

import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
			pass

if is_speech_available():
			from transformers import EncodecFeatureExtractor, MusicgenProcessor


a_         =      random.Random()

def 	lowerCamelCase__	(   _a							,     _a=1.0							,     _a=None							,     _a=None):
	if rng is None:
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    global_rng

	SCREAMING_SNAKE_CASE				: Tuple			       =    []
	for batch_idx in range(shape[0]):
		values.append([])
		for _ in range(shape[1]):
			values[-1].append(rng.random() * scale)

	return values






@require_torch
@require_sentencepiece
class _UpperCamelCase							(							unittest.TestCase				):
			'''simple docstring'''







			def 							__UpperCamelCase	(     self							:	List[str]		)							->    Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    "facebook/musicgen-small"
				SCREAMING_SNAKE_CASE				: int			       =    tempfile.mkdtemp()
			def 							__UpperCamelCase	(     self							:	str , **a							:	Optional[Any]		)							->    Union[str, Any]:

				"""simple docstring"""

				return TaTokenizer.from_pretrained(self.checkpoint , **a		)
			def 							__UpperCamelCase	(     self							:	Union[str, Any] , **a							:	Dict		)							->    Optional[int]:

				"""simple docstring"""

				return EncodecFeatureExtractor.from_pretrained(self.checkpoint , **a		)
			def 							__UpperCamelCase	(     self							:	Any		)							->    List[str]:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    self.get_tokenizer()
				SCREAMING_SNAKE_CASE				: str			       =    self.get_feature_extractor()

				SCREAMING_SNAKE_CASE				: int			       =    MusicgenProcessor(tokenizer=a , feature_extractor=a		)

				processor.save_pretrained(self.tmpdirname		)
				SCREAMING_SNAKE_CASE				: int			       =    MusicgenProcessor.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 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    MusicgenProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor()		)
				processor.save_pretrained(self.tmpdirname		)

				SCREAMING_SNAKE_CASE				: List[str]			       =    self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)"		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.get_feature_extractor(do_normalize=a , padding_value=1.0		)

				SCREAMING_SNAKE_CASE				: str			       =    MusicgenProcessor.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 							__UpperCamelCase	(     self							:	List[str]		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    self.get_feature_extractor()
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.get_tokenizer()

				SCREAMING_SNAKE_CASE				: str			       =    MusicgenProcessor(tokenizer=a , feature_extractor=a		)

				SCREAMING_SNAKE_CASE				: int			       =    floats_list((3, 1000)		)

				SCREAMING_SNAKE_CASE				: Any			       =    feature_extractor(a , return_tensors="np"		)
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    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 							__UpperCamelCase	(     self							:	Tuple		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.get_feature_extractor()
				SCREAMING_SNAKE_CASE				: Dict			       =    self.get_tokenizer()

				SCREAMING_SNAKE_CASE				: Tuple			       =    MusicgenProcessor(tokenizer=a , feature_extractor=a		)

				SCREAMING_SNAKE_CASE				: List[str]			       =    "This is a test string"

				SCREAMING_SNAKE_CASE				: int			       =    processor(text=a		)

				SCREAMING_SNAKE_CASE				: List[Any]			       =    tokenizer(a		)

				for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key] , encoded_processor[key]		)
			def 							__UpperCamelCase	(     self							:	Any		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    self.get_feature_extractor()
				SCREAMING_SNAKE_CASE				: str			       =    self.get_tokenizer()

				SCREAMING_SNAKE_CASE				: Tuple			       =    MusicgenProcessor(tokenizer=a , feature_extractor=a		)

				SCREAMING_SNAKE_CASE				: str			       =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				SCREAMING_SNAKE_CASE				: Any			       =    processor.batch_decode(sequences=a		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    tokenizer.batch_decode(a		)

				self.assertListEqual(a , a		)
			def 							__UpperCamelCase	(     self							:	str		)							->    List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: int			       =    self.get_feature_extractor()
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.get_tokenizer()

				SCREAMING_SNAKE_CASE				: int			       =    MusicgenProcessor(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" , )






			def 							__UpperCamelCase	(     self							:	str		)							->    Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.get_feature_extractor(padding_side="left"		)
				SCREAMING_SNAKE_CASE				: Dict			       =    self.get_tokenizer()

				SCREAMING_SNAKE_CASE				: Dict			       =    MusicgenProcessor(tokenizer=a , feature_extractor=a		)

				SCREAMING_SNAKE_CASE				: Any			       =    [floats_list((1, x)		)[0] for x in range(5 , 20 , 5		)]
				SCREAMING_SNAKE_CASE				: int			       =    processor(a		).padding_mask

				SCREAMING_SNAKE_CASE				: Any			       =    np.asarray(floats_list((3, 20)		)		)[:, None, :]
				SCREAMING_SNAKE_CASE				: int			       =    processor.batch_decode(a , padding_mask=a		)

				self.assertIsInstance(a , a		)

				for audio in decoded_audios:
					self.assertIsInstance(a , np.ndarray		)

				self.assertTrue(decoded_audios[0].shape == (1, 10)		)
				self.assertTrue(decoded_audios[1].shape == (1, 15)		)
				self.assertTrue(decoded_audios[2].shape == (1, 20)		)


import math
import os

import cva
import numpy as np

a_         =      255.0

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

	return 20 * math.logaa(PIXEL_MAX / math.sqrt(_a))

def 	lowerCamelCase__	(   ):
	SCREAMING_SNAKE_CASE				: List[Any]			       =    os.path.dirname(os.path.realpath(_a))
	# Loading images (original image and compressed image)
	SCREAMING_SNAKE_CASE				: Any			       =    cva.imread(os.path.join(_a							,     "image_data/original_image.png"))
	SCREAMING_SNAKE_CASE				: List[str]			       =    cva.imread(os.path.join(_a							,     "image_data/compressed_image.png")							,     1)

	SCREAMING_SNAKE_CASE				: List[Any]			       =    cva.imread(os.path.join(_a							,     "image_data/PSNR-example-base.png"))
	SCREAMING_SNAKE_CASE				: List[str]			       =    cva.imread(
	    os.path.join(_a							,     "image_data/PSNR-example-comp-10.jpg")							,     1)

	# Value expected: 29.73dB
	print("-- First Test --")
	print(f"PSNR value is {peak_signal_to_noise_ratio(_a							,     _a)} dB")

	# # Value expected: 31.53dB (Wikipedia Example)
	print("\n-- Second Test --")
	print(f"PSNR value is {peak_signal_to_noise_ratio(_a							,     _a)} dB")


if __name__ == "__main__":
			main()