



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 							A			(  _UpperCAmelCase       :     Tuple					)       ->				List[Any]:







			'''simple docstring'''
			return 1.0 / (1.0 + np.exp(-_outputs					))





def 							A			(  _UpperCAmelCase       :     Optional[int]					)       ->				int:







			'''simple docstring'''
			_UpperCAmelCase	 =     np.max(_outputs							,     axis=-1							,     keepdims=lowerCAmelCase__					)
			_UpperCAmelCase	 =     np.exp(_outputs - maxes					)
			return shifted_exp / shifted_exp.sum(axis=-1							,     keepdims=lowerCAmelCase__					)







class     __lowerCAmelCase					(    __a		):
			UpperCamelCase           =  '''sigmoid'''
			UpperCamelCase           =  '''softmax'''
			UpperCamelCase           =  '''none'''







@add_end_docstrings(
    __a       ,		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     __lowerCAmelCase					(    __a		):
			UpperCamelCase           =  False
			UpperCamelCase           =  ClassificationFunction.NONE



			def __init__(		self     :						Tuple				,	**A     :						int)     ->					List[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 					_lowerCamelCase (		self     :						Tuple				,	A     :						List[str]=None				,	A     :						List[str]=None				,	A     :						Optional[int]=""				,	**A     :						Tuple)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     tokenizer_kwargs

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

						if isinstance(snake_case__				,	snake_case__) or top_k is None:
									_UpperCAmelCase	 =     top_k
									_UpperCAmelCase	 =     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:
												_UpperCAmelCase	 =     None
									else:
												_UpperCAmelCase	 =     1

						if isinstance(snake_case__				,	snake_case__):
									_UpperCAmelCase	 =     ClassificationFunction[function_to_apply.upper()]

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



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



						"""simple docstring"""






						_UpperCAmelCase	 =     super().__call__(*snake_case__				,	**snake_case__)
						# TODO try and retrieve it in a nicer way from _sanitize_parameters.
						_UpperCAmelCase	 =     '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 					_lowerCamelCase (		self     :						str				,	A     :						List[str]				,	**A     :						Tuple)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     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 					_lowerCamelCase (		self     :						Dict				,	A     :						List[str])     ->					Optional[int]:



						"""simple docstring"""






						return self.model(**snake_case__)



			def 					_lowerCamelCase (		self     :						Optional[int]				,	A     :						Optional[Any]				,	A     :						Dict=None				,	A     :						Optional[Any]=1				,	A     :						List[Any]=True)     ->					Tuple:



						"""simple docstring"""






						if function_to_apply is None:
									if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
												_UpperCAmelCase	 =     ClassificationFunction.SIGMOID
									elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
												_UpperCAmelCase	 =     ClassificationFunction.SOFTMAX
									elif hasattr(self.model.config				,	'function_to_apply') and function_to_apply is None:
												_UpperCAmelCase	 =     self.model.config.function_to_apply
									else:
												_UpperCAmelCase	 =     ClassificationFunction.NONE

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

						if function_to_apply == ClassificationFunction.SIGMOID:
									_UpperCAmelCase	 =     sigmoid(snake_case__)
						elif function_to_apply == ClassificationFunction.SOFTMAX:
									_UpperCAmelCase	 =     softmax(snake_case__)
						elif function_to_apply == ClassificationFunction.NONE:
									_UpperCAmelCase	 =     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()}

						_UpperCAmelCase	 =     [
						    {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(snake_case__)
						]
						if not _legacy:
									dict_scores.sort(key=lambda A: x["score"]				,	reverse=snake_case__)
									if top_k is not None:
												_UpperCAmelCase	 =     dict_scores[:top_k]
						return dict_scores





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[int]					)       ->				int:







			'''simple docstring'''
			# Initialise PyTorch model
			_UpperCAmelCase	 =     TaConfig.from_json_file(_UpperCAmelCase					)
			print(F"Building PyTorch model from configuration: {config}"					)
			_UpperCAmelCase	 =     TaForConditionalGeneration(_UpperCAmelCase					)

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)

			# Save pytorch-model
			print(F"Save PyTorch model to {pytorch_dump_path}"					)
			model.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
							UpperCAmelCase__     = 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(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
							    ),
							)
							parser.add_argument(
							    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__     = parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
