


"""simple docstring"""



import math
from typing import Optional

import numpy as np

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


UpperCAmelCase      		=			logging.get_logger(__name__)

UpperCAmelCase      		=			{
    '''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 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      Dict        =   "encodec"






       def __init__(   self  :    List[Any]  ,     snake_case__  :    str=[1.5, 3.0, 6.0, 12.0, 24.0]  ,     snake_case__  :    Any=2_4_0_0_0  ,     snake_case__  :    Optional[int]=1  ,     snake_case__  :    Optional[int]=False  ,     snake_case__  :    str=None  ,     snake_case__  :    Tuple=None  ,     snake_case__  :    List[str]=1_2_8  ,     snake_case__  :    str=3_2  ,     snake_case__  :    List[Any]=1  ,     snake_case__  :    Tuple=[8, 5, 4, 2]  ,     snake_case__  :    str="weight_norm"  ,     snake_case__  :    Tuple=7  ,     snake_case__  :    int=7  ,     snake_case__  :    Dict=3  ,     snake_case__  :    Tuple=2  ,     snake_case__  :    int=True  ,     snake_case__  :    Union[str, Any]="reflect"  ,     snake_case__  :    Tuple=2  ,     snake_case__  :    Optional[int]=2  ,     snake_case__  :    List[str]=1.0  ,     snake_case__  :    List[str]=1_0_2_4  ,     snake_case__  :    Any=None  ,     snake_case__  :    List[str]=True  ,     **snake_case__  :    Union[str, Any]  ,     ):

              '''simple docstring'''






              lowercase		:List[str]       							=				target_bandwidths
              lowercase		:Optional[int]       							=				sampling_rate
              lowercase		:Tuple       							=				audio_channels
              lowercase		:Any       							=				normalize
              lowercase		:List[Any]       							=				chunk_length_s
              lowercase		:Optional[Any]       							=				overlap
              lowercase		:List[Any]       							=				hidden_size
              lowercase		:Tuple       							=				num_filters
              lowercase		:int       							=				num_residual_layers
              lowercase		:Tuple       							=				upsampling_ratios
              lowercase		:Union[str, Any]       							=				norm_type
              lowercase		:str       							=				kernel_size
              lowercase		:Optional[int]       							=				last_kernel_size
              lowercase		:List[Any]       							=				residual_kernel_size
              lowercase		:Optional[Any]       							=				dilation_growth_rate
              lowercase		:List[Any]       							=				use_causal_conv
              lowercase		:str       							=				pad_mode
              lowercase		:List[Any]       							=				compress
              lowercase		:int       							=				num_lstm_layers
              lowercase		:Union[str, Any]       							=				trim_right_ratio
              lowercase		:List[str]       							=				codebook_size
              lowercase		:Any       							=				codebook_dim if codebook_dim is not None else hidden_size
              lowercase		:str       							=				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__(**snake_case__				)






       @property
       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






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






       @property
       def  __snake_case						(   self  :    List[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  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:int       							=				np.prod(self.upsampling_ratios				)
              return math.ceil(self.sampling_rate / hop_length				)







       @property
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)				)





"""simple docstring"""



import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      Tuple        =   PriorTransformer
       __A			:      Any        =   "hidden_states"






       @property
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				4
              lowercase		:List[Any]       							=				8
              lowercase		:Any       							=				7

              lowercase		:Optional[Any]       							=				floats_tensor((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:List[Any]       							=				floats_tensor((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Union[str, Any]       							=				floats_tensor((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Optional[Any]=0				):

              '''simple docstring'''






              torch.manual_seed(snake_case__				)
              lowercase		:Union[str, Any]       							=				4
              lowercase		:Optional[int]       							=				8
              lowercase		:str       							=				7

              lowercase		:Dict       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:Union[str, Any]       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Tuple       							=				torch.randn((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }






       @property
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              return (4, 8)






       @property
       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              return (4, 8)






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				{
                  '''num_attention_heads''': 2,
                  '''attention_head_dim''': 4,
                  '''num_layers''': 2,
                  '''embedding_dim''': 8,
                  '''num_embeddings''': 7,
                  '''additional_embeddings''': 4,
              }
              lowercase		:List[str]       							=				self.dummy_input
              return init_dict, inputs_dict






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase							,  lowercase		:Optional[int]       							=				PriorTransformer.from_pretrained(
                  '''hf-internal-testing/prior-dummy'''  ,     output_loading_info=snake_case__				)
              self.assertIsNotNone(snake_case__				)
              self.assertEqual(len(loading_info['''missing_keys''']				)  ,     0				)

              model.to(snake_case__				)
              lowercase		:List[Any]       							=				model(**self.dummy_input				)[0]

              assert hidden_states is not None, "Make sure output is not None"






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase							,  lowercase		:List[Any]       							=				self.prepare_init_args_and_inputs_for_common()

              lowercase		:str       							=				self.model_class(**snake_case__				)
              lowercase		:Optional[Any]       							=				inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              lowercase		:List[Any]       							=				[*signature.parameters.keys()]

              lowercase		:Optional[Any]       							=				['''hidden_states''', '''timestep''']
              self.assertListEqual(arg_names[:2]  ,     snake_case__				)







       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''				)
              lowercase		:List[Any]       							=				model.to(snake_case__				)

              if hasattr(snake_case__  ,     '''set_default_attn_processor'''				):
                     model.set_default_attn_processor()

              lowercase		:List[str]       							=				self.get_dummy_seed_input()

              with torch.no_grad():
                     lowercase		:List[Any]       							=				model(**snake_case__				)[0]

              lowercase		:Dict       							=				output[0, :5].flatten().cpu()
              print(snake_case__				)

              # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
              # the expected output slices are not the same for CPU and GPU.
              lowercase		:str       							=				torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]				)
              self.assertTrue(torch_all_close(snake_case__  ,     snake_case__  ,     rtol=1e-2				)				)







@slow
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    int=1  ,     snake_case__  :    int=7_6_8  ,     snake_case__  :    Optional[Any]=7_7  ,     snake_case__  :    Union[str, Any]=0				):

              '''simple docstring'''






              torch.manual_seed(snake_case__				)
              lowercase		:Any       							=				batch_size
              lowercase		:Dict       							=				embedding_dim
              lowercase		:int       							=				num_embeddings

              lowercase		:int       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:List[str]       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Optional[Any]       							=				torch.randn((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()







       @parameterized.expand(
           [
               # fmt: off
               [1_3, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
               [3_7, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
               # fmt: on
           ]				)
       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Dict  ,     snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:str       							=				PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''  ,     subfolder='''prior'''				)
              model.to(snake_case__				)
              lowercase		:Union[str, Any]       							=				self.get_dummy_seed_input(seed=snake_case__				)

              with torch.no_grad():
                     lowercase		:int       							=				model(**snake_case__				)[0]

              assert list(sample.shape				) == [1, 7_6_8]

              lowercase		:List[Any]       							=				sample[0, :8].flatten().cpu()
              print(snake_case__				)
              lowercase		:Union[str, Any]       							=				torch.tensor(snake_case__				)

              assert torch_all_close(snake_case__  ,     snake_case__  ,     atol=1e-3				)

