



"""simple docstring"""






import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
	from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
	from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


def      _lowercase							(    __snake_case      )		->  Optional[Any]:
			return 1.0 / (1.0 + np.exp(-_outputs      ))


def      _lowercase							(    __snake_case      )		->  str:
			__lowerCAmelCase :    Optional[int]				=			np.max(_outputs   ,axis=-1   ,keepdims=_UpperCAmelCase      )
			__lowerCAmelCase :    Optional[int]				=			np.exp(_outputs - maxes      )
			return shifted_exp / shifted_exp.sum(axis=-1   ,keepdims=_UpperCAmelCase      )



class      A__  (    a__				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'sigmoid'
	SCREAMING_SNAKE_CASE        =      'softmax'
	SCREAMING_SNAKE_CASE        =      'none'






@add_end_docstrings(
    a__    ,						R'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `"default"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `"sigmoid"`: Applies the sigmoid function on the output.\n            - `"softmax"`: Applies the softmax function on the output.\n            - `"none"`: Does not apply any function on the output.\n    '    ,						)
class      A__  (    a__				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      ClassificationFunction.NONE

	def __init__(	self:						Any			,     **_SCREAMING_SNAKE_CASE:						List[str])							->			Optional[Any]:

				"""simple docstring"""

				super().__init__(**_snake_case)

				self.check_model_type(
				    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
				    if self.framework == "tf"
				    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=None			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=None			,     _SCREAMING_SNAKE_CASE:						List[str]=""			,     **_SCREAMING_SNAKE_CASE:						List[Any])							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			tokenizer_kwargs

				__lowerCAmelCase :    int				=			{}
				if hasattr(self.model.config			,     "return_all_scores") and return_all_scores is None:
							__lowerCAmelCase :    int				=			self.model.config.return_all_scores

				if isinstance(_snake_case			,     _snake_case) or top_k is None:
							__lowerCAmelCase :    str				=			top_k
							__lowerCAmelCase :    List[str]				=			False
				elif return_all_scores is not None:
							warnings.warn(
							    "`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"
							    " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`."			,     _snake_case			,     )
							if return_all_scores:
										__lowerCAmelCase :    Dict				=			None
							else:
										__lowerCAmelCase :    Optional[Any]				=			1

				if isinstance(_snake_case			,     _snake_case):
							__lowerCAmelCase :    List[str]				=			ClassificationFunction[function_to_apply.upper()]

				if function_to_apply is not None:
							__lowerCAmelCase :    Any				=			function_to_apply
				return preprocess_params, {}, postprocess_params

	def __call__(	self:						List[Any]			,     *_SCREAMING_SNAKE_CASE:						Tuple			,     **_SCREAMING_SNAKE_CASE:						List[str])							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			super().__call__(*_snake_case			,     **_snake_case)
				# TODO try and retrieve it in a nicer way from _sanitize_parameters.
				__lowerCAmelCase :    List[Any]				=			"top_k" not in kwargs
				if isinstance(args[0]			,     _snake_case) and _legacy:
							# This pipeline is odd, and return a list when single item is run
							return [result]
				else:
							return result

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						int			,     **_SCREAMING_SNAKE_CASE:						Dict)							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.framework
				if isinstance(_snake_case			,     _snake_case):
							return self.tokenizer(**_snake_case			,     return_tensors=_snake_case			,     **_snake_case)
				elif isinstance(_snake_case			,     _snake_case) and len(_snake_case) == 1 and isinstance(inputs[0]			,     _snake_case) and len(inputs[0]) == 2:
							# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
							return self.tokenizer(
							    text=inputs[0][0]			,     text_pair=inputs[0][1]			,     return_tensors=_snake_case			,     **_snake_case)
				elif isinstance(_snake_case			,     _snake_case):
							# This is likely an invalid usage of the pipeline attempting to pass text pairs.
							raise ValueError(
							    "The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
							    " dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair.")
				return self.tokenizer(_snake_case			,     return_tensors=_snake_case			,     **_snake_case)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			Any:

				"""simple docstring"""

				return self.model(**_snake_case)







	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Optional[int]=None			,     _SCREAMING_SNAKE_CASE:						Any=1			,     _SCREAMING_SNAKE_CASE:						Dict=True)							->			Dict:

				"""simple docstring"""

				if function_to_apply is None:
							if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
										__lowerCAmelCase :    List[Any]				=			ClassificationFunction.SIGMOID
							elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
										__lowerCAmelCase :    str				=			ClassificationFunction.SOFTMAX
							elif hasattr(self.model.config			,     "function_to_apply") and function_to_apply is None:
										__lowerCAmelCase :    Optional[int]				=			self.model.config.function_to_apply
							else:
										__lowerCAmelCase :    Any				=			ClassificationFunction.NONE

				__lowerCAmelCase :    Union[str, Any]				=			model_outputs["logits"][0]
				__lowerCAmelCase :    int				=			outputs.numpy()

				if function_to_apply == ClassificationFunction.SIGMOID:
							__lowerCAmelCase :    str				=			sigmoid(_snake_case)
				elif function_to_apply == ClassificationFunction.SOFTMAX:
							__lowerCAmelCase :    Optional[int]				=			softmax(_snake_case)
				elif function_to_apply == ClassificationFunction.NONE:
							__lowerCAmelCase :    List[Any]				=			outputs
				else:
							raise ValueError(F"""Unrecognized `function_to_apply` argument: {function_to_apply}""")

				if top_k == 1 and _legacy:
							return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

				__lowerCAmelCase :    List[str]				=			[
				    {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(_snake_case)
				]
				if not _legacy:
							dict_scores.sort(key=lambda _SCREAMING_SNAKE_CASE: x["score"]			,     reverse=_snake_case)
							if top_k is not None:
										__lowerCAmelCase :    Optional[Any]				=			dict_scores[:top_k]
				return dict_scores






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase     :						Optional[Any]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						List[Any]						       =  {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}







class       a							(   a__  ):
 snake_case__             =       '''megatron-bert'''



 def __init__(     self				,       _snake_case=2_90_56				,       _snake_case=10_24				,       _snake_case=24				,       _snake_case=16				,       _snake_case=40_96				,       _snake_case="gelu"				,       _snake_case=0.1				,       _snake_case=0.1				,       _snake_case=5_12				,       _snake_case=2				,       _snake_case=0.02				,       _snake_case=1E-12				,       _snake_case=0				,       _snake_case="absolute"				,       _snake_case=True				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(pad_token_id=_snake_case				,       **_snake_case     )

  lowerCAmelCase    				= vocab_size
  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= intermediate_size
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= max_position_embeddings
  lowerCAmelCase    				= type_vocab_size
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= layer_norm_eps
  lowerCAmelCase    				= position_embedding_type
  lowerCAmelCase    				= use_cache





