
"""simple docstring"""




import copy
import os
from typing import Union

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


_A     :			Any  						=			logging.get_logger(__name__)

_A     :			Union[str, Any]  						=			{
    """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 						a__		( a_		):
   __lowerCAmelCase				=							"""bridgetower_vision_model"""
   def __init__(     self      ,       _a=768      ,       _a=12      ,       _a=3      ,       _a=16      ,       _a=288      ,       _a=1      ,       _a=1E-05      ,       _a=False      ,       _a=True      ,       _a=False      ,       **_a      ,       ):
          super().__init__(**_a							)
          lowercase       :							List[str]									=				hidden_size
          lowercase       :							str									=				num_hidden_layers
          lowercase       :							Union[str, Any]									=				num_channels
          lowercase       :							Any									=				patch_size
          lowercase       :							Dict									=				image_size
          lowercase       :							List[str]									=				initializer_factor
          lowercase       :							List[Any]									=				layer_norm_eps
          lowercase       :							Dict									=				stop_gradient
          lowercase       :							List[Any]									=				share_layernorm
          lowercase       :							Dict									=				remove_last_layer

   @classmethod
   def      __magic_name__     (     cls      ,       _a      ,       **_a							):
          lowercase						,       lowercase       :							str									=				cls.get_config_dict(_a      ,       **_a							)

          if config_dict.get("model_type"							) == "bridgetower":
                 lowercase       :							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(_a      ,       **_a							)


class 						a__		( a_		):
   __lowerCAmelCase				=							"""bridgetower_text_model"""
   def __init__(     self      ,       _a=50_265      ,       _a=768      ,       _a=12      ,       _a=12      ,       _a=1      ,       _a=3_072      ,       _a="gelu"      ,       _a=0.1      ,       _a=0.1      ,       _a=514      ,       _a=1      ,       _a=1E-05      ,       _a=1      ,       _a=0      ,       _a=2      ,       _a="absolute"      ,       _a=True      ,       **_a      ,       ):
          super().__init__(**_a							)

          lowercase       :							List[Any]									=				vocab_size
          lowercase       :							Union[str, Any]									=				hidden_size
          lowercase       :							List[Any]									=				num_hidden_layers
          lowercase       :							Dict									=				num_attention_heads
          lowercase       :							Union[str, Any]									=				hidden_act
          lowercase       :							Optional[Any]									=				initializer_factor
          lowercase       :							Union[str, Any]									=				intermediate_size
          lowercase       :							List[str]									=				hidden_dropout_prob
          lowercase       :							Optional[Any]									=				attention_probs_dropout_prob
          lowercase       :							Union[str, Any]									=				max_position_embeddings
          lowercase       :							str									=				type_vocab_size
          lowercase       :							str									=				layer_norm_eps
          lowercase       :							Tuple									=				position_embedding_type
          lowercase       :							Union[str, Any]									=				use_cache
          lowercase       :							Tuple									=				pad_token_id
          lowercase       :							List[str]									=				bos_token_id
          lowercase       :							List[str]									=				eos_token_id

   @classmethod
   def      __magic_name__     (     cls      ,       _a      ,       **_a							):
          lowercase						,       lowercase       :							Optional[int]									=				cls.get_config_dict(_a      ,       **_a							)

          if config_dict.get("model_type"							) == "bridgetower":
                 lowercase       :							Optional[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(_a      ,       **_a							)


class 						a__		( a_		):
   __lowerCAmelCase				=							"""bridgetower"""
   def __init__(     self      ,       _a=True      ,       _a="gelu"      ,       _a=768      ,       _a=1      ,       _a=1E-05      ,       _a=False      ,       _a="add"      ,       _a=12      ,       _a=6      ,       _a=False      ,       _a=False      ,       _a=None      ,       _a=None      ,       **_a      ,       ):
          # TODO: remove this once the Hub files are updated.
          lowercase       :							str									=				kwargs.pop("text_config_dict"      ,       _a							)
          lowercase       :							List[Any]									=				kwargs.pop("vision_config_dict"      ,       _a							)

          super().__init__(**_a							)
          lowercase       :							Optional[Any]									=				share_cross_modal_transformer_layers
          lowercase       :							Union[str, Any]									=				hidden_act
          lowercase       :							str									=				hidden_size
          lowercase       :							Optional[int]									=				initializer_factor
          lowercase       :							int									=				layer_norm_eps
          lowercase       :							Union[str, Any]									=				share_link_tower_layers
          lowercase       :							List[str]									=				link_tower_type
          lowercase       :							Tuple									=				num_attention_heads
          lowercase       :							int									=				num_hidden_layers
          lowercase       :							Any									=				tie_word_embeddings
          lowercase       :							Optional[int]									=				init_layernorm_from_vision_encoder

          if text_config is None:
                 lowercase       :							List[str]									=				{}
                 logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."							)

          if vision_config is None:
                 lowercase       :							Optional[Any]									=				{}
                 logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."							)

          lowercase       :							Any									=				BridgeTowerTextConfig(**_a							)
          lowercase       :							List[Any]									=				BridgeTowerVisionConfig(**_a							)
   @classmethod
   def      __magic_name__     (     cls      ,       _a      ,       _a      ,       **_a							):
          return cls(text_config=text_config.to_dict()      ,       vision_config=vision_config.to_dict()      ,       **_a							)

   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				copy.deepcopy(self.__dict__							)
          lowercase       :							Dict									=				self.text_config.to_dict()
          lowercase       :							List[str]									=				self.vision_config.to_dict()
          lowercase       :							Tuple									=				self.__class__.model_type
          return output




"""simple docstring"""




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


_A     :			Union[str, Any]  						=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __magic_name__      ( __snake_case							:			str    )					->							List[str]:
       if not path:
              return "pipe"

       for ext in PipelineDataFormat.SUPPORTED_FORMATS:
              if path.endswith(__snake_case    ):
                     return ext

       raise Exception(
           f"""Unable to determine file format from file extension {path}. """
           f"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""    )


def     __magic_name__      ( __snake_case							:			str    )					->							Optional[int]:
       lowercase       :							int									=				pipeline(
           task=args.task	,					model=args.model if args.model else None	,					config=args.config	,					tokenizer=args.tokenizer	,					device=args.device	,					)
       lowercase       :							Optional[Any]									=				try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
       lowercase       :							Optional[int]									=				PipelineDataFormat.from_str(
           format=__snake_case	,					output_path=args.output	,					input_path=args.input	,					column=args.column if args.column else nlp.default_input_names	,					overwrite=args.overwrite	,					)
       return RunCommand(__snake_case	,					__snake_case    )

class 						a__		( a_		):
   def __init__(     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				nlp
          lowercase       :							Optional[Any]									=				reader
   @staticmethod
   def      __magic_name__     (     _a							):
          lowercase       :							Any									=				parser.add_parser("run"      ,       help="Run a pipeline through the CLI"							)
          run_parser.add_argument("--task"      ,       choices=get_supported_tasks()      ,       help="Task to run"							)
          run_parser.add_argument("--input"      ,       type=_a      ,       help="Path to the file to use for inference"							)
          run_parser.add_argument("--output"      ,       type=_a      ,       help="Path to the file that will be used post to write results."							)
          run_parser.add_argument("--model"      ,       type=_a      ,       help="Name or path to the model to instantiate."							)
          run_parser.add_argument("--config"      ,       type=_a      ,       help="Name or path to the model's config to instantiate."							)
          run_parser.add_argument(
              "--tokenizer"      ,       type=_a      ,       help="Name of the tokenizer to use. (default: same as the model name)"							)
          run_parser.add_argument(
              "--column"      ,       type=_a      ,       help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"      ,       )
          run_parser.add_argument(
              "--format"      ,       type=_a      ,       default="infer"      ,       choices=PipelineDataFormat.SUPPORTED_FORMATS      ,       help="Input format to read from"      ,       )
          run_parser.add_argument(
              "--device"      ,       type=_a      ,       default=-1      ,       help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"      ,       )
          run_parser.add_argument("--overwrite"      ,       action="store_true"      ,       help="Allow overwriting the output file."							)
          run_parser.set_defaults(func=_a							)

   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							int									=				self._nlp, []

          for entry in self._reader:
                 lowercase       :							str									=				nlp(**_a							) if self._reader.is_multi_columns else nlp(_a							)
                 if isinstance(_a      ,       _a							):
                        outputs.append(_a							)
                 else:
                        outputs += output

        # Saving data
          if self._nlp.binary_output:
                 lowercase       :							Dict									=				self._reader.save_binary(_a							)
                 logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""							)
          else:
                 self._reader.save(_a							)


