



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 _a   (				self		):
											a__					   =		"""<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(""" """		)
											a__					   =		dict(zip(a_	,	range(len(a_		)		)		)		)

											a__					   =		{
											    """pad_token""": """<pad>""",
											    """unk_token""": """<unk>""",
											    """bos_token""": """<s>""",
											    """eos_token""": """</s>""",
											}
											a__					   =		{
											    """feature_size""": 1,
											    """padding_value""": 0.0,
											    """sampling_rate""": 16_000,
											    """return_attention_mask""": False,
											    """do_normalize""": True,
											}

											a__					   =		tempfile.mkdtemp()
											a__					   =		os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["""vocab_file"""]		)
											a__					   =		os.path.join(self.tmpdirname	,	a_		)
											with open(self.vocab_file	,	"""w"""	,	encoding="""utf-8"""		) as fp:
															fp.write(json.dumps(a_		) + """\n"""		)

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


							def _a   (				self	,	**a_		):
											a__					   =		self.add_kwargs_tokens_map.copy()
											kwargs.update(a_		)
											return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,	**a_		)


							def _a   (				self	,	**a_		):
											return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,	**a_		)


							def _a   (				self		):
											shutil.rmtree(self.tmpdirname		)


							def _a   (				self		):
											a__					   =		self.get_tokenizer()
											a__					   =		self.get_feature_extractor()

											a__					   =		WavaVecaProcessor(tokenizer=a_	,	feature_extractor=a_		)

											processor.save_pretrained(self.tmpdirname		)
											a__					   =		WavaVecaProcessor.from_pretrained(self.tmpdirname		)

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

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


							def _a   (				self		):
											a__					   =		WavaVecaProcessor(tokenizer=self.get_tokenizer()	,	feature_extractor=self.get_feature_extractor()		)
											processor.save_pretrained(self.tmpdirname		)

											a__					   =		self.get_tokenizer(bos_token="""(BOS)"""	,	eos_token="""(EOS)"""		)
											a__					   =		self.get_feature_extractor(do_normalize=a_	,	padding_value=1.0		)

											a__					   =		WavaVecaProcessor.from_pretrained(
											    self.tmpdirname	,	bos_token="""(BOS)"""	,	eos_token="""(EOS)"""	,	do_normalize=a_	,	padding_value=1.0		)

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

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


							def _a   (				self		):
											a__					   =		self.get_feature_extractor()
											a__					   =		self.get_tokenizer()

											a__					   =		WavaVecaProcessor(tokenizer=a_	,	feature_extractor=a_		)

											a__					   =		floats_list((3, 1_000)		)

											a__					   =		feature_extractor(a_	,	return_tensors="""np"""		)
											a__					   =		processor(a_	,	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 _a   (				self		):
											a__					   =		self.get_feature_extractor()
											a__					   =		self.get_tokenizer()

											a__					   =		WavaVecaProcessor(tokenizer=a_	,	feature_extractor=a_		)

											a__					   =		"""This is a test string"""

											a__					   =		processor(text=a_		)

											a__					   =		tokenizer(a_		)

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


							def _a   (				self		):
											a__					   =		self.get_feature_extractor()
											a__					   =		self.get_tokenizer()

											a__					   =		WavaVecaProcessor(tokenizer=a_	,	feature_extractor=a_		)

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

											a__					   =		processor.batch_decode(a_		)
											a__					   =		tokenizer.batch_decode(a_		)

											self.assertListEqual(a_	,	a_		)







							def _a   (				self		):
											a__					   =		self.get_feature_extractor()
											a__					   =		self.get_tokenizer()

											a__					   =		WavaVecaProcessor(tokenizer=a_	,	feature_extractor=a_		)

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



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__SCREAMING_SNAKE_CASE		:  Tuple        =      {
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  Dict        =      ['SegformerFeatureExtractor']
					__SCREAMING_SNAKE_CASE		:  str        =      ['SegformerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  List[str]        =      [
					    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SegformerDecodeHead',
					    'SegformerForImageClassification',
					    'SegformerForSemanticSegmentation',
					    'SegformerLayer',
					    'SegformerModel',
					    'SegformerPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  List[str]        =      [
					    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFSegformerDecodeHead',
					    'TFSegformerForImageClassification',
					    'TFSegformerForSemanticSegmentation',
					    'TFSegformerModel',
					    'TFSegformerPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_segformer import SegformerFeatureExtractor
										from .image_processing_segformer import SegformerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_segformer import (
										    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SegformerDecodeHead,
										    SegformerForImageClassification,
										    SegformerForSemanticSegmentation,
										    SegformerLayer,
										    SegformerModel,
										    SegformerPreTrainedModel,
										)
					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_segformer import (
										    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSegformerDecodeHead,
										    TFSegformerForImageClassification,
										    TFSegformerForSemanticSegmentation,
										    TFSegformerModel,
										    TFSegformerPreTrainedModel,
										)

else:
					import sys

					__SCREAMING_SNAKE_CASE		:  Optional[Any]        =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)