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

class      _SCREAMING_SNAKE_CASE				(		nn.Module				):
				lowerCamelCase_	  =  42
				lowerCamelCase_	  =  jnp.floataa





				def 							_UpperCAmelCase    (		self			:		Optional[Any]				):


								"""simple docstring"""

								A  :	Any				=					nn.Conv(
								    self.out_channels       ,   kernel_size=(3, 3)       ,   strides=(1, 1)       ,   padding=((1, 1), (1, 1))       ,   dtype=self.dtype       ,   )






				def __call__(		self			:		List[Any]       ,   snake_case_			:		Tuple				):


								"""simple docstring"""

								A			,							A			,							A			,							A  :	List[str]				=					hidden_states.shape
								A  :	Optional[int]				=					jax.image.resize(
								    __lowerCAmelCase       ,   shape=(batch, height * 2, width * 2, channels)       ,   method='''nearest'''       ,   )
								A  :	Optional[int]				=					self.conv(__lowerCAmelCase				)
								return hidden_states



class      _SCREAMING_SNAKE_CASE				(		nn.Module				):
				lowerCamelCase_	  =  42
				lowerCamelCase_	  =  jnp.floataa





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


								"""simple docstring"""

								A  :	List[Any]				=					nn.Conv(
								    self.out_channels       ,   kernel_size=(3, 3)       ,   strides=(2, 2)       ,   padding=((1, 1), (1, 1))       ,   dtype=self.dtype       ,   )






				def __call__(		self			:		Dict       ,   snake_case_			:		Tuple				):


								"""simple docstring"""

								A  :	List[str]				=					self.conv(__lowerCAmelCase				)
								return hidden_states



class      _SCREAMING_SNAKE_CASE				(		nn.Module				):
				lowerCamelCase_	  =  42
				lowerCamelCase_	  =  None
				lowerCamelCase_	  =  0.0
				lowerCamelCase_	  =  None
				lowerCamelCase_	  =  jnp.floataa





				def 							_UpperCAmelCase    (		self			:		int				):


								"""simple docstring"""

								A  :	Optional[int]				=					self.in_channels if self.out_channels is None else self.out_channels

								A  :	Tuple				=					nn.GroupNorm(num_groups=32       ,   epsilon=1E-5				)
								A  :	Union[str, Any]				=					nn.Conv(
								    __lowerCAmelCase       ,   kernel_size=(3, 3)       ,   strides=(1, 1)       ,   padding=((1, 1), (1, 1))       ,   dtype=self.dtype       ,   )

								A  :	Optional[Any]				=					nn.Dense(__lowerCAmelCase       ,   dtype=self.dtype				)

								A  :	str				=					nn.GroupNorm(num_groups=32       ,   epsilon=1E-5				)
								A  :	Optional[Any]				=					nn.Dropout(self.dropout_prob				)
								A  :	Dict				=					nn.Conv(
								    __lowerCAmelCase       ,   kernel_size=(3, 3)       ,   strides=(1, 1)       ,   padding=((1, 1), (1, 1))       ,   dtype=self.dtype       ,   )

								A  :	List[Any]				=					self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

								A  :	Optional[int]				=					None
								if use_nin_shortcut:
												A  :	List[str]				=					nn.Conv(
												    __lowerCAmelCase       ,   kernel_size=(1, 1)       ,   strides=(1, 1)       ,   padding='''VALID'''       ,   dtype=self.dtype       ,   )






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


								"""simple docstring"""

								A  :	str				=					hidden_states
								A  :	Optional[int]				=					self.norma(__lowerCAmelCase				)
								A  :	Optional[int]				=					nn.swish(__lowerCAmelCase				)
								A  :	List[Any]				=					self.conva(__lowerCAmelCase				)

								A  :	str				=					self.time_emb_proj(nn.swish(__lowerCAmelCase				)				)
								A  :	Optional[int]				=					jnp.expand_dims(jnp.expand_dims(__lowerCAmelCase       ,   1				)       ,   1				)
								A  :	Tuple				=					hidden_states + temb

								A  :	Dict				=					self.norma(__lowerCAmelCase				)
								A  :	Any				=					nn.swish(__lowerCAmelCase				)
								A  :	List[str]				=					self.dropout(__lowerCAmelCase       ,   __lowerCAmelCase				)
								A  :	Union[str, Any]				=					self.conva(__lowerCAmelCase				)

								if self.conv_shortcut is not None:
												A  :	Any				=					self.conv_shortcut(__lowerCAmelCase				)

								return hidden_states + residual





'''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 						a_				(			unittest.TestCase						):




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   'ylacombe/bark-small'
							__snake_case						 =   tempfile.mkdtemp()
							__snake_case						 =   'en_speaker_1'
							__snake_case						 =   'This is a test string'
							__snake_case						 =   'speaker_embeddings_path.json'
							__snake_case						 =   'speaker_embeddings'




	def   lowercase__   (       self     :  int       ,		**__lowerCAmelCase     :  str      ):
							return AutoTokenizer.from_pretrained(self.checkpoint       ,		**__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[Any]      ):
							shutil.rmtree(self.tmpdirname      )




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   BarkProcessor(tokenizer=__lowerCAmelCase      )

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

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




	@slow
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   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       ,		)

							__snake_case						 =   self.get_tokenizer(bos_token='(BOS)'       ,		eos_token='(EOS)'      )

							__snake_case						 =   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   lowercase__   (       self     :  str      ):
							__snake_case						 =   BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint       ,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,		)

							__snake_case						 =   3_5
							__snake_case						 =   2
							__snake_case						 =   8

							__snake_case						 =   {
							    'semantic_prompt': np.ones(__lowerCAmelCase      ),
							    'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)      ),
							    'fine_prompt': np.ones((nb_codebooks_total, seq_len)      ),
							}

							# test providing already loaded voice_preset
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=__lowerCAmelCase      )

							__snake_case						 =   inputs['history_prompt']
							for key in voice_preset:
													self.assertListEqual(voice_preset[key].tolist()       ,		processed_voice_preset.get(__lowerCAmelCase       ,		np.array([]      )      ).tolist()      )

							# test loading voice preset from npz file
							__snake_case						 =   os.path.join(self.tmpdirname       ,		'file.npz'      )
							np.savez(__lowerCAmelCase       ,		**__lowerCAmelCase      )
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=__lowerCAmelCase      )
							__snake_case						 =   inputs['history_prompt']

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

							# test loading voice preset from the hub
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=self.voice_preset      )




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   BarkProcessor(tokenizer=__lowerCAmelCase      )

							__snake_case						 =   processor(text=self.input_string      )

							__snake_case						 =   tokenizer(
							    self.input_string       ,		padding='max_length'       ,		max_length=2_5_6       ,		add_special_tokens=__lowerCAmelCase       ,		return_attention_mask=__lowerCAmelCase       ,		return_token_type_ids=__lowerCAmelCase       ,		)

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

