import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
	from transformers import TvltImageProcessor

if is_speech_available():
	from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self						)				->				Optional[int]:
												UpperCamelCase_		:							List[str]  =		"""ZinengTang/tvlt-base"""
												UpperCamelCase_		:							List[Any]  =		tempfile.mkdtemp()





						def        A_							(self			,     **__UpperCamelCase						)				->				str:
												return TvltImageProcessor.from_pretrained(self.checkpoint			,     **__UpperCamelCase						)





						def        A_							(self			,     **__UpperCamelCase						)				->				List[str]:
												return TvltFeatureExtractor.from_pretrained(self.checkpoint			,     **__UpperCamelCase						)





						def        A_							(self						)				->				str:
												shutil.rmtree(self.tmpdirname						)





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							int  =		self.get_image_processor()
												UpperCamelCase_		:							str  =		self.get_feature_extractor()

												UpperCamelCase_		:							Dict  =		TvltProcessor(image_processor=__UpperCamelCase			,     feature_extractor=__UpperCamelCase						)
												processor.save_pretrained(self.tmpdirname						)
												UpperCamelCase_		:							List[Any]  =		TvltProcessor.from_pretrained(self.tmpdirname						)

												self.assertIsInstance(processor.feature_extractor			,     __UpperCamelCase						)
												self.assertIsInstance(processor.image_processor			,     __UpperCamelCase						)





						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							str  =		self.get_image_processor()
												UpperCamelCase_		:							int  =		self.get_feature_extractor()

												UpperCamelCase_		:							Optional[int]  =		TvltProcessor(image_processor=__UpperCamelCase			,     feature_extractor=__UpperCamelCase						)

												UpperCamelCase_		:							Optional[Any]  =		np.ones([12_000]						)

												UpperCamelCase_		:							Optional[Any]  =		feature_extractor(__UpperCamelCase			,     return_tensors="""np"""						)
												UpperCamelCase_		:							Union[str, Any]  =		processor(audio=__UpperCamelCase			,     return_tensors="""np"""						)

												for key in audio_dict.keys():
																		self.assertAlmostEqual(audio_dict[key].sum()			,     input_processor[key].sum()			,     delta=1E-2						)





						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_		:							List[Any]  =		self.get_image_processor()
												UpperCamelCase_		:							Any  =		self.get_feature_extractor()

												UpperCamelCase_		:							str  =		TvltProcessor(image_processor=__UpperCamelCase			,     feature_extractor=__UpperCamelCase						)

												UpperCamelCase_		:							Optional[Any]  =		np.ones([3, 224, 224]						)

												UpperCamelCase_		:							Union[str, Any]  =		image_processor(__UpperCamelCase			,     return_tensors="""np"""						)
												UpperCamelCase_		:							str  =		processor(images=__UpperCamelCase			,     return_tensors="""np"""						)

												for key in image_dict.keys():
																		self.assertAlmostEqual(image_dict[key].sum()			,     input_processor[key].sum()			,     delta=1E-2						)





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							int  =		self.get_image_processor()
												UpperCamelCase_		:							int  =		self.get_feature_extractor()

												UpperCamelCase_		:							str  =		TvltProcessor(image_processor=__UpperCamelCase			,     feature_extractor=__UpperCamelCase						)

												UpperCamelCase_		:							Optional[int]  =		np.ones([12_000]						)
												UpperCamelCase_		:							Union[str, Any]  =		np.ones([3, 224, 224]						)

												UpperCamelCase_		:							List[Any]  =		processor(audio=__UpperCamelCase			,     images=__UpperCamelCase						)

												self.assertListEqual(list(inputs.keys()						)			,     ["""audio_values""", """audio_mask""", """pixel_values""", """pixel_mask"""]						)

												# test if it raises when no input is passed
												with pytest.raises(__UpperCamelCase						):
																		processor()





						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_		:							str  =		self.get_image_processor()
												UpperCamelCase_		:							Union[str, Any]  =		self.get_feature_extractor()

												UpperCamelCase_		:							Optional[Any]  =		TvltProcessor(image_processor=__UpperCamelCase			,     feature_extractor=__UpperCamelCase						)

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







import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     Tuple         =	{
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}

class        UpperCamelCase						(				__a				):
						a__		:int      =			'''encodec'''





						def __init__(self			,     __UpperCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0]			,     __UpperCamelCase=24_000			,     __UpperCamelCase=1			,     __UpperCamelCase=False			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=128			,     __UpperCamelCase=32			,     __UpperCamelCase=1			,     __UpperCamelCase=[8, 5, 4, 2]			,     __UpperCamelCase="weight_norm"			,     __UpperCamelCase=7			,     __UpperCamelCase=7			,     __UpperCamelCase=3			,     __UpperCamelCase=2			,     __UpperCamelCase=True			,     __UpperCamelCase="reflect"			,     __UpperCamelCase=2			,     __UpperCamelCase=2			,     __UpperCamelCase=1.0			,     __UpperCamelCase=1_024			,     __UpperCamelCase=None			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				Dict:
												UpperCamelCase_		:							str  =		target_bandwidths
												UpperCamelCase_		:							Tuple  =		sampling_rate
												UpperCamelCase_		:							str  =		audio_channels
												UpperCamelCase_		:							Any  =		normalize
												UpperCamelCase_		:							Union[str, Any]  =		chunk_length_s
												UpperCamelCase_		:							Optional[Any]  =		overlap
												UpperCamelCase_		:							Any  =		hidden_size
												UpperCamelCase_		:							Dict  =		num_filters
												UpperCamelCase_		:							List[str]  =		num_residual_layers
												UpperCamelCase_		:							Optional[int]  =		upsampling_ratios
												UpperCamelCase_		:							str  =		norm_type
												UpperCamelCase_		:							str  =		kernel_size
												UpperCamelCase_		:							Tuple  =		last_kernel_size
												UpperCamelCase_		:							int  =		residual_kernel_size
												UpperCamelCase_		:							str  =		dilation_growth_rate
												UpperCamelCase_		:							str  =		use_causal_conv
												UpperCamelCase_		:							str  =		pad_mode
												UpperCamelCase_		:							Union[str, Any]  =		compress
												UpperCamelCase_		:							Dict  =		num_lstm_layers
												UpperCamelCase_		:							Optional[int]  =		trim_right_ratio
												UpperCamelCase_		:							int  =		codebook_size
												UpperCamelCase_		:							str  =		codebook_dim if codebook_dim is not None else hidden_size
												UpperCamelCase_		:							Optional[int]  =		use_conv_shortcut

												if self.norm_type not in ["weight_norm", "time_group_norm"]:
																		raise ValueError(
																		    f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''						)

												super().__init__(**__UpperCamelCase						)





						@property
						def        A_							(self						)				->				Optional[int]:
												if self.chunk_length_s is None:
																		return None
												else:
																		return int(self.chunk_length_s * self.sampling_rate						)





						@property
						def        A_							(self						)				->				Optional[int]:
												if self.chunk_length_s is None or self.overlap is None:
																		return None
												else:
																		return max(1			,     int((1.0 - self.overlap) * self.chunk_length						)						)





						@property
						def        A_							(self						)				->				int:
												UpperCamelCase_		:							int  =		np.prod(self.upsampling_ratios						)
												return math.ceil(self.sampling_rate / hop_length						)





						@property
						def        A_							(self						)				->				int:
												return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)						)






