




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        UpperCAmelCase__				:



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					str     					=    42
     _SCREAMING_SNAKE_CASE      :					Union[str, Any]     					=    42
     _SCREAMING_SNAKE_CASE      :					Optional[int]     					=    None
     _SCREAMING_SNAKE_CASE      :					Union[str, Any]     					=    None
     def        lowerCAmelCase__  (				self	):
            return json.dumps(dataclasses.asdict(self	) ,    indent=2	) + "\n"



@dataclass(frozen=lowercase__				)
class        UpperCAmelCase__				:



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Tuple     					=    42
     _SCREAMING_SNAKE_CASE      :					int     					=    None
     _SCREAMING_SNAKE_CASE      :					Any     					=    None
     _SCREAMING_SNAKE_CASE      :					Dict     					=    None
     def        lowerCAmelCase__  (				self	):
            return json.dumps(dataclasses.asdict(self	)	) + "\n"



class        UpperCAmelCase__				:



     '''simple docstring'''
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            raise NotImplementedError()
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            raise NotImplementedError()
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            raise NotImplementedError()
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            raise NotImplementedError()
     def        lowerCAmelCase__  (				self	):
            raise NotImplementedError()
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            if len(self.get_labels()	) > 1:
                   a			   =self.get_labels()[int(example.label	)]
            return example






     @classmethod
     def        lowerCAmelCase__  (				cls ,    _lowerCAmelCase ,    _lowerCAmelCase=None	):
            with open(_lowercase ,    """r""" ,    encoding="""utf-8-sig"""	) as f:
                   return list(csv.reader(_lowercase ,    delimiter="""\t""" ,    quotechar=_lowercase	)	)



class        UpperCAmelCase__				(							lowercase__				):



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase=None ,    _lowerCAmelCase=None ,    _lowerCAmelCase="classification" ,    _lowerCAmelCase=False	):
            a			   =[] if labels is None else labels
            a			   =[] if examples is None else examples
            a			   =mode
            a			   =verbose
     def __len__(				self	):
            return len(self.examples	)
     def __getitem__(				self ,    _lowerCAmelCase	):
            if isinstance(_lowercase ,    _lowercase	):
                   return SingleSentenceClassificationProcessor(labels=self.labels ,    examples=self.examples[idx]	)
            return self.examples[idx]
     @classmethod
     def        lowerCAmelCase__  (				cls ,    _lowerCAmelCase ,    _lowerCAmelCase="" ,    _lowerCAmelCase=0 ,    _lowerCAmelCase=1 ,    _lowerCAmelCase=None ,    _lowerCAmelCase=False ,    **_lowerCAmelCase	):
            a			   =cls(**_lowercase	)
            processor.add_examples_from_csv(
                _lowercase ,    split_name=_lowercase ,    column_label=_lowercase ,    column_text=_lowercase ,    column_id=_lowercase ,    skip_first_row=_lowercase ,    overwrite_labels=_lowercase ,    overwrite_examples=_lowercase ,    )
            return processor
     @classmethod
     def        lowerCAmelCase__  (				cls ,    _lowerCAmelCase ,    _lowerCAmelCase=None ,    **_lowerCAmelCase	):
            a			   =cls(**_lowercase	)
            processor.add_examples(_lowercase ,    labels=_lowercase	)
            return processor
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase="" ,    _lowerCAmelCase=0 ,    _lowerCAmelCase=1 ,    _lowerCAmelCase=None ,    _lowerCAmelCase=False ,    _lowerCAmelCase=False ,    _lowerCAmelCase=False ,    ):
            a			   =self._read_tsv(_lowercase	)
            if skip_first_row:
                   a			   =lines[1:]
            a			   =[]
            a			   =[]
            a			   =[]
            for i, line in enumerate(_lowercase	):
                   texts.append(line[column_text]	)
                   labels.append(line[column_label]	)
                   if column_id is not None:
                          ids.append(line[column_id]	)
                   else:
                          a			   =F'''{split_name}-{i}''' if split_name else str(_lowercase	)
                          ids.append(_lowercase	)

            return self.add_examples(
                _lowercase ,    _lowercase ,    _lowercase ,    overwrite_labels=_lowercase ,    overwrite_examples=_lowercase	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase=None ,    _lowerCAmelCase=None ,    _lowerCAmelCase=False ,    _lowerCAmelCase=False	):
            if labels is not None and len(_lowercase	) != len(_lowercase	):
                   raise ValueError(
                       F'''Text and labels have mismatched lengths {len(_lowercase	)} and {len(_lowercase	)}'''	)
            if ids is not None and len(_lowercase	) != len(_lowercase	):
                   raise ValueError(F'''Text and ids have mismatched lengths {len(_lowercase	)} and {len(_lowercase	)}'''	)
            if ids is None:
                   a			   =[None] * len(_lowercase	)
            if labels is None:
                   a			   =[None] * len(_lowercase	)
            a			   =[]
            a			   =set()
            for text_or_text_and_label, label, guid in zip(_lowercase ,    _lowercase ,    _lowercase	):
                   if isinstance(_lowercase ,    (tuple, list)	) and label is None:
                          a   ,      a			   =text_or_text_and_label
                   else:
                          a			   =text_or_text_and_label
                   added_labels.add(_lowercase	)
                   examples.append(InputExample(guid=_lowercase ,    text_a=_lowercase ,    text_b=_lowercase ,    label=_lowercase	)	)

            # Update examples
            if overwrite_examples:
                   a			   =examples
            else:
                   self.examples.extend(_lowercase	)

            # Update labels
            if overwrite_labels:
                   a			   =list(_lowercase	)
            else:
                   a			   =list(set(self.labels	).union(_lowercase	)	)

            return self.examples






     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase=None ,    _lowerCAmelCase=False ,    _lowerCAmelCase=0 ,    _lowerCAmelCase=True ,    _lowerCAmelCase=None ,    ):
            if max_length is None:
                   a			   =tokenizer.max_len

            a			   ={label: i for i, label in enumerate(self.labels	)}

            a			   =[]
            for ex_index, example in enumerate(self.examples	):
                   if ex_index % 10_000 == 0:
                          logger.info(F'''Tokenizing example {ex_index}'''	)

                   a			   =tokenizer.encode(
                       example.text_a ,    add_special_tokens=_lowercase ,    max_length=min(_lowercase ,    tokenizer.max_len	) ,    )
                   all_input_ids.append(_lowercase	)

            a			   =max(len(_lowercase	) for input_ids in all_input_ids	)

            a			   =[]
            for ex_index, (input_ids, example) in enumerate(zip(_lowercase ,    self.examples	)	):
                   if ex_index % 10_000 == 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.
                   a			   =[1 if mask_padding_with_zero else 0] * len(_lowercase	)

                   # Zero-pad up to the sequence length.
                   a			   =batch_length - len(_lowercase	)
                   if pad_on_left:
                          a			   =([pad_token] * padding_length) + input_ids
                          a			   =([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                   else:
                          a			   =input_ids + ([pad_token] * padding_length)
                          a			   =attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

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

                   if self.mode == "classification":
                          a			   =label_map[example.label]
                   elif self.mode == "regression":
                          a			   =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(_lowercase	) for x in input_ids]	)}'''	)
                          logger.info(F'''attention_mask: {' '.join([str(_lowercase	) for x in attention_mask]	)}'''	)
                          logger.info(F'''label: {example.label} (id = {label})'''	)

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

            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)

                   a			   =tf.data.Dataset.from_generator(
                       _lowercase ,    ({"""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

                   a			   =torch.tensor([f.input_ids for f in features] ,    dtype=torch.long	)
                   a			   =torch.tensor([f.attention_mask for f in features] ,    dtype=torch.long	)
                   if self.mode == "classification":
                          a			   =torch.tensor([f.label for f in features] ,    dtype=torch.long	)
                   elif self.mode == "regression":
                          a			   =torch.tensor([f.label for f in features] ,    dtype=torch.float	)

                   a			   =TensorDataset(_lowercase ,    _lowercase ,    _lowercase	)
                   return dataset
            else:
                   raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""	)

"""simple docstring"""


__UpperCamelCase       :    List[Any]         =		"\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n"

__UpperCamelCase       :    Any         =		[{"type": "code", "content": INSTALL_CONTENT}]