import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


UpperCAmelCase_								=							logging.get_logger(__name__)

def 							UpperCamelCase				(    lowerCAmelCase_=None       ,					lowerCAmelCase_=None   )		->						str:




       '''simple docstring'''






       return field(default_factory=lambda: default       ,					metadata=lowerCAmelCase_   )





@dataclass
class      lowerCAmelCase						:
     _SCREAMING_SNAKE_CASE     :     List[str]		=list_field(
         default=[]			,		metadata={
             """help""": (
                 """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
                 """ of all available models"""
             )
         }			,		)

     _SCREAMING_SNAKE_CASE     :     List[int]		=list_field(
         default=[8]			,		metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}			)

     _SCREAMING_SNAKE_CASE     :     List[int]		=list_field(
         default=[8, 32, 128, 512]			,		metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}			,		)

     _SCREAMING_SNAKE_CASE     :     bool		=field(
         default=_a			,		metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}			,		)
     _SCREAMING_SNAKE_CASE     :     bool		=field(
         default=_a			,		metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}			,		)
     _SCREAMING_SNAKE_CASE     :     bool		=field(
         default=_a			,		metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}			)
     _SCREAMING_SNAKE_CASE     :     bool		=field(default=_a			,		metadata={"""help""": """Use FP16 to accelerate inference."""}			)
     _SCREAMING_SNAKE_CASE     :     bool		=field(default=_a			,		metadata={"""help""": """Benchmark training of model"""}			)
     _SCREAMING_SNAKE_CASE     :     bool		=field(default=_a			,		metadata={"""help""": """Verbose memory tracing"""}			)
     _SCREAMING_SNAKE_CASE     :     bool		=field(
         default=_a			,		metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}			,		)
     _SCREAMING_SNAKE_CASE     :     bool		=field(
         default=_a			,		metadata={
             """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
         }			,		)
     _SCREAMING_SNAKE_CASE     :     bool		=field(default=_a			,		metadata={"""help""": """Trace memory line by line"""}			)
     _SCREAMING_SNAKE_CASE     :     bool		=field(default=_a			,		metadata={"""help""": """Save result to a CSV file"""}			)
     _SCREAMING_SNAKE_CASE     :     bool		=field(default=_a			,		metadata={"""help""": """Save all print statements in a log file"""}			)
     _SCREAMING_SNAKE_CASE     :     bool		=field(default=_a			,		metadata={"""help""": """Whether to print environment information"""}			)
     _SCREAMING_SNAKE_CASE     :     bool		=field(
         default=_a			,		metadata={
             """help""": (
                 """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
                 """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
                 """ for debugging / testing and on TPU."""
             )
         }			,		)
     _SCREAMING_SNAKE_CASE     :     str		=field(
         default=F'inference_time_{round(time()			)}.csv'			,		metadata={"""help""": """CSV filename used if saving time results to csv."""}			,		)
     _SCREAMING_SNAKE_CASE     :     str		=field(
         default=F'inference_memory_{round(time()			)}.csv'			,		metadata={"""help""": """CSV filename used if saving memory results to csv."""}			,		)
     _SCREAMING_SNAKE_CASE     :     str		=field(
         default=F'train_time_{round(time()			)}.csv'			,		metadata={"""help""": """CSV filename used if saving time results to csv for training."""}			,		)
     _SCREAMING_SNAKE_CASE     :     str		=field(
         default=F'train_memory_{round(time()			)}.csv'			,		metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}			,		)
     _SCREAMING_SNAKE_CASE     :     str		=field(
         default=F'env_info_{round(time()			)}.csv'			,		metadata={"""help""": """CSV filename used if saving environment information."""}			,		)
     _SCREAMING_SNAKE_CASE     :     str		=field(
         default=F'log_{round(time()			)}.csv'			,		metadata={"""help""": """Log filename used if print statements are saved in log."""}			,		)
     _SCREAMING_SNAKE_CASE     :     int		=field(default=3			,		metadata={"""help""": """Times an experiment will be run."""}			)
     _SCREAMING_SNAKE_CASE     :     bool		=field(
         default=_a			,		metadata={
             """help""": (
                 """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
                 """ model weights."""
             )
         }			,		)



     def 			a__  (					self       ):
            warnings.warn(
                f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
                ' are deprecated in general and it is advised to use external Benchmarking libraries '
                ' to benchmark Transformer models.'				,      lowerCAmelCase__				,      )



     def 			a__  (					self       ):
            return json.dumps(dataclasses.asdict(self       )				,      indent=2       )



     @property
     def 			a__  (					self       ):
            if len(self.models       ) <= 0:
                   raise ValueError(
                       'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
                       ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'       )
            return self.models





     @property
     def 			a__  (					self       ):
            if not self.multi_process:
                   return False
            elif self.is_tpu:
                   logger.info('Multiprocessing is currently not possible on TPU.'       )
                   return False
            else:
                   return True
import copy
import os
from typing import Union

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


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
    '''BridgeTower/bridgetower-base-itm-mlm''': (
        '''https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'''
    ),
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[Any]		="""bridgetower_vision_model"""



     def __init__(					self				,      lowerCAmelCase__=768				,      lowerCAmelCase__=12				,      lowerCAmelCase__=3				,      lowerCAmelCase__=16				,      lowerCAmelCase__=288				,      lowerCAmelCase__=1				,      lowerCAmelCase__=1E-05				,      lowerCAmelCase__=False				,      lowerCAmelCase__=True				,      lowerCAmelCase__=False				,      **lowerCAmelCase__				,      ):
            super().__init__(**lowerCAmelCase__       )
            _A=	hidden_size
            _A=	num_hidden_layers
            _A=	num_channels
            _A=	patch_size
            _A=	image_size
            _A=	initializer_factor
            _A=	layer_norm_eps
            _A=	stop_gradient
            _A=	share_layernorm
            _A=	remove_last_layer





     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _A,			_A=	cls.get_config_dict(lowerCAmelCase__				,      **lowerCAmelCase__       )

            if config_dict.get('model_type'       ) == "bridgetower":
                   _A=	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(lowerCAmelCase__				,      **lowerCAmelCase__       )





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[Any]		="""bridgetower_text_model"""



     def __init__(					self				,      lowerCAmelCase__=50265				,      lowerCAmelCase__=768				,      lowerCAmelCase__=12				,      lowerCAmelCase__=12				,      lowerCAmelCase__=1				,      lowerCAmelCase__=3072				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=514				,      lowerCAmelCase__=1				,      lowerCAmelCase__=1E-05				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0				,      lowerCAmelCase__=2				,      lowerCAmelCase__="absolute"				,      lowerCAmelCase__=True				,      **lowerCAmelCase__				,      ):
            super().__init__(**lowerCAmelCase__       )

            _A=	vocab_size
            _A=	hidden_size
            _A=	num_hidden_layers
            _A=	num_attention_heads
            _A=	hidden_act
            _A=	initializer_factor
            _A=	intermediate_size
            _A=	hidden_dropout_prob
            _A=	attention_probs_dropout_prob
            _A=	max_position_embeddings
            _A=	type_vocab_size
            _A=	layer_norm_eps
            _A=	position_embedding_type
            _A=	use_cache
            _A=	pad_token_id
            _A=	bos_token_id
            _A=	eos_token_id





     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _A,			_A=	cls.get_config_dict(lowerCAmelCase__				,      **lowerCAmelCase__       )

            if config_dict.get('model_type'       ) == "bridgetower":
                   _A=	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(lowerCAmelCase__				,      **lowerCAmelCase__       )





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Dict		="""bridgetower"""



     def __init__(					self				,      lowerCAmelCase__=True				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=768				,      lowerCAmelCase__=1				,      lowerCAmelCase__=1E-05				,      lowerCAmelCase__=False				,      lowerCAmelCase__="add"				,      lowerCAmelCase__=12				,      lowerCAmelCase__=6				,      lowerCAmelCase__=False				,      lowerCAmelCase__=False				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      **lowerCAmelCase__				,      ):
            # TODO: remove this once the Hub files are updated.
            _A=	kwargs.pop('text_config_dict'				,      lowerCAmelCase__       )
            _A=	kwargs.pop('vision_config_dict'				,      lowerCAmelCase__       )

            super().__init__(**lowerCAmelCase__       )
            _A=	share_cross_modal_transformer_layers
            _A=	hidden_act
            _A=	hidden_size
            _A=	initializer_factor
            _A=	layer_norm_eps
            _A=	share_link_tower_layers
            _A=	link_tower_type
            _A=	num_attention_heads
            _A=	num_hidden_layers
            _A=	tie_word_embeddings
            _A=	init_layernorm_from_vision_encoder

            if text_config is None:
                   _A=	{}
                   logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.'       )

            if vision_config is None:
                   _A=	{}
                   logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'       )

            _A=	BridgeTowerTextConfig(**lowerCAmelCase__       )
            _A=	BridgeTowerVisionConfig(**lowerCAmelCase__       )



     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            return cls(text_config=text_config.to_dict()				,      vision_config=vision_config.to_dict()				,      **lowerCAmelCase__       )





     def 			a__  (					self       ):
            _A=	copy.deepcopy(self.__dict__       )
            _A=	self.text_config.to_dict()
            _A=	self.vision_config.to_dict()
            _A=	self.__class__.model_type
            return output