





'''simple docstring'''






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


lowerCAmelCase       		=      logging.get_logger(__name__)

lowerCAmelCase       		=      {
    """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   "xglm"
	snake_case_      =   ["past_key_values"]

	snake_case_      =   {
	    "num_attention_heads": "attention_heads",
	    "hidden_size": "d_model",
	    "num_hidden_layers": "num_layers",
	}



	def __init__(   self		,      a_=256_008		,      a_=2_048		,      a_=1_024		,      a_=4_096		,      a_=24		,      a_=16		,      a_="gelu"		,      a_=0.1		,      a_=0.1		,      a_=0.0		,      a_=0.0		,      a_=0.02		,      a_=True		,      a_=True		,      a_=2		,      a_=1		,      a_=0		,      a_=2		,      **a_		,      ):
			lowerCAmelCase     :    List[Any]						=   vocab_size
			lowerCAmelCase     :    Optional[int]						=   max_position_embeddings
			lowerCAmelCase     :    Union[str, Any]						=   d_model
			lowerCAmelCase     :    int						=   ffn_dim
			lowerCAmelCase     :    Tuple						=   num_layers
			lowerCAmelCase     :    Optional[int]						=   attention_heads
			lowerCAmelCase     :    int						=   activation_function
			lowerCAmelCase     :    Dict						=   dropout
			lowerCAmelCase     :    Optional[int]						=   attention_dropout
			lowerCAmelCase     :    Optional[int]						=   activation_dropout
			lowerCAmelCase     :    Tuple						=   layerdrop
			lowerCAmelCase     :    Dict						=   init_std
			lowerCAmelCase     :    Optional[int]						=   scale_embedding  # scale factor will be sqrt(d_model) if True
			lowerCAmelCase     :    Any						=   use_cache

			super().__init__(
			    pad_token_id=a_		,      bos_token_id=a_		,      eos_token_id=a_		,      decoder_start_token_id=a_		,      **a_		,      )










'''simple docstring'''






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   ["image_processor", "tokenizer"]
	snake_case_      =   "ChineseCLIPImageProcessor"
	snake_case_      =   ("BertTokenizer", "BertTokenizerFast")



	def __init__(   self		,      a_=None		,      a_=None		,      **a_	):
			lowerCAmelCase     :    Any						=   None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
					    " instead."		,      a_		,      )
					lowerCAmelCase     :    Optional[Any]						=   kwargs.pop("feature_extractor"	)

			lowerCAmelCase     :    int						=   image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError("You need to specify an `image_processor`."	)
			if tokenizer is None:
					raise ValueError("You need to specify a `tokenizer`."	)

			super().__init__(a_		,      a_	)
			lowerCAmelCase     :    Dict						=   self.image_processor



	def __call__(   self		,      a_=None		,      a_=None		,      a_=None		,      **a_	):

			if text is None and images is None:
					raise ValueError("You have to specify either text or images. Both cannot be none."	)

			if text is not None:
					lowerCAmelCase     :    List[str]						=   self.tokenizer(a_		,      return_tensors=a_		,      **a_	)

			if images is not None:
					lowerCAmelCase     :    List[Any]						=   self.image_processor(a_		,      return_tensors=a_		,      **a_	)

			if text is not None and images is not None:
					lowerCAmelCase     :    str						=   image_features.pixel_values
					return encoding
			elif text is not None:
					return encoding
			else:
					return BatchEncoding(data=dict(**a_	)		,      tensor_type=a_	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.tokenizer.batch_decode(*a_		,      **a_	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			return self.tokenizer.decode(*a_		,      **a_	)



	@property
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.tokenizer.model_input_names
			lowerCAmelCase     :    List[Any]						=   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)





	@property
	def   _lowerCamelCase    (   self	):
			warnings.warn(
			    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,      a_		,      )
			return self.image_processor_class



