


import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.12.2"):
			raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
			raise Exception("requires fairseq < v2")

logging.set_verbosity_info()
_snake_case					:		int      =    logging.get_logger(__name__)

_snake_case					:		Optional[int]      =    "Hello, World!"
_snake_case					:		Tuple      =    "en_XX"







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Optional[Any]										=       Path("data_bin"     )
							__snake_case		:      Union[str, Any]										=       FairseqXmodModel.from_pretrained(
							    model_name_or_path=str(Path(__lowerCamelCase     ).parent     )							,     checkpoint_file=Path(__lowerCamelCase     ).name							,     _name="xmod_base"							,     arch="xmod_base"							,     task="multilingual_masked_lm"							,     data_name_or_path=str(__lowerCamelCase     )							,     bpe="sentencepiece"							,     sentencepiece_model=str(Path(__lowerCamelCase     ).parent / "sentencepiece.bpe.model"     )							,     src_dict=str(data_dir / "dict.txt"     )							,     )
							xmod.eval()  # disable dropout
							print(__lowerCamelCase     )

							__snake_case		:      Optional[Any]										=       xmod.model.encoder.sentence_encoder
							__snake_case		:      Optional[int]										=       XmodConfig(
							    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings							,     hidden_size=xmod.cfg.model.encoder_embed_dim							,     num_hidden_layers=xmod.cfg.model.encoder_layers							,     num_attention_heads=xmod.cfg.model.encoder_attention_heads							,     intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim							,     max_position_embeddings=5_1_4							,     type_vocab_size=1							,     layer_norm_eps=1e-5							,     pre_norm=xmod.cfg.model.encoder_normalize_before							,     adapter_reduction_factor=getattr(xmod.cfg.model							,     "bottleneck"							,     2     )							,     adapter_layer_norm=xmod.cfg.model.adapter_layer_norm							,     adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm							,     ln_before_adapter=xmod.cfg.model.ln_before_adapter							,     languages=xmod.cfg.model.languages							,     )
							if classification_head:
														__snake_case		:      List[Any]										=       xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

							print("Our X-MOD config:"							,     __lowerCamelCase     )

							__snake_case		:      Tuple										=       XmodForSequenceClassification(__lowerCamelCase     ) if classification_head else XmodForMaskedLM(__lowerCamelCase     )
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							__snake_case		:      int										=       xmod_sent_encoder.embed_tokens.weight
							__snake_case		:      Dict										=       xmod_sent_encoder.embed_positions.weight
							__snake_case		:      List[Any]										=       torch.zeros_like(
							    model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c xmod doesn't use them.

							__snake_case		:      Tuple										=       xmod_sent_encoder.layernorm_embedding.weight
							__snake_case		:      Dict										=       xmod_sent_encoder.layernorm_embedding.bias

							for i in range(config.num_hidden_layers     ):
														# Encoder: start of layer
														__snake_case		:      Union[str, Any]										=       model.roberta.encoder.layer[i]
														__snake_case		:      Any										=       xmod_sent_encoder.layers[i]

														# self attention
														__snake_case		:      Optional[int]										=       layer.attention.self
														if not (
														    xmod_layer.self_attn.k_proj.weight.data.shape
														    == xmod_layer.self_attn.q_proj.weight.data.shape
														    == xmod_layer.self_attn.v_proj.weight.data.shape
														    == torch.Size((config.hidden_size, config.hidden_size)     )
														):
																					raise AssertionError("Dimensions of self-attention weights do not match."     )

														__snake_case		:      int										=       xmod_layer.self_attn.q_proj.weight
														__snake_case		:      Tuple										=       xmod_layer.self_attn.q_proj.bias
														__snake_case		:      List[str]										=       xmod_layer.self_attn.k_proj.weight
														__snake_case		:      str										=       xmod_layer.self_attn.k_proj.bias
														__snake_case		:      Any										=       xmod_layer.self_attn.v_proj.weight
														__snake_case		:      Optional[int]										=       xmod_layer.self_attn.v_proj.bias

														# self-attention output
														__snake_case		:      Optional[int]										=       layer.attention.output
														if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
																					raise AssertionError("Dimensions of self-attention output weights do not match."     )
														__snake_case		:      str										=       xmod_layer.self_attn.out_proj.weight
														__snake_case		:      Optional[Any]										=       xmod_layer.self_attn.out_proj.bias
														__snake_case		:      int										=       xmod_layer.self_attn_layer_norm.weight
														__snake_case		:      int										=       xmod_layer.self_attn_layer_norm.bias

														# intermediate
														__snake_case		:      int										=       layer.intermediate
														if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError("Dimensions of intermediate weights do not match."     )
														__snake_case		:      List[Any]										=       xmod_layer.fca.weight
														__snake_case		:      Union[str, Any]										=       xmod_layer.fca.bias

														# output
														__snake_case		:      List[str]										=       layer.output
														if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
																					raise AssertionError("Dimensions of feed-forward weights do not match."     )
														__snake_case		:      int										=       xmod_layer.fca.weight
														__snake_case		:      Union[str, Any]										=       xmod_layer.fca.bias
														__snake_case		:      Any										=       xmod_layer.final_layer_norm.weight
														__snake_case		:      Union[str, Any]										=       xmod_layer.final_layer_norm.bias
														if bert_output.adapter_layer_norm is not None:
																					__snake_case		:      Any										=       xmod_layer.adapter_layer_norm.weight
																					__snake_case		:      List[Any]										=       xmod_layer.adapter_layer_norm.bias

														if sorted(bert_output.adapter_modules.keys()     ) != sorted(xmod_layer.adapter_modules.keys()     ):
																					raise AssertionError("Lists of language adapters do not match."     )
														for lang_code, adapter in xmod_layer.adapter_modules.items():
																					__snake_case		:      int										=       bert_output.adapter_modules[lang_code]
																					__snake_case		:      Tuple										=       xmod_layer.adapter_modules[lang_code]
																					__snake_case		:      Optional[Any]										=       from_adapter.fca.weight
																					__snake_case		:      Optional[Any]										=       from_adapter.fca.bias
																					__snake_case		:      Optional[Any]										=       from_adapter.fca.weight
																					__snake_case		:      Tuple										=       from_adapter.fca.bias

														# end of layer

							if xmod_sent_encoder.layer_norm is not None:
														__snake_case		:      List[Any]										=       xmod_sent_encoder.layer_norm.weight
														__snake_case		:      Optional[Any]										=       xmod_sent_encoder.layer_norm.bias

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

							# Let's check that we get the same results.
							__snake_case		:      int										=       xmod.encode(__lowerCamelCase     ).unsqueeze(0     )  # batch of size 1
							model.roberta.set_default_language(__lowerCamelCase     )

							__snake_case		:      List[Any]										=       model(__lowerCamelCase     )[0]
							if classification_head:
														__snake_case		:      List[Any]										=       xmod.model.classification_heads["mnli"](xmod.extract_features(__lowerCamelCase     )     )
							else:
														__snake_case		:      Optional[int]										=       xmod.model(__lowerCamelCase							,     lang_id=[SAMPLE_LANGUAGE]     )[0]
							print(our_output.shape							,     their_output.shape     )
							__snake_case		:      Tuple										=       torch.max(torch.abs(our_output - their_output     )     ).item()
							print(F'max_absolute_diff = {max_absolute_diff}'     )  # ~ 1e-7
							__snake_case		:      List[str]										=       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"     )

							Path(__lowerCamelCase     ).mkdir(parents=__lowerCamelCase							,     exist_ok=__lowerCamelCase     )
							print(F'Saving model to {pytorch_dump_folder_path}'     )
							model.save_pretrained(__lowerCamelCase     )


if __name__ == "__main__":
			_snake_case					:		int      =    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--xmod_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."
			)
			_snake_case					:		Optional[Any]      =    parser.parse_args()
			convert_xmod_checkpoint_to_pytorch(
			    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)





import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case					:		Union[str, Any]      =    logging.get_logger(__name__)

_snake_case					:		List[str]      =    {
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "encodec"

							def __init__(							self	:  Any   ,     lowerCamelCase	:  Optional[int]=[1.5, 3.0, 6.0, 12.0, 24.0]   ,     lowerCamelCase	:  List[str]=24000   ,     lowerCamelCase	:  int=1   ,     lowerCamelCase	:  Optional[int]=False   ,     lowerCamelCase	:  Dict=None   ,     lowerCamelCase	:  Tuple=None   ,     lowerCamelCase	:  Optional[int]=128   ,     lowerCamelCase	:  Optional[int]=32   ,     lowerCamelCase	:  List[str]=1   ,     lowerCamelCase	:  str=[8, 5, 4, 2]   ,     lowerCamelCase	:  List[str]="weight_norm"   ,     lowerCamelCase	:  Any=7   ,     lowerCamelCase	:  Tuple=7   ,     lowerCamelCase	:  int=3   ,     lowerCamelCase	:  int=2   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  List[Any]="reflect"   ,     lowerCamelCase	:  Union[str, Any]=2   ,     lowerCamelCase	:  Optional[int]=2   ,     lowerCamelCase	:  int=1.0   ,     lowerCamelCase	:  Optional[Any]=1024   ,     lowerCamelCase	:  Optional[Any]=None   ,     lowerCamelCase	:  str=True   ,     **lowerCamelCase	:  Dict   ,     )     ->   Any:
														__snake_case		:      Tuple										=       target_bandwidths
														__snake_case		:      Union[str, Any]										=       sampling_rate
														__snake_case		:      Union[str, Any]										=       audio_channels
														__snake_case		:      Dict										=       normalize
														__snake_case		:      List[Any]										=       chunk_length_s
														__snake_case		:      Tuple										=       overlap
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      List[Any]										=       num_filters
														__snake_case		:      Union[str, Any]										=       num_residual_layers
														__snake_case		:      Optional[int]										=       upsampling_ratios
														__snake_case		:      List[str]										=       norm_type
														__snake_case		:      Optional[int]										=       kernel_size
														__snake_case		:      Dict										=       last_kernel_size
														__snake_case		:      Tuple										=       residual_kernel_size
														__snake_case		:      List[Any]										=       dilation_growth_rate
														__snake_case		:      Optional[int]										=       use_causal_conv
														__snake_case		:      Tuple										=       pad_mode
														__snake_case		:      Union[str, Any]										=       compress
														__snake_case		:      Union[str, Any]										=       num_lstm_layers
														__snake_case		:      int										=       trim_right_ratio
														__snake_case		:      Tuple										=       codebook_size
														__snake_case		:      Optional[Any]										=       codebook_dim if codebook_dim is not None else hidden_size
														__snake_case		:      int										=       use_conv_shortcut

														if self.norm_type not in ["weight_norm", "time_group_norm"]:
																					raise ValueError(
																					    F'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'    )

														super().__init__(**lowerCamelCase    )

							@property
							def    __snake_case (							self	:  int    )     ->   Optional[int]:
														if self.chunk_length_s is None:
																					return None
														else:
																					return int(self.chunk_length_s * self.sampling_rate    )

							@property
							def    __snake_case (							self	:  Union[str, Any]    )     ->   Optional[int]:
														if self.chunk_length_s is None or self.overlap is None:
																					return None
														else:
																					return max(1   ,     int((1.0 - self.overlap) * self.chunk_length    )    )

							@property
							def    __snake_case (							self	:  Optional[Any]    )     ->   int:
														__snake_case		:      Union[str, Any]										=       np.prod(self.upsampling_ratios    )
														return math.ceil(self.sampling_rate / hop_length    )






							@property
							def    __snake_case (							self	:  Optional[Any]    )     ->   int:
														return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)    )

