



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							42
				__lowerCamelCase					=							42


if is_transformers_available() and is_torch_available():
	from .modeling_roberta_series import RobertaSeriesModelWithTransformation
	from .pipeline_alt_diffusion import AltDiffusionPipeline
	from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline




'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class    lowerCamelCase__  (   unittest.TestCase     ):



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :Any       ="""<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z""".split(""" """     )
        _UpperCamelCase  :Optional[Any]       =dict(zip(lowerCAmelCase__					,      range(len(lowerCAmelCase__     )     )     )     )

        _UpperCamelCase  :int       ={
            """pad_token""": """<pad>""",
            """unk_token""": """<unk>""",
            """bos_token""": """<s>""",
            """eos_token""": """</s>""",
        }
        _UpperCamelCase  :Union[str, Any]       ={
            """feature_size""": 1,
            """padding_value""": 0.0,
            """sampling_rate""": 16_000,
            """return_attention_mask""": False,
            """do_normalize""": True,
        }

        _UpperCamelCase  :List[str]       =tempfile.mkdtemp()
        _UpperCamelCase  :Optional[Any]       =os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["""vocab_file"""]     )
        _UpperCamelCase  :Tuple       =os.path.join(self.tmpdirname					,      lowerCAmelCase__     )
        with open(self.vocab_file					,      """w"""					,      encoding="""utf-8"""     ) as fp:
             fp.write(json.dumps(lowerCAmelCase__     ) + """\n"""     )

        with open(self.feature_extraction_file					,      """w"""					,      encoding="""utf-8"""     ) as fp:
             fp.write(json.dumps(lowerCAmelCase__     ) + """\n"""     )



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :int       =self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCAmelCase__     )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname					,      **lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     str:
        """simple docstring"""





        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname					,      **lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        shutil.rmtree(self.tmpdirname     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =self.get_tokenizer()
        _UpperCamelCase  :int       =self.get_feature_extractor()

        _UpperCamelCase  :Union[str, Any]       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        processor.save_pretrained(self.tmpdirname     )
        _UpperCamelCase  :Dict       =WavaVecaProcessor.from_pretrained(self.tmpdirname     )

        self.assertEqual(processor.tokenizer.get_vocab()					,      tokenizer.get_vocab()     )
        self.assertIsInstance(processor.tokenizer					,      lowerCAmelCase__     )

        self.assertEqual(processor.feature_extractor.to_json_string()					,      feature_extractor.to_json_string()     )
        self.assertIsInstance(processor.feature_extractor					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Union[str, Any]       =WavaVecaProcessor(tokenizer=self.get_tokenizer()					,      feature_extractor=self.get_feature_extractor()     )
        processor.save_pretrained(self.tmpdirname     )

        _UpperCamelCase  :Optional[int]       =self.get_tokenizer(bos_token="""(BOS)"""					,      eos_token="""(EOS)"""     )
        _UpperCamelCase  :Optional[int]       =self.get_feature_extractor(do_normalize=lowerCAmelCase__					,      padding_value=1.0     )

        _UpperCamelCase  :Optional[int]       =WavaVecaProcessor.from_pretrained(
            self.tmpdirname					,      bos_token="""(BOS)"""					,      eos_token="""(EOS)"""					,      do_normalize=lowerCAmelCase__					,      padding_value=1.0     )

        self.assertEqual(processor.tokenizer.get_vocab()					,      tokenizer_add_kwargs.get_vocab()     )
        self.assertIsInstance(processor.tokenizer					,      lowerCAmelCase__     )

        self.assertEqual(processor.feature_extractor.to_json_string()					,      feature_extractor_add_kwargs.to_json_string()     )
        self.assertIsInstance(processor.feature_extractor					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.get_feature_extractor()
        _UpperCamelCase  :List[str]       =self.get_tokenizer()

        _UpperCamelCase  :Any       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Tuple       =floats_list((3, 1_000)     )

        _UpperCamelCase  :Tuple       =feature_extractor(lowerCAmelCase__					,      return_tensors="""np"""     )
        _UpperCamelCase  :Union[str, Any]       =processor(lowerCAmelCase__					,      return_tensors="""np"""     )

        for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum()					,      input_processor[key].sum()					,      delta=1e-2     )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.get_feature_extractor()
        _UpperCamelCase  :List[Any]       =self.get_tokenizer()

        _UpperCamelCase  :Tuple       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[Any]       ="""This is a test string"""

        _UpperCamelCase  :Union[str, Any]       =processor(text=lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =tokenizer(lowerCAmelCase__     )

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]					,      encoded_processor[key]     )



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =self.get_feature_extractor()
        _UpperCamelCase  :Any       =self.get_tokenizer()

        _UpperCamelCase  :str       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[int]       =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCamelCase  :List[Any]       =processor.batch_decode(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =tokenizer.batch_decode(lowerCAmelCase__     )

        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )




   def  _UpperCamelCase		(    self     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :List[Any]       =self.get_feature_extractor()
        _UpperCamelCase  :Any       =self.get_tokenizer()

        _UpperCamelCase  :List[str]       =WavaVecaProcessor(tokenizer=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__     )

        self.assertListEqual(
            processor.model_input_names					,      feature_extractor.model_input_names					,      msg="""`processor` and `feature_extractor` model input names do not match"""					,      )