from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE			:						List[str]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Any				  =		{
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """t5"""
  _SCREAMING_SNAKE_CASE     					=      ["""past_key_values"""]
  _SCREAMING_SNAKE_CASE     					=      {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

  def __init__(		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Any=3_21_28  ,				__SCREAMING_SNAKE_CASE			:   List[str]=5_12  ,				__SCREAMING_SNAKE_CASE			:   Dict=64  ,				__SCREAMING_SNAKE_CASE			:   str=20_48  ,				__SCREAMING_SNAKE_CASE			:   int=6  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   List[str]=8  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=32  ,				__SCREAMING_SNAKE_CASE			:   Any=1_28  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.1  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1E-6  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=1.0  ,				__SCREAMING_SNAKE_CASE			:   Any="relu"  ,				__SCREAMING_SNAKE_CASE			:   Dict=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0  ,				__SCREAMING_SNAKE_CASE			:   Any=1  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				):
   __a							=	vocab_size
   __a							=	d_model
   __a							=	d_kv
   __a							=	d_ff
   __a							=	num_layers
   __a							=	(
       num_decoder_layers if num_decoder_layers is not None else self.num_layers
   )  # default = symmetry
   __a							=	num_heads
   __a							=	relative_attention_num_buckets
   __a							=	relative_attention_max_distance
   __a							=	dropout_rate
   __a							=	layer_norm_epsilon
   __a							=	initializer_factor
   __a							=	feed_forward_proj
   __a							=	use_cache

   __a							=	self.feed_forward_proj.split("-"							)
   __a							=	act_info[-1]
   __a							=	act_info[0] == "gated"

   if len(__SCREAMING_SNAKE_CASE							) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE							) > 2:
    raise ValueError(
        f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
        "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
        "'gated-gelu' or 'relu'"							)

   # for backwards compatibility
   if feed_forward_proj == "gated-gelu":
    __a							=	"gelu_new"

   super().__init__(
       pad_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				is_encoder_decoder=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE  ,				)
class  A_			(							a_			):

  @property
  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	{
       "input_ids": {0: "batch", 1: "encoder_sequence"},
       "attention_mask": {0: "batch", 1: "encoder_sequence"},
   }
   if self.use_past:
    __a							=	"past_encoder_sequence + sequence"
    __a							=	{0: "batch"}
    __a							=	{0: "batch", 1: "past_decoder_sequence + sequence"}
   else:
    __a							=	{0: "batch", 1: "decoder_sequence"}
    __a							=	{0: "batch", 1: "decoder_sequence"}

   if self.use_past:
    self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE  ,				direction="inputs"							)

   return common_inputs

  @property
  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   return 13



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""image_processor""", """tokenizer"""]
  _SCREAMING_SNAKE_CASE     					=      """ChineseCLIPImageProcessor"""
  _SCREAMING_SNAKE_CASE     					=      ("""BertTokenizer""", """BertTokenizerFast""")

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

   __a							=	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__(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	self.image_processor

  def __call__(		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   Dict=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]							):

   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:
    __a							=	self.tokenizer(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if images is not None:
    __a							=	self.image_processor(__SCREAMING_SNAKE_CASE  ,				return_tensors=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

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

  def 		_UpperCAmelCase (		self			:   int  ,				*__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				**__SCREAMING_SNAKE_CASE			:   str							):
   return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				*__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  @property
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	self.tokenizer.model_input_names
   __a							=	self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)

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


