





"""simple docstring"""
import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


lowerCamelCase_          =	logging.get_logger(__name__)






@dataclass
class       _SCREAMING_SNAKE_CASE:
       SCREAMING_SNAKE_CASE_     :   str
       SCREAMING_SNAKE_CASE_     :   str
       SCREAMING_SNAKE_CASE_     :   Optional[str]					=						None
       SCREAMING_SNAKE_CASE_     :   Optional[str]					=						None




       def 					_UpperCamelCase  (		self							)    ->   Optional[int]:





             """simple docstring"""






             return json.dumps(dataclasses.asdict(self							)	,indent=2							) + "\n"






@dataclass(frozen=A						)
class       _SCREAMING_SNAKE_CASE:
       SCREAMING_SNAKE_CASE_     :   List[int]
       SCREAMING_SNAKE_CASE_     :   Optional[List[int]]					=						None
       SCREAMING_SNAKE_CASE_     :   Optional[List[int]]					=						None
       SCREAMING_SNAKE_CASE_     :   Optional[Union[int, float]]					=						None




       def 					_UpperCamelCase  (		self							)    ->   Any:





             """simple docstring"""






             return json.dumps(dataclasses.asdict(self							)							) + "\n"






class       _SCREAMING_SNAKE_CASE:




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             raise NotImplementedError()




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Any:





             """simple docstring"""






             raise NotImplementedError()




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             raise NotImplementedError()




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             raise NotImplementedError()




       def 					_UpperCamelCase  (		self							)    ->   Optional[int]:





             """simple docstring"""






             raise NotImplementedError()




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             if len(self.get_labels()							) > 1:
                   __SCREAMING_SNAKE_CASE     :int	       =						self.get_labels()[int(example.label							)]
             return example




       @classmethod
       def 					_UpperCamelCase  (		cls	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=None							)    ->   Any:





             """simple docstring"""






             with open(SCREAMING_SNAKE_CASE__	,'''r'''	,encoding='''utf-8-sig'''							) as f:
                   return list(csv.reader(SCREAMING_SNAKE_CASE__	,delimiter='''\t'''	,quotechar=SCREAMING_SNAKE_CASE__							)							)






class       _SCREAMING_SNAKE_CASE(     A						):




       def __init__(		self	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__="classification"	,SCREAMING_SNAKE_CASE__=False							)    ->   Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						[] if labels is None else labels
             __SCREAMING_SNAKE_CASE     :Any	       =						[] if examples is None else examples
             __SCREAMING_SNAKE_CASE     :int	       =						mode
             __SCREAMING_SNAKE_CASE     :Tuple	       =						verbose




       def __len__(		self							)    ->   Dict:





             """simple docstring"""






             return len(self.examples							)




       def __getitem__(		self	,SCREAMING_SNAKE_CASE__							)    ->   Dict:





             """simple docstring"""






             if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							):
                   return SingleSentenceClassificationProcessor(labels=self.labels	,examples=self.examples[idx]							)
             return self.examples[idx]




       @classmethod
       def 					_UpperCamelCase  (		cls	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=""	,SCREAMING_SNAKE_CASE__=0	,SCREAMING_SNAKE_CASE__=1	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=False	,**SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[str]	       =						cls(**SCREAMING_SNAKE_CASE__							)
             processor.add_examples_from_csv(
                 SCREAMING_SNAKE_CASE__	,split_name=SCREAMING_SNAKE_CASE__	,column_label=SCREAMING_SNAKE_CASE__	,column_text=SCREAMING_SNAKE_CASE__	,column_id=SCREAMING_SNAKE_CASE__	,skip_first_row=SCREAMING_SNAKE_CASE__	,overwrite_labels=SCREAMING_SNAKE_CASE__	,overwrite_examples=SCREAMING_SNAKE_CASE__	,)
             return processor




       @classmethod
       def 					_UpperCamelCase  (		cls	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=None	,**SCREAMING_SNAKE_CASE__							)    ->   List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						cls(**SCREAMING_SNAKE_CASE__							)
             processor.add_examples(SCREAMING_SNAKE_CASE__	,labels=SCREAMING_SNAKE_CASE__							)
             return processor




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=""	,SCREAMING_SNAKE_CASE__=0	,SCREAMING_SNAKE_CASE__=1	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=False	,SCREAMING_SNAKE_CASE__=False	,SCREAMING_SNAKE_CASE__=False	,)    ->   Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :int	       =						self._read_tsv(SCREAMING_SNAKE_CASE__							)
             if skip_first_row:
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						lines[1:]
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[]
             __SCREAMING_SNAKE_CASE     :str	       =						[]
             __SCREAMING_SNAKE_CASE     :int	       =						[]
             for i, line in enumerate(SCREAMING_SNAKE_CASE__							):
                   texts.append(line[column_text]							)
                   labels.append(line[column_label]							)
                   if column_id is not None:
                         ids.append(line[column_id]							)
                   else:
                         __SCREAMING_SNAKE_CASE     :int	       =						f'''{split_name}-{i}''' if split_name else str(SCREAMING_SNAKE_CASE__							)
                         ids.append(SCREAMING_SNAKE_CASE__							)

             return self.add_examples(
                 SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,overwrite_labels=SCREAMING_SNAKE_CASE__	,overwrite_examples=SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=False	,SCREAMING_SNAKE_CASE__=False							)    ->   List[str]:





             """simple docstring"""






             if labels is not None and len(SCREAMING_SNAKE_CASE__							) != len(SCREAMING_SNAKE_CASE__							):
                   raise ValueError(
                       f'''Text and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__							)} and {len(SCREAMING_SNAKE_CASE__							)}'''							)
             if ids is not None and len(SCREAMING_SNAKE_CASE__							) != len(SCREAMING_SNAKE_CASE__							):
                   raise ValueError(f'''Text and ids have mismatched lengths {len(SCREAMING_SNAKE_CASE__							)} and {len(SCREAMING_SNAKE_CASE__							)}'''							)
             if ids is None:
                   __SCREAMING_SNAKE_CASE     :Any	       =						[None] * len(SCREAMING_SNAKE_CASE__							)
             if labels is None:
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						[None] * len(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Any	       =						[]
             __SCREAMING_SNAKE_CASE     :Tuple	       =						set()
             for text_or_text_and_label, label, guid in zip(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							):
                   if isinstance(SCREAMING_SNAKE_CASE__	,(tuple, list)							) and label is None:
                         __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Tuple	       =						text_or_text_and_label
                   else:
                         __SCREAMING_SNAKE_CASE     :Tuple	       =						text_or_text_and_label
                   added_labels.add(SCREAMING_SNAKE_CASE__							)
                   examples.append(InputExample(guid=SCREAMING_SNAKE_CASE__	,text_a=SCREAMING_SNAKE_CASE__	,text_b=SCREAMING_SNAKE_CASE__	,label=SCREAMING_SNAKE_CASE__							)							)

             # Update examples
             if overwrite_examples:
                   __SCREAMING_SNAKE_CASE     :str	       =						examples
             else:
                   self.examples.extend(SCREAMING_SNAKE_CASE__							)

             # Update labels
             if overwrite_labels:
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						list(SCREAMING_SNAKE_CASE__							)
             else:
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						list(set(self.labels							).union(SCREAMING_SNAKE_CASE__							)							)

             return self.examples




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=None	,SCREAMING_SNAKE_CASE__=False	,SCREAMING_SNAKE_CASE__=0	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__=None	,)    ->   str:





             """simple docstring"""






             if max_length is None:
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						tokenizer.max_len

             __SCREAMING_SNAKE_CASE     :List[Any]	       =						{label: i for i, label in enumerate(self.labels							)}

             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						[]
             for ex_index, example in enumerate(self.examples							):
                   if ex_index % 1_00_00 == 0:
                         logger.info(f'''Tokenizing example {ex_index}'''							)

                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						tokenizer.encode(
                       example.text_a	,add_special_tokens=SCREAMING_SNAKE_CASE__	,max_length=min(SCREAMING_SNAKE_CASE__	,tokenizer.max_len							)	,)
                   all_input_ids.append(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :List[str]	       =						max(len(SCREAMING_SNAKE_CASE__							) for input_ids in all_input_ids							)

             __SCREAMING_SNAKE_CASE     :Any	       =						[]
             for ex_index, (input_ids, example) in enumerate(zip(SCREAMING_SNAKE_CASE__	,self.examples							)							):
                   if ex_index % 1_00_00 == 0:
                         logger.info(f'''Writing example {ex_index}/{len(self.examples							)}'''							)
                   # The mask has 1 for real tokens and 0 for padding tokens. Only real
                   # tokens are attended to.
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						[1 if mask_padding_with_zero else 0] * len(SCREAMING_SNAKE_CASE__							)

                   # Zero-pad up to the sequence length.
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						batch_length - len(SCREAMING_SNAKE_CASE__							)
                   if pad_on_left:
                         __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						([pad_token] * padding_length) + input_ids
                         __SCREAMING_SNAKE_CASE     :int	       =						([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                   else:
                         __SCREAMING_SNAKE_CASE     :List[str]	       =						input_ids + ([pad_token] * padding_length)
                         __SCREAMING_SNAKE_CASE     :List[Any]	       =						attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

                   if len(SCREAMING_SNAKE_CASE__							) != batch_length:
                         raise ValueError(f'''Error with input length {len(SCREAMING_SNAKE_CASE__							)} vs {batch_length}'''							)
                   if len(SCREAMING_SNAKE_CASE__							) != batch_length:
                         raise ValueError(f'''Error with input length {len(SCREAMING_SNAKE_CASE__							)} vs {batch_length}'''							)

                   if self.mode == "classification":
                         __SCREAMING_SNAKE_CASE     :Dict	       =						label_map[example.label]
                   elif self.mode == "regression":
                         __SCREAMING_SNAKE_CASE     :Dict	       =						float(example.label							)
                   else:
                         raise ValueError(self.mode							)

                   if ex_index < 5 and self.verbose:
                         logger.info('''*** Example ***'''							)
                         logger.info(f'''guid: {example.guid}'''							)
                         logger.info(f'''input_ids: {" ".join([str(SCREAMING_SNAKE_CASE__							) for x in input_ids]							)}'''							)
                         logger.info(f'''attention_mask: {" ".join([str(SCREAMING_SNAKE_CASE__							) for x in attention_mask]							)}'''							)
                         logger.info(f'''label: {example.label} (id = {label})'''							)

                   features.append(InputFeatures(input_ids=SCREAMING_SNAKE_CASE__	,attention_mask=SCREAMING_SNAKE_CASE__	,label=SCREAMING_SNAKE_CASE__							)							)

             if return_tensors is None:
                   return features
             elif return_tensors == "tf":
                   if not is_tf_available():
                         raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''							)
                   import tensorflow as tf

                   def gen():
                         for ex in features:
                               yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

                   __SCREAMING_SNAKE_CASE     :str	       =						tf.data.Dataset.from_generator(
                       SCREAMING_SNAKE_CASE__	,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)	,({'''input_ids''': tf.TensorShape([None]							), '''attention_mask''': tf.TensorShape([None]							)}, tf.TensorShape([]							))	,)
                   return dataset
             elif return_tensors == "pt":
                   if not is_torch_available():
                         raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''							)
                   import torch
                   from torch.utils.data import TensorDataset

                   __SCREAMING_SNAKE_CASE     :Any	       =						torch.tensor([f.input_ids for f in features]	,dtype=torch.long							)
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						torch.tensor([f.attention_mask for f in features]	,dtype=torch.long							)
                   if self.mode == "classification":
                         __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						torch.tensor([f.label for f in features]	,dtype=torch.long							)
                   elif self.mode == "regression":
                         __SCREAMING_SNAKE_CASE     :Optional[int]	       =						torch.tensor([f.label for f in features]	,dtype=torch.float							)

                   __SCREAMING_SNAKE_CASE     :Dict	       =						TensorDataset(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)
                   return dataset
             else:
                   raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''							)






"""simple docstring"""
import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


lowerCamelCase_          =	(3_0, 4_8_7)
lowerCamelCase_          =	(3_6, 1_0_2_4)
lowerCamelCase_          =	(1_0_0, 1_0_0)
lowerCamelCase_          =	1_0_0

lowerCamelCase_          =	datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

lowerCamelCase_    ,  lowerCamelCase_          =	os.path.split(__file__)
lowerCamelCase_          =	os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))



@get_duration
def   __lowerCamelCase     (							a_  :	Optional[Any]				,     a_  :	List[str]				,     a_  :	List[str]							)    ->   List[str]:
      with ArrowWriter(features=a_				,     path=os.path.join(a_				,     '''beta.arrow'''							)							) as writer:
            for key, record in dummy_data:
                  __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						my_features.encode_example(a_							)
                  writer.write(a_							)
            __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						writer.finalize()



@get_duration
def   __lowerCamelCase     (							a_  :	Tuple				,     a_  :	Optional[Any]							)    ->   Dict:
      __SCREAMING_SNAKE_CASE     :Tuple	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      for _ in dataset:
            pass



@get_duration
def   __lowerCamelCase     (							a_  :	List[str]				,     a_  :	List[str]							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      dataset.set_format('''numpy'''							)
      for _ in dataset:
            pass



@get_duration
def   __lowerCamelCase     (							a_  :	Tuple				,     a_  :	Optional[int]							)    ->   Optional[int]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						10
      __SCREAMING_SNAKE_CASE     :Dict	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      for i in range(0				,     len(a_							)				,     a_							):
            __SCREAMING_SNAKE_CASE     :Dict	       =						dataset[i : i + batch_size]



@get_duration
def   __lowerCamelCase     (							a_  :	Any				,     a_  :	Optional[Any]							)    ->   Optional[Any]:
      __SCREAMING_SNAKE_CASE     :List[Any]	       =						10
      __SCREAMING_SNAKE_CASE     :List[str]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      dataset.set_format('''numpy'''							)
      for i in range(0				,     len(a_							)				,     a_							):
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						dataset[i : i + batch_size]



@get_duration
def   __lowerCamelCase     (							a_  :	List[Any]				,     a_  :	Optional[Any]							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      for col in feats:
            __SCREAMING_SNAKE_CASE     :Tuple	       =						dataset[col]



@get_duration
def   __lowerCamelCase     (							a_  :	int				,     a_  :	List[str]							)    ->   str:
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						datasets.Dataset.from_file(
          filename=os.path.join(a_				,     '''beta.arrow'''							)				,     info=datasets.DatasetInfo(features=a_							)							)
      dataset.set_format('''numpy'''							)
      for col in feats:
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						dataset[col]



def   __lowerCamelCase     (							)    ->   Union[str, Any]:
      __SCREAMING_SNAKE_CASE     :Tuple	       =						{}
      __SCREAMING_SNAKE_CASE     :int	       =						(
          read_unformated,
          read_formatted_as_numpy,
          read_batch_unformated,
          read_batch_formatted_as_numpy,
          read_col_unformated,
          read_col_formatted_as_numpy,
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
            __SCREAMING_SNAKE_CASE     :Dict	       =						datasets.Features({'''image''': ArrayaD(a_				,     dtype='''float32'''							)}							)
            __SCREAMING_SNAKE_CASE     :Any	       =						generate_examples(features=a_				,     num_examples=a_							)
            __SCREAMING_SNAKE_CASE     :Dict	       =						write(a_				,     a_				,     a_							)
            for read_func in read_functions:
                  __SCREAMING_SNAKE_CASE     :str	       =						read_func(a_				,     a_							)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
            # )
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''							)							)							)}							)
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						generate_examples(
                features=a_				,     num_examples=a_				,     seq_shapes={'''image''': SPEED_TEST_SHAPE}							)
            __SCREAMING_SNAKE_CASE     :Tuple	       =						write(a_				,     a_				,     a_							)
            for read_func in read_functions:
                  __SCREAMING_SNAKE_CASE     :Tuple	       =						read_func(a_				,     a_							)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
            # )
            __SCREAMING_SNAKE_CASE     :Dict	       =						datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''							)							)}							)
            __SCREAMING_SNAKE_CASE     :Dict	       =						generate_examples(
                features=a_				,     num_examples=a_				,     seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}				,     )
            __SCREAMING_SNAKE_CASE     :Any	       =						write(a_				,     a_				,     a_							)
            for read_func in read_functions:
                  __SCREAMING_SNAKE_CASE     :str	       =						read_func(a_				,     a_							)

      with open(a_				,     '''wb'''							) as f:
            f.write(json.dumps(a_							).encode('''utf-8'''							)							)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_array_xd()