


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
A      = logging.get_logger(__name__)





def __UpperCAmelCase       (		__A			)						->					Union[str, Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     SwinConfig(
           embed_dim=1_9_2 ,						depths=(2, 2, 1_8, 2) ,						num_heads=(6, 1_2, 2_4, 4_8) ,						window_size=1_2 ,						out_features=["stage2", "stage3", "stage4"] ,						)

       UpperCAmelCase__										=     DetaConfig(
           backbone_config=__A ,						num_queries=9_0_0 ,						encoder_ffn_dim=2_0_4_8 ,						decoder_ffn_dim=2_0_4_8 ,						num_feature_levels=5 ,						assign_first_stage=__A ,						with_box_refine=__A ,						two_stage=__A ,						)

       # set labels
       UpperCAmelCase__										=     "huggingface/label-files"
       if "o365" in model_name:
              UpperCAmelCase__										=     3_6_6
              UpperCAmelCase__										=     "object365-id2label.json"
       else:
              UpperCAmelCase__										=     9_1
              UpperCAmelCase__										=     "coco-detection-id2label.json"

       UpperCAmelCase__										=     num_labels
       UpperCAmelCase__										=     json.load(open(cached_download(hf_hub_url(__A ,						__A ,						repo_type="dataset"			)			) ,						"r"			)			)
       UpperCAmelCase__										=     {int(__A			): v for k, v in idalabel.items()}
       UpperCAmelCase__										=     idalabel
       UpperCAmelCase__										=     {v: k for k, v in idalabel.items()}

       return config





def __UpperCAmelCase       (		__A			)						->					Optional[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     []

       # stem
       # fmt: off
       rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")			)
       rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")			)
       rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")			)
       rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")			)
       # stages
       for i in range(len(config.backbone_config.depths			)			):
              for j in range(config.backbone_config.depths[i]			):
                     rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm1.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")			)
                     rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm1.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")			)
                     rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")			)
                     rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")			)
                     rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")			)
                     rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")			)
                     rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm2.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")			)
                     rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.norm2.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")			)
                     rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")			)
                     rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")			)
                     rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")			)
                     rename_keys.append((F"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")			)

              if i < 3:
                     rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.reduction.weight""", F"""model.backbone.model.encoder.layers.{i}.downsample.reduction.weight""")			)
                     rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.norm.weight""", F"""model.backbone.model.encoder.layers.{i}.downsample.norm.weight""")			)
                     rename_keys.append((F"""backbone.0.body.layers.{i}.downsample.norm.bias""", F"""model.backbone.model.encoder.layers.{i}.downsample.norm.bias""")			)

       rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")			)
       rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")			)
       rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")			)
       rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")			)
       rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")			)
       rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")			)

       # transformer encoder
       for i in range(config.encoder_layers			):
              rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", F"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", F"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", F"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", F"""model.encoder.layers.{i}.self_attn.value_proj.weight""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", F"""model.encoder.layers.{i}.self_attn.value_proj.bias""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", F"""model.encoder.layers.{i}.self_attn.output_proj.weight""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", F"""model.encoder.layers.{i}.self_attn.output_proj.bias""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.weight""", F"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""model.encoder.layers.{i}.fc1.weight""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""model.encoder.layers.{i}.fc1.bias""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""model.encoder.layers.{i}.fc2.weight""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""model.encoder.layers.{i}.fc2.bias""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""model.encoder.layers.{i}.final_layer_norm.weight""")			)
              rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""model.encoder.layers.{i}.final_layer_norm.bias""")			)

       # transformer decoder
       for i in range(config.decoder_layers			):
              rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", F"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", F"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", F"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.weight""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""model.decoder.layers.{i}.self_attn.out_proj.weight""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""model.decoder.layers.{i}.self_attn.out_proj.bias""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.weight""", F"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.norm2.bias""", F"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""model.decoder.layers.{i}.fc1.weight""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""model.decoder.layers.{i}.fc1.bias""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""model.decoder.layers.{i}.fc2.weight""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""model.decoder.layers.{i}.fc2.bias""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""model.decoder.layers.{i}.final_layer_norm.weight""")			)
              rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""model.decoder.layers.{i}.final_layer_norm.bias""")			)

       # fmt: on

       return rename_keys





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					List[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     dct.pop(__A			)
       UpperCAmelCase__										=     val





def __UpperCAmelCase       (		__A ,						__A			)						->					List[str]:



       '''simple docstring'''





       UpperCAmelCase__										=     [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
       for i in range(len(backbone_config.depths			)			):
              UpperCAmelCase__										=     num_features[i]
              for j in range(backbone_config.depths[i]			):
                     # fmt: off
                     # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                     UpperCAmelCase__										=     state_dict.pop(F"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""			)
                     UpperCAmelCase__										=     state_dict.pop(F"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"""			)
                     # next, add query, keys and values (in that order) to the state dict
                     UpperCAmelCase__										=     in_proj_weight[:dim, :]
                     UpperCAmelCase__										=     in_proj_bias[: dim]
                     UpperCAmelCase__										=     in_proj_weight[
                         dim : dim * 2, :
                     ]
                     UpperCAmelCase__										=     in_proj_bias[
                         dim : dim * 2
                     ]
                     UpperCAmelCase__										=     in_proj_weight[
                         -dim :, :
                     ]
                     UpperCAmelCase__										=     in_proj_bias[-dim :]
                     # fmt: on





def __UpperCAmelCase       (		__A ,						__A			)						->					Optional[int]:



       '''simple docstring'''





       UpperCAmelCase__										=     config.d_model
       for i in range(config.decoder_layers			):
              # read in weights + bias of input projection layer of self-attention
              UpperCAmelCase__										=     state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""			)
              UpperCAmelCase__										=     state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""			)
              # next, add query, keys and values (in that order) to the state dict
              UpperCAmelCase__										=     in_proj_weight[:hidden_size, :]
              UpperCAmelCase__										=     in_proj_bias[:hidden_size]
              UpperCAmelCase__										=     in_proj_weight[
                  hidden_size : hidden_size * 2, :
              ]
              UpperCAmelCase__										=     in_proj_bias[hidden_size : hidden_size * 2]
              UpperCAmelCase__										=     in_proj_weight[-hidden_size:, :]
              UpperCAmelCase__										=     in_proj_bias[-hidden_size:]





def __UpperCAmelCase       (		)						->					int:



       '''simple docstring'''





       UpperCAmelCase__										=     "http://images.cocodataset.org/val2017/000000039769.jpg"
       UpperCAmelCase__										=     Image.open(requests.get(__A ,						stream=__A			).raw			)

       return im





@torch.no_grad()
def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					Tuple:



       '''simple docstring'''





       UpperCAmelCase__										=     get_deta_config(__A			)

       # load original state dict
       if model_name == "deta-swin-large":
              UpperCAmelCase__										=     hf_hub_download(repo_id="nielsr/deta-checkpoints" ,						filename="adet_swin_ft.pth"			)
       elif model_name == "deta-swin-large-o365":
              UpperCAmelCase__										=     hf_hub_download(repo_id="jozhang97/deta-swin-l-o365" ,						filename="deta_swin_pt_o365.pth"			)
       else:
              raise ValueError(F"""Model name {model_name} not supported"""			)

       UpperCAmelCase__										=     torch.load(__A ,						map_location="cpu"			)["model"]

       # original state dict
       for name, param in state_dict.items():
              print(__A ,						param.shape			)

       # rename keys
       UpperCAmelCase__										=     create_rename_keys(__A			)
       for src, dest in rename_keys:
              rename_key(__A ,						__A ,						__A			)
       read_in_swin_q_k_v(__A ,						config.backbone_config			)
       read_in_decoder_q_k_v(__A ,						__A			)

       # fix some prefixes
       for key in state_dict.copy().keys():
              if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                     UpperCAmelCase__										=     state_dict.pop(__A			)
                     UpperCAmelCase__										=     val
              if "input_proj" in key:
                     UpperCAmelCase__										=     state_dict.pop(__A			)
                     UpperCAmelCase__										=     val
              if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                     UpperCAmelCase__										=     state_dict.pop(__A			)
                     UpperCAmelCase__										=     val

    # finally, create HuggingFace model and load state dict
       UpperCAmelCase__										=     DetaForObjectDetection(__A			)
       model.load_state_dict(__A			)
       model.eval()

       UpperCAmelCase__										=     "cuda" if torch.cuda.is_available() else "cpu"
       model.to(__A			)

       # load image processor
       UpperCAmelCase__										=     DetaImageProcessor(format="coco_detection"			)

       # verify our conversion on image
       UpperCAmelCase__										=     prepare_img()
       UpperCAmelCase__										=     processor(images=__A ,						return_tensors="pt"			)
       UpperCAmelCase__										=     encoding["pixel_values"]
       UpperCAmelCase__										=     model(pixel_values.to(__A			)			)

       # verify logits
       print("Logits:" ,						outputs.logits[0, :3, :3]			)
       print("Boxes:" ,						outputs.pred_boxes[0, :3, :3]			)
       if model_name == "deta-swin-large":
              UpperCAmelCase__										=     torch.tensor(
                  [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]			)
              UpperCAmelCase__										=     torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]			)
       elif model_name == "deta-swin-large-o365":
              UpperCAmelCase__										=     torch.tensor(
                  [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]			)
              UpperCAmelCase__										=     torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]			)
       assert torch.allclose(outputs.logits[0, :3, :3] ,						expected_logits.to(__A			) ,						atol=1E-4			)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,						expected_boxes.to(__A			) ,						atol=1E-4			)
       print("Everything ok!"			)

       if pytorch_dump_folder_path:
              # Save model and processor
              logger.info(F"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""			)
              Path(__A			).mkdir(exist_ok=__A			)
              model.save_pretrained(__A			)
              processor.save_pretrained(__A			)

       # Push to hub
       if push_to_hub:
              print("Pushing model and processor to hub..."			)
              model.push_to_hub(F"""jozhang97/{model_name}"""			)
              processor.push_to_hub(F"""jozhang97/{model_name}"""			)


if __name__ == "__main__":
    A      = argparse.ArgumentParser()

    parser.add_argument(
        "--model_name",
        type=str,
        default="deta-swin-large",
        choices=["deta-swin-large", "deta-swin-large-o365"],
        help="Name of the model you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        help="Path to the folder to output PyTorch model.",
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
    )
    A      = parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









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() )





