


import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD


class     lowercase__  (					nn.Module		):
  A__=  42
  A__=  42
  A__=  0.0
  A__=  1
  A__=  1
  A__=  True
  A__=  False
  A__=  False
  A__=  False
  A__=  jnp.floataa






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     []
         UpperCAmelCase__										=     []

         for i in range(self.num_layers ):
                UpperCAmelCase__										=     self.in_channels if i == 0 else self.out_channels

                UpperCAmelCase__										=     FlaxResnetBlockaD(
                    in_channels=_lowercase		,						out_channels=self.out_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
                resnets.append(_lowercase )

                UpperCAmelCase__										=     FlaxTransformeraDModel(
                    in_channels=self.out_channels		,						n_heads=self.num_attention_heads		,						d_head=self.out_channels // self.num_attention_heads		,						depth=1		,						use_linear_projection=self.use_linear_projection		,						only_cross_attention=self.only_cross_attention		,						use_memory_efficient_attention=self.use_memory_efficient_attention		,						dtype=self.dtype		,						)
                attentions.append(_lowercase )

         UpperCAmelCase__										=     resnets
         UpperCAmelCase__										=     attentions

         if self.add_downsample:
                UpperCAmelCase__										=     FlaxDownsampleaD(self.out_channels		,						dtype=self.dtype )






  def __call__(					self      :					Dict		,						_lowercase      :					int		,						_lowercase      :					str		,						_lowercase      :					int		,						_lowercase      :					Optional[int]=True ):




         """simple docstring"""







         UpperCAmelCase__										=     ()

         for resnet, attn in zip(self.resnets		,						self.attentions ):
                UpperCAmelCase__										=     resnet(_lowercase		,						_lowercase		,						deterministic=_lowercase )
                UpperCAmelCase__										=     attn(_lowercase		,						_lowercase		,						deterministic=_lowercase )
                output_states += (hidden_states,)

         if self.add_downsample:
                UpperCAmelCase__										=     self.downsamplers_a(_lowercase )
                output_states += (hidden_states,)

         return hidden_states, output_states




class     lowercase__  (					nn.Module		):
  A__=  42
  A__=  42
  A__=  0.0
  A__=  1
  A__=  True
  A__=  jnp.floataa






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     []

         for i in range(self.num_layers ):
                UpperCAmelCase__										=     self.in_channels if i == 0 else self.out_channels

                UpperCAmelCase__										=     FlaxResnetBlockaD(
                    in_channels=_lowercase		,						out_channels=self.out_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
                resnets.append(_lowercase )
         UpperCAmelCase__										=     resnets

         if self.add_downsample:
                UpperCAmelCase__										=     FlaxDownsampleaD(self.out_channels		,						dtype=self.dtype )






  def __call__(					self      :					List[Any]		,						_lowercase      :					str		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Dict=True ):




         """simple docstring"""







         UpperCAmelCase__										=     ()

         for resnet in self.resnets:
                UpperCAmelCase__										=     resnet(_lowercase		,						_lowercase		,						deterministic=_lowercase )
                output_states += (hidden_states,)

         if self.add_downsample:
                UpperCAmelCase__										=     self.downsamplers_a(_lowercase )
                output_states += (hidden_states,)

         return hidden_states, output_states




class     lowercase__  (					nn.Module		):
  A__=  42
  A__=  42
  A__=  42
  A__=  0.0
  A__=  1
  A__=  1
  A__=  True
  A__=  False
  A__=  False
  A__=  False
  A__=  jnp.floataa






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     []
         UpperCAmelCase__										=     []

         for i in range(self.num_layers ):
                UpperCAmelCase__										=     self.in_channels if (i == self.num_layers - 1) else self.out_channels
                UpperCAmelCase__										=     self.prev_output_channel if i == 0 else self.out_channels

                UpperCAmelCase__										=     FlaxResnetBlockaD(
                    in_channels=resnet_in_channels + res_skip_channels		,						out_channels=self.out_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
                resnets.append(_lowercase )

                UpperCAmelCase__										=     FlaxTransformeraDModel(
                    in_channels=self.out_channels		,						n_heads=self.num_attention_heads		,						d_head=self.out_channels // self.num_attention_heads		,						depth=1		,						use_linear_projection=self.use_linear_projection		,						only_cross_attention=self.only_cross_attention		,						use_memory_efficient_attention=self.use_memory_efficient_attention		,						dtype=self.dtype		,						)
                attentions.append(_lowercase )

         UpperCAmelCase__										=     resnets
         UpperCAmelCase__										=     attentions

         if self.add_upsample:
                UpperCAmelCase__										=     FlaxUpsampleaD(self.out_channels		,						dtype=self.dtype )






  def __call__(					self      :					Dict		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Tuple		,						_lowercase      :					Any		,						_lowercase      :					List[str]		,						_lowercase      :					List[Any]=True ):




         """simple docstring"""







         for resnet, attn in zip(self.resnets		,						self.attentions ):
                # pop res hidden states
                UpperCAmelCase__										=     res_hidden_states_tuple[-1]
                UpperCAmelCase__										=     res_hidden_states_tuple[:-1]
                UpperCAmelCase__										=     jnp.concatenate((hidden_states, res_hidden_states)		,						axis=-1 )

                UpperCAmelCase__										=     resnet(_lowercase		,						_lowercase		,						deterministic=_lowercase )
                UpperCAmelCase__										=     attn(_lowercase		,						_lowercase		,						deterministic=_lowercase )

         if self.add_upsample:
                UpperCAmelCase__										=     self.upsamplers_a(_lowercase )

         return hidden_states




class     lowercase__  (					nn.Module		):
  A__=  42
  A__=  42
  A__=  42
  A__=  0.0
  A__=  1
  A__=  True
  A__=  jnp.floataa






  def 			_UpperCAmelCase  (					self      :					Tuple ):




         """simple docstring"""







         UpperCAmelCase__										=     []

         for i in range(self.num_layers ):
                UpperCAmelCase__										=     self.in_channels if (i == self.num_layers - 1) else self.out_channels
                UpperCAmelCase__										=     self.prev_output_channel if i == 0 else self.out_channels

                UpperCAmelCase__										=     FlaxResnetBlockaD(
                    in_channels=resnet_in_channels + res_skip_channels		,						out_channels=self.out_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
                resnets.append(_lowercase )

         UpperCAmelCase__										=     resnets

         if self.add_upsample:
                UpperCAmelCase__										=     FlaxUpsampleaD(self.out_channels		,						dtype=self.dtype )






  def __call__(					self      :					Optional[Any]		,						_lowercase      :					int		,						_lowercase      :					Tuple		,						_lowercase      :					Tuple		,						_lowercase      :					Tuple=True ):




         """simple docstring"""







         for resnet in self.resnets:
                # pop res hidden states
                UpperCAmelCase__										=     res_hidden_states_tuple[-1]
                UpperCAmelCase__										=     res_hidden_states_tuple[:-1]
                UpperCAmelCase__										=     jnp.concatenate((hidden_states, res_hidden_states)		,						axis=-1 )

                UpperCAmelCase__										=     resnet(_lowercase		,						_lowercase		,						deterministic=_lowercase )

         if self.add_upsample:
                UpperCAmelCase__										=     self.upsamplers_a(_lowercase )

         return hidden_states




class     lowercase__  (					nn.Module		):
  A__=  42
  A__=  0.0
  A__=  1
  A__=  1
  A__=  False
  A__=  False
  A__=  jnp.floataa






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     [
             FlaxResnetBlockaD(
                 in_channels=self.in_channels		,						out_channels=self.in_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
         ]

         UpperCAmelCase__										=     []

         for _ in range(self.num_layers ):
                UpperCAmelCase__										=     FlaxTransformeraDModel(
                    in_channels=self.in_channels		,						n_heads=self.num_attention_heads		,						d_head=self.in_channels // self.num_attention_heads		,						depth=1		,						use_linear_projection=self.use_linear_projection		,						use_memory_efficient_attention=self.use_memory_efficient_attention		,						dtype=self.dtype		,						)
                attentions.append(_lowercase )

                UpperCAmelCase__										=     FlaxResnetBlockaD(
                    in_channels=self.in_channels		,						out_channels=self.in_channels		,						dropout_prob=self.dropout		,						dtype=self.dtype		,						)
                resnets.append(_lowercase )

         UpperCAmelCase__										=     resnets
         UpperCAmelCase__										=     attentions






  def __call__(					self      :					Optional[Any]		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Tuple		,						_lowercase      :					Optional[Any]		,						_lowercase      :					str=True ):




         """simple docstring"""







         UpperCAmelCase__										=     self.resnets[0](_lowercase		,						_lowercase )
         for attn, resnet in zip(self.attentions		,						self.resnets[1:] ):
                UpperCAmelCase__										=     attn(_lowercase		,						_lowercase		,						deterministic=_lowercase )
                UpperCAmelCase__										=     resnet(_lowercase		,						_lowercase		,						deterministic=_lowercase )

         return hidden_states









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     lowercase__  (					unittest.TestCase		):






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     "ylacombe/bark-small"
         UpperCAmelCase__										=     tempfile.mkdtemp()
         UpperCAmelCase__										=     "en_speaker_1"
         UpperCAmelCase__										=     "This is a test string"
         UpperCAmelCase__										=     "speaker_embeddings_path.json"
         UpperCAmelCase__										=     "speaker_embeddings"






  def 			_UpperCAmelCase  (					self      :					List[str]		,						**_lowercase      :					str ):




         """simple docstring"""







         return AutoTokenizer.from_pretrained(self.checkpoint		,						**_lowercase )






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         shutil.rmtree(self.tmpdirname )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer()

         UpperCAmelCase__										=     BarkProcessor(tokenizer=_lowercase )

         processor.save_pretrained(self.tmpdirname )
         UpperCAmelCase__										=     BarkProcessor.from_pretrained(self.tmpdirname )

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






  @slow
  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         UpperCAmelCase__										=     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		,						)

         UpperCAmelCase__										=     self.get_tokenizer(bos_token="(BOS)"		,						eos_token="(EOS)" )

         UpperCAmelCase__										=     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 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint		,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,						)

         UpperCAmelCase__										=     35
         UpperCAmelCase__										=     2
         UpperCAmelCase__										=     8

         UpperCAmelCase__										=     {
             "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
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=_lowercase )

         UpperCAmelCase__										=     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
         UpperCAmelCase__										=     os.path.join(self.tmpdirname		,						"file.npz" )
         np.savez(_lowercase		,						**_lowercase )
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=_lowercase )
         UpperCAmelCase__										=     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
         UpperCAmelCase__										=     processor(text=self.input_string		,						voice_preset=self.voice_preset )






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




         """simple docstring"""







         UpperCAmelCase__										=     self.get_tokenizer()

         UpperCAmelCase__										=     BarkProcessor(tokenizer=_lowercase )

         UpperCAmelCase__										=     processor(text=self.input_string )

         UpperCAmelCase__										=     tokenizer(
             self.input_string		,						padding="max_length"		,						max_length=2_56		,						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() )





