
import math
from typing import Optional

import numpy as np

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


A     :	Any    = logging.get_logger(__name__)

A     :	Union[str, Any]    = {
    "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 			__A( SCREAMING_SNAKE_CASE__			):
				snake_case_						   =    '''encodec'''




				def __init__(     self		,     _snake_case=[1.5, 3.0, 6.0, 12.0, 24.0]		,     _snake_case=24_000		,     _snake_case=1		,     _snake_case=False		,     _snake_case=None		,     _snake_case=None		,     _snake_case=128		,     _snake_case=32		,     _snake_case=1		,     _snake_case=[8, 5, 4, 2]		,     _snake_case="weight_norm"		,     _snake_case=7		,     _snake_case=7		,     _snake_case=3		,     _snake_case=2		,     _snake_case=True		,     _snake_case="reflect"		,     _snake_case=2		,     _snake_case=2		,     _snake_case=1.0		,     _snake_case=1_024		,     _snake_case=None		,     _snake_case=True		,     **_snake_case		,     )    ->      int:


								'''simple docstring'''




								__a										=    target_bandwidths
								__a										=    sampling_rate
								__a										=    audio_channels
								__a										=    normalize
								__a										=    chunk_length_s
								__a										=    overlap
								__a										=    hidden_size
								__a										=    num_filters
								__a										=    num_residual_layers
								__a										=    upsampling_ratios
								__a										=    norm_type
								__a										=    kernel_size
								__a										=    last_kernel_size
								__a										=    residual_kernel_size
								__a										=    dilation_growth_rate
								__a										=    use_causal_conv
								__a										=    pad_mode
								__a										=    compress
								__a										=    num_lstm_layers
								__a										=    trim_right_ratio
								__a										=    codebook_size
								__a										=    codebook_dim if codebook_dim is not None else hidden_size
								__a										=    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__(**A__				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								if self.chunk_length_s is None:
												return None
								else:
												return int(self.chunk_length_s * self.sampling_rate				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								__a										=    np.prod(self.upsampling_ratios				)
								return math.ceil(self.sampling_rate / hop_length				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)				)

import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       List[Any]       					=IFInpaintingPipeline
							lowercase_       :       Optional[int]       					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
							lowercase_       :       Any       					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowercase_       :       str       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




							def  A__      (				self):
												return self._get_dummy_components()




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)

												lowercase							=						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''image''': image,
												    '''mask_image''': mask_image,
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs




							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
							def  A__      (				self):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)




							def  A__      (				self):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA''')
							def  A__      (				self):
												# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
												super().test_save_load_floataa(expected_max_diff=1E-1)




							def  A__      (				self):
												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)




							def  A__      (				self):
												self._test_save_load_local()





							def  A__      (				self):
												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2   ,)




