






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 	_UpperCamelCase   (    UpperCamelCase_       :					Tuple		)  ->   Dict:

						"""simple docstring"""



						return 1.0 / (1.0 + np.exp(-_outputs		))







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]		)  ->   Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.max(_outputs    ,       axis=-1    ,       keepdims=UpperCamelCase_		)
						lowerCAmelCase__    						=						np.exp(_outputs - maxes		)
						return shifted_exp / shifted_exp.sum(axis=-1    ,       keepdims=UpperCamelCase_		)


class     __SCREAMING_SNAKE_CASE					(   __lowercase):
						_SCREAMING_SNAKE_CASE	:       str   =					'''sigmoid'''
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					'''softmax'''
						_SCREAMING_SNAKE_CASE	:       Any   =					'''none'''




@add_end_docstrings(
    __lowercase		,					R'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    '''		,					)
class     __SCREAMING_SNAKE_CASE					(   __lowercase):
						_SCREAMING_SNAKE_CASE	:       List[str]   =					False
						_SCREAMING_SNAKE_CASE	:       List[Any]   =					ClassificationFunction.NONE






						def __init__(	self     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												super().__init__(**_UpperCamelCase   )

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






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase=""     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												# Using "" as default argument because we're going to use `top_k=None` in user code to declare
												# "No top_k"
												lowerCAmelCase__    						=						tokenizer_kwargs

												lowerCAmelCase__    						=						{}
												if hasattr(self.model.config     ,						'return_all_scores'   ) and return_all_scores is None:
																		lowerCAmelCase__    						=						self.model.config.return_all_scores

												if isinstance(_UpperCamelCase     ,						_UpperCamelCase   ) or top_k is None:
																		lowerCAmelCase__    						=						top_k
																		lowerCAmelCase__    						=						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`.'     ,						_UpperCamelCase     ,						)
																		if return_all_scores:
																								lowerCAmelCase__    						=						None
																		else:
																								lowerCAmelCase__    						=						1

												if isinstance(_UpperCamelCase     ,						_UpperCamelCase   ):
																		lowerCAmelCase__    						=						ClassificationFunction[function_to_apply.upper()]

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






						def __call__(	self     ,						*_UpperCamelCase     ,						**_UpperCamelCase   ):






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






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.framework
												if isinstance(_UpperCamelCase     ,						_UpperCamelCase   ):
																		return self.tokenizer(**_UpperCamelCase     ,						return_tensors=_UpperCamelCase     ,						**_UpperCamelCase   )
												elif isinstance(_UpperCamelCase     ,						_UpperCamelCase   ) and len(_UpperCamelCase   ) == 1 and isinstance(inputs[0]     ,						_UpperCamelCase   ) 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=_UpperCamelCase     ,						**_UpperCamelCase   )
												elif isinstance(_UpperCamelCase     ,						_UpperCamelCase   ):
																		# 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(_UpperCamelCase     ,						return_tensors=_UpperCamelCase     ,						**_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												return self.model(**_UpperCamelCase   )







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase=None     ,						_UpperCamelCase=1     ,						_UpperCamelCase=True   ):






												"""simple docstring"""
												# `_legacy` is used to determine if we're running the naked pipeline and in backward
												# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
												# the more natural result containing the list.
												# Default value before `set_parameters`
												if function_to_apply is None:
																		if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																								lowerCAmelCase__    						=						ClassificationFunction.SIGMOID
																		elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																								lowerCAmelCase__    						=						ClassificationFunction.SOFTMAX
																		elif hasattr(self.model.config     ,						'function_to_apply'   ) and function_to_apply is None:
																								lowerCAmelCase__    						=						self.model.config.function_to_apply
																		else:
																								lowerCAmelCase__    						=						ClassificationFunction.NONE

												lowerCAmelCase__    						=						model_outputs['logits'][0]
												lowerCAmelCase__    						=						outputs.numpy()

												if function_to_apply == ClassificationFunction.SIGMOID:
																		lowerCAmelCase__    						=						sigmoid(_UpperCamelCase   )
												elif function_to_apply == ClassificationFunction.SOFTMAX:
																		lowerCAmelCase__    						=						softmax(_UpperCamelCase   )
												elif function_to_apply == ClassificationFunction.NONE:
																		lowerCAmelCase__    						=						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__    						=						[
												    {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(_UpperCamelCase   )
												]
												if not _legacy:
																		dict_scores.sort(key=lambda _UpperCamelCase   : x["score"]     ,						reverse=_UpperCamelCase   )
																		if top_k is not None:
																								lowerCAmelCase__    						=						dict_scores[:top_k]
												return dict_scores













import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Optional[Any]		)  ->   Any:

						"""simple docstring"""



						lowerCAmelCase__    						=						BertConfig.from_json_file(UpperCamelCase_		)
						print(F"Building PyTorch model from configuration: {config}"		)
						lowerCAmelCase__    						=						BertForPreTraining(UpperCamelCase_		)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)

						# Save pytorch-model
						print(F"Save PyTorch model to {pytorch_dump_path}"		)
						torch.save(model.state_dict()    ,       UpperCamelCase_		)


if __name__ == "__main__":
					__snake_case    :    Union[str, Any]         =     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--bert_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					__snake_case    :    List[str]         =     parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





