

import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


A_		:			Optional[int]										=   logging.getLogger(__name__)
class 					A_   (      _a						):


    '''simple docstring'''






    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__=None     ,      lowercase__=None  )     ->      List[Any]:
        if current_layer == 0:
            __UpperCAmelCase									=			self.embedding_hidden_mapping_in(lowercase__  )
        else:
            __UpperCAmelCase									=			hidden_states[0]

        __UpperCAmelCase									=			int(self.config.num_hidden_layers / self.config.num_hidden_groups  )

        # Index of the hidden group
        __UpperCAmelCase									=			int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)  )

        __UpperCAmelCase									=			self.albert_layer_groups[group_idx](
            lowercase__     ,      lowercase__     ,      head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]     ,      )
        __UpperCAmelCase									=			layer_group_output[0]

        return (hidden_states,)






@add_start_docstrings(
    "The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top." ,     _a ,     )
class 					A_   (      _a						):


    '''simple docstring'''






    def __init__(self     ,      lowercase__  )     ->      Tuple:
        super().__init__(lowercase__  )

        __UpperCAmelCase									=			AlbertTransformerWithPabee(lowercase__  )

        self.init_weights()
        __UpperCAmelCase									=			0
        __UpperCAmelCase									=			0
        __UpperCAmelCase									=			0

        __UpperCAmelCase									=			0


    def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      Tuple:
        __UpperCAmelCase									=			threshold


    def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
        __UpperCAmelCase									=			patience


    def      lowerCAmelCase_						(self  )     ->      Dict:
        __UpperCAmelCase									=			0
        __UpperCAmelCase									=			0


    def      lowerCAmelCase_						(self  )     ->      Any:
        __UpperCAmelCase									=			self.inference_layers_num / self.inference_instances_num
        __UpperCAmelCase									=			(
            F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
            F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
        )
        print(lowercase__  )






    @add_start_docstrings_to_model_forward(lowercase__  )
    def      lowerCAmelCase_						(self     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=False     ,      )     ->      Optional[int]:

        if input_ids is not None and inputs_embeds is not None:
            raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''  )
        elif input_ids is not None:
            __UpperCAmelCase									=			input_ids.size()
        elif inputs_embeds is not None:
            __UpperCAmelCase									=			inputs_embeds.size()[:-1]
        else:
            raise ValueError('''You have to specify either input_ids or inputs_embeds'''  )

        __UpperCAmelCase									=			input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            __UpperCAmelCase									=			torch.ones(lowercase__     ,      device=lowercase__  )
        if token_type_ids is None:
            __UpperCAmelCase									=			torch.zeros(lowercase__     ,      dtype=torch.long     ,      device=lowercase__  )

        __UpperCAmelCase									=			attention_mask.unsqueeze(1  ).unsqueeze(2  )
        __UpperCAmelCase									=			extended_attention_mask.to(dtype=self.dtype  )  # fp16 compatibility
        __UpperCAmelCase									=			(1.0 - extended_attention_mask) * -10000.0
        __UpperCAmelCase									=			self.get_head_mask(lowercase__     ,      self.config.num_hidden_layers  )

        __UpperCAmelCase									=			self.embeddings(
            lowercase__     ,      position_ids=lowercase__     ,      token_type_ids=lowercase__     ,      inputs_embeds=lowercase__  )
        __UpperCAmelCase									=			embedding_output

        if self.training:
            __UpperCAmelCase									=			[]
            for i in range(self.config.num_hidden_layers  ):
                __UpperCAmelCase									=			self.encoder.adaptive_forward(
                    lowercase__     ,      current_layer=lowercase__     ,      attention_mask=lowercase__     ,      head_mask=lowercase__     ,      )

                __UpperCAmelCase									=			self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]  )  )
                __UpperCAmelCase									=			output_layers[i](output_dropout(lowercase__  )  )
                res.append(lowercase__  )
        elif self.patience == 0:  # Use all layers for inference
            __UpperCAmelCase									=			self.encoder(lowercase__     ,      lowercase__     ,      head_mask=lowercase__  )
            __UpperCAmelCase									=			self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]  )  )
            __UpperCAmelCase									=			[output_layers[self.config.num_hidden_layers - 1](lowercase__  )]
        else:
            __UpperCAmelCase									=			0
            __UpperCAmelCase									=			None
            __UpperCAmelCase									=			0
            for i in range(self.config.num_hidden_layers  ):
                calculated_layer_num += 1
                __UpperCAmelCase									=			self.encoder.adaptive_forward(
                    lowercase__     ,      current_layer=lowercase__     ,      attention_mask=lowercase__     ,      head_mask=lowercase__     ,      )

                __UpperCAmelCase									=			self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]  )  )
                __UpperCAmelCase									=			output_layers[i](lowercase__  )
                if regression:
                    __UpperCAmelCase									=			logits.detach()
                    if patient_result is not None:
                        __UpperCAmelCase									=			patient_result.detach()
                    if (patient_result is not None) and torch.abs(patient_result - labels  ) < self.regression_threshold:
                        patient_counter += 1
                    else:
                        __UpperCAmelCase									=			0
                else:
                    __UpperCAmelCase									=			logits.detach().argmax(dim=1  )
                    if patient_result is not None:
                        __UpperCAmelCase									=			patient_result.detach().argmax(dim=1  )
                    if (patient_result is not None) and torch.all(labels.eq(lowercase__  )  ):
                        patient_counter += 1
                    else:
                        __UpperCAmelCase									=			0

                __UpperCAmelCase									=			logits
                if patient_counter == self.patience:
                    break
            __UpperCAmelCase									=			[patient_result]
            self.inference_layers_num += calculated_layer_num
            self.inference_instances_num += 1

        return res






@add_start_docstrings(
    "Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. " ,     _a ,     )
class 					A_   (      _a						):


    '''simple docstring'''






    def __init__(self     ,      lowercase__  )     ->      Union[str, Any]:
        super().__init__(lowercase__  )
        __UpperCAmelCase									=			config.num_labels

        __UpperCAmelCase									=			AlbertModelWithPabee(lowercase__  )
        __UpperCAmelCase									=			nn.Dropout(config.classifier_dropout_prob  )
        __UpperCAmelCase									=			nn.ModuleList(
            [nn.Linear(config.hidden_size     ,      self.config.num_labels  ) for _ in range(config.num_hidden_layers  )]  )

        self.init_weights()






    @add_start_docstrings_to_model_forward(lowercase__  )
    def      lowerCAmelCase_						(self     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      lowercase__=None     ,      )     ->      List[str]:
        __UpperCAmelCase									=			self.albert(
            input_ids=lowercase__     ,      attention_mask=lowercase__     ,      token_type_ids=lowercase__     ,      position_ids=lowercase__     ,      head_mask=lowercase__     ,      inputs_embeds=lowercase__     ,      output_dropout=self.dropout     ,      output_layers=self.classifiers     ,      regression=self.num_labels == 1     ,      )

        __UpperCAmelCase									=			(logits[-1],)

        if labels is not None:
            __UpperCAmelCase									=			None
            __UpperCAmelCase									=			0
            for ix, logits_item in enumerate(lowercase__  ):
                if self.num_labels == 1:
                    #  We are doing regression
                    __UpperCAmelCase									=			MSELoss()
                    __UpperCAmelCase									=			loss_fct(logits_item.view(-1  )     ,      labels.view(-1  )  )
                else:
                    __UpperCAmelCase									=			CrossEntropyLoss()
                    __UpperCAmelCase									=			loss_fct(logits_item.view(-1     ,      self.num_labels  )     ,      labels.view(-1  )  )
                if total_loss is None:
                    __UpperCAmelCase									=			loss
                else:
                    total_loss += loss * (ix + 1)
                total_weights += ix + 1
            __UpperCAmelCase									=			(total_loss / total_weights,) + outputs

        return outputs







import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
A_		:			Any										=   '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'

# TODO: Add description of the dataset here
# You can copy an official description
A_		:			Optional[Any]										=   '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'

# TODO: Add a link to an official homepage for the dataset here
A_		:			Dict										=   ''

# TODO: Add the licence for the dataset here if you can find it
A_		:			Dict										=   ''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
A_		:			Union[str, Any]										=   {
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}
class 					A_   (      datasets.GeneratorBasedBuilder						):


    '''simple docstring'''






    a__     							=       datasets.Version("1.1.0"						)

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    a__     							=       [
        datasets.BuilderConfig(name="first_domain" ,     version=_a ,     description="This part of my dataset covers a first domain"						),
        datasets.BuilderConfig(name="second_domain" ,     version=_a ,     description="This part of my dataset covers a second domain"						),
    ]

    a__     							=       "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.


    def      lowerCAmelCase_						(self  )     ->      Tuple:
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            __UpperCAmelCase									=			datasets.Features(
                {
                    '''sentence''': datasets.Value('''string'''  ),
                    '''option1''': datasets.Value('''string'''  ),
                    '''answer''': datasets.Value('''string'''  )
                    # These are the features of your dataset like images, labels ...
                }  )
        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
            __UpperCAmelCase									=			datasets.Features(
                {
                    '''sentence''': datasets.Value('''string'''  ),
                    '''option2''': datasets.Value('''string'''  ),
                    '''second_domain_answer''': datasets.Value('''string'''  )
                    # These are the features of your dataset like images, labels ...
                }  )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION     ,      features=lowercase__     ,      homepage=_HOMEPAGE     ,      license=_LICENSE     ,      citation=_CITATION     ,      )


    def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        __UpperCAmelCase									=			_URLS[self.config.name]
        __UpperCAmelCase									=			dl_manager.download_and_extract(lowercase__  )
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN     ,      gen_kwargs={
                    '''filepath''': os.path.join(lowercase__     ,      '''train.jsonl'''  ),
                    '''split''': '''train''',
                }     ,      ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION     ,      gen_kwargs={
                    '''filepath''': os.path.join(lowercase__     ,      '''dev.jsonl'''  ),
                    '''split''': '''dev''',
                }     ,      ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST     ,      gen_kwargs={
                    '''filepath''': os.path.join(lowercase__     ,      '''test.jsonl'''  ),
                    '''split''': '''test'''
                }     ,      ),
        ]






    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(lowercase__     ,      encoding='''utf-8'''  ) as f:
            for key, row in enumerate(lowercase__  ):
                __UpperCAmelCase									=			json.loads(lowercase__  )
                if self.config.name == "first_domain":
                    # Yields examples as (key, example) tuples
                    yield key, {
                        "sentence": data["sentence"],
                        "option1": data["option1"],
                        "answer": "" if split == "test" else data["answer"],
                    }
                else:
                    yield key, {
                        "sentence": data["sentence"],
                        "option2": data["option2"],
                        "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                    }




