,		"r"  ) as f:
      UpperCAmelCase_					:					List[Any]			     =    f.readlines()

   UpperCAmelCase_					:					int			     =    f"""class {class_name}("""
   UpperCAmelCase_					:					Optional[Any]			     =    f"""{4 * " "}def {test_name}("""
   UpperCAmelCase_					:					Optional[Any]			     =    f"""{8 * " "}{correct_line.split()[0]}"""
   UpperCAmelCase_					:					Tuple			     =    f"""{16 * " "}{correct_line.split()[0]}"""
   UpperCAmelCase_					:					int			     =    False
   UpperCAmelCase_					:					Union[str, Any]			     =    False
   UpperCAmelCase_					:					str			     =    False
   UpperCAmelCase_					:					Optional[Any]			     =    False
   UpperCAmelCase_					:					List[str]			     =    0
   UpperCAmelCase_					:					Optional[int]			     =    0

   UpperCAmelCase_					:					int			     =    []
   for line in lines:
      if line.startswith(__lowerCamelCase  ):
         UpperCAmelCase_					:					Tuple			     =    True
      elif in_class and line.startswith(__lowerCamelCase  ):
         UpperCAmelCase_					:					Optional[int]			     =    True
      elif in_class and in_func and (line.startswith(__lowerCamelCase  ) or line.startswith(__lowerCamelCase  )):
         UpperCAmelCase_					:					Any			     =    len(line.split(correct_line.split()[0]  )[0]  )
         count += 1

         if count == done_test[_id]:
            UpperCAmelCase_					:					Union[str, Any]			     =    True

      if in_class and in_func and in_line:
         if ")" not in line:
            continue
         else:
            UpperCAmelCase_					:					Any			     =    True

      if in_class and in_func and in_line and insert_line:
         new_lines.append(f"""{spaces * " "}{correct_line}"""  )
         UpperCAmelCase_					:					int			     =    False
      else:
         new_lines.append(__lowerCamelCase  )

   with open(__lowerCamelCase,		"w"  ) as f:
      for line in new_lines:
         f.write(__lowerCamelCase  )







def 	__a     (		__lowerCamelCase,		__lowerCamelCase=None  ):
   if fail is not None:
      with open(__lowerCamelCase,		"r"  ) as f:
         UpperCAmelCase_					:					Tuple			     =    {l.strip() for l in f.readlines()}
   else:
      UpperCAmelCase_					:					str			     =    None

   with open(__lowerCamelCase,		"r"  ) as f:
      UpperCAmelCase_					:					Optional[int]			     =    f.readlines()

   UpperCAmelCase_					:					Any			     =    defaultdict(__lowerCamelCase  )
   for line in correct_lines:
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    line.split(";"  )
      if test_failures is None or "::".join([file, class_name, test_name]  ) in test_failures:
         overwrite_file(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )


if __name__ == "__main__":
    _a		       =							argparse.ArgumentParser()
    parser.add_argument('--correct_filename', help='filename of tests with expected result')
    parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
    _a		       =							parser.parse_args()

    main(args.correct_filename, args.fail_filename)


