


"""simple docstring"""



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Dict							  =  '''Speech2TextFeatureExtractor'''
   __UpperCAmelCase			:	Optional[Any]							  =  '''Speech2TextTokenizer'''







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




       super().__init__(UpperCamelCase__						,     UpperCamelCase__						)
       snake_case   :				List[str]				 =   self.feature_extractor
       snake_case   :				str				 =   False







   def __call__(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		int:

       '''simple docstring'''




       if self._in_target_context_manager:
           return self.current_processor(*UpperCamelCase__						,     **UpperCamelCase__						)

       if "raw_speech" in kwargs:
           warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."						)
           snake_case   :				List[str]				 =   kwargs.pop("raw_speech"						)
       else:
           snake_case   :				Any				 =   kwargs.pop("audio"						,     UpperCamelCase__						)
       snake_case   :				str				 =   kwargs.pop("sampling_rate"						,     UpperCamelCase__						)
       snake_case   :				Union[str, Any]				 =   kwargs.pop("text"						,     UpperCamelCase__						)
       if len(UpperCamelCase__						) > 0:
           snake_case   :				str				 =   args[0]
           snake_case   :				List[str]				 =   args[1:]

       if audio is None and text is None:
           raise ValueError("You need to specify either an `audio` or `text` input to process."						)

       if audio is not None:
           snake_case   :				str				 =   self.feature_extractor(UpperCamelCase__						,     *UpperCamelCase__						,     sampling_rate=UpperCamelCase__						,     **UpperCamelCase__						)
       if text is not None:
           snake_case   :				Tuple				 =   self.tokenizer(UpperCamelCase__						,     **UpperCamelCase__						)

       if text is None:
           return inputs
       elif audio is None:
           return encodings
       else:
           snake_case   :				List[Any]				 =   encodings["input_ids"]
           return inputs







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




       return self.tokenizer.batch_decode(*UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Any:

       '''simple docstring'''




       return self.tokenizer.decode(*UpperCamelCase__						,     **UpperCamelCase__						)







   @contextmanager
   def 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




       warnings.warn(
           "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
           "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
           "your audio inputs, or in a separate call."						)
       snake_case   :				Optional[int]				 =   True
       snake_case   :				List[Any]				 =   self.tokenizer
       yield
       snake_case   :				int				 =   self.feature_extractor
       snake_case   :				Dict				 =   False









"""simple docstring"""



import copy
import os
from typing import Union

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


__snake_case             =   logging.get_logger(__name__)

__snake_case             =   {
    """BAAI/AltCLIP""": """https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json""",
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Tuple							  =  '''altclip_text_model'''







   def __init__(     self						,     UpperCamelCase__=25_0002						,     UpperCamelCase__=1024						,     UpperCamelCase__=24						,     UpperCamelCase__=16						,     UpperCamelCase__=4096						,     UpperCamelCase__="gelu"						,     UpperCamelCase__=0.1						,     UpperCamelCase__=0.1						,     UpperCamelCase__=514						,     UpperCamelCase__=1						,     UpperCamelCase__=0.02						,     UpperCamelCase__=0.02						,     UpperCamelCase__=1e-05						,     UpperCamelCase__=1						,     UpperCamelCase__=0						,     UpperCamelCase__=2						,     UpperCamelCase__="absolute"						,     UpperCamelCase__=True						,     UpperCamelCase__=768						,     **UpperCamelCase__						,     )     ->		Optional[int]:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__						,     bos_token_id=UpperCamelCase__						,     eos_token_id=UpperCamelCase__						,     **UpperCamelCase__						)

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







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Tuple							  =  '''altclip_vision_model'''







   def __init__(     self						,     UpperCamelCase__=768						,     UpperCamelCase__=3072						,     UpperCamelCase__=512						,     UpperCamelCase__=12						,     UpperCamelCase__=12						,     UpperCamelCase__=3						,     UpperCamelCase__=224						,     UpperCamelCase__=32						,     UpperCamelCase__="quick_gelu"						,     UpperCamelCase__=1e-5						,     UpperCamelCase__=0.0						,     UpperCamelCase__=0.02						,     UpperCamelCase__=1.0						,     **UpperCamelCase__						,     )     ->		str:

       '''simple docstring'''




       super().__init__(**UpperCamelCase__						)

       snake_case   :				Optional[int]				 =   hidden_size
       snake_case   :				str				 =   intermediate_size
       snake_case   :				List[str]				 =   projection_dim
       snake_case   :				Optional[Any]				 =   num_hidden_layers
       snake_case   :				Optional[int]				 =   num_attention_heads
       snake_case   :				str				 =   num_channels
       snake_case   :				List[str]				 =   patch_size
       snake_case   :				List[Any]				 =   image_size
       snake_case   :				Union[str, Any]				 =   initializer_range
       snake_case   :				Optional[Any]				 =   initializer_factor
       snake_case   :				Any				 =   attention_dropout
       snake_case   :				Dict				 =   layer_norm_eps
       snake_case   :				List[str]				 =   hidden_act







   @classmethod
   def 			lowerCamelCase		(     cls						,     UpperCamelCase__						,     **UpperCamelCase__						)     ->		"PretrainedConfig":

       '''simple docstring'''




       cls._set_token_in_kwargs(UpperCamelCase__						)

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

       # get the vision config dict if we are loading from AltCLIPConfig
       if config_dict.get("model_type"						) == "altclip":
           snake_case   :				Optional[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(UpperCamelCase__						,     **UpperCamelCase__						)







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	str							  =  '''altclip'''
   __UpperCAmelCase			:	Optional[Any]							  =  True







   def __init__(     self						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     UpperCamelCase__=768						,     UpperCamelCase__=2.6592						,     **UpperCamelCase__						)     ->		Any:

       '''simple docstring'''




       snake_case   :				List[str]				 =   kwargs.pop("text_config_dict"						,     UpperCamelCase__						)
       snake_case   :				Union[str, Any]				 =   kwargs.pop("vision_config_dict"						,     UpperCamelCase__						)

       super().__init__(**UpperCamelCase__						)

       # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
       # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
       # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
       if text_config_dict is not None:
           if text_config is None:
               snake_case   :				List[str]				 =   {}

           # This is the complete result when using `text_config_dict`.
           snake_case   :				Dict				 =   AltCLIPTextConfig(**UpperCamelCase__						).to_dict()

           # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
           for key, value in _text_config_dict.items():
               if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
                   # If specified in `text_config_dict`
                   if key in text_config_dict:
                       snake_case   :				Optional[Any]				 =   (
                           F'`{key}` is found in both `text_config_dict` and `text_config` but with different values. '
                           F'The value `text_config_dict["{key}"]` will be used instead.'
                       )
                   # If inferred from default argument values (just to be super careful)
                   else:
                       snake_case   :				Any				 =   (
                           F'`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '
                           F'value `text_config["{key}"]` will be overriden.'
                       )
                   logger.warning(UpperCamelCase__						)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
           text_config.update(_text_config_dict						)

       if vision_config_dict is not None:
           if vision_config is None:
               snake_case   :				Union[str, Any]				 =   {}

           # This is the complete result when using `vision_config_dict`.
           snake_case   :				int				 =   AltCLIPVisionConfig(**UpperCamelCase__						).to_dict()
           # convert keys to string instead of integer
           if "id2label" in _vision_config_dict:
               snake_case   :				Optional[int]				 =   {
                   str(UpperCamelCase__						): value for key, value in _vision_config_dict["id2label"].items()
               }

           # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
           for key, value in _vision_config_dict.items():
               if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
                   # If specified in `vision_config_dict`
                   if key in vision_config_dict:
                       snake_case   :				int				 =   (
                           F'`{key}` is found in both `vision_config_dict` and `vision_config` but with different '
                           F'values. The value `vision_config_dict["{key}"]` will be used instead.'
                       )
                   # If inferred from default argument values (just to be super careful)
                   else:
                       snake_case   :				Optional[Any]				 =   (
                           F'`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '
                           F'The value `vision_config["{key}"]` will be overriden.'
                       )
                   logger.warning(UpperCamelCase__						)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
           vision_config.update(_vision_config_dict						)

       if text_config is None:
           snake_case   :				Optional[int]				 =   {}
           logger.info("`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values."						)

       if vision_config is None:
           snake_case   :				Dict				 =   {}
           logger.info("`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values."						)

       snake_case   :				Dict				 =   AltCLIPTextConfig(**UpperCamelCase__						)
       snake_case   :				Tuple				 =   AltCLIPVisionConfig(**UpperCamelCase__						)

       snake_case   :				int				 =   projection_dim
       snake_case   :				List[str]				 =   logit_scale_init_value
       snake_case   :				int				 =   1.0







   @classmethod
   def 			lowerCamelCase		(     cls						,     UpperCamelCase__						,     UpperCamelCase__						,     **UpperCamelCase__						)     ->		Union[str, Any]:

       '''simple docstring'''




       return cls(text_config=text_config.to_dict()						,     vision_config=vision_config.to_dict()						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				Tuple				 =   copy.deepcopy(self.__dict__						)
       snake_case   :				Optional[int]				 =   self.text_config.to_dict()
       snake_case   :				str				 =   self.vision_config.to_dict()
       snake_case   :				Optional[int]				 =   self.__class__.model_type
       return output





