

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


__snake_case							:			Union[str, Any]      		= logging.get_logger(__name__)



@dataclass
class 					A__:
						"""simple docstring"""






						_A						:							str
						_A						:							str
						_A						:							Optional[str]									= None
						_A						:							Optional[str]									= None





						def  UpperCamelCase__				(						self						)    ->   List[Any]:
									return json.dumps(dataclasses.asdict(self						)     ,			indent=2						) + "\n"







@dataclass(frozen=a_       )
class 					A__:
						"""simple docstring"""






						_A						:							List[int]
						_A						:							Optional[List[int]]									= None
						_A						:							Optional[List[int]]									= None
						_A						:							Optional[Union[int, float]]									= None





						def  UpperCamelCase__				(						self						)    ->   int:
									return json.dumps(dataclasses.asdict(self						)						) + "\n"







class 					A__:
						"""simple docstring"""






						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   List[Any]:
									raise NotImplementedError()





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   List[str]:
									raise NotImplementedError()





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   Any:
									raise NotImplementedError()





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   Union[str, Any]:
									raise NotImplementedError()





						def  UpperCamelCase__				(						self						)    ->   Tuple:
									raise NotImplementedError()





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   Any:
									if len(self.get_labels()						) > 1:
												a_	:	Any          =   self.get_labels()[int(example.label						)]
									return example





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







class 					A__(a_       ):
						"""simple docstring"""






						def __init__(						self     ,			_lowercase=None     ,			_lowercase=None     ,			_lowercase="classification"     ,			_lowercase=False						)    ->   Union[str, Any]:
									a_	:	List[str]          =   [] if labels is None else labels
									a_	:	Optional[Any]          =   [] if examples is None else examples
									a_	:	Union[str, Any]          =   mode
									a_	:	Optional[Any]          =   verbose





						def __len__(						self						)    ->   Tuple:
									return len(self.examples						)





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





						@classmethod
						def  UpperCamelCase__				(						cls     ,			_lowercase     ,			_lowercase=""     ,			_lowercase=0     ,			_lowercase=1     ,			_lowercase=None     ,			_lowercase=False     ,			**_lowercase						)    ->   Any:
									a_	:	List[Any]          =   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  UpperCamelCase__				(						cls     ,			_lowercase     ,			_lowercase=None     ,			**_lowercase						)    ->   Dict:
									a_	:	Tuple          =   cls(**_lowercase						)
									processor.add_examples(_lowercase     ,			labels=_lowercase						)
									return processor





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase=""     ,			_lowercase=0     ,			_lowercase=1     ,			_lowercase=None     ,			_lowercase=False     ,			_lowercase=False     ,			_lowercase=False     ,			)    ->   List[Any]:
									a_	:	List[str]          =   self._read_tsv(_lowercase						)
									if skip_first_row:
												a_	:	Union[str, Any]          =   lines[1:]
									a_	:	int          =   []
									a_	:	int          =   []
									a_	:	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:
															a_	:	int          =   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  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase=None     ,			_lowercase=None     ,			_lowercase=False     ,			_lowercase=False						)    ->   int:
									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_	:	str          =   [None] * len(_lowercase						)
									if labels is None:
												a_	:	Union[str, Any]          =   [None] * len(_lowercase						)
									a_	:	str          =   []
									a_	:	Union[str, Any]          =   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_	:	Any          =   text_or_text_and_label
												else:
															a_	:	List[Any]          =   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_	:	Union[str, Any]          =   examples
									else:
												self.examples.extend(_lowercase						)

									# Update labels
									if overwrite_labels:
												a_	:	List[str]          =   list(_lowercase						)
									else:
												a_	:	List[Any]          =   list(set(self.labels						).union(_lowercase						)						)

									return self.examples





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase=None     ,			_lowercase=False     ,			_lowercase=0     ,			_lowercase=True     ,			_lowercase=None     ,			)    ->   Any:
									if max_length is None:
												a_	:	int          =   tokenizer.max_len

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

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

												a_	:	List[Any]          =   tokenizer.encode(
												    example.text_a     ,			add_special_tokens=_lowercase     ,			max_length=min(_lowercase     ,			tokenizer.max_len						)     ,			)
												all_input_ids.append(_lowercase						)

									a_	:	List[Any]          =   max(len(_lowercase						) for input_ids in all_input_ids						)

									a_	:	Optional[int]          =   []
									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_	:	Union[str, Any]          =   [1 if mask_padding_with_zero else 0] * len(_lowercase						)

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

												a_	:	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

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

												a_	:	Union[str, Any]          =   TensorDataset(_lowercase     ,			_lowercase     ,			_lowercase						)
												return dataset
									else:
												raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""						)





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__snake_case							:			Any      		= {
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:			Dict      		= [
	    """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """ConvNextV2ForImageClassification""",
	    """ConvNextV2Model""",
	    """ConvNextV2PreTrainedModel""",
	    """ConvNextV2Backbone""",
	]


if TYPE_CHECKING:
	from .configuration_convnextva import (
	    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    ConvNextVaConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_convnextva import (
		    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConvNextVaBackbone,
		    ConvNextVaForImageClassification,
		    ConvNextVaModel,
		    ConvNextVaPreTrainedModel,
		)

else:
	import sys

	__snake_case							:			Dict      		= _LazyModule(__name__, globals()["""__file__"""], _import_structure)


