





import unittest

from diffusers import __version__
from diffusers.utils import deprecate







class        __lowerCAmelCase (					unittest.TestCase):
      _lowercase       :      Optional[Any]        =    """.""".join([str(int(__version__.split(""".""")[0]) + 1)] + __version__.split(""".""")[1:])
      _lowercase       :      int        =    """0.0.1"""







      def      _lowercase						(					self							)     ->		Tuple:



        '''simple docstring'''




        a__			:      List[str]            ={"deprecated_arg": 4}

        with self.assertWarns(lowerCAmelCase__							) as warning:
          a__			:      List[Any]            =deprecate("deprecated_arg"	,   self.higher_version	,   "message"	,   take_from=lowerCAmelCase__							)

        assert output == 4
        assert (
            str(warning.warning							)
            == F'''The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.'''
            " message"
        )







      def      _lowercase						(					self							)     ->		str:



        '''simple docstring'''




        a__			:      Optional[int]            ={"deprecated_arg": 4}

        with self.assertWarns(lowerCAmelCase__							) as warning:
          a__			:      Optional[Any]            =deprecate(("deprecated_arg", self.higher_version, "message")	,   take_from=lowerCAmelCase__							)

        assert output == 4
        assert (
            str(warning.warning							)
            == F'''The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.'''
            " message"
        )







      def      _lowercase						(					self							)     ->		Optional[int]:



        '''simple docstring'''




        a__			:      int            ={"deprecated_arg_1": 4, "deprecated_arg_2": 8}
        with self.assertWarns(lowerCAmelCase__							) as warning:
          a__			:      Optional[Any]            =deprecate(
              ("deprecated_arg_1", self.higher_version, "Hey")	,   ("deprecated_arg_2", self.higher_version, "Hey")	,   take_from=lowerCAmelCase__	,   )
        assert output_a == 4
        assert output_a == 8
        assert (
            str(warning.warnings[0].message							)
            == "The `deprecated_arg_1` argument is deprecated and will be removed in version"
            F''' {self.higher_version}. Hey'''
        )
        assert (
            str(warning.warnings[1].message							)
            == "The `deprecated_arg_2` argument is deprecated and will be removed in version"
            F''' {self.higher_version}. Hey'''
        )







      def      _lowercase						(					self							)     ->		Optional[int]:



        '''simple docstring'''




        a__			:      Optional[Any]            ={"deprecated_arg": 4}

        with self.assertRaises(lowerCAmelCase__							) as error:
          deprecate(("wrong_arg", self.higher_version, "message")	,   take_from=lowerCAmelCase__							)

        assert "test_deprecate_function_incorrect_arg in" in str(error.exception							)
        assert "line" in str(error.exception							)
        assert "got an unexpected keyword argument `deprecated_arg`" in str(error.exception							)







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



        '''simple docstring'''




        with self.assertWarns(lowerCAmelCase__							) as warning:
          deprecate(("deprecated_arg", self.higher_version, "message")							)

        assert (
            str(warning.warning							)
            == F'''`deprecated_arg` is deprecated and will be removed in version {self.higher_version}. message'''
        )







      def      _lowercase						(					self							)     ->		Dict:



        '''simple docstring'''




        with self.assertWarns(lowerCAmelCase__							) as warning:
          deprecate(
              ("deprecated_arg_1", self.higher_version, "Hey")	,   ("deprecated_arg_2", self.higher_version, "Hey")	,   )
        assert (
            str(warning.warnings[0].message							)
            == F'''`deprecated_arg_1` is deprecated and will be removed in version {self.higher_version}. Hey'''
        )
        assert (
            str(warning.warnings[1].message							)
            == F'''`deprecated_arg_2` is deprecated and will be removed in version {self.higher_version}. Hey'''
        )







      def      _lowercase						(					self							)     ->		Dict:



        '''simple docstring'''




        class        __lowerCAmelCase :
              _lowercase       :      List[str]        =    5

        with self.assertWarns(lowerCAmelCase__							) as warning:
          a__			:      str            =deprecate(("arg", self.higher_version, "message")	,   take_from=Args()							)

        assert arg == 5
        assert (
            str(warning.warning							)
            == F'''The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'''
        )







      def      _lowercase						(					self							)     ->		Optional[Any]:



        '''simple docstring'''




        class        __lowerCAmelCase :
              _lowercase       :      Union[str, Any]        =    5
              _lowercase       :      Dict        =    7

        with self.assertWarns(lowerCAmelCase__							) as warning:
          a__			:      Dict            =deprecate(
              ("arg", self.higher_version, "message")	,   ("foo", self.higher_version, "message")	,   ("does not exist", self.higher_version, "message")	,   take_from=Args()	,   )

        assert arg_a == 5
        assert arg_a == 7
        assert (
            str(warning.warning							)
            == F'''The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'''
        )
        assert (
            str(warning.warnings[0].message							)
            == F'''The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'''
        )
        assert (
            str(warning.warnings[1].message							)
            == F'''The `foo` attribute is deprecated and will be removed in version {self.higher_version}. message'''
        )







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



        '''simple docstring'''




        a__			:      List[str]            ={"deprecated_arg": 4}

        with self.assertRaises(lowerCAmelCase__							) as error:
          deprecate(("wrong_arg", self.lower_version, "message")	,   take_from=lowerCAmelCase__							)

        assert (
            str(error.exception							)
            == "The deprecation tuple ('wrong_arg', '0.0.1', 'message') should be removed since diffusers' version"
            F''' {__version__} is >= {self.lower_version}'''
        )







      def      _lowercase						(					self							)     ->		Dict:



        '''simple docstring'''




        with self.assertWarns(lowerCAmelCase__							) as warning:
          deprecate(("deprecated_arg", self.higher_version, "This message is better!!!")	,   standard_warn=lowerCAmelCase__							)

        assert str(warning.warning							) == "This message is better!!!"







      def      _lowercase						(					self							)     ->		int:



        '''simple docstring'''




        with self.assertWarns(lowerCAmelCase__							) as warning:
          deprecate(("deprecated_arg", self.higher_version, "This message is better!!!")	,   standard_warn=lowerCAmelCase__							)
        assert str(warning.warning							) == "This message is better!!!"
        assert "diffusers/tests/others/test_utils.py" in warning.filename













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			:			int	  = logging.get_logger(__name__)







@dataclass
class        __lowerCAmelCase :
      _lowercase       :      str
      _lowercase       :      str
      _lowercase       :      Optional[str]        =    None
      _lowercase       :      Optional[str]        =    None







      def      _lowercase						(					self							)     ->		List[str]:



        '''simple docstring'''




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







@dataclass(frozen=UpperCamelCase__)
class        __lowerCAmelCase :
      _lowercase       :      List[int]
      _lowercase       :      Optional[List[int]]        =    None
      _lowercase       :      Optional[List[int]]        =    None
      _lowercase       :      Optional[Union[int, float]]        =    None







      def      _lowercase						(					self							)     ->		Optional[int]:



        '''simple docstring'''




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







class        __lowerCAmelCase :







      def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Union[str, Any]:



        '''simple docstring'''




        raise NotImplementedError()







      def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		str:



        '''simple docstring'''




        raise NotImplementedError()







      def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[Any]:



        '''simple docstring'''




        raise NotImplementedError()







      def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Optional[int]:



        '''simple docstring'''




        raise NotImplementedError()







      def      _lowercase						(					self							)     ->		Any:



        '''simple docstring'''




        raise NotImplementedError()







      def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Dict:



        '''simple docstring'''




        if len(self.get_labels()							) > 1:
          a__			:      Dict            =self.get_labels()[int(example.label							)]
        return example







      @classmethod
      def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=None							)     ->		Union[str, Any]:



        '''simple docstring'''




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







class        __lowerCAmelCase (					UpperCamelCase__):







      def __init__(					self	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   lowerCAmelCase__="classification"	,   lowerCAmelCase__=False							)     ->		Any:



        '''simple docstring'''




        a__			:      Any            =[] if labels is None else labels
        a__			:      List[Any]            =[] if examples is None else examples
        a__			:      List[str]            =mode
        a__			:      Tuple            =verbose







      def __len__(					self							)     ->		Optional[int]:



        '''simple docstring'''




        return len(self.examples							)







      def __getitem__(					self	,   lowerCAmelCase__							)     ->		int:



        '''simple docstring'''




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







      @classmethod
      def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=""	,   lowerCAmelCase__=0	,   lowerCAmelCase__=1	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   **lowerCAmelCase__							)     ->		List[str]:



        '''simple docstring'''




        a__			:      Tuple            =cls(**lowerCAmelCase__							)
        processor.add_examples_from_csv(
            lowerCAmelCase__	,   split_name=lowerCAmelCase__	,   column_label=lowerCAmelCase__	,   column_text=lowerCAmelCase__	,   column_id=lowerCAmelCase__	,   skip_first_row=lowerCAmelCase__	,   overwrite_labels=lowerCAmelCase__	,   overwrite_examples=lowerCAmelCase__	,   )
        return processor







      @classmethod
      def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   **lowerCAmelCase__							)     ->		int:



        '''simple docstring'''




        a__			:      List[str]            =cls(**lowerCAmelCase__							)
        processor.add_examples(lowerCAmelCase__	,   labels=lowerCAmelCase__							)
        return processor







      def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=""	,   lowerCAmelCase__=0	,   lowerCAmelCase__=1	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False	,   )     ->		Dict:



        '''simple docstring'''




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

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







      def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False							)     ->		List[Any]:



        '''simple docstring'''




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

        # Update examples
        if overwrite_examples:
          a__			:      Dict            =examples
        else:
          self.examples.extend(lowerCAmelCase__							)

        # Update labels
        if overwrite_labels:
          a__			:      Any            =list(lowerCAmelCase__							)
        else:
          a__			:      List[Any]            =list(set(self.labels							).union(lowerCAmelCase__							)							)

        return self.examples







      def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=0	,   lowerCAmelCase__=True	,   lowerCAmelCase__=None	,   )     ->		int:



        '''simple docstring'''




        if max_length is None:
          a__			:      int            =tokenizer.max_len

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

        a__			:      Union[str, Any]            =[]
        for ex_index, example in enumerate(self.examples							):
          if ex_index % 1_0_0_0_0 == 0:
            logger.info(F'''Tokenizing example {ex_index}'''							)

          a__			:      Dict            =tokenizer.encode(
              example.text_a	,   add_special_tokens=lowerCAmelCase__	,   max_length=min(lowerCAmelCase__	,   tokenizer.max_len							)	,   )
          all_input_ids.append(lowerCAmelCase__							)

        a__			:      List[Any]            =max(len(lowerCAmelCase__							) for input_ids in all_input_ids							)

        a__			:      Dict            =[]
        for ex_index, (input_ids, example) in enumerate(zip(lowerCAmelCase__	,   self.examples							)							):
          if ex_index % 1_0_0_0_0 == 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__			:      Optional[Any]            =[1 if mask_padding_with_zero else 0] * len(lowerCAmelCase__							)

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

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

          if self.mode == "classification":
            a__			:      Dict            =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(lowerCAmelCase__							) for x in input_ids]							)}'''							)
            logger.info(F'''attention_mask: {" ".join([str(lowerCAmelCase__							) for x in attention_mask]							)}'''							)
            logger.info(F'''label: {example.label} (id = {label})'''							)

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

        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__			:      Any            =tf.data.Dataset.from_generator(
              lowerCAmelCase__	,   ({"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__			:      str            =torch.tensor([f.input_ids for f in features]	,   dtype=torch.long							)
          a__			:      str            =torch.tensor([f.attention_mask for f in features]	,   dtype=torch.long							)
          if self.mode == "classification":
            a__			:      Dict            =torch.tensor([f.label for f in features]	,   dtype=torch.long							)
          elif self.mode == "regression":
            a__			:      Tuple            =torch.tensor([f.label for f in features]	,   dtype=torch.float							)

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






