


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


A      = logging.get_logger(__name__)

A      = {
    "Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  'dpt'






  def __init__(					self      :					List[Any]		,						_lowercase      :					Dict=7_68		,						_lowercase      :					Tuple=12		,						_lowercase      :					str=12		,						_lowercase      :					Optional[int]=30_72		,						_lowercase      :					Optional[int]="gelu"		,						_lowercase      :					int=0.0		,						_lowercase      :					Dict=0.0		,						_lowercase      :					Optional[int]=0.0_2		,						_lowercase      :					List[Any]=1E-12		,						_lowercase      :					Optional[Any]=3_84		,						_lowercase      :					Tuple=16		,						_lowercase      :					str=3		,						_lowercase      :					str=False		,						_lowercase      :					List[Any]=True		,						_lowercase      :					str=[2, 5, 8, 11]		,						_lowercase      :					str="project"		,						_lowercase      :					Any=[4, 2, 1, 0.5]		,						_lowercase      :					Optional[Any]=[96, 1_92, 3_84, 7_68]		,						_lowercase      :					List[str]=2_56		,						_lowercase      :					List[str]=-1		,						_lowercase      :					int=False		,						_lowercase      :					Union[str, Any]=True		,						_lowercase      :					Optional[int]=0.4		,						_lowercase      :					Union[str, Any]=2_55		,						_lowercase      :					str=0.1		,						_lowercase      :					str=[1, 10_24, 24, 24]		,						_lowercase      :					int=[0, 1]		,						_lowercase      :					Any=None		,						**_lowercase      :					Any		,						):




         """simple docstring"""







         super().__init__(**_lowercase )

         UpperCAmelCase__										=     hidden_size
         UpperCAmelCase__										=     is_hybrid

         if self.is_hybrid:
                if backbone_config is None:
                       logger.info("Initializing the config with a `BiT` backbone." )
                       UpperCAmelCase__										=     {
                           "global_padding": "same",
                           "layer_type": "bottleneck",
                           "depths": [3, 4, 9],
                           "out_features": ["stage1", "stage2", "stage3"],
                           "embedding_dynamic_padding": True,
                       }
                       UpperCAmelCase__										=     BitConfig(**_lowercase )
                elif isinstance(_lowercase		,						_lowercase ):
                       logger.info("Initializing the config with a `BiT` backbone." )
                       UpperCAmelCase__										=     BitConfig(**_lowercase )
                elif isinstance(_lowercase		,						_lowercase ):
                       UpperCAmelCase__										=     backbone_config
                else:
                       raise ValueError(
                           F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.""" )

                UpperCAmelCase__										=     backbone_featmap_shape
                UpperCAmelCase__										=     neck_ignore_stages

                if readout_type != "project":
                       raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode." )
         else:
                UpperCAmelCase__										=     None
                UpperCAmelCase__										=     None
                UpperCAmelCase__										=     []

         UpperCAmelCase__										=     num_hidden_layers
         UpperCAmelCase__										=     num_attention_heads
         UpperCAmelCase__										=     intermediate_size
         UpperCAmelCase__										=     hidden_act
         UpperCAmelCase__										=     hidden_dropout_prob
         UpperCAmelCase__										=     attention_probs_dropout_prob
         UpperCAmelCase__										=     initializer_range
         UpperCAmelCase__										=     layer_norm_eps
         UpperCAmelCase__										=     image_size
         UpperCAmelCase__										=     patch_size
         UpperCAmelCase__										=     num_channels
         UpperCAmelCase__										=     qkv_bias
         UpperCAmelCase__										=     backbone_out_indices
         if readout_type not in ["ignore", "add", "project"]:
                raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']" )
         UpperCAmelCase__										=     readout_type
         UpperCAmelCase__										=     reassemble_factors
         UpperCAmelCase__										=     neck_hidden_sizes
         UpperCAmelCase__										=     fusion_hidden_size
         UpperCAmelCase__										=     head_in_index
         UpperCAmelCase__										=     use_batch_norm_in_fusion_residual
         # auxiliary head attributes (semantic segmentation)
         UpperCAmelCase__										=     use_auxiliary_head
         UpperCAmelCase__										=     auxiliary_loss_weight
         UpperCAmelCase__										=     semantic_loss_ignore_index
         UpperCAmelCase__										=     semantic_classifier_dropout






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     copy.deepcopy(self.__dict__ )

         if output["backbone_config"] is not None:
                UpperCAmelCase__										=     self.backbone_config.to_dict()

         UpperCAmelCase__										=     self.__class__.model_type
         return output









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() )





