'''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


_lowercase		:					int													=		logging.get_logger(__name__)





@dataclass
class        __magic_name__		:
			UpperCamelCase__							=			42
			UpperCamelCase__							=			42
			UpperCamelCase__							=			None
			UpperCamelCase__							=			None







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





@dataclass(frozen=_UpperCAmelCase)
class        __magic_name__		:
			UpperCamelCase__							=			42
			UpperCamelCase__							=			None
			UpperCamelCase__							=			None
			UpperCamelCase__							=			None







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any       ):
										return json.dumps(dataclasses.asdict(self       )       ) + "\n"





class        __magic_name__		:







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]							,     lowercase_							:    Optional[int]       ):
										raise NotImplementedError()







			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple							,     lowercase_							:    str       ):
										raise NotImplementedError()







			def       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    str       ):
										raise NotImplementedError()







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict							,     lowercase_							:    int       ):
										raise NotImplementedError()







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										raise NotImplementedError()







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]							,     lowercase_							:    Union[str, Any]       ):
										if len(self.get_labels()       ) > 1:
																	lowercase_   :     Union[str, Any]									=			self.get_labels()[int(example.label       )]
										return example







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





class        __magic_name__		(       _UpperCAmelCase):







			def __init__(      self							:    Optional[int]							,     lowercase_							:    Tuple=None							,     lowercase_							:    Tuple=None							,     lowercase_							:    List[str]="classification"							,     lowercase_							:    Dict=False       ):
										lowercase_   :     int									=			[] if labels is None else labels
										lowercase_   :     str									=			[] if examples is None else examples
										lowercase_   :     Union[str, Any]									=			mode
										lowercase_   :     Union[str, Any]									=			verbose







			def __len__(      self							:    List[str]       ):
										return len(self.examples       )







			def __getitem__(      self							:    Union[str, Any]							,     lowercase_							:    int       ):
										if isinstance(lowercase_							,     lowercase_       ):
																	return SingleSentenceClassificationProcessor(labels=self.labels							,     examples=self.examples[idx]       )
										return self.examples[idx]







			@classmethod
			def       SCREAMING_SNAKE_CASE_	(      cls							:    str							,     lowercase_							:    Dict							,     lowercase_							:    Any=""							,     lowercase_							:    Any=0							,     lowercase_							:    Any=1							,     lowercase_							:    Optional[int]=None							,     lowercase_							:    str=False							,     **lowercase_							:    List[str]       ):
										lowercase_   :     str									=			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       SCREAMING_SNAKE_CASE_	(      cls							:    Optional[int]							,     lowercase_							:    Any							,     lowercase_							:    Union[str, Any]=None							,     **lowercase_							:    Optional[Any]       ):
										lowercase_   :     Optional[Any]									=			cls(**lowercase_       )
										processor.add_examples(lowercase_							,     labels=lowercase_       )
										return processor







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict							,     lowercase_							:    Any							,     lowercase_							:    Dict=""							,     lowercase_							:    str=0							,     lowercase_							:    List[str]=1							,     lowercase_							:    Optional[int]=None							,     lowercase_							:    Optional[Any]=False							,     lowercase_							:    Tuple=False							,     lowercase_							:    Dict=False							,     ):
										lowercase_   :     Optional[Any]									=			self._read_tsv(lowercase_       )
										if skip_first_row:
																	lowercase_   :     List[str]									=			lines[1:]
										lowercase_   :     List[Any]									=			[]
										lowercase_   :     List[str]									=			[]
										lowercase_   :     List[Any]									=			[]
										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:
																								lowercase_   :     Union[str, Any]									=			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       SCREAMING_SNAKE_CASE_	(      self							:    Any							,     lowercase_							:    Dict							,     lowercase_							:    str=None							,     lowercase_							:    Tuple=None							,     lowercase_							:    Union[str, Any]=False							,     lowercase_							:    Dict=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:
																	lowercase_   :     Union[str, Any]									=			[None] * len(lowercase_       )
										if labels is None:
																	lowercase_   :     List[Any]									=			[None] * len(lowercase_       )
										lowercase_   :     List[Any]									=			[]
										lowercase_   :     Any									=			set()
										for text_or_text_and_label, label, guid in zip(lowercase_							,     lowercase_							,     lowercase_       ):
																	if isinstance(lowercase_							,     (tuple, list)       ) and label is None:
																								lowercase_		,		lowercase_   :     Optional[Any]									=			text_or_text_and_label
																	else:
																								lowercase_   :     int									=			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:
																	lowercase_   :     Any									=			examples
										else:
																	self.examples.extend(lowercase_       )

										# Update labels
										if overwrite_labels:
																	lowercase_   :     Dict									=			list(lowercase_       )
										else:
																	lowercase_   :     List[str]									=			list(set(self.labels       ).union(lowercase_       )       )

										return self.examples







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[int]							,     lowercase_							:    Union[str, Any]							,     lowercase_							:    Dict=None							,     lowercase_							:    List[str]=False							,     lowercase_							:    Tuple=0							,     lowercase_							:    Tuple=True							,     lowercase_							:    str=None							,     ):
										if max_length is None:
																	lowercase_   :     Optional[Any]									=			tokenizer.max_len

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

										lowercase_   :     Any									=			[]
										for ex_index, example in enumerate(self.examples       ):
																	if ex_index % 10000 == 0:
																								logger.info(f'''Tokenizing example {ex_index}'''       )

																	lowercase_   :     Optional[Any]									=			tokenizer.encode(
																	    example.text_a							,     add_special_tokens=lowercase_							,     max_length=min(lowercase_							,     tokenizer.max_len       )							,     )
																	all_input_ids.append(lowercase_       )

										lowercase_   :     int									=			max(len(lowercase_       ) for input_ids in all_input_ids       )

										lowercase_   :     Optional[Any]									=			[]
										for ex_index, (input_ids, example) in enumerate(zip(lowercase_							,     self.examples       )       ):
																	if ex_index % 10000 == 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.
																	lowercase_   :     Any									=			[1 if mask_padding_with_zero else 0] * len(lowercase_       )

																	# Zero-pad up to the sequence length.
																	lowercase_   :     Tuple									=			batch_length - len(lowercase_       )
																	if pad_on_left:
																								lowercase_   :     Tuple									=			([pad_token] * padding_length) + input_ids
																								lowercase_   :     int									=			([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
																	else:
																								lowercase_   :     Optional[Any]									=			input_ids + ([pad_token] * padding_length)
																								lowercase_   :     Optional[int]									=			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":
																								lowercase_   :     List[Any]									=			label_map[example.label]
																	elif self.mode == "regression":
																								lowercase_   :     Optional[Any]									=			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)

																	lowercase_   :     Union[str, Any]									=			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

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

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






'''simple docstring'''




_lowercase		:					List[str]													=		"\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"

_lowercase		:					Union[str, Any]													=		[{"type": "code", "content": INSTALL_CONTENT}]





