






import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
							raise Exception('''requires fairseq >= 1.0.0a''')

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

SCREAMING_SNAKE_CASE__				     =			'''Hello world! cécé herlolip'''

def        UpperCAmelCase__		(						lowerCamelCase_       :       str   ,  lowerCamelCase_       :       str   ,  lowerCamelCase_       :       bool				):
			__a						:  Union[str, Any]									=     FairseqRobertaModel.from_pretrained(lowerCamelCase_				)
			roberta.eval()  # disable dropout
			__a						:  Optional[int]									=     roberta.model.encoder.sentence_encoder
			__a						:  Any									=     XLMRobertaConfig(
			    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,  hidden_size=roberta.cfg.model.encoder_embed_dim   ,  num_hidden_layers=roberta.cfg.model.encoder_layers   ,  num_attention_heads=roberta.cfg.model.encoder_attention_heads   ,  intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim   ,  max_position_embeddings=5_1_4   ,  type_vocab_size=1   ,  layer_norm_eps=1e-5   ,  )
			if classification_head:
						__a						:  Optional[int]									=     roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

			print('Our RoBERTa config:'   ,  lowerCamelCase_				)

			__a						:  Dict									=     XLMRobertaXLForSequenceClassification(lowerCamelCase_				) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase_				)
			model.eval()

			# Now let's copy all the weights.
			# Embeddings
			__a						:  Optional[int]									=     roberta_sent_encoder.embed_tokens.weight
			__a						:  Union[str, Any]									=     roberta_sent_encoder.embed_positions.weight
			__a						:  List[Any]									=     torch.zeros_like(
			    model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c RoBERTa doesn't use them.

			__a						:  int									=     roberta_sent_encoder.layer_norm.weight
			__a						:  Union[str, Any]									=     roberta_sent_encoder.layer_norm.bias

			for i in range(config.num_hidden_layers				):
						# Encoder: start of layer
						__a						:  BertLayer									=     model.roberta.encoder.layer[i]
						__a						:  TransformerSentenceEncoderLayer									=     roberta_sent_encoder.layers[i]

						__a						:  RobertaAttention									=     layer.attention
						__a						:  Any									=     roberta_layer.self_attn_layer_norm.weight
						__a						:  Any									=     roberta_layer.self_attn_layer_norm.bias

						# self attention
						__a						:  BertSelfAttention									=     layer.attention.self
						assert (
						    roberta_layer.self_attn.k_proj.weight.data.shape
						    == roberta_layer.self_attn.q_proj.weight.data.shape
						    == roberta_layer.self_attn.v_proj.weight.data.shape
						    == torch.Size((config.hidden_size, config.hidden_size)				)
						)

						__a						:  int									=     roberta_layer.self_attn.q_proj.weight
						__a						:  Dict									=     roberta_layer.self_attn.q_proj.bias
						__a						:  List[Any]									=     roberta_layer.self_attn.k_proj.weight
						__a						:  Any									=     roberta_layer.self_attn.k_proj.bias
						__a						:  List[str]									=     roberta_layer.self_attn.v_proj.weight
						__a						:  Union[str, Any]									=     roberta_layer.self_attn.v_proj.bias

						# self-attention output
						__a						:  BertSelfOutput									=     layer.attention.output
						assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
						__a						:  int									=     roberta_layer.self_attn.out_proj.weight
						__a						:  Union[str, Any]									=     roberta_layer.self_attn.out_proj.bias

						# this one is final layer norm
						__a						:  Tuple									=     roberta_layer.final_layer_norm.weight
						__a						:  Dict									=     roberta_layer.final_layer_norm.bias

						# intermediate
						__a						:  BertIntermediate									=     layer.intermediate
						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
						__a						:  Union[str, Any]									=     roberta_layer.fca.weight
						__a						:  Tuple									=     roberta_layer.fca.bias

						# output
						__a						:  BertOutput									=     layer.output
						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
						__a						:  int									=     roberta_layer.fca.weight
						__a						:  Optional[Any]									=     roberta_layer.fca.bias
						# end of layer

			if classification_head:
						__a						:  Union[str, Any]									=     roberta.model.classification_heads['mnli'].dense.weight
						__a						:  Dict									=     roberta.model.classification_heads['mnli'].dense.bias
						__a						:  Optional[int]									=     roberta.model.classification_heads['mnli'].out_proj.weight
						__a						:  Tuple									=     roberta.model.classification_heads['mnli'].out_proj.bias
			else:
						# LM Head
						__a						:  Optional[Any]									=     roberta.model.encoder.lm_head.dense.weight
						__a						:  Tuple									=     roberta.model.encoder.lm_head.dense.bias
						__a						:  Dict									=     roberta.model.encoder.lm_head.layer_norm.weight
						__a						:  Optional[Any]									=     roberta.model.encoder.lm_head.layer_norm.bias
						__a						:  Union[str, Any]									=     roberta.model.encoder.lm_head.weight
						__a						:  Optional[Any]									=     roberta.model.encoder.lm_head.bias

			# Let's check that we get the same results.
			__a						:  torch.Tensor									=     roberta.encode(lowerCamelCase_				).unsqueeze(0				)  # batch of size 1

			__a						:  Tuple									=     model(lowerCamelCase_				)[0]
			if classification_head:
						__a						:  Union[str, Any]									=     roberta.model.classification_heads['mnli'](roberta.extract_features(lowerCamelCase_				)				)
			else:
						__a						:  int									=     roberta.model(lowerCamelCase_				)[0]
			print(our_output.shape   ,  their_output.shape				)
			__a						:  Any									=     torch.max(torch.abs(our_output - their_output				)				).item()
			print(f'''max_absolute_diff = {max_absolute_diff}'''				)  # ~ 1e-7
			__a						:  Union[str, Any]									=     torch.allclose(lowerCamelCase_   ,  lowerCamelCase_   ,  atol=1e-3				)
			print('Do both models output the same tensors?'   ,  '🔥' if success else '💩'				)
			if not success:
						raise Exception('Something went wRoNg'				)

			pathlib.Path(lowerCamelCase_				).mkdir(parents=lowerCamelCase_   ,  exist_ok=lowerCamelCase_				)
			print(f'''Saving model to {pytorch_dump_folder_path}'''				)
			model.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__				     =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							parser.add_argument(
							    '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
							)
							SCREAMING_SNAKE_CASE__				     =			parser.parse_args()
							convert_xlm_roberta_xl_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)











'''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   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   'ylacombe/bark-small'
							__snake_case						 =   tempfile.mkdtemp()
							__snake_case						 =   'en_speaker_1'
							__snake_case						 =   'This is a test string'
							__snake_case						 =   'speaker_embeddings_path.json'
							__snake_case						 =   'speaker_embeddings'




	def   lowercase__   (       self     :  int       ,		**__lowerCAmelCase     :  str      ):
							return AutoTokenizer.from_pretrained(self.checkpoint       ,		**__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[Any]      ):
							shutil.rmtree(self.tmpdirname      )




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   BarkProcessor(tokenizer=__lowerCAmelCase      )

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

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




	@slow
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   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       ,		)

							__snake_case						 =   self.get_tokenizer(bos_token='(BOS)'       ,		eos_token='(EOS)'      )

							__snake_case						 =   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   lowercase__   (       self     :  str      ):
							__snake_case						 =   BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint       ,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,		)

							__snake_case						 =   3_5
							__snake_case						 =   2
							__snake_case						 =   8

							__snake_case						 =   {
							    '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
							__snake_case						 =   processor(text=self.input_string       ,		voice_preset=__lowerCAmelCase      )

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




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.get_tokenizer()

							__snake_case						 =   BarkProcessor(tokenizer=__lowerCAmelCase      )

							__snake_case						 =   processor(text=self.input_string      )

							__snake_case						 =   tokenizer(
							    self.input_string       ,		padding='max_length'       ,		max_length=2_5_6       ,		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()      )

