'''simple docstring'''




from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)





class 					__magic_name__		(					_UpperCAmelCase):







   def __init__(			self       :						Tuple       ,    lowercase_       :						Distribution       ,    lowercase_       :						Tuple=None       ,    lowercase_       :						List[str]=None       ,    lowercase_       :						int=0					):
          lowercase_      :    Union[str, Any]       		= 1.0 if scale is None else scale
          lowercase_      :    List[Any]       		= 0.0 if loc is None else loc

          super().__init__(lowercase_       ,    [AffineTransform(loc=self.loc       ,    scale=self.scale       ,    event_dim=lowercase_					)]					)







   @property
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
          return self.base_dist.mean * self.scale + self.loc







   @property
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
          return self.base_dist.variance * self.scale**2







   @property
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
          return self.variance.sqrt()





class 					__magic_name__		(					nn.Module):







   def __init__(			self       :						Union[str, Any]       ,    lowercase_       :						int       ,    lowercase_       :						Dict[str, int]       ,    lowercase_       :						Callable[..., Tuple[torch.Tensor]]       ,    **lowercase_       :						List[Any]					):
          super().__init__(**lowercase_					)
          lowercase_      :    List[Any]       		= args_dim
          lowercase_      :    Tuple       		= nn.ModuleList([nn.Linear(lowercase_       ,    lowercase_					) for dim in args_dim.values()]					)
          lowercase_      :    int       		= domain_map







   def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						torch.Tensor					):
          lowercase_      :    Any       		= [proj(lowercase_					) for proj in self.proj]

          return self.domain_map(*lowercase_					)





class 					__magic_name__		(					nn.Module):







   def __init__(			self       :						Optional[Any]       ,    lowercase_       :						List[Any]					):
          super().__init__()
          lowercase_      :    Dict       		= function







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						Tuple       ,    *lowercase_       :						Optional[int]					):
          return self.function(lowercase_       ,    *lowercase_					)





class 					__magic_name__		:
   UpperCamelCase__       = 42
   UpperCamelCase__       = 42
   UpperCamelCase__       = 42







   def __init__(			self       :						Dict       ,    lowercase_       :						int = 1					):
          lowercase_      :    str       		= dim
          lowercase_      :    int       		= {k: dim * self.args_dim[k] for k in self.args_dim}







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						Optional[Any]					):
          if self.dim == 1:
                 return self.distribution_class(*lowercase_					)
          else:
                 return Independent(self.distribution_class(*lowercase_					)       ,    1					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						str       ,    lowercase_       :						Optional[torch.Tensor] = None       ,    lowercase_       :						Optional[torch.Tensor] = None       ,    ):
          lowercase_      :    str       		= self._base_distribution(lowercase_					)
          if loc is None and scale is None:
                 return distr
          else:
                 return AffineTransformed(lowercase_       ,    loc=lowercase_       ,    scale=lowercase_       ,    event_dim=self.event_dim					)







   @property
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
          return () if self.dim == 1 else (self.dim,)







   @property
   def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
          return len(self.event_shape					)







   @property
   def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
          return 0.0







   def 			SCREAMING_SNAKE_CASE_  (			self       :						str       ,    lowercase_       :						int					):
          return ParameterProjection(
              in_features=lowercase_       ,    args_dim=self.args_dim       ,    domain_map=LambdaLayer(self.domain_map					)       ,    )







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    *lowercase_       :						torch.Tensor					):
          raise NotImplementedError()







   @staticmethod
   def 			SCREAMING_SNAKE_CASE_  (			lowercase_       :						torch.Tensor					):
          return (x + torch.sqrt(torch.square(lowercase_					) + 4.0					)) / 2.0





class 					__magic_name__		(					_UpperCAmelCase):
   UpperCamelCase__       = {'''df''': 1, '''loc''': 1, '''scale''': 1}
   UpperCamelCase__       = StudentT







   @classmethod
   def 			SCREAMING_SNAKE_CASE_  (			cls       :						List[Any]       ,    lowercase_       :						torch.Tensor       ,    lowercase_       :						torch.Tensor       ,    lowercase_       :						torch.Tensor					):
          lowercase_      :    List[str]       		= cls.squareplus(lowercase_					).clamp_min(torch.finfo(scale.dtype					).eps					)
          lowercase_      :    Optional[Any]       		= 2.0 + cls.squareplus(lowercase_					)
          return df.squeeze(-1					), loc.squeeze(-1					), scale.squeeze(-1					)





class 					__magic_name__		(					_UpperCAmelCase):
   UpperCamelCase__       = {'''loc''': 1, '''scale''': 1}
   UpperCamelCase__       = Normal







   @classmethod
   def 			SCREAMING_SNAKE_CASE_  (			cls       :						int       ,    lowercase_       :						torch.Tensor       ,    lowercase_       :						torch.Tensor					):
          lowercase_      :    Any       		= cls.squareplus(lowercase_					).clamp_min(torch.finfo(scale.dtype					).eps					)
          return loc.squeeze(-1					), scale.squeeze(-1					)





class 					__magic_name__		(					_UpperCAmelCase):
   UpperCamelCase__       = {'''total_count''': 1, '''logits''': 1}
   UpperCamelCase__       = NegativeBinomial







   @classmethod
   def 			SCREAMING_SNAKE_CASE_  (			cls       :						Optional[int]       ,    lowercase_       :						torch.Tensor       ,    lowercase_       :						torch.Tensor					):
          lowercase_      :    int       		= cls.squareplus(lowercase_					)
          return total_count.squeeze(-1					), logits.squeeze(-1					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						Tuple					):
          lowercase_      :    Any       		= distr_args
          if self.dim == 1:
                 return self.distribution_class(total_count=lowercase_       ,    logits=lowercase_					)
          else:
                 return Independent(self.distribution_class(total_count=lowercase_       ,    logits=lowercase_					)       ,    1					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						str       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Optional[torch.Tensor] = None       ,    lowercase_       :						Optional[torch.Tensor] = None					):
          lowercase_      :    Union[str, Any]       		= distr_args

          if scale is not None:
                 # See scaling property of Gamma.
                 logits += scale.log()

          return self._base_distribution((total_count, logits)					)






'''simple docstring'''




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class 					__magic_name__		(					unittest.TestCase):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    List[Any]       		= """ylacombe/bark-small"""
										lowercase_      :    List[str]       		= tempfile.mkdtemp()
										lowercase_      :    Tuple       		= """en_speaker_1"""
										lowercase_      :    Union[str, Any]       		= """This is a test string"""
										lowercase_      :    int       		= """speaker_embeddings_path.json"""
										lowercase_      :    Any       		= """speaker_embeddings"""







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    **lowercase_       :						Optional[int]					):
										return AutoTokenizer.from_pretrained(self.checkpoint       ,    **lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										shutil.rmtree(self.tmpdirname					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
										lowercase_      :    Any       		= self.get_tokenizer()

										lowercase_      :    Optional[Any]       		= BarkProcessor(tokenizer=lowercase_					)

										processor.save_pretrained(self.tmpdirname					)
										lowercase_      :    Union[str, Any]       		= BarkProcessor.from_pretrained(self.tmpdirname					)

										self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer.get_vocab()					)







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										lowercase_      :    Any       		= BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint       ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,    )
										processor.save_pretrained(
										    self.tmpdirname       ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,    speaker_embeddings_directory=self.speaker_embeddings_directory       ,    )

										lowercase_      :    List[str]       		= self.get_tokenizer(bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""					)

										lowercase_      :    Optional[Any]       		= BarkProcessor.from_pretrained(
										    self.tmpdirname       ,    self.speaker_embeddings_dict_path       ,    bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""       ,    )

										self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer_add_kwargs.get_vocab()					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    Optional[int]       		= BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint       ,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,    )

										lowercase_      :    Optional[int]       		= 35
										lowercase_      :    int       		= 2
										lowercase_      :    Union[str, Any]       		= 8

										lowercase_      :    Union[str, Any]       		= {
										    """semantic_prompt""": np.ones(lowercase_					),
										    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)					),
										    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)					),
										}

										# test providing already loaded voice_preset
										lowercase_      :    str       		= processor(text=self.input_string       ,    voice_preset=lowercase_					)

										lowercase_      :    Dict       		= inputs["""history_prompt"""]
										for key in voice_preset:
																	self.assertListEqual(voice_preset[key].tolist()       ,    processed_voice_preset.get(lowercase_       ,    np.array([]					)					).tolist()					)

										# test loading voice preset from npz file
										lowercase_      :    Any       		= os.path.join(self.tmpdirname       ,    """file.npz"""					)
										np.savez(lowercase_       ,    **lowercase_					)
										lowercase_      :    Optional[Any]       		= processor(text=self.input_string       ,    voice_preset=lowercase_					)
										lowercase_      :    List[Any]       		= inputs["""history_prompt"""]

										for key in voice_preset:
																	self.assertListEqual(voice_preset[key].tolist()       ,    processed_voice_preset.get(lowercase_       ,    np.array([]					)					).tolist()					)

										# test loading voice preset from the hub
										lowercase_      :    Union[str, Any]       		= processor(text=self.input_string       ,    voice_preset=self.voice_preset					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
										lowercase_      :    List[str]       		= self.get_tokenizer()

										lowercase_      :    int       		= BarkProcessor(tokenizer=lowercase_					)

										lowercase_      :    Any       		= processor(text=self.input_string					)

										lowercase_      :    List[str]       		= tokenizer(
										    self.input_string       ,    padding="""max_length"""       ,    max_length=256       ,    add_special_tokens=lowercase_       ,    return_attention_mask=lowercase_       ,    return_token_type_ids=lowercase_       ,    )

										for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key].squeeze().tolist()					)





