




"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def 					_UpperCamelCase    (			UpperCamelCase							)			->			Union[str, Any]:


			"""simple docstring"""
			__UpperCAmelCase	: Union[str, Any]		=					[
			    "encoder.version",
			    "decoder.version",
			    "model.encoder.version",
			    "model.decoder.version",
			    "_float_tensor",
			    "decoder.output_projection.weight",
			]
			for k in ignore_keys:
						state_dict.pop(UpperCamelCase				,       UpperCamelCase							)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			Optional[Any]:


			"""simple docstring"""
			__UpperCAmelCase	,     __UpperCAmelCase	: Optional[Any]		=					emb.weight.shape
			__UpperCAmelCase	: int		=					nn.Linear(UpperCamelCase				,       UpperCamelCase				,       bias=UpperCamelCase							)
			__UpperCAmelCase	: Optional[Any]		=					emb.weight.data
			return lin_layer

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase="facebook/mbart-large-en-ro"				,       UpperCamelCase=False				,       UpperCamelCase=False							)			->			str:


			"""simple docstring"""
			__UpperCAmelCase	: str		=					torch.load(UpperCamelCase				,       map_location="cpu"							)["model"]
			remove_ignore_keys_(UpperCamelCase							)
			__UpperCAmelCase	: str		=					state_dict["encoder.embed_tokens.weight"].shape[0]

			__UpperCAmelCase	: Any		=					MBartConfig.from_pretrained(UpperCamelCase				,       vocab_size=UpperCamelCase							)
			if mbart_aa and finetuned:
						__UpperCAmelCase	: Any		=					"relu"

			__UpperCAmelCase	: str		=					state_dict["decoder.embed_tokens.weight"]
			__UpperCAmelCase	: Any		=					MBartForConditionalGeneration(UpperCamelCase							)
			model.model.load_state_dict(UpperCamelCase							)

			if finetuned:
						__UpperCAmelCase	: List[str]		=					make_linear_from_emb(model.model.shared							)

			return model


if __name__ == "__main__":
					A   =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
					)
					parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument(
					    """--hf_config""",
					    default="""facebook/mbart-large-cc25""",
					    type=str,
					    help="""Which huggingface architecture to use: mbart-large""",
					)
					parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
					parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
					A   =       parser.parse_args()
					A   =       convert_fairseq_mbart_checkpoint_from_disk(
					    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
					)
					model.save_pretrained(args.pytorch_dump_folder_path)








"""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       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					"ylacombe/bark-small"
						__UpperCAmelCase	: Any		=					tempfile.mkdtemp()
						__UpperCAmelCase	: List[str]		=					"en_speaker_1"
						__UpperCAmelCase	: List[Any]		=					"This is a test string"
						__UpperCAmelCase	: List[str]		=					"speaker_embeddings_path.json"
						__UpperCAmelCase	: Tuple		=					"speaker_embeddings"


			def       a_     (      self      :    Optional[int]      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




						return AutoTokenizer.from_pretrained(self.checkpoint      ,  **UpperCamelCase_)


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						shutil.rmtree(self.tmpdirname)


			def       a_     (      self      :    int):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.get_tokenizer()

						__UpperCAmelCase	: Optional[Any]		=					BarkProcessor(tokenizer=UpperCamelCase_)

						processor.save_pretrained(self.tmpdirname)
						__UpperCAmelCase	: Optional[int]		=					BarkProcessor.from_pretrained(self.tmpdirname)

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


			@slow
			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					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	: Optional[Any]		=					self.get_tokenizer(bos_token="(BOS)"      ,  eos_token="(EOS)")

						__UpperCAmelCase	: Optional[Any]		=					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      :    Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					BarkProcessor.from_pretrained(
						    pretrained_processor_name_or_path=self.checkpoint      ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,  )

						__UpperCAmelCase	: Optional[int]		=					35
						__UpperCAmelCase	: Union[str, Any]		=					2
						__UpperCAmelCase	: List[Any]		=					8

						__UpperCAmelCase	: str		=					{
						    "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
						__UpperCAmelCase	: Union[str, Any]		=					processor(text=self.input_string      ,  voice_preset=UpperCamelCase_)

						__UpperCAmelCase	: str		=					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
						__UpperCAmelCase	: int		=					os.path.join(self.tmpdirname      ,  "file.npz")
						np.savez(UpperCamelCase_      ,  **UpperCamelCase_)
						__UpperCAmelCase	: List[Any]		=					processor(text=self.input_string      ,  voice_preset=UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					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
						__UpperCAmelCase	: Tuple		=					processor(text=self.input_string      ,  voice_preset=self.voice_preset)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					self.get_tokenizer()

						__UpperCAmelCase	: Any		=					BarkProcessor(tokenizer=UpperCamelCase_)

						__UpperCAmelCase	: List[Any]		=					processor(text=self.input_string)

						__UpperCAmelCase	: List[Any]		=					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())


