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 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		_lowercase   ="<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(" "       )
		_lowercase   =dict(zip(lowerCAmelCase_       ,  range(len(lowerCAmelCase_       )       )       )       )

		_lowercase   ={
		    "pad_token": "<pad>",
		    "unk_token": "<unk>",
		    "bos_token": "<s>",
		    "eos_token": "</s>",
		}
		_lowercase   ={
		    "feature_size": 1,
		    "padding_value": 0.0,
		    "sampling_rate": 16000,
		    "return_attention_mask": False,
		    "do_normalize": True,
		}

		_lowercase   =tempfile.mkdtemp()
		_lowercase   =os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["vocab_file"]       )
		_lowercase   =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 __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		_lowercase   =self.add_kwargs_tokens_map.copy()
		kwargs.update(lowerCAmelCase_       )
		return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       )


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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_tokenizer()
		_lowercase   =self.get_feature_extractor()

		_lowercase   =WavaVecaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		processor.save_pretrained(self.tmpdirname       )
		_lowercase   =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 __lowerCAmelCase      (   self       ):
		_lowercase   =WavaVecaProcessor(tokenizer=self.get_tokenizer()       ,  feature_extractor=self.get_feature_extractor()       )
		processor.save_pretrained(self.tmpdirname       )

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

		_lowercase   =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 __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =WavaVecaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   =floats_list((3, 1000)       )

		_lowercase   =feature_extractor(lowerCAmelCase_       ,  return_tensors="np"       )
		_lowercase   =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 __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =WavaVecaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

		_lowercase   ="This is a test string"

		_lowercase   =processor(text=lowerCAmelCase_       )

		_lowercase   =tokenizer(lowerCAmelCase_       )

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =WavaVecaProcessor(tokenizer=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       )

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

		_lowercase   =processor.batch_decode(lowerCAmelCase_       )
		_lowercase   =tokenizer.batch_decode(lowerCAmelCase_       )

		self.assertListEqual(lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_feature_extractor()
		_lowercase   =self.get_tokenizer()

		_lowercase   =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"       ,  )







"""simple docstring"""
from collections import defaultdict







def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     dict:



       """simple docstring"""







       __magic_name__			:		defaultdict[str, int]        =     defaultdict(lowerCamelCase   )
       # Creating a dictionary containing count of each word
       for word in sentence.split():
              occurrence[word] += 1
       return occurrence


if __name__ == "__main__":
 for word, count in word_occurrence("""INPUT STRING""").items():
  print(F"""{word}: {count}""")
