



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_       :List[str]				  =				{
    "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
    "processing_donut": ["DonutProcessor"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :Optional[int]				  =				[
     "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DonutSwinModel",
     "DonutSwinPreTrainedModel",
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :Union[str, Any]				  =				["DonutFeatureExtractor"]
 a_       :Any				  =				["DonutImageProcessor"]


if TYPE_CHECKING:
 from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
 from .processing_donut import DonutProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_donut_swin import (
      DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
      DonutSwinModel,
      DonutSwinPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_donut import DonutFeatureExtractor
  from .image_processing_donut import DonutImageProcessor

else:
 import sys

 a_       :List[str]				  =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











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  snake_case__						(	unittest.TestCase					):






     """simple docstring"""


     def        lowercase_       (	self  :  Optional[int] )       ->Optional[Any]:
            snake_case__							:     Tuple            =     '<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(' ' )
            snake_case__							:     Any            =     dict(zip(_snake_case,			range(len(_snake_case ) ) ) )

            snake_case__							:     List[str]            =     {
                'pad_token': '<pad>',
                'unk_token': '<unk>',
                'bos_token': '<s>',
                'eos_token': '</s>',
            }
            snake_case__							:     Optional[int]            =     {
                'feature_size': 1,
                'padding_value': 0.0,
                'sampling_rate': 1_6_0_0_0,
                'return_attention_mask': False,
                'do_normalize': True,
            }

            snake_case__							:     Union[str, Any]            =     tempfile.mkdtemp()
            snake_case__							:     Optional[int]            =     os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['vocab_file'] )
            snake_case__							:     int            =     os.path.join(self.tmpdirname,			_snake_case )
            with open(self.vocab_file,			'w',			encoding='utf-8' ) as fp:
                   fp.write(json.dumps(_snake_case ) + '\n' )

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






     def        lowercase_       (	self  :  Tuple,			**_snake_case  :  Dict )       ->Union[str, Any]:
            snake_case__							:     Tuple            =     self.add_kwargs_tokens_map.copy()
            kwargs.update(_snake_case )
            return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,			**_snake_case )






     def        lowercase_       (	self  :  Optional[Any],			**_snake_case  :  Dict )       ->Union[str, Any]:
            return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,			**_snake_case )






     def        lowercase_       (	self  :  List[Any] )       ->str:
            shutil.rmtree(self.tmpdirname )






     def        lowercase_       (	self  :  Optional[int] )       ->Any:
            snake_case__							:     Any            =     self.get_tokenizer()
            snake_case__							:     Any            =     self.get_feature_extractor()

            snake_case__							:     Tuple            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            processor.save_pretrained(self.tmpdirname )
            snake_case__							:     List[Any]            =     WavaVecaProcessor.from_pretrained(self.tmpdirname )

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

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






     def        lowercase_       (	self  :  Optional[Any] )       ->Any:
            snake_case__							:     str            =     WavaVecaProcessor(tokenizer=self.get_tokenizer(),			feature_extractor=self.get_feature_extractor() )
            processor.save_pretrained(self.tmpdirname )

            snake_case__							:     Optional[int]            =     self.get_tokenizer(bos_token='(BOS)',			eos_token='(EOS)' )
            snake_case__							:     List[str]            =     self.get_feature_extractor(do_normalize=_snake_case,			padding_value=1.0 )

            snake_case__							:     List[str]            =     WavaVecaProcessor.from_pretrained(
                self.tmpdirname,			bos_token='(BOS)',			eos_token='(EOS)',			do_normalize=_snake_case,			padding_value=1.0 )

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

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






     def        lowercase_       (	self  :  str )       ->Optional[Any]:
            snake_case__							:     Optional[Any]            =     self.get_feature_extractor()
            snake_case__							:     Tuple            =     self.get_tokenizer()

            snake_case__							:     List[Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            snake_case__							:     Any            =     floats_list((3, 1_0_0_0) )

            snake_case__							:     int            =     feature_extractor(_snake_case,			return_tensors='np' )
            snake_case__							:     Union[str, Any]            =     processor(_snake_case,			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        lowercase_       (	self  :  Optional[int] )       ->str:
            snake_case__							:     int            =     self.get_feature_extractor()
            snake_case__							:     Any            =     self.get_tokenizer()

            snake_case__							:     List[str]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            snake_case__							:     Optional[Any]            =     'This is a test string'

            snake_case__							:     Optional[Any]            =     processor(text=_snake_case )

            snake_case__							:     Any            =     tokenizer(_snake_case )

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






     def        lowercase_       (	self  :  Union[str, Any] )       ->List[str]:
            snake_case__							:     Union[str, Any]            =     self.get_feature_extractor()
            snake_case__							:     List[str]            =     self.get_tokenizer()

            snake_case__							:     Union[str, Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

            snake_case__							:     Optional[Any]            =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            snake_case__							:     Dict            =     processor.batch_decode(_snake_case )
            snake_case__							:     Optional[int]            =     tokenizer.batch_decode(_snake_case )

            self.assertListEqual(_snake_case,			_snake_case )






     def        lowercase_       (	self  :  List[str] )       ->Any:
            snake_case__							:     List[Any]            =     self.get_feature_extractor()
            snake_case__							:     List[str]            =     self.get_tokenizer()

            snake_case__							:     Union[str, Any]            =     WavaVecaProcessor(tokenizer=_snake_case,			feature_extractor=_snake_case )

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






