




"""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


__A							=     logging.get_logger(__name__)
@dataclass
class      lowerCamelCase__      :
			a__	: str
			a__	: str
			a__	: Optional[str]        =       None
			a__	: Optional[str]        =       None


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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







@dataclass(frozen=lowerCAmelCase__     )
class      lowerCamelCase__      :
			a__	: List[int]
			a__	: Optional[List[int]]        =       None
			a__	: Optional[List[int]]        =       None
			a__	: Optional[Union[int, float]]        =       None


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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







class      lowerCamelCase__      :


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					raise NotImplementedError()


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					raise NotImplementedError()


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					raise NotImplementedError()


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					raise NotImplementedError()


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					raise NotImplementedError()


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







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



			@classmethod
			def 							lowerCamelCase_			(       cls			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None							):



					"""simple docstring"""







					with open(_UpperCAmelCase			,	"r"			,	encoding="utf-8-sig"							) as f:
							return list(csv.reader(_UpperCAmelCase			,	delimiter="\t"			,	quotechar=_UpperCAmelCase							)							)







class      lowerCamelCase__      (					lowerCAmelCase__     ):


			def __init__(       self			,	SCREAMING_SNAKE_CASE=None			,	SCREAMING_SNAKE_CASE=None			,	SCREAMING_SNAKE_CASE="classification"			,	SCREAMING_SNAKE_CASE=False							):



					"""simple docstring"""







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


			def __len__(       self							):



					"""simple docstring"""







					return len(self.examples							)


			def __getitem__(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







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


			@classmethod
			def 							lowerCamelCase_			(       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							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			cls(**_UpperCAmelCase							)
					processor.add_examples_from_csv(
					    _UpperCAmelCase			,	split_name=_UpperCAmelCase			,	column_label=_UpperCAmelCase			,	column_text=_UpperCAmelCase			,	column_id=_UpperCAmelCase			,	skip_first_row=_UpperCAmelCase			,	overwrite_labels=_UpperCAmelCase			,	overwrite_examples=_UpperCAmelCase			,	)
					return processor


			@classmethod
			def 							lowerCamelCase_			(       cls			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None			,	**SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  int        =			cls(**_UpperCAmelCase							)
					processor.add_examples(_UpperCAmelCase			,	labels=_UpperCAmelCase							)
					return processor


			def 							lowerCamelCase_			(       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			,	):



					"""simple docstring"""







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

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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None			,	SCREAMING_SNAKE_CASE=None			,	SCREAMING_SNAKE_CASE=False			,	SCREAMING_SNAKE_CASE=False							):



					"""simple docstring"""







					if labels is not None and len(_UpperCAmelCase							) != len(_UpperCAmelCase							):
							raise ValueError(
							    F'''Text and labels have mismatched lengths {len(_UpperCAmelCase							)} and {len(_UpperCAmelCase							)}'''							)
					if ids is not None and len(_UpperCAmelCase							) != len(_UpperCAmelCase							):
							raise ValueError(F'''Text and ids have mismatched lengths {len(_UpperCAmelCase							)} and {len(_UpperCAmelCase							)}'''							)
					if ids is None:
							snake_case						:  int        =			[None] * len(_UpperCAmelCase							)
					if labels is None:
							snake_case						:  List[Any]        =			[None] * len(_UpperCAmelCase							)
					snake_case						:  List[str]        =			[]
					snake_case						:  Optional[Any]        =			set()
					for text_or_text_and_label, label, guid in zip(_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase							):
							if isinstance(_UpperCAmelCase			,	(tuple, list)							) and label is None:
									snake_case     ,			snake_case						:  Union[str, Any]        =			text_or_text_and_label
							else:
									snake_case						:  Dict        =			text_or_text_and_label
							added_labels.add(_UpperCAmelCase							)
							examples.append(InputExample(guid=_UpperCAmelCase			,	text_a=_UpperCAmelCase			,	text_b=_UpperCAmelCase			,	label=_UpperCAmelCase							)							)

					# Update examples
					if overwrite_examples:
							snake_case						:  str        =			examples
					else:
							self.examples.extend(_UpperCAmelCase							)

					# Update labels
					if overwrite_labels:
							snake_case						:  Optional[int]        =			list(_UpperCAmelCase							)
					else:
							snake_case						:  str        =			list(set(self.labels							).union(_UpperCAmelCase							)							)

					return self.examples



			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None			,	SCREAMING_SNAKE_CASE=False			,	SCREAMING_SNAKE_CASE=0			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=None			,	):



					"""simple docstring"""







					if max_length is None:
							snake_case						:  Any        =			tokenizer.max_len

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

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

							snake_case						:  Any        =			tokenizer.encode(
							    example.text_a			,	add_special_tokens=_UpperCAmelCase			,	max_length=min(_UpperCAmelCase			,	tokenizer.max_len							)			,	)
							all_input_ids.append(_UpperCAmelCase							)

					snake_case						:  int        =			max(len(_UpperCAmelCase							) for input_ids in all_input_ids							)

					snake_case						:  Any        =			[]
					for ex_index, (input_ids, example) in enumerate(zip(_UpperCAmelCase			,	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.
							snake_case						:  Optional[Any]        =			[1 if mask_padding_with_zero else 0] * len(_UpperCAmelCase							)

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

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

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

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

					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)

							snake_case						:  int        =			tf.data.Dataset.from_generator(
							    _UpperCAmelCase			,	({"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

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

							snake_case						:  Any        =			TensorDataset(_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase							)
							return dataset
					else:
							raise ValueError("return_tensors should be one of \'tf\' or \'pt\'"							)







"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__A							=     {"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "GPTJForCausalLM",
							    "GPTJForQuestionAnswering",
							    "GPTJForSequenceClassification",
							    "GPTJModel",
							    "GPTJPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "TFGPTJForCausalLM",
							    "TFGPTJForQuestionAnswering",
							    "TFGPTJForSequenceClassification",
							    "TFGPTJModel",
							    "TFGPTJPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "FlaxGPTJForCausalLM",
							    "FlaxGPTJModel",
							    "FlaxGPTJPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

							__A							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

