


'''simple docstring'''




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


__lowercase  :      int      		=						random.Random()





def 							a_       (						lowercase							,						lowercase=1.0							,						lowercase=None							,						lowercase=None    )			->int:






				'''simple docstring'''

				if rng is None:
								__lowerCamelCase		:      Union[str, Any]											=					global_rng

				__lowerCamelCase		:      Dict											=					[]
				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 _snake_case   ( unittest.TestCase   ):
							"""simple docstring"""





							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:



											"""simple docstring"""







											__lowerCamelCase		:      Any											=					"facebook/musicgen-small"
											__lowerCamelCase		:      Any											=					tempfile.mkdtemp()







							def 		__SCREAMING_SNAKE_CASE(   self			,				**__A	)     ->Optional[int]:



											"""simple docstring"""







											return TaTokenizer.from_pretrained(self.checkpoint			,				**lowerCAmelCase__	)







							def 		__SCREAMING_SNAKE_CASE(   self			,				**__A	)     ->str:



											"""simple docstring"""







											return EncodecFeatureExtractor.from_pretrained(self.checkpoint			,				**lowerCAmelCase__	)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[str]:



											"""simple docstring"""







											shutil.rmtree(self.tmpdirname	)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:      List[Any]											=					self.get_tokenizer()
											__lowerCamelCase		:      str											=					self.get_feature_extractor()

											__lowerCamelCase		:      Optional[Any]											=					MusicgenProcessor(tokenizer=lowerCAmelCase__			,				feature_extractor=lowerCAmelCase__	)

											processor.save_pretrained(self.tmpdirname	)
											__lowerCamelCase		:      List[str]											=					MusicgenProcessor.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 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:      List[Any]											=					MusicgenProcessor(tokenizer=self.get_tokenizer()			,				feature_extractor=self.get_feature_extractor()	)
											processor.save_pretrained(self.tmpdirname	)

											__lowerCamelCase		:      Optional[Any]											=					self.get_tokenizer(bos_token="(BOS)"			,				eos_token="(EOS)"	)
											__lowerCamelCase		:      Union[str, Any]											=					self.get_feature_extractor(do_normalize=lowerCAmelCase__			,				padding_value=1.0	)

											__lowerCamelCase		:      str											=					MusicgenProcessor.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 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:



											"""simple docstring"""







											__lowerCamelCase		:      Tuple											=					self.get_feature_extractor()
											__lowerCamelCase		:      Optional[int]											=					self.get_tokenizer()

											__lowerCamelCase		:      str											=					MusicgenProcessor(tokenizer=lowerCAmelCase__			,				feature_extractor=lowerCAmelCase__	)

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

											__lowerCamelCase		:      List[Any]											=					feature_extractor(lowerCAmelCase__			,				return_tensors="np"	)
											__lowerCamelCase		:      Union[str, Any]											=					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 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:



											"""simple docstring"""







											__lowerCamelCase		:      Any											=					self.get_feature_extractor()
											__lowerCamelCase		:      Dict											=					self.get_tokenizer()

											__lowerCamelCase		:      Optional[int]											=					MusicgenProcessor(tokenizer=lowerCAmelCase__			,				feature_extractor=lowerCAmelCase__	)

											__lowerCamelCase		:      Any											=					"This is a test string"

											__lowerCamelCase		:      Optional[int]											=					processor(text=lowerCAmelCase__	)

											__lowerCamelCase		:      int											=					tokenizer(lowerCAmelCase__	)

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







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:



											"""simple docstring"""







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

											__lowerCamelCase		:      Tuple											=					MusicgenProcessor(tokenizer=lowerCAmelCase__			,				feature_extractor=lowerCAmelCase__	)

											__lowerCamelCase		:      List[Any]											=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											__lowerCamelCase		:      Dict											=					processor.batch_decode(sequences=lowerCAmelCase__	)
											__lowerCamelCase		:      Any											=					tokenizer.batch_decode(lowerCAmelCase__	)

											self.assertListEqual(lowerCAmelCase__			,				lowerCAmelCase__	)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Union[str, Any]:



											"""simple docstring"""







											__lowerCamelCase		:      Any											=					self.get_feature_extractor()
											__lowerCamelCase		:      List[str]											=					self.get_tokenizer()

											__lowerCamelCase		:      List[str]											=					MusicgenProcessor(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"			,				)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->str:



											"""simple docstring"""







											__lowerCamelCase		:      Dict											=					self.get_feature_extractor(padding_side="left"	)
											__lowerCamelCase		:      Any											=					self.get_tokenizer()

											__lowerCamelCase		:      List[str]											=					MusicgenProcessor(tokenizer=lowerCAmelCase__			,				feature_extractor=lowerCAmelCase__	)

											__lowerCamelCase		:      List[str]											=					[floats_list((1, x)	)[0] for x in range(5			,				20			,				5	)]
											__lowerCamelCase		:      List[Any]											=					processor(lowerCAmelCase__	).padding_mask

											__lowerCamelCase		:      Optional[int]											=					np.asarray(floats_list((3, 20)	)	)[:, None, :]
											__lowerCamelCase		:      Union[str, Any]											=					processor.batch_decode(lowerCAmelCase__			,				padding_mask=lowerCAmelCase__	)

											self.assertIsInstance(lowerCAmelCase__			,				lowerCAmelCase__	)

											for audio in decoded_audios:
															self.assertIsInstance(lowerCAmelCase__			,				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)	)









'''simple docstring'''




from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize





class _snake_case   ( a__   ):
							"""simple docstring"""





							__a												=     1
							__a												=     2
							__a												=     3







							def __repr__(   self	)     ->Tuple:



											"""simple docstring"""







											return f'{self.__class__.__name__}.{self.name}'





def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->float:






				'''simple docstring'''

				__lowerCamelCase						,					__lowerCamelCase		:      str											=					point
				return degrees(atana(y - miny							,						x - minx    )    )





def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Direction:






				'''simple docstring'''

				__lowerCamelCase						,					__lowerCamelCase		:      List[str]											=					starting
				__lowerCamelCase						,					__lowerCamelCase		:      Any											=					via
				__lowerCamelCase						,					__lowerCamelCase		:      str											=					target
				__lowerCamelCase		:      str											=					degrees(atana(ya - ya							,						xa - xa    )    )
				via_angle %= 360
				__lowerCamelCase		:      List[Any]											=					degrees(atana(ya - ya							,						xa - xa    )    )
				target_angle %= 360
				# t-
				#  \ \
				#   \ v
				#    \|
				#     s
				# via_angle is always lower than target_angle, if direction is left.
				# If they are same, it means they are on a same line of convex hull.
				if target_angle > via_angle:
								return Direction.left
				elif target_angle == via_angle:
								return Direction.straight
				else:
								return Direction.right





def 							a_       (						lowercase    )			->list[tuple[int, int]]:






				'''simple docstring'''

				if len(lowercase    ) <= 2:
								# There is no convex hull
								raise ValueError("graham_scan: argument must contain more than 3 points."    )
				if len(lowercase    ) == 3:
								return points
				# find the lowest and the most left point
				__lowerCamelCase		:      Optional[Any]											=					0
				__lowerCamelCase						,					__lowerCamelCase		:      List[Any]											=					maxsize, maxsize
				for i, point in enumerate(lowercase    ):
								__lowerCamelCase		:      Dict											=					point[0]
								__lowerCamelCase		:      Optional[int]											=					point[1]
								if y < miny:
												__lowerCamelCase		:      Dict											=					y
												__lowerCamelCase		:      int											=					x
												__lowerCamelCase		:      List[Any]											=					i
								if y == miny and x < minx:
												__lowerCamelCase		:      Optional[int]											=					x
												__lowerCamelCase		:      Any											=					i

    # remove the lowest and the most left point from points for preparing for sort
				points.pop(lowercase    )

				__lowerCamelCase		:      Optional[Any]											=					sorted(lowercase							,						key=lambda lowercase    : angle_comparer(lowercase							,						lowercase							,						lowercase    )    )
				# This insert actually costs complexity,
				# and you should instead add (minx, miny) into stack later.
				# I'm using insert just for easy understanding.
				sorted_points.insert(0							,						(minx, miny)    )

				__lowerCamelCase		:      deque[tuple[int, int]]											=					deque()
				stack.append(sorted_points[0]    )
				stack.append(sorted_points[1]    )
				stack.append(sorted_points[2]    )
				# In any ways, the first 3 points line are towards left.
				# Because we sort them the angle from minx, miny.
				__lowerCamelCase		:      str											=					Direction.left

				for i in range(3							,						len(lowercase    )    ):
								while True:
												__lowerCamelCase		:      List[Any]											=					stack[-2]
												__lowerCamelCase		:      str											=					stack[-1]
												__lowerCamelCase		:      int											=					sorted_points[i]
												__lowerCamelCase		:      List[Any]											=					check_direction(lowercase							,						lowercase							,						lowercase    )

												if next_direction == Direction.left:
																__lowerCamelCase		:      Optional[int]											=					Direction.left
																break
												if next_direction == Direction.straight:
																if current_direction == Direction.left:
																				# We keep current_direction as left.
																				# Because if the straight line keeps as straight,
																				# we want to know if this straight line is towards left.
																				break
																elif current_direction == Direction.right:
																				# If the straight line is towards right,
																				# every previous points on those straigh line is not convex hull.
																				stack.pop()
												if next_direction == Direction.right:
																stack.pop()
								stack.append(sorted_points[i]    )
				return list(lowercase    )





