
import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class 							UpperCAmelCase				(  tf.keras.layers.Layer  ):




	'''simple docstring'''





	def __init__(	self :			List[Any]							,A :			Dict[str, int]							,A :			List[str]							,A :			int = None							,A :			int = None			):
					super().__init__()
					__A             =			pad_token_id
					__A             =			max_length
					__A             =			vocab
					__A             =			merges
					__A             =			BytePairTokenizer(A							,A							,sequence_length=A			)






	@classmethod
	def     UpperCamelCase_  (	cls :			Optional[Any]							,A :			GPTaTokenizer							,*A :			Any							,**A :			Union[str, Any]			):
					__A             =			[" ".join(A			) for m in tokenizer.bpe_ranks.keys()]
					__A             =			tokenizer.get_vocab()
					return cls(A							,A							,*A							,**A			)






	@classmethod
	def     UpperCamelCase_  (	cls :			List[str]							,A :			Union[str, os.PathLike]							,*A :			Tuple							,**A :			Optional[Any]			):
					__A             =			GPTaTokenizer.from_pretrained(A							,*A							,**A			)
					return cls.from_tokenizer(A							,*A							,**A			)






	@classmethod
	def     UpperCamelCase_  (	cls :			List[Any]							,A :			Optional[Any]			):
					return cls(**A			)






	def     UpperCamelCase_  (	self :			Any			):
					return {
					    "vocab": self.vocab,
					    "merges": self.merges,
					    "max_length": self.max_length,
					    "pad_token_id": self.pad_token_id,
					}







	def     UpperCamelCase_  (	self :			str							,A :			Optional[Any]							,A :			int = None			):
					__A             =			self.tf_tokenizer(A			)
					__A             =			tf.ones_like(A			)

					if self.pad_token_id is not None:
									# pad the tokens up to max length
									__A             =			max_length if max_length is not None else self.max_length

									if max_length is not None:
													__A      ,							__A             =			pad_model_inputs(
													    A							,max_seq_length=A							,pad_value=self.pad_token_id			)

					return {"attention_mask": attention_mask, "input_ids": input_ids}


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"facebook/bart-large-mnli"
	snake_case_    =					(
	    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
	    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
	    "It returns the most likely label in the list of provided `labels` for the input text."
	)
	snake_case_    =					"text_classifier"
	snake_case_    =					AutoTokenizer
	snake_case_    =					AutoModelForSequenceClassification

	snake_case_    =					["text", ["text"]]
	snake_case_    =					["text"]






	def     UpperCamelCase_  (	self :			str			):
					super().setup()
					__A             =			self.model.config
					__A             =			-1
					for idx, label in config.idalabel.items():
									if label.lower().startswith("entail"			):
													__A             =			int(A			)
					if self.entailment_id == -1:
									raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."			)






	def     UpperCamelCase_  (	self :			int							,A :			Optional[Any]							,A :			Dict			):
					__A             =			labels
					return self.pre_processor(
					    [text] * len(A			)							,[f'''This example is {label}''' for label in labels]							,return_tensors="pt"							,padding="max_length"							,)







	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Tuple			):
					__A             =			outputs.logits
					__A             =			torch.argmax(logits[:, 2]			).item()
					return self._labels[label_id]
