



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  _lowercase				(unittest.TestCase ):
    '''simple docstring'''







    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				"<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_											=				dict(zip(snake_case__       ,	range(len(snake_case__      )      )      )      )

         UpperCamelCase_											=				{
             "pad_token": "<pad>",
             "unk_token": "<unk>",
             "bos_token": "<s>",
             "eos_token": "</s>",
         }
         UpperCamelCase_											=				{
             "feature_size": 1,
             "padding_value": 0.0,
             "sampling_rate": 1_6000,
             "return_attention_mask": False,
             "do_normalize": True,
         }

         UpperCamelCase_											=				tempfile.mkdtemp()
         UpperCamelCase_											=				os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES["vocab_file"]      )
         UpperCamelCase_											=				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  _lowerCamelCase   (	self       ,	**snake_case__      ):

         '''simple docstring'''


         UpperCamelCase_											=				self.add_kwargs_tokens_map.copy()
         kwargs.update(snake_case__      )
         return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,	**snake_case__      )

    def  _lowerCamelCase   (	self       ,	**snake_case__      ):

         '''simple docstring'''


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

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         shutil.rmtree(self.tmpdirname      )

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				self.get_tokenizer()
         UpperCamelCase_											=				self.get_feature_extractor()

         UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

         processor.save_pretrained(self.tmpdirname      )
         UpperCamelCase_											=				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  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				WavaVecaProcessor(tokenizer=self.get_tokenizer()       ,	feature_extractor=self.get_feature_extractor()      )
         processor.save_pretrained(self.tmpdirname      )

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

         UpperCamelCase_											=				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  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				self.get_feature_extractor()
         UpperCamelCase_											=				self.get_tokenizer()

         UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

         UpperCamelCase_											=				floats_list((3, 1000)      )

         UpperCamelCase_											=				feature_extractor(snake_case__       ,	return_tensors="np"      )
         UpperCamelCase_											=				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  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				self.get_feature_extractor()
         UpperCamelCase_											=				self.get_tokenizer()

         UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

         UpperCamelCase_											=				"This is a test string"

         UpperCamelCase_											=				processor(text=snake_case__      )

         UpperCamelCase_											=				tokenizer(snake_case__      )

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

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				self.get_feature_extractor()
         UpperCamelCase_											=				self.get_tokenizer()

         UpperCamelCase_											=				WavaVecaProcessor(tokenizer=snake_case__       ,	feature_extractor=snake_case__      )

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

         UpperCamelCase_											=				processor.batch_decode(snake_case__      )
         UpperCamelCase_											=				tokenizer.batch_decode(snake_case__      )

         self.assertListEqual(snake_case__       ,	snake_case__      )

    def  _lowerCamelCase   (	self      ):

         '''simple docstring'''


         UpperCamelCase_											=				self.get_feature_extractor()
         UpperCamelCase_											=				self.get_tokenizer()

         UpperCamelCase_											=				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"       ,	)





from typing import TYPE_CHECKING

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


UpperCAmelCase				:							Optional[int]								={
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase				:							Optional[int]								=["""RealmTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase				:							Union[str, Any]								=[
	    """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RealmEmbedder""",
	    """RealmForOpenQA""",
	    """RealmKnowledgeAugEncoder""",
	    """RealmPreTrainedModel""",
	    """RealmReader""",
	    """RealmScorer""",
	    """load_tf_weights_in_realm""",
	]
	UpperCAmelCase				:							List[str]								=["""RealmRetriever"""]


if TYPE_CHECKING:
	from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
	from .tokenization_realm import RealmTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_realm import RealmTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_realm import (
		    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RealmEmbedder,
		    RealmForOpenQA,
		    RealmKnowledgeAugEncoder,
		    RealmPreTrainedModel,
		    RealmReader,
		    RealmScorer,
		    load_tf_weights_in_realm,
		)
		from .retrieval_realm import RealmRetriever


else:
	import sys

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