
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       _lowerCAmelCase   (      unittest.TestCase			):




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      "ylacombe/bark-small"
							lowerCAmelCase_         =      tempfile.mkdtemp()
							lowerCAmelCase_         =      "en_speaker_1"
							lowerCAmelCase_         =      "This is a test string"
							lowerCAmelCase_         =      "speaker_embeddings_path.json"
							lowerCAmelCase_         =      "speaker_embeddings"




				def 	__a		(   self							,							**_UpperCamelCase	)							-> Optional[Any]:
							return AutoTokenizer.from_pretrained(self.checkpoint							,							**_UpperCamelCase	)




				def 	__a		(   self	)							-> Optional[int]:
							shutil.rmtree(self.tmpdirname	)




				def 	__a		(   self	)							-> Optional[Any]:
							lowerCAmelCase_         =      self.get_tokenizer()

							lowerCAmelCase_         =      BarkProcessor(tokenizer=_UpperCamelCase	)

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

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




				@slow
				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      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							,							)

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

							lowerCAmelCase_         =      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 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint							,							speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,							)

							lowerCAmelCase_         =      35
							lowerCAmelCase_         =      2
							lowerCAmelCase_         =      8

							lowerCAmelCase_         =      {
							    "semantic_prompt": np.ones(_UpperCamelCase	),
							    "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)	),
							    "fine_prompt": np.ones((nb_codebooks_total, seq_len)	),
							}

							# test providing already loaded voice_preset
							lowerCAmelCase_         =      processor(text=self.input_string							,							voice_preset=_UpperCamelCase	)

							lowerCAmelCase_         =      inputs["history_prompt"]
							for key in voice_preset:
										self.assertListEqual(voice_preset[key].tolist()							,							processed_voice_preset.get(_UpperCamelCase							,							np.array([]	)	).tolist()	)

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

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

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




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.get_tokenizer()

							lowerCAmelCase_         =      BarkProcessor(tokenizer=_UpperCamelCase	)

							lowerCAmelCase_         =      processor(text=self.input_string	)

							lowerCAmelCase_         =      tokenizer(
							    self.input_string							,							padding="max_length"							,							max_length=256							,							add_special_tokens=_UpperCamelCase							,							return_attention_mask=_UpperCamelCase							,							return_token_type_ids=_UpperCamelCase							,							)

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





import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


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






def   lowerCamelCase__							(							__lowerCAmelCase    :    Any							):




			"""simple docstring"""


			lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)
			if "model" in sd.keys():
						lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)["model"]

			# pop unnecessary weights
			lowerCAmelCase_         =      [
			    "decoder.version",
			    "decoder.output_projection.weight",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(__lowerCAmelCase							)

			lowerCAmelCase_         =      {
			    "decoder.project_in_dim.weight": "decoder.project_in.weight",
			    "decoder.project_out_dim.weight": "decoder.project_out.weight",
			    "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
			    "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									lowerCAmelCase_         =      sd.pop(__lowerCAmelCase							)

			lowerCAmelCase_         =      list(sd.keys()							)
			for key in keys:
						if ".qkv_proj." in key:
									lowerCAmelCase_         =      sd[key]
									# We split QKV in separate Q,K,V

									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".q_proj."							)
									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".k_proj."							)
									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".v_proj."							)

									lowerCAmelCase_         =      value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      torch.split(__lowerCAmelCase       ,				depth // 3       ,				dim=0							)

									lowerCAmelCase_         =      q
									lowerCAmelCase_         =      k
									lowerCAmelCase_         =      v
									del sd[key]

			return sd






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[Any]=None							):




			"""simple docstring"""


			lowerCAmelCase_         =      load_checkpoint(__lowerCAmelCase							)

			if config is not None:
						lowerCAmelCase_         =      OPTConfig.from_pretrained(__lowerCAmelCase							)
			else:
						lowerCAmelCase_         =      OPTConfig()

			lowerCAmelCase_         =      OPTModel(__lowerCAmelCase							).half().eval()
			model.load_state_dict(__lowerCAmelCase							)

			# Check results
			Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
			model.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
			_A  =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--fairseq_path",
			    type=str,
			    help=(
			        "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
			        " https://huggingface.co/models?other=opt_metasq"
			    ),
			)
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
			_A  =	parser.parse_args()
			convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



