




from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


UpperCAmelCase_		:				str									=     logging.get_logger(__name__)




class 		__A      (			UpperCamelCase__			):

						def __init__(		self   :Union[str, Any]    , __snake_case   :int    , __snake_case   :int    , __snake_case   :float    , **__snake_case   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Any								=feature_size
										__magic_name__	:	str								=sampling_rate
										__magic_name__	:	Optional[Any]								=padding_value

										__magic_name__	:	int								=kwargs.pop("""padding_side"""    , """right""" )
										__magic_name__	:	Any								=kwargs.pop("""return_attention_mask"""    , __snake_case )

										super().__init__(**__snake_case )

						def     A__						(		self   :List[str]    , __snake_case   :Union[
						        BatchFeature,
						        List[BatchFeature],
						        Dict[str, BatchFeature],
						        Dict[str, List[BatchFeature]],
						        List[Dict[str, BatchFeature]],
						    ]    , __snake_case   :Union[bool, str, PaddingStrategy] = True    , __snake_case   :Optional[int] = None    , __snake_case   :bool = False    , __snake_case   :Optional[int] = None    , __snake_case   :Optional[bool] = None    , __snake_case   :Optional[Union[str, TensorType]] = None    , ):
										'''simple docstring'''

										if isinstance(__snake_case    , (list, tuple) ) and isinstance(processed_features[0]    , (dict, BatchFeature) ):
														__magic_name__	:	Tuple								={
														    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
														}

										# The model's main input name, usually `input_values`, has be passed for padding
										if self.model_input_names[0] not in processed_features:
														raise ValueError(
														    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
														    f" to this method that includes {self.model_input_names[0]}, but you provided"
														    f" {list(processed_features.keys() )}" )

										__magic_name__	:	Dict								=processed_features[self.model_input_names[0]]
										__magic_name__	:	Union[str, Any]								=(
										    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
										)

										if len(__snake_case ) == 0:
														if return_attention_mask:
																		__magic_name__	:	Any								=[]
														return processed_features

										# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
										# and rebuild them afterwards if no return_tensors is specified
										# Note that we lose the specific device the tensor may be on for PyTorch

										__magic_name__	:	Tuple								=required_input[0]
										if isinstance(__snake_case    , (list, tuple) ):
														# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
														__magic_name__	:	Optional[Any]								=0
														while len(required_input[index] ) == 0:
																		index += 1
														if index < len(__snake_case ):
																		__magic_name__	:	Dict								=required_input[index][0]

										if return_tensors is None:
														if is_tf_tensor(__snake_case ):
																		__magic_name__	:	List[Any]								="""tf"""
														elif is_torch_tensor(__snake_case ):
																		__magic_name__	:	str								="""pt"""
														elif isinstance(__snake_case    , (int, float, list, tuple, np.ndarray) ):
																		__magic_name__	:	List[str]								="""np"""
														else:
																		raise ValueError(
																		    f"type of {first_element} unknown: {type(__snake_case )}. "
																		    """Should be one of a python, numpy, pytorch or tensorflow object.""" )

										for key, value in processed_features.items():
														if isinstance(value[0]    , (int, float) ):
																		__magic_name__	:	int								=to_numpy(__snake_case )
														else:
																		__magic_name__	:	int								=[to_numpy(__snake_case ) for v in value]

        # Convert padding_strategy in PaddingStrategy
										__magic_name__	:	Optional[int]								=self._get_padding_strategies(padding=__snake_case    , max_length=__snake_case )

										__magic_name__	:	Dict								=processed_features[self.model_input_names[0]]

										__magic_name__	:	Dict								=len(__snake_case )
										if not all(len(__snake_case ) == batch_size for v in processed_features.values() ):
														raise ValueError("""Some items in the output dictionary have a different batch size than others.""" )

										__magic_name__	:	Union[str, Any]								=[]
										for i in range(__snake_case ):
														__magic_name__	:	Optional[int]								={k: v[i] for k, v in processed_features.items()}
														# truncation
														__magic_name__	:	Optional[int]								=self._truncate(
														    __snake_case    , max_length=__snake_case    , pad_to_multiple_of=__snake_case    , truncation=__snake_case    , )
														truncated_inputs.append(__snake_case )

										if padding_strategy == PaddingStrategy.LONGEST:
														# make sure that `max_length` cannot be longer than the longest truncated length
														__magic_name__	:	List[Any]								=max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
														__magic_name__	:	Optional[Any]								=PaddingStrategy.MAX_LENGTH

										__magic_name__	:	Optional[int]								={}
										for i in range(__snake_case ):
														# padding
														__magic_name__	:	Optional[int]								=self._pad(
														    truncated_inputs[i]    , max_length=__snake_case    , padding_strategy=__snake_case    , pad_to_multiple_of=__snake_case    , return_attention_mask=__snake_case    , )

														for key, value in outputs.items():
																		if key not in batch_outputs:
																						__magic_name__	:	Optional[int]								=[]
																		if value.dtype is np.dtype(np.floataa ):
																						__magic_name__	:	Optional[Any]								=value.astype(np.floataa )
																		batch_outputs[key].append(__snake_case )

										return BatchFeature(__snake_case    , tensor_type=__snake_case )

						def     A__						(		self   :Dict    , __snake_case   :Union[Dict[str, np.ndarray], BatchFeature]    , __snake_case   :Optional[int] = None    , __snake_case   :PaddingStrategy = PaddingStrategy.DO_NOT_PAD    , __snake_case   :Optional[int] = None    , __snake_case   :Optional[bool] = None    , ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=processed_features[self.model_input_names[0]]

										if padding_strategy == PaddingStrategy.LONGEST:
														__magic_name__	:	List[str]								=len(__snake_case )

										if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
														__magic_name__	:	List[Any]								=((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										__magic_name__	:	str								=padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case ) < max_length

										if return_attention_mask and "attention_mask" not in processed_features:
														__magic_name__	:	str								=np.ones(len(__snake_case )    , dtype=np.intaa )

										if needs_to_be_padded:
														__magic_name__	:	Dict								=max_length - len(__snake_case )
														if self.padding_side == "right":
																		if return_attention_mask:
																						__magic_name__	:	Union[str, Any]								=np.pad(
																						    processed_features["""attention_mask"""]    , (0, difference) )
																		__magic_name__	:	Optional[Any]								=((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																		__magic_name__	:	Tuple								=np.pad(
																		    __snake_case    , __snake_case    , """constant"""    , constant_values=self.padding_value )
														elif self.padding_side == "left":
																		if return_attention_mask:
																						__magic_name__	:	int								=np.pad(
																						    processed_features["""attention_mask"""]    , (difference, 0) )
																		__magic_name__	:	Optional[int]								=((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																		__magic_name__	:	Dict								=np.pad(
																		    __snake_case    , __snake_case    , """constant"""    , constant_values=self.padding_value )
														else:
																		raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )

										return processed_features

						def     A__						(		self   :Dict    , __snake_case   :Union[Dict[str, np.ndarray], BatchFeature]    , __snake_case   :Optional[int] = None    , __snake_case   :Optional[int] = None    , __snake_case   :Optional[bool] = None    , ):
										'''simple docstring'''

										if not truncation:
														return processed_features
										elif truncation and max_length is None:
														raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" )

										__magic_name__	:	Optional[int]								=processed_features[self.model_input_names[0]]

										# find `max_length` that fits `pad_to_multiple_of`
										if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
														__magic_name__	:	Optional[Any]								=((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

										__magic_name__	:	int								=len(__snake_case ) > max_length

										if needs_to_be_truncated:
														__magic_name__	:	str								=processed_features[self.model_input_names[0]][:max_length]
														if "attention_mask" in processed_features:
																		__magic_name__	:	Any								=processed_features["""attention_mask"""][:max_length]

										return processed_features


						def     A__						(		self   :str    , __snake_case   :Any=False    , __snake_case   :Optional[int]=None ):
										'''simple docstring'''

										if padding is not False:
														if padding is True:
																		__magic_name__	:	str								=PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
														elif not isinstance(__snake_case    , __snake_case ):
																		__magic_name__	:	int								=PaddingStrategy(__snake_case )
														elif isinstance(__snake_case    , __snake_case ):
																		__magic_name__	:	Optional[Any]								=padding
										else:
														__magic_name__	:	Tuple								=PaddingStrategy.DO_NOT_PAD

										# Set max length if needed
										if max_length is None:
														if padding_strategy == PaddingStrategy.MAX_LENGTH:
																		raise ValueError(
																		    f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined" )

        # Test if we have a padding value
										if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
														raise ValueError(
														    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
														    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" )

										return padding_strategy










from typing import Mapping

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


UpperCAmelCase_		:				str									=     logging.get_logger(__name__)

UpperCAmelCase_		:				Tuple									=     {
    "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      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""t5"""
						UpperCamelCase     						=					["""past_key_values"""]
						UpperCamelCase     						=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

						def __init__(		self   :Optional[Any]    , __snake_case   :Optional[Any]=3_21_28    , __snake_case   :Optional[int]=5_12    , __snake_case   :str=64    , __snake_case   :int=20_48    , __snake_case   :Union[str, Any]=6    , __snake_case   :Union[str, Any]=None    , __snake_case   :Union[str, Any]=8    , __snake_case   :List[str]=32    , __snake_case   :Dict=1_28    , __snake_case   :List[Any]=0.1    , __snake_case   :int=1E-6    , __snake_case   :List[str]=1.0    , __snake_case   :Optional[int]="relu"    , __snake_case   :List[str]=True    , __snake_case   :Union[str, Any]=True    , __snake_case   :Union[str, Any]=0    , __snake_case   :str=1    , **__snake_case   :int    , ):
										'''simple docstring'''

										__magic_name__	:	str								=vocab_size
										__magic_name__	:	str								=d_model
										__magic_name__	:	Optional[Any]								=d_kv
										__magic_name__	:	int								=d_ff
										__magic_name__	:	Union[str, Any]								=num_layers
										__magic_name__	:	Tuple								=(
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										__magic_name__	:	List[str]								=num_heads
										__magic_name__	:	Optional[Any]								=relative_attention_num_buckets
										__magic_name__	:	Union[str, Any]								=relative_attention_max_distance
										__magic_name__	:	Dict								=dropout_rate
										__magic_name__	:	Dict								=layer_norm_epsilon
										__magic_name__	:	List[str]								=initializer_factor
										__magic_name__	:	Any								=feed_forward_proj
										__magic_name__	:	Optional[int]								=use_cache

										__magic_name__	:	Tuple								=self.feed_forward_proj.split("""-""" )
										__magic_name__	:	int								=act_info[-1]
										__magic_name__	:	int								=act_info[0] == """gated"""

										if len(__snake_case ) > 1 and act_info[0] != "gated" or len(__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":
														__magic_name__	:	str								="""gelu_new"""

										super().__init__(
										    pad_token_id=__snake_case    , eos_token_id=__snake_case    , is_encoder_decoder=__snake_case    , **__snake_case    , )




class 		__A      (			UpperCamelCase__			):

						@property
						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	int								={
										    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
										    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
										}
										if self.use_past:
														__magic_name__	:	List[str]								="""past_encoder_sequence + sequence"""
														__magic_name__	:	List[Any]								={0: """batch"""}
														__magic_name__	:	Union[str, Any]								={0: """batch""", 1: """past_decoder_sequence + sequence"""}
										else:
														__magic_name__	:	int								={0: """batch""", 1: """decoder_sequence"""}
														__magic_name__	:	Tuple								={0: """batch""", 1: """decoder_sequence"""}

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

										return common_inputs


						@property
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										return 13




