





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


__UpperCAmelCase						=      logging.get_logger(__name__)

@dataclass
class 		__a							:
       __snake_case						:   Any      =		42
       __snake_case						:   Dict      =		42
       __snake_case						:   Tuple      =		None
       __snake_case						:   Optional[Any]      =		None



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






@dataclass(frozen=UpperCAmelCase_      )
class 		__a							:
       __snake_case						:   str      =		42
       __snake_case						:   Optional[Any]      =		None
       __snake_case						:   List[Any]      =		None
       __snake_case						:   List[Any]      =		None



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






class 		__a							:



       def  A							(     self	:   Optional[Any]					,			UpperCAmelCase	:   Any			):
           raise NotImplementedError()



       def  A							(     self	:   Union[str, Any]					,			UpperCAmelCase	:   str			):
           raise NotImplementedError()



       def  A							(     self	:   List[str]					,			UpperCAmelCase	:   Tuple			):
           raise NotImplementedError()



       def  A							(     self	:   List[Any]					,			UpperCAmelCase	:   int			):
           raise NotImplementedError()



       def  A							(     self	:   Optional[int]			):
           raise NotImplementedError()



       def  A							(     self	:   Optional[int]					,			UpperCAmelCase	:   List[str]			):
           if len(self.get_labels()			) > 1:
               lowerCAmelCase_					:	Dict						=   self.get_labels()[int(example.label			)]
           return example



       @classmethod
       def  A							(     cls	:   Optional[Any]					,			UpperCAmelCase	:   List[Any]					,			UpperCAmelCase	:   int=None			):
           with open(UpperCAmelCase					,			"""r"""					,			encoding="""utf-8-sig"""			) as f:
               return list(csv.reader(UpperCAmelCase					,			delimiter="""\t"""					,			quotechar=UpperCAmelCase			)			)






class 		__a							(	UpperCAmelCase_      ):



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



       def __len__(     self	:   int			):
           return len(self.examples			)



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



       @classmethod
       def  A							(     cls	:   int					,			UpperCAmelCase	:   Any					,			UpperCAmelCase	:   int=""					,			UpperCAmelCase	:   List[Any]=0					,			UpperCAmelCase	:   str=1					,			UpperCAmelCase	:   Optional[int]=None					,			UpperCAmelCase	:   List[str]=False					,			**UpperCAmelCase	:   List[str]			):
           lowerCAmelCase_					:	List[str]						=   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  A							(     cls	:   int					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   Any=None					,			**UpperCAmelCase	:   str			):
           lowerCAmelCase_					:	Tuple						=   cls(**UpperCAmelCase			)
           processor.add_examples(UpperCAmelCase					,			labels=UpperCAmelCase			)
           return processor



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

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

           return self.examples



       def  A							(     self	:   List[str]					,			UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   Optional[Any]=None					,			UpperCAmelCase	:   List[Any]=False					,			UpperCAmelCase	:   int=0					,			UpperCAmelCase	:   Optional[Any]=True					,			UpperCAmelCase	:   Optional[int]=None					,			):
           if max_length is None:
               lowerCAmelCase_					:	str						=   tokenizer.max_len

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

           lowerCAmelCase_					:	Dict						=   []
           for ex_index, example in enumerate(self.examples			):
               if ex_index % 1_00_00 == 0:
                   logger.info(F'Tokenizing example {ex_index}'			)

               lowerCAmelCase_					:	Optional[int]						=   tokenizer.encode(
                   example.text_a					,			add_special_tokens=UpperCAmelCase					,			max_length=min(UpperCAmelCase					,			tokenizer.max_len			)					,			)
               all_input_ids.append(UpperCAmelCase			)

           lowerCAmelCase_					:	Optional[Any]						=   max(len(UpperCAmelCase			) for input_ids in all_input_ids			)

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

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

               lowerCAmelCase_					:	Union[str, Any]						=   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

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

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









from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    'cross_attention',
    '0.20.0',
    'Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.',
    standard_warn=False,
)


__UpperCAmelCase						=      AttentionProcessor

class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   List[Any]					,			*UpperCAmelCase	:   List[Any]					,			**UpperCAmelCase	:   Any			):
           lowerCAmelCase_					:	str						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   Optional[Any]					,			*UpperCAmelCase	:   List[Any]					,			**UpperCAmelCase	:   List[str]			):
           lowerCAmelCase_					:	List[Any]						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   List[str]					,			*UpperCAmelCase	:   str					,			**UpperCAmelCase	:   Union[str, Any]			):
           lowerCAmelCase_					:	Tuple						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   Optional[int]					,			*UpperCAmelCase	:   Union[str, Any]					,			**UpperCAmelCase	:   Any			):
           lowerCAmelCase_					:	Tuple						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   str					,			*UpperCAmelCase	:   Any					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   List[str]					,			*UpperCAmelCase	:   Union[str, Any]					,			**UpperCAmelCase	:   Tuple			):
           lowerCAmelCase_					:	Tuple						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   Dict					,			*UpperCAmelCase	:   int					,			**UpperCAmelCase	:   int			):
           lowerCAmelCase_					:	List[str]						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)






class 		__a							(	__UpperCamelCase      ):



       def __init__(     self	:   Optional[Any]					,			*UpperCAmelCase	:   Any					,			**UpperCAmelCase	:   int			):
           lowerCAmelCase_					:	Union[str, Any]						=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"			)			)} instead.'
           deprecate("""cross_attention"""					,			"""0.20.0"""					,			UpperCAmelCase					,			standard_warn=UpperCAmelCase			)
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)


