


'''simple docstring'''




import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


__lowercase  :      Dict      		=				10_00





class _snake_case   :
							"""simple docstring"""





							def __init__(   self			,				__A			,				__A=1.2			,				__A="g"			,				__A=0.5			,				__A="-"			,				__A="test_out.jpg"			,				__A=True			,				__A=False			,				__A=None			,				__A=None			,				__A=0.7			,				)     ->Optional[Any]:



											"""simple docstring"""







											if isinstance(__A			,				torch.Tensor	):
															__lowerCamelCase		:      int											=					img.numpy().astype("np.uint8"	)
											if isinstance(__A			,				__A	):
															__lowerCamelCase		:      Tuple											=					img_tensorize(__A	)
											assert isinstance(__A			,				np.ndarray	)

											__lowerCamelCase						,					__lowerCamelCase		:      str											=					img.shape[1], img.shape[0]
											__lowerCamelCase		:      Optional[int]											=					mplfigure.Figure(frameon=__A	)
											__lowerCamelCase		:      Optional[int]											=					fig.get_dpi()
											__lowerCamelCase		:      Dict											=					(width * scale + 1E-2) / dpi
											__lowerCamelCase		:      Union[str, Any]											=					(height * scale + 1E-2) / dpi
											fig.set_size_inches(__A			,				__A	)
											__lowerCamelCase		:      List[Any]											=					fig.add_axes([0.0, 0.0, 1.0, 1.0]	)
											ax.axis("off"	)
											ax.set_xlim(0.0			,				__A	)
											ax.set_ylim(__A	)

											__lowerCamelCase		:      str											=					saveas
											__lowerCamelCase		:      Optional[Any]											=					rgb
											__lowerCamelCase		:      Union[str, Any]											=					pynb
											__lowerCamelCase		:      Optional[Any]											=					img
											__lowerCamelCase		:      List[Any]											=					edgecolor
											__lowerCamelCase		:      Any											=					0.5
											__lowerCamelCase		:      List[str]											=					linestyle
											__lowerCamelCase		:      Tuple											=					int(np.sqrt(min(__A			,				__A	)	) * scale // 3	)
											__lowerCamelCase		:      str											=					width
											__lowerCamelCase		:      Optional[int]											=					height
											__lowerCamelCase		:      List[Any]											=					scale
											__lowerCamelCase		:      Union[str, Any]											=					fig
											__lowerCamelCase		:      Tuple											=					ax
											__lowerCamelCase		:      List[Any]											=					pad
											__lowerCamelCase		:      Union[str, Any]											=					idaobj
											__lowerCamelCase		:      Optional[int]											=					idaattr
											__lowerCamelCase		:      Dict											=					FigureCanvasAgg(__A	)







							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A=None	)     ->Optional[Any]:



											"""simple docstring"""







											if color is None:
															__lowerCamelCase		:      Any											=					self.edgecolor
											((__lowerCamelCase)						,					(__lowerCamelCase)						,					(__lowerCamelCase)						,					(__lowerCamelCase))		:      Union[str, Any]											=					box
											__lowerCamelCase		:      Optional[Any]											=					xa - xa
											__lowerCamelCase		:      Any											=					ya - ya
											self.ax.add_patch(
											    mpl.patches.Rectangle(
											        (xa, ya)			,				__A			,				__A			,				fill=__A			,				edgecolor=__A			,				linewidth=self.font_size // 3			,				alpha=self.alpha			,				linestyle=self.linestyle			,				)	)







							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A=None			,				__A=None			,				__A=None			,				__A=None	)     ->Any:



											"""simple docstring"""







											if len(boxes.shape	) > 2:
															__lowerCamelCase		:      Union[str, Any]											=					boxes[0]
											if len(obj_ids.shape	) > 1:
															__lowerCamelCase		:      List[Any]											=					obj_ids[0]
											if len(obj_scores.shape	) > 1:
															__lowerCamelCase		:      Optional[Any]											=					obj_scores[0]
											if len(attr_ids.shape	) > 1:
															__lowerCamelCase		:      str											=					attr_ids[0]
											if len(attr_scores.shape	) > 1:
															__lowerCamelCase		:      str											=					attr_scores[0]
											if isinstance(__A			,				torch.Tensor	):
															__lowerCamelCase		:      List[str]											=					boxes.numpy()
											if isinstance(__A			,				__A	):
															__lowerCamelCase		:      Optional[Any]											=					np.array(__A	)
											assert isinstance(__A			,				np.ndarray	)
											__lowerCamelCase		:      Union[str, Any]											=					np.prod(boxes[:, 2:] - boxes[:, :2]			,				axis=1	)
											__lowerCamelCase		:      int											=					np.argsort(-areas	).tolist()
											__lowerCamelCase		:      List[Any]											=					boxes[sorted_idxs] if boxes is not None else None
											__lowerCamelCase		:      List[Any]											=					obj_ids[sorted_idxs] if obj_ids is not None else None
											__lowerCamelCase		:      str											=					obj_scores[sorted_idxs] if obj_scores is not None else None
											__lowerCamelCase		:      int											=					attr_ids[sorted_idxs] if attr_ids is not None else None
											__lowerCamelCase		:      Dict											=					attr_scores[sorted_idxs] if attr_scores is not None else None

											__lowerCamelCase		:      Any											=					[self._random_color(maximum=1	) for _ in range(len(__A	)	)]
											__lowerCamelCase		:      Tuple											=					[assigned_colors[idx] for idx in sorted_idxs]
											if obj_ids is not None:
															__lowerCamelCase		:      Union[str, Any]											=					self._create_text_labels_attr(__A			,				__A			,				__A			,				__A	)
															for i in range(len(__A	)	):
																			__lowerCamelCase		:      Optional[int]											=					assigned_colors[i]
																			self.add_box(boxes[i]			,				__A	)
																			self.draw_labels(labels[i]			,				boxes[i]			,				__A	)







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



											"""simple docstring"""







											__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Union[str, Any]											=					box
											__lowerCamelCase		:      Dict											=					(xa, ya)
											__lowerCamelCase		:      int											=					(ya - ya) * (xa - xa)
											__lowerCamelCase		:      Optional[int]											=					_SMALL_OBJ * self.scale
											if instance_area < small or ya - ya < 40 * self.scale:
															if ya >= self.height - 5:
																			__lowerCamelCase		:      Union[str, Any]											=					(xa, ya)
															else:
																			__lowerCamelCase		:      Tuple											=					(xa, ya)

											__lowerCamelCase		:      Union[str, Any]											=					(ya - ya) / np.sqrt(self.height * self.width	)
											__lowerCamelCase		:      Optional[Any]											=					self._change_color_brightness(__A			,				brightness_factor=0.7	)
											__lowerCamelCase		:      Optional[int]											=					np.clip((height_ratio - 0.02) / 0.08 + 1			,				1.2			,				2	)
											font_size *= 0.75 * self.font_size

											self.draw_text(
											    text=__A			,				position=__A			,				color=__A			,				)







							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A			,				__A="g"			,				__A="left"			,				)     ->Tuple:



											"""simple docstring"""







											__lowerCamelCase		:      Union[str, Any]											=					0
											__lowerCamelCase		:      int											=					self.font_size
											__lowerCamelCase		:      Union[str, Any]											=					np.maximum(list(mplc.to_rgb(__A	)	)			,				0.2	)
											__lowerCamelCase		:      Any											=					max(0.8			,				np.max(__A	)	)
											__lowerCamelCase		:      Optional[Any]											=					{
											    "facecolor": "black",
											    "alpha": self.alpha,
											    "pad": self.pad,
											    "edgecolor": "none",
											}
											__lowerCamelCase						,					__lowerCamelCase		:      Optional[int]											=					position
											self.ax.text(
											    __A			,				__A			,				__A			,				size=font_size * self.scale			,				family="sans-serif"			,				bbox=__A			,				verticalalignment="top"			,				horizontalalignment=__A			,				color=__A			,				zorder=10			,				rotation=__A			,				)







							def 		__SCREAMING_SNAKE_CASE(   self			,				__A=None	)     ->List[str]:



											"""simple docstring"""







											if saveas is None:
															__lowerCamelCase		:      int											=					self.saveas
											if saveas.lower().endswith(".jpg"	) or saveas.lower().endswith(".png"	):
															cva.imwrite(
															    __A			,				self._get_buffer()[:, :, ::-1]			,				)
											else:
															self.fig.savefig(__A	)







							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A			,				__A			,				__A	)     ->Any:



											"""simple docstring"""







											__lowerCamelCase		:      str											=					[self.idaobj[i] for i in classes]
											__lowerCamelCase		:      Tuple											=					[self.idaattr[i] for i in attr_classes]
											__lowerCamelCase		:      List[str]											=					[
											    f'{label} {score:.2f} {attr} {attr_score:.2f}'
											    for label, score, attr, attr_score in zip(__A			,				__A			,				__A			,				__A	)
											]
											return labels







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



											"""simple docstring"""







											__lowerCamelCase		:      Optional[int]											=					[self.idaobj[i] for i in classes]
											if scores is not None:
															if labels is None:
																			__lowerCamelCase		:      str											=					["{:.0f}%".format(s * 100	) for s in scores]
															else:
																			__lowerCamelCase		:      List[Any]											=					["{} {:.0f}%".format(__A			,				s * 100	) for li, s in zip(__A			,				__A	)]
											return labels







							def 		__SCREAMING_SNAKE_CASE(   self			,				__A=255	)     ->int:



											"""simple docstring"""







											__lowerCamelCase		:      Optional[Any]											=					np.random.randint(0			,				len(_COLORS	)	)
											__lowerCamelCase		:      int											=					_COLORS[idx] * maximum
											if not self.rgb:
															__lowerCamelCase		:      Tuple											=					ret[::-1]
											return ret







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:



											"""simple docstring"""







											if not self.pynb:
															__lowerCamelCase						,					__lowerCamelCase		:      int											=					self.canvas.print_to_buffer()
															if (width, height) != (self.width, self.height):
																			__lowerCamelCase		:      Optional[Any]											=					cva.resize(self.img			,				(width, height)	)
															else:
																			__lowerCamelCase		:      int											=					self.img
											else:
															__lowerCamelCase		:      Optional[int]											=					io.BytesIO()  # works for cairo backend
															self.canvas.print_rgba(__A	)
															__lowerCamelCase						,					__lowerCamelCase		:      str											=					self.width, self.height
															__lowerCamelCase		:      Optional[int]											=					buf.getvalue()
															__lowerCamelCase		:      Tuple											=					self.img

											__lowerCamelCase		:      Dict											=					np.frombuffer(__A			,				dtype="uint8"	)
											__lowerCamelCase		:      Any											=					buffer.reshape(__A			,				__A			,				4	)
											__lowerCamelCase						,					__lowerCamelCase		:      Optional[Any]											=					np.split(__A			,				[3]			,				axis=2	)

											try:
															import numexpr as ne  # fuse them with numexpr

															__lowerCamelCase		:      Any											=					ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"	)
											except ImportError:
															__lowerCamelCase		:      List[Any]											=					alpha.astype("float32"	) / 255.0
															__lowerCamelCase		:      Tuple											=					img * (1 - alpha) + rgb * alpha

											return visualized_image.astype("uint8"	)







							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A	)     ->List[Any]:



											"""simple docstring"""







											assert brightness_factor >= -1.0 and brightness_factor <= 1.0
											__lowerCamelCase		:      Optional[int]											=					mplc.to_rgb(__A	)
											__lowerCamelCase		:      Tuple											=					colorsys.rgb_to_hls(*mplc.to_rgb(__A	)	)
											__lowerCamelCase		:      Dict											=					polygon_color[1] + (brightness_factor * polygon_color[1])
											__lowerCamelCase		:      Optional[int]											=					0.0 if modified_lightness < 0.0 else modified_lightness
											__lowerCamelCase		:      str											=					1.0 if modified_lightness > 1.0 else modified_lightness
											__lowerCamelCase		:      str											=					colorsys.hls_to_rgb(polygon_color[0]			,				__A			,				polygon_color[2]	)
											return modified_color

__lowercase  :      List[Any]      		=				(
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
            0.0_98,
            0.9_29,
            0.6_94,
            0.1_25,
            0.4_94,
            0.1_84,
            0.5_56,
            0.4_66,
            0.6_74,
            0.1_88,
            0.3_01,
            0.7_45,
            0.9_33,
            0.6_35,
            0.0_78,
            0.1_84,
            0.3_00,
            0.3_00,
            0.3_00,
            0.6_00,
            0.6_00,
            0.6_00,
            1.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.7_49,
            0.7_49,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            0.0_00,
            0.3_33,
            0.6_67,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            0.3_33,
            0.0_00,
            0.6_67,
            0.6_67,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.3_33,
            0.5_00,
            0.0_00,
            0.6_67,
            0.5_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.3_33,
            0.0_00,
            0.5_00,
            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
            0.6_67,
            0.5_00,
            0.3_33,
            1.0_00,
            0.5_00,
            0.6_67,
            0.0_00,
            0.5_00,
            0.6_67,
            0.3_33,
            0.5_00,
            0.6_67,
            0.6_67,
            0.5_00,
            0.6_67,
            1.0_00,
            0.5_00,
            1.0_00,
            0.0_00,
            0.5_00,
            1.0_00,
            0.3_33,
            0.5_00,
            1.0_00,
            0.6_67,
            0.5_00,
            1.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            1.0_00,
            0.3_33,
            0.6_67,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)









'''simple docstring'''




import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
				import torch


__lowercase  :      Any      		=				random.Random()





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






				'''simple docstring'''

				if rng is None:
								__lowerCamelCase		:      Any											=					global_rng

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

				return values





@require_torch
class _snake_case   ( unittest.TestCase   ):
							"""simple docstring"""





							def __init__(   self			,				__A			,				__A=7			,				__A=400			,				__A=2000			,				__A=1			,				__A=0.0			,				__A=24000			,				__A=True			,				)     ->Optional[int]:



											"""simple docstring"""







											__lowerCamelCase		:      Dict											=					parent
											__lowerCamelCase		:      Tuple											=					batch_size
											__lowerCamelCase		:      str											=					min_seq_length
											__lowerCamelCase		:      Optional[Any]											=					max_seq_length
											__lowerCamelCase		:      int											=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											__lowerCamelCase		:      List[Any]											=					feature_size
											__lowerCamelCase		:      int											=					padding_value
											__lowerCamelCase		:      Optional[Any]											=					sampling_rate
											__lowerCamelCase		:      List[str]											=					return_attention_mask







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:



											"""simple docstring"""







											return {
											    "feature_size": self.feature_size,
											    "padding_value": self.padding_value,
											    "sampling_rate": self.sampling_rate,
											    "return_attention_mask": self.return_attention_mask,
											}







							def 		__SCREAMING_SNAKE_CASE(   self			,				__A=False			,				__A=False	)     ->int:



											"""simple docstring"""







											def _flatten(__A	):
															return list(itertools.chain(*__A	)	)

											if equal_length:
															__lowerCamelCase		:      Optional[int]											=					floats_list((self.batch_size, self.max_seq_length)	)
											else:
															# make sure that inputs increase in size
															__lowerCamelCase		:      Optional[Any]											=					[
															    _flatten(floats_list((x, self.feature_size)	)	)
															    for x in range(self.min_seq_length			,				self.max_seq_length			,				self.seq_length_diff	)
															]

											if numpify:
															__lowerCamelCase		:      int											=					[np.asarray(__A	) for x in audio_inputs]

											return audio_inputs





@require_torch
class _snake_case   ( a__    ,   unittest.TestCase   ):
							"""simple docstring"""





							__a												=     EncodecFeatureExtractor







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:



											"""simple docstring"""







											__lowerCamelCase		:      str											=					EnCodecFeatureExtractionTester(self	)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:



											"""simple docstring"""







											__lowerCamelCase		:      Optional[int]											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											# create three inputs of length 800, 1000, and 1200
											__lowerCamelCase		:      Optional[Any]											=					[floats_list((1, x)	)[0] for x in range(800			,				1400			,				200	)]
											__lowerCamelCase		:      Optional[Any]											=					[np.asarray(__A	) for audio_input in audio_inputs]

											# Test not batched input
											__lowerCamelCase		:      List[str]											=					feat_extract(audio_inputs[0]			,				return_tensors="np"	).input_values
											__lowerCamelCase		:      Union[str, Any]											=					feat_extract(np_audio_inputs[0]			,				return_tensors="np"	).input_values
											self.assertTrue(np.allclose(__A			,				__A			,				atol=1E-3	)	)

											# Test batched
											__lowerCamelCase		:      Optional[int]											=					feat_extract(__A			,				padding=__A			,				return_tensors="np"	).input_values
											__lowerCamelCase		:      List[str]											=					feat_extract(__A			,				padding=__A			,				return_tensors="np"	).input_values
											for enc_seq_a, enc_seq_a in zip(__A			,				__A	):
															self.assertTrue(np.allclose(__A			,				__A			,				atol=1E-3	)	)







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



											"""simple docstring"""







											__lowerCamelCase		:      Optional[Any]											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											__lowerCamelCase		:      Tuple											=					np.random.rand(100	).astype(np.floataa	)
											__lowerCamelCase		:      Any											=					np_audio_inputs.tolist()

											for inputs in [py_audio_inputs, np_audio_inputs]:
															__lowerCamelCase		:      int											=					feature_extractor.pad([{"input_values": inputs}]			,				return_tensors="np"	)
															self.assertTrue(np_processed.input_values.dtype == np.floataa	)
															__lowerCamelCase		:      List[Any]											=					feature_extractor.pad([{"input_values": inputs}]			,				return_tensors="pt"	)
															self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)







							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->int:



											"""simple docstring"""







											from datasets import load_dataset

											__lowerCamelCase		:      List[Any]											=					load_dataset("hf-internal-testing/librispeech_asr_dummy"			,				"clean"			,				split="validation"	)
											# automatic decoding with librispeech
											__lowerCamelCase		:      str											=					ds.sort("id"	).select(range(__A	)	)[:num_samples]["audio"]

											return [x["array"] for x in audio_samples]







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:



											"""simple docstring"""







											__lowerCamelCase		:      Dict											=					torch.tensor(
											    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
											     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
											     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
											     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
											     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
											     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]	)
											# fmt: on
											__lowerCamelCase		:      Union[str, Any]											=					self._load_datasamples(1	)
											__lowerCamelCase		:      str											=					EncodecFeatureExtractor()
											__lowerCamelCase		:      Optional[int]											=					feature_extractor(__A			,				return_tensors="pt"	).input_values
											self.assertEquals(input_values.shape			,				(1, 1, 93680)	)
											self.assertTrue(torch.allclose(input_values[0, 0, :30]			,				__A			,				atol=1E-6	)	)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:



											"""simple docstring"""







											__lowerCamelCase		:      Optional[Any]											=					torch.tensor(
											    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
											     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
											     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
											     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
											     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
											     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]	)
											# fmt: on
											__lowerCamelCase		:      Union[str, Any]											=					self._load_datasamples(1	)
											__lowerCamelCase		:      str											=					[np.tile(input_audio[0][None]			,				reps=(2, 1)	)]
											input_audio[0][1] *= 0.5
											__lowerCamelCase		:      Optional[int]											=					EncodecFeatureExtractor(feature_size=2	)
											__lowerCamelCase		:      str											=					feature_extractor(__A			,				return_tensors="pt"	).input_values
											self.assertEquals(input_values.shape			,				(1, 2, 93680)	)
											self.assertTrue(torch.allclose(input_values[0, 0, :30]			,				__A			,				atol=1E-6	)	)
											self.assertTrue(torch.allclose(input_values[0, 1, :30]			,				EXPECTED_INPUT_VALUES * 0.5			,				atol=1E-6	)	)







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:



											"""simple docstring"""







											__lowerCamelCase		:      str											=					self._load_datasamples(2	)
											# would be easier if the stride was like
											__lowerCamelCase		:      List[Any]											=					EncodecFeatureExtractor(feature_size=1			,				chunk_length_s=1			,				overlap=0.01	)

											# pad and trunc raise an error ?
											with self.assertRaisesRegex(
											    __A			,				"^Both padding and truncation were set. Make sure you only set one.$"			,				):
															__lowerCamelCase		:      Dict											=					feature_extractor(
															    __A			,				padding="max_length"			,				truncation=__A			,				return_tensors="pt"	).input_values

											# truncate to chunk
											__lowerCamelCase		:      Optional[Any]											=					feature_extractor(__A			,				truncation=__A			,				return_tensors="pt"	).input_values
											self.assertEquals(truncated_outputs.shape			,				(2, 1, 71520)	)  # 2 chunks

											# force truncate to max_length
											__lowerCamelCase		:      List[str]											=					feature_extractor(
											    __A			,				truncation=__A			,				max_length=48000			,				return_tensors="pt"	).input_values
											self.assertEquals(truncated_outputs.shape			,				(2, 1, 48000)	)

											# pad to chunk
											__lowerCamelCase		:      List[str]											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values
											self.assertEquals(padded_outputs.shape			,				(2, 1, 95280)	)

											# pad to chunk
											__lowerCamelCase		:      Optional[Any]											=					feature_extractor(__A			,				return_tensors="pt"	).input_values
											self.assertEquals(truncated_outputs.shape			,				(2, 1, 95280)	)

											# force pad to max length
											__lowerCamelCase		:      int											=					feature_extractor(
											    __A			,				padding="max_length"			,				max_length=100000			,				return_tensors="pt"	).input_values
											self.assertEquals(truncated_outputs.shape			,				(2, 1, 100000)	)

											# force no pad
											with self.assertRaisesRegex(
											    __A			,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"			,				):
															__lowerCamelCase		:      Optional[Any]											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values

											__lowerCamelCase		:      int											=					feature_extractor(input_audio[0]			,				padding=__A			,				return_tensors="pt"	).input_values
											self.assertEquals(truncated_outputs.shape			,				(1, 1, 93680)	)

											# no pad if no chunk_length_s
											__lowerCamelCase		:      Any											=					None
											with self.assertRaisesRegex(
											    __A			,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"			,				):
															__lowerCamelCase		:      int											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values

											__lowerCamelCase		:      Any											=					feature_extractor(input_audio[0]			,				padding=__A			,				return_tensors="pt"	).input_values
											self.assertEquals(truncated_outputs.shape			,				(1, 1, 93680)	)

											# no pad if no overlap
											__lowerCamelCase		:      List[Any]											=					2
											__lowerCamelCase		:      Any											=					None
											with self.assertRaisesRegex(
											    __A			,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"			,				):
															__lowerCamelCase		:      Optional[int]											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values

											__lowerCamelCase		:      List[str]											=					feature_extractor(input_audio[0]			,				padding=__A			,				return_tensors="pt"	).input_values
											self.assertEquals(truncated_outputs.shape			,				(1, 1, 93680)	)





