





import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def      lowerCAmelCase_ (       __a	,    __a		)     ->		Optional[Any]:







  """simple docstring"""





  assert isinstance(__a	,    __a		)
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("keep_in_memory"	,    [False, True]		)
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      Any			  =tmp_path / "cache"
  lowerCamelCase__:      Optional[int]			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowerCamelCase__:      int			  =ParquetDatasetReader(__a	,    cache_dir=__a	,    keep_in_memory=__a		).read()
  _check_parquet_dataset(__a	,    __a		)





@pytest.mark.parametrize(
    "features"	,    [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]	,    )
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      int			  =tmp_path / "cache"
  lowerCamelCase__:      Tuple			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Union[str, Any]			  =features.copy() if features else default_expected_features
  lowerCamelCase__:      Union[str, Any]			  =(
      Features({feature: Value(__a		) for feature, dtype in features.items()}		) if features is not None else None
  )
  lowerCamelCase__:      int			  =ParquetDatasetReader(__a	,    features=__a	,    cache_dir=__a		).read()
  _check_parquet_dataset(__a	,    __a		)





@pytest.mark.parametrize("split"	,    [None, NamedSplit("train"		), "train", "test"]		)
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      Union[str, Any]			  =tmp_path / "cache"
  lowerCamelCase__:      Dict			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Optional[int]			  =ParquetDatasetReader(__a	,    cache_dir=__a	,    split=__a		).read()
  _check_parquet_dataset(__a	,    __a		)
  assert dataset.split == split if split else "train"





@pytest.mark.parametrize("path_type"	,    [str, list]		)
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Dict:







  """simple docstring"""





  if issubclass(__a	,    __a		):
    lowerCamelCase__:      str			  =parquet_path
  elif issubclass(__a	,    __a		):
    lowerCamelCase__:      str			  =[parquet_path]
  lowerCamelCase__:      Optional[Any]			  =tmp_path / "cache"
  lowerCamelCase__:      Any			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Optional[int]			  =ParquetDatasetReader(__a	,    cache_dir=__a		).read()
  _check_parquet_dataset(__a	,    __a		)





def      lowerCAmelCase_ (       __a	,    __a	,    __a=("train",)		)     ->		Union[str, Any]:







  """simple docstring"""





  assert isinstance(__a	,    __a		)
  for split in splits:
    lowerCamelCase__:      Optional[Any]			  =dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("keep_in_memory"	,    [False, True]		)
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      Any			  =tmp_path / "cache"
  lowerCamelCase__:      str			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowerCamelCase__:      List[str]			  =ParquetDatasetReader(
        {"train": parquet_path}	,    cache_dir=__a	,    keep_in_memory=__a		).read()
  _check_parquet_datasetdict(__a	,    __a		)





@pytest.mark.parametrize(
    "features"	,    [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]	,    )
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      List[Any]			  =tmp_path / "cache"
  lowerCamelCase__:      Any			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      int			  =features.copy() if features else default_expected_features
  lowerCamelCase__:      Union[str, Any]			  =(
      Features({feature: Value(__a		) for feature, dtype in features.items()}		) if features is not None else None
  )
  lowerCamelCase__:      Union[str, Any]			  =ParquetDatasetReader({"train": parquet_path}	,    features=__a	,    cache_dir=__a		).read()
  _check_parquet_datasetdict(__a	,    __a		)





@pytest.mark.parametrize("split"	,    [None, NamedSplit("train"		), "train", "test"]		)
def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[str]:







  """simple docstring"""





  if split:
    lowerCamelCase__:      Union[str, Any]			  ={split: parquet_path}
  else:
    lowerCamelCase__:      int			  ="train"
    lowerCamelCase__:      Union[str, Any]			  ={"train": parquet_path, "test": parquet_path}
  lowerCamelCase__:      int			  =tmp_path / "cache"
  lowerCamelCase__:      Union[str, Any]			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  lowerCamelCase__:      Optional[Any]			  =ParquetDatasetReader(__a	,    cache_dir=__a		).read()
  _check_parquet_datasetdict(__a	,    __a	,    splits=list(path.keys()		)		)
  assert all(dataset[split].split == split for split in path.keys()		)





def      lowerCAmelCase_ (       __a	,    __a		)     ->		Tuple:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =ParquetDatasetWriter(__a	,    tmp_path / "foo.parquet"		)
  assert writer.write() > 0
  lowerCamelCase__:      Tuple			  =pq.ParquetFile(tmp_path / "foo.parquet"		)
  lowerCamelCase__:      Optional[int]			  =pf.read()
  assert dataset.data.table == output_table





def      lowerCAmelCase_ (       __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      List[str]			  =str(shared_datadir / "test_image_rgb.jpg"		)
  lowerCamelCase__:      Union[str, Any]			  ={"image": [image_path]}
  lowerCamelCase__:      int			  =Features({"image": Image()}		)
  lowerCamelCase__:      Tuple			  =Dataset.from_dict(__a	,    features=__a		)
  lowerCamelCase__:      Optional[int]			  =ParquetDatasetWriter(__a	,    tmp_path / "foo.parquet"		)
  assert writer.write() > 0

  lowerCamelCase__:      Optional[Any]			  =Dataset.from_parquet(str(tmp_path / "foo.parquet"		)		)
  assert dataset.features == reloaded_dataset.features

  lowerCamelCase__:      List[str]			  =ParquetDatasetReader(str(tmp_path / "foo.parquet"		)	,    streaming=__a		).read()
  assert dataset.features == reloaded_iterable_dataset.features






@pytest.mark.parametrize(
    "feature, expected"	,    [
        (Features({"foo": Value("int32"		)}		), None),
        (Features({"image": Image(), "foo": Value("int32"		)}		), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"nested": Sequence(Audio()		)}		), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]	,    )
def      lowerCAmelCase_ (       __a	,    __a		)     ->		Any:







  """simple docstring"""





  assert get_writer_batch_size(__a		) == expected








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.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


a__:      int									=		logging.getLogger(__name__)




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=None,__lowerCamelCase=None ):
        A__											=						self.layer[current_layer](__lowerCamelCase,__lowerCamelCase,head_mask[current_layer] )

        A__											=						layer_outputs[0]

        return hidden_states




@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''				,					UpperCamelCase__				,					)
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def __init__(							self,__lowerCamelCase ):
        super().__init__(__lowerCamelCase )

        A__											=						BertEncoderWithPabee(__lowerCamelCase )

        self.init_weights()
        A__											=						0
        A__											=						0
        A__											=						0

        A__											=						0



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						threshold



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						patience



 def 				UpperCamelCase			(							self ):
        A__											=						0
        A__											=						0



 def 				UpperCamelCase			(							self ):
        A__											=						self.inference_layers_num / self.inference_instances_num
        A__											=						(
            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(__lowerCamelCase )




 @add_start_docstrings_to_model_forward(__lowerCamelCase )
 def 				UpperCamelCase			(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=False,):

        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:
               A__											=						input_ids.size()
        elif inputs_embeds is not None:
               A__											=						inputs_embeds.size()[:-1]
        else:
               raise ValueError('''You have to specify either input_ids or inputs_embeds''' )

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

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

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        A__											=						self.get_extended_attention_mask(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
               A__					,	A__					,	A__											=						encoder_hidden_states.size()
               A__											=						(encoder_batch_size, encoder_sequence_length)
               if encoder_attention_mask is None:
                      A__											=						torch.ones(__lowerCamelCase,device=__lowerCamelCase )
               A__											=						self.invert_attention_mask(__lowerCamelCase )
        else:
               A__											=						None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        A__											=						self.get_head_mask(__lowerCamelCase,self.config.num_hidden_layers )

        A__											=						self.embeddings(
            input_ids=__lowerCamelCase,position_ids=__lowerCamelCase,token_type_ids=__lowerCamelCase,inputs_embeds=__lowerCamelCase )
        A__											=						embedding_output

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

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

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

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

        return res




@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''				,					UpperCamelCase__				,					)
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def __init__(							self,__lowerCamelCase ):
        super().__init__(__lowerCamelCase )
        A__											=						config.num_labels

        A__											=						BertModelWithPabee(__lowerCamelCase )
        A__											=						nn.Dropout(config.hidden_dropout_prob )
        A__											=						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(__lowerCamelCase )
 def 				UpperCamelCase			(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,):
        A__											=						self.bert(
            input_ids=__lowerCamelCase,attention_mask=__lowerCamelCase,token_type_ids=__lowerCamelCase,position_ids=__lowerCamelCase,head_mask=__lowerCamelCase,inputs_embeds=__lowerCamelCase,output_dropout=self.dropout,output_layers=self.classifiers,regression=self.num_labels == 1,)

        A__											=						(logits[-1],)

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

        return outputs





