

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


SCREAMING_SNAKE_CASE		:Optional[Any]				  =    get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")






@require_torch
class 	__magic_name__				(       unittest.TestCase							):






     def   UpperCAmelCase_							(							self  )->    Dict:
         UpperCamelCase_      =	tempfile.mkdtemp()

         UpperCamelCase_      =	SpeechTaTokenizer(_lowercase  )
         tokenizer.save_pretrained(self.tmpdirname  )

         UpperCamelCase_      =	{
             "feature_size": 1,
             "padding_value": 0.0,
             "sampling_rate": 16_000,
             "do_normalize": False,
             "num_mel_bins": 80,
             "hop_length": 16,
             "win_length": 64,
             "win_function": "hann_window",
             "fmin": 80,
             "fmax": 7_600,
             "mel_floor": 1e-1_0,
             "reduction_factor": 2,
             "return_attention_mask": True,
         }

         UpperCamelCase_      =	os.path.join(self.tmpdirname		,	_lowercase  )
         with open(self.feature_extraction_file		,	"w"		,	encoding="utf-8"  ) as fp:
             fp.write(json.dumps(_lowercase  ) + "\n"  )






     def   UpperCAmelCase_							(							self		,	**_lowercase  )->    List[str]:
         return SpeechTaTokenizer.from_pretrained(self.tmpdirname		,	**_lowercase  )






     def   UpperCAmelCase_							(							self		,	**_lowercase  )->    str:
         return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname		,	**_lowercase  )






     def   UpperCAmelCase_							(							self  )->    Any:
         shutil.rmtree(self.tmpdirname  )






     def   UpperCAmelCase_							(							self  )->    Dict:
         UpperCamelCase_      =	self.get_tokenizer()
         UpperCamelCase_      =	self.get_feature_extractor()

         UpperCamelCase_      =	SpeechTaProcessor(tokenizer=_lowercase		,	feature_extractor=_lowercase  )

         processor.save_pretrained(self.tmpdirname  )
         UpperCamelCase_      =	SpeechTaProcessor.from_pretrained(self.tmpdirname  )

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

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






     def   UpperCAmelCase_							(							self  )->    int:
         UpperCamelCase_      =	SpeechTaProcessor(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=_lowercase		,	padding_value=1.0  )

         UpperCamelCase_      =	SpeechTaProcessor.from_pretrained(
             self.tmpdirname		,	bos_token="(BOS)"		,	eos_token="(EOS)"		,	do_normalize=_lowercase		,	padding_value=1.0  )

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

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






     def   UpperCAmelCase_							(							self  )->    Any:
         UpperCamelCase_      =	self.get_feature_extractor()
         UpperCamelCase_      =	self.get_tokenizer()

         UpperCamelCase_      =	SpeechTaProcessor(tokenizer=_lowercase		,	feature_extractor=_lowercase  )

         UpperCamelCase_      =	floats_list((3, 1_000)  )

         UpperCamelCase_      =	feature_extractor(audio=_lowercase		,	return_tensors="np"  )
         UpperCamelCase_      =	processor(audio=_lowercase		,	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   UpperCAmelCase_							(							self  )->    str:
         UpperCamelCase_      =	self.get_feature_extractor()
         UpperCamelCase_      =	self.get_tokenizer()

         UpperCamelCase_      =	SpeechTaProcessor(tokenizer=_lowercase		,	feature_extractor=_lowercase  )

         UpperCamelCase_      =	floats_list((3, 1_000)  )

         UpperCamelCase_      =	feature_extractor(audio_target=_lowercase		,	return_tensors="np"  )
         UpperCamelCase_      =	processor(audio_target=_lowercase		,	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   UpperCAmelCase_							(							self  )->    Tuple:
         UpperCamelCase_      =	self.get_feature_extractor()
         UpperCamelCase_      =	self.get_tokenizer()

         UpperCamelCase_      =	SpeechTaProcessor(tokenizer=_lowercase		,	feature_extractor=_lowercase  )

         UpperCamelCase_      =	"This is a test string"

         UpperCamelCase_      =	processor(text=_lowercase  )
         UpperCamelCase_      =	tokenizer(_lowercase  )

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






     def   UpperCAmelCase_							(							self  )->    List[str]:
         UpperCamelCase_      =	self.get_feature_extractor()
         UpperCamelCase_      =	self.get_tokenizer()

         UpperCamelCase_      =	SpeechTaProcessor(tokenizer=_lowercase		,	feature_extractor=_lowercase  )

         UpperCamelCase_      =	"This is a test string"

         UpperCamelCase_      =	processor(text_target=_lowercase  )
         UpperCamelCase_      =	tokenizer(_lowercase  )

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






     def   UpperCAmelCase_							(							self  )->    List[str]:
         UpperCamelCase_      =	self.get_feature_extractor()
         UpperCamelCase_      =	self.get_tokenizer()

         UpperCamelCase_      =	SpeechTaProcessor(tokenizer=_lowercase		,	feature_extractor=_lowercase  )

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

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

         self.assertListEqual(_lowercase		,	_lowercase  )






     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         UpperCamelCase_      =	self.get_feature_extractor()
         UpperCamelCase_      =	self.get_tokenizer()

         UpperCamelCase_      =	SpeechTaProcessor(tokenizer=_lowercase		,	feature_extractor=_lowercase  )

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



import unittest

from transformers.image_processing_utils import get_size_dict






class 	__magic_name__				(       unittest.TestCase							):






     def   UpperCAmelCase_							(							self  )->    int:
         # Test a dict with the wrong keys raises an error
         UpperCamelCase_      =	{"wrong_key": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         UpperCamelCase_      =	{"height": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         UpperCamelCase_      =	{"width": 224, "shortest_edge": 224}
         with self.assertRaises(_lowercase  ):
             get_size_dict(_lowercase  )

         # Test a dict with the correct keys is returned as is
         UpperCamelCase_      =	{"height": 224, "width": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	_lowercase  )

         UpperCamelCase_      =	{"shortest_edge": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224}  )

         UpperCamelCase_      =	{"longest_edge": 224, "shortest_edge": 224}
         UpperCamelCase_      =	get_size_dict(_lowercase  )
         self.assertEqual(_lowercase		,	{"longest_edge": 224, "shortest_edge": 224}  )

         # Test a single int value which  represents (size, size)
         UpperCamelCase_      =	get_size_dict(224  )
         self.assertEqual(_lowercase		,	{"height": 224, "width": 224}  )

         # Test a single int value which represents the shortest edge
         UpperCamelCase_      =	get_size_dict(224		,	default_to_square=_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224}  )

         # Test a tuple of ints which represents (height, width)
         UpperCamelCase_      =	get_size_dict((150, 200)  )
         self.assertEqual(_lowercase		,	{"height": 150, "width": 200}  )

         # Test a tuple of ints which represents (width, height)
         UpperCamelCase_      =	get_size_dict((150, 200)		,	height_width_order=_lowercase  )
         self.assertEqual(_lowercase		,	{"height": 200, "width": 150}  )

         # Test an int representing the shortest edge and max_size which represents the longest edge
         UpperCamelCase_      =	get_size_dict(224		,	max_size=256		,	default_to_square=_lowercase  )
         self.assertEqual(_lowercase		,	{"shortest_edge": 224, "longest_edge": 256}  )

         # Test int with default_to_square=True and max_size fails
         with self.assertRaises(_lowercase  ):
             get_size_dict(224		,	max_size=256		,	default_to_square=_lowercase  )
