





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     __SCREAMING_SNAKE_CASE   (       unittest.TestCase    ):






							def 			_lowerCamelCase						(  self					):
														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 			_lowerCamelCase						(  self					,	**__lowerCAmelCase					):
														return AutoTokenizer.from_pretrained(self.checkpoint					,	**__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														shutil.rmtree(self.tmpdirname					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_tokenizer()

														UpperCamelCase__			       =     BarkProcessor(tokenizer=__lowerCAmelCase					)

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

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






							@slow
							def 			_lowerCamelCase						(  self					):
														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 			_lowerCamelCase						(  self					):
														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(__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
														UpperCamelCase__			       =     processor(text=self.input_string					,	voice_preset=__lowerCAmelCase					)

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







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_tokenizer()

														UpperCamelCase__			       =     BarkProcessor(tokenizer=__lowerCAmelCase					)

														UpperCamelCase__			       =     processor(text=self.input_string					)

														UpperCamelCase__			       =     tokenizer(
														    self.input_string					,	padding="""max_length"""					,	max_length=256					,	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()					)











from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map





def       _UpperCamelCase       (a__				:Dict[str, torch.Tensor]    ):






							"""simple docstring"""





							UpperCamelCase__			       =     []
							UpperCamelCase__			       =     []
							UpperCamelCase__			       =     []

							for rt in rc.restypes:
														UpperCamelCase__			       =     rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
														restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]    )
														UpperCamelCase__			       =     {name: i for i, name in enumerate(a__    )}
														restype_atomaa_to_atomaa_list.append(
														    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]    )

														restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]    )

							# Add dummy mapping for restype 'UNK'
							restype_atomaa_to_atomaa_list.append([0] * 14    )
							restype_atomaa_to_atomaa_list.append([0] * 37    )
							restype_atomaa_mask_list.append([0.0] * 14    )

							UpperCamelCase__			       =     torch.tensor(
							    a__				,     dtype=torch.intaa				,     device=protein["""aatype"""].device				,     )
							UpperCamelCase__			       =     torch.tensor(
							    a__				,     dtype=torch.intaa				,     device=protein["""aatype"""].device				,     )
							UpperCamelCase__			       =     torch.tensor(
							    a__				,     dtype=torch.floataa				,     device=protein["""aatype"""].device				,     )
							UpperCamelCase__			       =     protein["""aatype"""].to(torch.long    )

							# create the mapping for (residx, atom14) --> atom37, i.e. an array
							# with shape (num_res, 14) containing the atom37 indices for this protein
							UpperCamelCase__			       =     restype_atomaa_to_atomaa[protein_aatype]
							UpperCamelCase__			       =     restype_atomaa_mask[protein_aatype]

							UpperCamelCase__			       =     residx_atomaa_mask
							UpperCamelCase__			       =     residx_atomaa_to_atomaa.long()

							# create the gather indices for mapping back
							UpperCamelCase__			       =     restype_atomaa_to_atomaa[protein_aatype]
							UpperCamelCase__			       =     residx_atomaa_to_atomaa.long()

							# create the corresponding mask
							UpperCamelCase__			       =     torch.zeros([21, 37]				,     dtype=torch.floataa				,     device=protein["""aatype"""].device    )
							for restype, restype_letter in enumerate(rc.restypes    ):
														UpperCamelCase__			       =     rc.restype_atoa[restype_letter]
														UpperCamelCase__			       =     rc.residue_atoms[restype_name]
														for atom_name in atom_names:
																					UpperCamelCase__			       =     rc.atom_order[atom_name]
																					UpperCamelCase__			       =     1

							UpperCamelCase__			       =     restype_atomaa_mask[protein_aatype]
							UpperCamelCase__			       =     residx_atomaa_mask

							return protein





def       _UpperCamelCase       (a__				:Dict[str, torch.Tensor]    ):






							"""simple docstring"""





							UpperCamelCase__			       =     tree_map(lambda a__    : torch.tensor(a__				,     device=batch["""aatype"""].device    )				,     a__				,     np.ndarray    )
							UpperCamelCase__			       =     tensor_tree_map(lambda a__    : np.array(a__    )				,     make_atomaa_masks(a__    )    )
							return out




