


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__			 =						logging.get_logger(__name__)

snake_case__			 =						'''Hello, World!'''
snake_case__			 =						'''en_XX'''


def 				lowerCamelCase__	(      a	:   str	,  a	:   str	,  a	:   bool				)    ->	List[Any]:





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

			a__ :List[str]						  =    xmod.model.encoder.sentence_encoder
			a__ :Dict						  =    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=514	,  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:
						a__ :List[str]						  =    xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

			a__ :Optional[int]						  =    XmodForSequenceClassification(a				) if classification_head else XmodForMaskedLM(a				)
			model.eval()

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

			a__ :Union[str, Any]						  =    xmod_sent_encoder.layernorm_embedding.weight
			a__ :Tuple						  =    xmod_sent_encoder.layernorm_embedding.bias

			for i in range(config.num_hidden_layers				):
						# Encoder: start of layer
						a__ :Dict						  =    model.roberta.encoder.layer[i]
						a__ :List[str]						  =    xmod_sent_encoder.layers[i]

						# self attention
						a__ :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."				)

						a__ :List[str]						  =    xmod_layer.self_attn.q_proj.weight
						a__ :Union[str, Any]						  =    xmod_layer.self_attn.q_proj.bias
						a__ :Optional[Any]						  =    xmod_layer.self_attn.k_proj.weight
						a__ :Any						  =    xmod_layer.self_attn.k_proj.bias
						a__ :Dict						  =    xmod_layer.self_attn.v_proj.weight
						a__ :List[str]						  =    xmod_layer.self_attn.v_proj.bias

						# self-attention output
						a__ :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."				)
						a__ :Optional[Any]						  =    xmod_layer.self_attn.out_proj.weight
						a__ :int						  =    xmod_layer.self_attn.out_proj.bias
						a__ :Dict						  =    xmod_layer.self_attn_layer_norm.weight
						a__ :Dict						  =    xmod_layer.self_attn_layer_norm.bias

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

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

						# end of layer

			if xmod_sent_encoder.layer_norm is not None:
						a__ :List[Any]						  =    xmod_sent_encoder.layer_norm.weight
						a__ :Tuple						  =    xmod_sent_encoder.layer_norm.bias

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

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

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

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


if __name__ == "__main__":
		snake_case__			 =						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__			 =						parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)






import functools
import operator

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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'sew'




						def __init__(	self						:   Any   ,     __A						:   str=32   ,     __A						:   Dict=768   ,     __A						:   int=12   ,     __A						:   Dict=12   ,     __A						:   Dict=3072   ,     __A						:   int=2   ,     __A						:   Union[str, Any]="gelu"   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Optional[Any]=0.1   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   str=0.0   ,     __A						:   Union[str, Any]=0.1   ,     __A						:   Optional[Any]=0.1   ,     __A						:   Tuple=0.02   ,     __A						:   Any=1E-5   ,     __A						:   Optional[Any]="group"   ,     __A						:   str="gelu"   ,     __A						:   Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,     __A						:   Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,     __A						:   int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,     __A						:   List[str]=False   ,     __A						:   Tuple=128   ,     __A						:   Tuple=16   ,     __A						:   Optional[int]=True   ,     __A						:   Union[str, Any]=0.05   ,     __A						:   List[str]=10   ,     __A						:   Optional[int]=2   ,     __A						:   List[Any]=0.0   ,     __A						:   Optional[Any]=10   ,     __A						:   Tuple=0   ,     __A						:   Tuple="mean"   ,     __A						:   Any=False   ,     __A						:   str=False   ,     __A						:   Dict=256   ,     __A						:   Union[str, Any]=0   ,     __A						:   Optional[int]=1   ,     __A						:   Optional[Any]=2   ,     **__A						:   Tuple   ,     )      ->Tuple:






									"""simple docstring"""






									super().__init__(**__A   ,     pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A						)
									a__ :List[Any]						  =    hidden_size
									a__ :List[Any]						  =    feat_extract_norm
									a__ :List[str]						  =    feat_extract_activation
									a__ :Any						  =    list(__A						)
									a__ :Dict						  =    list(__A						)
									a__ :Optional[int]						  =    list(__A						)
									a__ :Any						  =    conv_bias
									a__ :List[str]						  =    num_conv_pos_embeddings
									a__ :str						  =    num_conv_pos_embedding_groups
									a__ :Optional[int]						  =    len(self.conv_dim						)
									a__ :List[Any]						  =    num_hidden_layers
									a__ :str						  =    intermediate_size
									a__ :Dict						  =    squeeze_factor
									a__ :List[Any]						  =    hidden_act
									a__ :Optional[Any]						  =    num_attention_heads
									a__ :Tuple						  =    hidden_dropout
									a__ :Tuple						  =    attention_dropout
									a__ :List[Any]						  =    activation_dropout
									a__ :str						  =    feat_proj_dropout
									a__ :Any						  =    final_dropout
									a__ :Dict						  =    layerdrop
									a__ :List[str]						  =    layer_norm_eps
									a__ :Tuple						  =    initializer_range
									a__ :Dict						  =    vocab_size

									if (
									    (len(self.conv_stride						) != self.num_feat_extract_layers)
									    or (len(self.conv_kernel						) != self.num_feat_extract_layers)
									    or (len(self.conv_dim						) != self.num_feat_extract_layers)
									):
												raise ValueError(
												    "Configuration for convolutional layers is incorrect."
												    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
												    F'''but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)'''
												    F'''= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

									# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
									a__ :int						  =    apply_spec_augment
									a__ :Optional[Any]						  =    mask_time_prob
									a__ :List[Any]						  =    mask_time_length
									a__ :Any						  =    mask_time_min_masks
									a__ :Any						  =    mask_feature_prob
									a__ :Dict						  =    mask_feature_length
									a__ :Union[str, Any]						  =    mask_feature_min_masks

									# ctc loss
									a__ :Dict						  =    ctc_loss_reduction
									a__ :Any						  =    ctc_zero_infinity

									# sequence classification
									a__ :Optional[int]						  =    use_weighted_layer_sum
									a__ :Tuple						  =    classifier_proj_size






						@property
						def  _snake_case					(	self						:   Dict						)      ->Union[str, Any]:






									"""simple docstring"""






									return functools.reduce(operator.mul   ,     self.conv_stride   ,     1						)


