'''simple docstring'''




import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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 	lowerCamelCase_	(snake_case__							, snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Union[str, Any]						=						IFInpaintingSuperResolutionPipeline
      __UpperCamelCase:  Any						=						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
      __UpperCamelCase:  Optional[Any]						=						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}    )
      __UpperCamelCase:  Optional[Any]						=						PipelineTesterMixin.required_optional_params - {"latents"}




      def      _A	(       self						:				Optional[Any]       ):
             return self._get_superresolution_dummy_components()




      def      _A	(       self						:				List[str]						,					A						:				Any						,					A						:				str=0       ):
             if str(A       ).startswith("mps"       ):
                    _UpperCAmelCase     :  List[Any]							      =    torch.manual_seed(A       )
             else:
                    _UpperCAmelCase     :  int							      =    torch.Generator(device=A       ).manual_seed(A       )

             _UpperCAmelCase     :  Any							      =    floats_tensor((1, 3, 16, 16)						,					rng=random.Random(A       )       ).to(A       )
             _UpperCAmelCase     :  List[Any]							      =    floats_tensor((1, 3, 32, 32)						,					rng=random.Random(A       )       ).to(A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    floats_tensor((1, 3, 32, 32)						,					rng=random.Random(A       )       ).to(A       )

             _UpperCAmelCase     :  int							      =    {
                 "prompt": "A painting of a squirrel eating a burger",
                 "image": image,
                 "original_image": original_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						:				List[str]       ):
             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )




      def      _A	(       self						:				Optional[Any]       ):
             self._test_save_load_optional_components()




      @unittest.skipIf(torch_device != "cuda"						,					reason="float16 requires CUDA"       )
      def      _A	(       self						:				List[str]       ):
             # 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						:				Optional[Any]       ):
             self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )




      def      _A	(       self						:				int       ):
             self._test_save_load_local()





      def      _A	(       self						:				Union[str, Any]       ):
             self._test_inference_batch_single_identical(
                 expected_max_diff=1E-2						,					)


'''simple docstring'''




import math
from typing import Optional

import numpy as np

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


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     List[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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  str						=						"encodec"




      def __init__(       self						:				Optional[int]						,					A						:				Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]						,					A						:				List[Any]=24000						,					A						:				Union[str, Any]=1						,					A						:				List[Any]=False						,					A						:				Optional[int]=None						,					A						:				int=None						,					A						:				str=128						,					A						:				List[Any]=32						,					A						:				List[Any]=1						,					A						:				int=[8, 5, 4, 2]						,					A						:				Optional[int]="weight_norm"						,					A						:				List[Any]=7						,					A						:				Any=7						,					A						:				Dict=3						,					A						:				Optional[int]=2						,					A						:				Dict=True						,					A						:				Dict="reflect"						,					A						:				Any=2						,					A						:				Dict=2						,					A						:				str=1.0						,					A						:				Optional[int]=1024						,					A						:				Any=None						,					A						:				Any=True						,					**A						:				str						,					):
             _UpperCAmelCase     :  Optional[int]							      =    target_bandwidths
             _UpperCAmelCase     :  List[str]							      =    sampling_rate
             _UpperCAmelCase     :  Optional[int]							      =    audio_channels
             _UpperCAmelCase     :  str							      =    normalize
             _UpperCAmelCase     :  int							      =    chunk_length_s
             _UpperCAmelCase     :  str							      =    overlap
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_size
             _UpperCAmelCase     :  int							      =    num_filters
             _UpperCAmelCase     :  Optional[Any]							      =    num_residual_layers
             _UpperCAmelCase     :  Optional[int]							      =    upsampling_ratios
             _UpperCAmelCase     :  int							      =    norm_type
             _UpperCAmelCase     :  List[Any]							      =    kernel_size
             _UpperCAmelCase     :  List[Any]							      =    last_kernel_size
             _UpperCAmelCase     :  List[Any]							      =    residual_kernel_size
             _UpperCAmelCase     :  List[str]							      =    dilation_growth_rate
             _UpperCAmelCase     :  Dict							      =    use_causal_conv
             _UpperCAmelCase     :  Tuple							      =    pad_mode
             _UpperCAmelCase     :  Tuple							      =    compress
             _UpperCAmelCase     :  List[str]							      =    num_lstm_layers
             _UpperCAmelCase     :  List[Any]							      =    trim_right_ratio
             _UpperCAmelCase     :  int							      =    codebook_size
             _UpperCAmelCase     :  Optional[Any]							      =    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__(**A       )




      @property
      def      _A	(       self						:				Any       ):
             if self.chunk_length_s is None:
                    return None
             else:
                    return int(self.chunk_length_s * self.sampling_rate       )




      @property
      def      _A	(       self						:				Union[str, Any]       ):
             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						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Dict							      =    np.prod(self.upsampling_ratios       )
             return math.ceil(self.sampling_rate / hop_length       )





      @property
      def      _A	(       self						:				str       ):
             return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)       )

