


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 						UpperCAmelCase__   :



     '''simple docstring'''
     UpperCAmelCase_         =  42
     UpperCAmelCase_         =  42
     UpperCAmelCase_         =  None
     UpperCAmelCase_         =  None




     def   lowerCAmelCase_   ( self       :       Optional[Any]		):





            """simple docstring"""



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




@dataclass(frozen=A_		)
class 						UpperCAmelCase__   :



     '''simple docstring'''
     UpperCAmelCase_         =  42
     UpperCAmelCase_         =  None
     UpperCAmelCase_         =  None
     UpperCAmelCase_         =  None




     def   lowerCAmelCase_   ( self       :       int		):





            """simple docstring"""



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




class 						UpperCAmelCase__   :



     '''simple docstring'''




     def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       str		):





            """simple docstring"""



            raise NotImplementedError()




     def   lowerCAmelCase_   ( self       :       List[Any]	,      UpperCamelCase       :       Any		):





            """simple docstring"""



            raise NotImplementedError()




     def   lowerCAmelCase_   ( self       :       Dict	,      UpperCamelCase       :       List[str]		):





            """simple docstring"""



            raise NotImplementedError()




     def   lowerCAmelCase_   ( self       :       str	,      UpperCamelCase       :       Dict		):





            """simple docstring"""



            raise NotImplementedError()




     def   lowerCAmelCase_   ( self       :       Any		):





            """simple docstring"""



            raise NotImplementedError()




     def   lowerCAmelCase_   ( self       :       List[Any]	,      UpperCamelCase       :       str		):





            """simple docstring"""



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




     @classmethod
     def   lowerCAmelCase_   ( cls       :       List[Any]	,      UpperCamelCase       :       int	,      UpperCamelCase       :       List[str]=None		):





            """simple docstring"""



            with open(UpperCamelCase	,      '''r'''	,      encoding='''utf-8-sig'''		) as f:
                   return list(csv.reader(UpperCamelCase	,      delimiter='''\t'''	,      quotechar=UpperCamelCase		)		)




class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''




     def __init__( self       :       List[Any]	,      UpperCamelCase       :       Any=None	,      UpperCamelCase       :       Optional[int]=None	,      UpperCamelCase       :       Union[str, Any]="classification"	,      UpperCamelCase       :       Optional[Any]=False		):





            """simple docstring"""



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




     def __len__( self       :       List[str]		):





            """simple docstring"""



            return len(self.examples		)




     def __getitem__( self       :       Any	,      UpperCamelCase       :       List[Any]		):





            """simple docstring"""



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




     @classmethod
     def   lowerCAmelCase_   ( cls       :       Union[str, Any]	,      UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       Tuple=""	,      UpperCamelCase       :       List[Any]=0	,      UpperCamelCase       :       str=1	,      UpperCamelCase       :       Dict=None	,      UpperCamelCase       :       str=False	,      **UpperCamelCase       :       Any		):





            """simple docstring"""



            _lowercase					:				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       :       List[str]	,      UpperCamelCase       :       int	,      UpperCamelCase       :       Tuple=None	,      **UpperCamelCase       :       Union[str, Any]		):





            """simple docstring"""



            _lowercase					:				Optional[Any]           =			cls(**UpperCamelCase		)
            processor.add_examples(UpperCamelCase	,      labels=UpperCamelCase		)
            return processor




     def   lowerCAmelCase_   ( self       :       Optional[int]	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       Optional[Any]=""	,      UpperCamelCase       :       List[Any]=0	,      UpperCamelCase       :       Optional[Any]=1	,      UpperCamelCase       :       Tuple=None	,      UpperCamelCase       :       int=False	,      UpperCamelCase       :       Optional[int]=False	,      UpperCamelCase       :       List[str]=False	,      ):





            """simple docstring"""



            _lowercase					:				Union[str, Any]           =			self._read_tsv(UpperCamelCase		)
            if skip_first_row:
                   _lowercase					:				List[str]           =			lines[1:]
            _lowercase					:				Optional[int]           =			[]
            _lowercase					:				Any           =			[]
            _lowercase					:				Optional[Any]           =			[]
            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:
                          _lowercase					:				Optional[Any]           =			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       :       Dict	,      UpperCamelCase       :       Dict	,      UpperCamelCase       :       Optional[int]=None	,      UpperCamelCase       :       List[str]=None	,      UpperCamelCase       :       List[str]=False	,      UpperCamelCase       :       Union[str, Any]=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:
                   _lowercase					:				List[str]           =			[None] * len(UpperCamelCase		)
            if labels is None:
                   _lowercase					:				Tuple           =			[None] * len(UpperCamelCase		)
            _lowercase					:				List[str]           =			[]
            _lowercase					:				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:
                          _lowercase       ,	_lowercase					:				Union[str, Any]           =			text_or_text_and_label
                   else:
                          _lowercase					:				List[Any]           =			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:
                   _lowercase					:				int           =			examples
            else:
                   self.examples.extend(UpperCamelCase		)

            # Update labels
            if overwrite_labels:
                   _lowercase					:				Union[str, Any]           =			list(UpperCamelCase		)
            else:
                   _lowercase					:				Tuple           =			list(set(self.labels		).union(UpperCamelCase		)		)

            return self.examples




     def   lowerCAmelCase_   ( self       :       str	,      UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       Union[str, Any]=None	,      UpperCamelCase       :       int=False	,      UpperCamelCase       :       Tuple=0	,      UpperCamelCase       :       Tuple=True	,      UpperCamelCase       :       Union[str, Any]=None	,      ):





            """simple docstring"""



            if max_length is None:
                   _lowercase					:				Union[str, 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 % 1_00_00 == 0:
                          logger.info(F'Tokenizing example {ex_index}'		)

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

            _lowercase					:				List[str]           =			max(len(UpperCamelCase		) for input_ids in all_input_ids		)

            _lowercase					:				Tuple           =			[]
            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.
                   _lowercase					:				Tuple           =			[1 if mask_padding_with_zero else 0] * len(UpperCamelCase		)

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

                   _lowercase					:				Tuple           =			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

                   _lowercase					:				Dict           =			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[str]           =			torch.tensor([f.label for f in features]	,      dtype=torch.long		)
                   elif self.mode == "regression":
                          _lowercase					:				List[Any]           =			torch.tensor([f.label for f in features]	,      dtype=torch.float		)

                   _lowercase					:				List[str]           =			TensorDataset(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		)
                   return dataset
            else:
                   raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''		)



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''




     def __init__( self       :       List[str]	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       int		):





            """simple docstring"""



            super().__init__()
            self.register_modules(unet=UpperCamelCase	,      scheduler=UpperCamelCase		)




     @torch.no_grad()
     def __call__( self       :       Any	,      UpperCamelCase       :       int = 1	,      UpperCamelCase       :       Optional[torch.Generator] = None	,      UpperCamelCase       :       int = 50	,      UpperCamelCase       :       Optional[str] = "pil"	,      UpperCamelCase       :       bool = True	,      **UpperCamelCase       :       int	,      ):





            """simple docstring"""



            _lowercase					:				Optional[int]           =			torch.randn(
                (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,      generator=UpperCamelCase	,      )
            _lowercase					:				Dict           =			image.to(self.device		)

            # set step values
            self.scheduler.set_timesteps(UpperCamelCase		)

            for t in self.progress_bar(self.scheduler.timesteps		):
                   # 1. predict noise model_output
                   _lowercase					:				Dict           =			self.unet(UpperCamelCase	,      UpperCamelCase		).sample

                   # 2. predict previous mean of image x_t-1 and add variance depending on eta
                   # eta corresponds to η in paper and should be between [0, 1]
                   # do x_t -> x_t-1
                   _lowercase					:				Tuple           =			self.scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		).prev_sample

            _lowercase					:				Optional[int]           =			(image / 2 + 0.5).clamp(0	,      1		)
            _lowercase					:				int           =			image.cpu().permute(0	,      2	,      3	,      1		).numpy()
            if output_type == "pil":
                   _lowercase					:				List[Any]           =			self.numpy_to_pil(UpperCamelCase		)

            if not return_dict:
                   return (image,), "This is a local test"

            return ImagePipelineOutput(images=UpperCamelCase		), "This is a local test"