



import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
		from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

		from .test_feature_extraction_speechta import floats_list


__UpperCAmelCase    	=  get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')


@require_torch
class 						lowerCamelCase__			(		unittest.TestCase ):


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							str									=tempfile.mkdtemp()

													a__:							Union[str, Any]									=SpeechTaTokenizer(UpperCAmelCase_      )
													tokenizer.save_pretrained(self.tmpdirname      )

													a__:							Union[str, Any]									={
													    "feature_size": 1,
													    "padding_value": 0.0,
													    "sampling_rate": 1_6_0_0_0,
													    "do_normalize": False,
													    "num_mel_bins": 8_0,
													    "hop_length": 1_6,
													    "win_length": 6_4,
													    "win_function": "hann_window",
													    "fmin": 8_0,
													    "fmax": 7_6_0_0,
													    "mel_floor": 1e-10,
													    "reduction_factor": 2,
													    "return_attention_mask": True,
													}

													a__:							List[str]									=os.path.join(self.tmpdirname   ,							UpperCAmelCase_      )
													with open(self.feature_extraction_file   ,							"w"   ,							encoding="utf-8"      ) as fp:
																				fp.write(json.dumps(UpperCAmelCase_      ) + "\n"      )


						def 	_lowerCamelCase      (			self    :	Tuple   ,							**_a    :	Dict      ):
													return SpeechTaTokenizer.from_pretrained(self.tmpdirname   ,							**UpperCAmelCase_      )


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							**_a    :	Dict      ):
													return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname   ,							**UpperCAmelCase_      )


						def 	_lowerCamelCase      (			self    :	Any      ):
													shutil.rmtree(self.tmpdirname      )


						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__:							int									=self.get_tokenizer()
													a__:							List[str]									=self.get_feature_extractor()

													a__:							List[Any]									=SpeechTaProcessor(tokenizer=UpperCAmelCase_   ,							feature_extractor=UpperCAmelCase_      )

													processor.save_pretrained(self.tmpdirname      )
													a__:							Any									=SpeechTaProcessor.from_pretrained(self.tmpdirname      )

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

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


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													a__:							Dict									=SpeechTaProcessor(tokenizer=self.get_tokenizer()   ,							feature_extractor=self.get_feature_extractor()      )
													processor.save_pretrained(self.tmpdirname      )

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

													a__:							Tuple									=SpeechTaProcessor.from_pretrained(
													    self.tmpdirname   ,							bos_token="(BOS)"   ,							eos_token="(EOS)"   ,							do_normalize=UpperCAmelCase_   ,							padding_value=1.0      )

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

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


						def 	_lowerCamelCase      (			self    :	int      ):
													a__:							int									=self.get_feature_extractor()
													a__:							List[str]									=self.get_tokenizer()

													a__:							str									=SpeechTaProcessor(tokenizer=UpperCAmelCase_   ,							feature_extractor=UpperCAmelCase_      )

													a__:							Dict									=floats_list((3, 1_0_0_0)      )

													a__:							Any									=feature_extractor(audio=UpperCAmelCase_   ,							return_tensors="np"      )
													a__:							Dict									=processor(audio=UpperCAmelCase_   ,							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 	_lowerCamelCase      (			self    :	str      ):
													a__:							Any									=self.get_feature_extractor()
													a__:							List[str]									=self.get_tokenizer()

													a__:							List[str]									=SpeechTaProcessor(tokenizer=UpperCAmelCase_   ,							feature_extractor=UpperCAmelCase_      )

													a__:							Tuple									=floats_list((3, 1_0_0_0)      )

													a__:							List[str]									=feature_extractor(audio_target=UpperCAmelCase_   ,							return_tensors="np"      )
													a__:							Optional[int]									=processor(audio_target=UpperCAmelCase_   ,							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 	_lowerCamelCase      (			self    :	Tuple      ):
													a__:							Dict									=self.get_feature_extractor()
													a__:							Tuple									=self.get_tokenizer()

													a__:							Optional[int]									=SpeechTaProcessor(tokenizer=UpperCAmelCase_   ,							feature_extractor=UpperCAmelCase_      )

													a__:							int									="This is a test string"

													a__:							Optional[Any]									=processor(text=UpperCAmelCase_      )
													a__:							List[Any]									=tokenizer(UpperCAmelCase_      )

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


						def 	_lowerCamelCase      (			self    :	Tuple      ):
													a__:							str									=self.get_feature_extractor()
													a__:							Any									=self.get_tokenizer()

													a__:							Optional[int]									=SpeechTaProcessor(tokenizer=UpperCAmelCase_   ,							feature_extractor=UpperCAmelCase_      )

													a__:							Dict									="This is a test string"

													a__:							List[Any]									=processor(text_target=UpperCAmelCase_      )
													a__:							Optional[Any]									=tokenizer(UpperCAmelCase_      )

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


						def 	_lowerCamelCase      (			self    :	str      ):
													a__:							List[str]									=self.get_feature_extractor()
													a__:							Optional[Any]									=self.get_tokenizer()

													a__:							Any									=SpeechTaProcessor(tokenizer=UpperCAmelCase_   ,							feature_extractor=UpperCAmelCase_      )

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

													a__:							Optional[Any]									=processor.batch_decode(UpperCAmelCase_      )
													a__:							Any									=tokenizer.batch_decode(UpperCAmelCase_      )

													self.assertListEqual(UpperCAmelCase_   ,							UpperCAmelCase_      )







						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							Tuple									=self.get_feature_extractor()
													a__:							Any									=self.get_tokenizer()

													a__:							Tuple									=SpeechTaProcessor(tokenizer=UpperCAmelCase_   ,							feature_extractor=UpperCAmelCase_      )

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












from typing import TYPE_CHECKING

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


UpperCAmelCase__    :      Optional[Any]       					={
    '''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:
							UpperCAmelCase__    :      Any       					=[
							    '''DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DonutSwinModel''',
							    '''DonutSwinPreTrainedModel''',
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      Tuple       					=['''DonutFeatureExtractor''']
							UpperCAmelCase__    :      List[str]       					=['''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

							UpperCAmelCase__    :      int       					=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





