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


A__           =							logging.get_logger(__name__)

A__           =							{
    """funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/config.json""",
    """funnel-transformer/small-base""": """https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json""",
    """funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/config.json""",
    """funnel-transformer/medium-base""": """https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json""",
    """funnel-transformer/intermediate""": (
        """https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json"""
    ),
    """funnel-transformer/intermediate-base""": (
        """https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json"""
    ),
    """funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/config.json""",
    """funnel-transformer/large-base""": """https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json""",
    """funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json""",
    """funnel-transformer/xlarge-base""": """https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json""",
}
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'funnel'
							_UpperCAmelCase						    =     {
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'n_head',
							}







							def __init__(				self     :    Any	,					__snake_case     :    List[Any]=30522	,					__snake_case     :    Dict=[4, 4, 4]	,					__snake_case     :    Dict=None	,					__snake_case     :    Optional[int]=2	,					__snake_case     :    Any=768	,					__snake_case     :    Union[str, Any]=12	,					__snake_case     :    Optional[int]=64	,					__snake_case     :    Any=3072	,					__snake_case     :    int="gelu_new"	,					__snake_case     :    Dict=0.1	,					__snake_case     :    int=0.1	,					__snake_case     :    Union[str, Any]=0.0	,					__snake_case     :    int=0.1	,					__snake_case     :    Optional[int]=None	,					__snake_case     :    List[str]=1e-9	,					__snake_case     :    Optional[Any]="mean"	,					__snake_case     :    Any="relative_shift"	,					__snake_case     :    str=True	,					__snake_case     :    Optional[Any]=True	,					__snake_case     :    Dict=True	,					**__snake_case     :    Union[str, Any]	,					):
										lowerCamelCase						:str						  =     vocab_size
										lowerCamelCase						:Dict						  =     block_sizes
										lowerCamelCase						:Any						  =     [1] * len(__snake_case		) if block_repeats is None else block_repeats
										assert len(__snake_case		) == len(
										    self.block_repeats		), "`block_sizes` and `block_repeats` should have the same length."
										lowerCamelCase						:str						  =     num_decoder_layers
										lowerCamelCase						:List[str]						  =     d_model
										lowerCamelCase						:Dict						  =     n_head
										lowerCamelCase						:Any						  =     d_head
										lowerCamelCase						:Optional[int]						  =     d_inner
										lowerCamelCase						:List[Any]						  =     hidden_act
										lowerCamelCase						:Optional[int]						  =     hidden_dropout
										lowerCamelCase						:Optional[int]						  =     attention_dropout
										lowerCamelCase						:Dict						  =     activation_dropout
										lowerCamelCase						:Union[str, Any]						  =     initializer_range
										lowerCamelCase						:List[str]						  =     initializer_std
										lowerCamelCase						:Tuple						  =     layer_norm_eps
										assert pooling_type in [
										    "mean",
										    "max",
										], F"Got {pooling_type} for `pooling_type` but only 'mean' and 'max' are supported."
										lowerCamelCase						:List[str]						  =     pooling_type
										assert attention_type in [
										    "relative_shift",
										    "factorized",
										], F"Got {attention_type} for `attention_type` but only 'relative_shift' and 'factorized' are supported."
										lowerCamelCase						:List[Any]						  =     attention_type
										lowerCamelCase						:List[Any]						  =     separate_cls
										lowerCamelCase						:Optional[Any]						  =     truncate_seq
										lowerCamelCase						:List[Any]						  =     pool_q_only

										super().__init__(**__snake_case		)







							@property
							def 				snake_case  (				self     :    str		):
										return sum(self.block_sizes		)







							@num_hidden_layers.setter
							def 				snake_case  (				self     :    Union[str, Any]	,					__snake_case     :    Optional[Any]		):
										raise NotImplementedError(
										    '''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'''		)







							@property
							def 				snake_case  (				self     :    Any		):
										return len(self.block_sizes		)







							@num_blocks.setter
							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    Optional[int]		):
										raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.'''		)




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()
A__           =							logging.get_logger(__name__)

A__           =							"""Hello, World!"""
A__           =							"""en_XX"""





def  _lowerCamelCase					(   a_				: str						,			a_				: str						,			a_				: bool):
			lowerCamelCase						:int						  =     Path('''data_bin''')
			lowerCamelCase						:Union[str, 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_)

			lowerCamelCase						:Any						  =     xmod.model.encoder.sentence_encoder
			lowerCamelCase						:List[str]						  =     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_14						,			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:
						lowerCamelCase						:Dict						  =     xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

			print('''Our X-MOD config:'''						,			a_)

			lowerCamelCase						:List[Any]						  =     XmodForSequenceClassification(a_) if classification_head else XmodForMaskedLM(a_)
			model.eval()

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

			lowerCamelCase						:List[Any]						  =     xmod_sent_encoder.layernorm_embedding.weight
			lowerCamelCase						:Optional[int]						  =     xmod_sent_encoder.layernorm_embedding.bias

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

						# self attention
						lowerCamelCase						: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.''')

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

						# self-attention output
						lowerCamelCase						: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.''')
						lowerCamelCase						:List[Any]						  =     xmod_layer.self_attn.out_proj.weight
						lowerCamelCase						:Union[str, Any]						  =     xmod_layer.self_attn.out_proj.bias
						lowerCamelCase						:str						  =     xmod_layer.self_attn_layer_norm.weight
						lowerCamelCase						:List[Any]						  =     xmod_layer.self_attn_layer_norm.bias

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

						# output
						lowerCamelCase						: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.''')
						lowerCamelCase						:str						  =     xmod_layer.fca.weight
						lowerCamelCase						:int						  =     xmod_layer.fca.bias
						lowerCamelCase						:List[Any]						  =     xmod_layer.final_layer_norm.weight
						lowerCamelCase						:List[str]						  =     xmod_layer.final_layer_norm.bias
						if bert_output.adapter_layer_norm is not None:
									lowerCamelCase						:List[str]						  =     xmod_layer.adapter_layer_norm.weight
									lowerCamelCase						: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():
									lowerCamelCase						:Optional[int]						  =     bert_output.adapter_modules[lang_code]
									lowerCamelCase						:Dict						  =     xmod_layer.adapter_modules[lang_code]
									lowerCamelCase						:List[Any]						  =     from_adapter.fca.weight
									lowerCamelCase						:List[Any]						  =     from_adapter.fca.bias
									lowerCamelCase						:Dict						  =     from_adapter.fca.weight
									lowerCamelCase						:Optional[Any]						  =     from_adapter.fca.bias

						# end of layer

			if xmod_sent_encoder.layer_norm is not None:
						lowerCamelCase						:Dict						  =     xmod_sent_encoder.layer_norm.weight
						lowerCamelCase						:List[Any]						  =     xmod_sent_encoder.layer_norm.bias

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

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

			lowerCamelCase						:Any						  =     model(a_)[0]
			if classification_head:
						lowerCamelCase						:Dict						  =     xmod.model.classification_heads['''mnli'''](xmod.extract_features(a_))
			else:
						lowerCamelCase						:int						  =     xmod.model(a_						,			lang_id=[SAMPLE_LANGUAGE])[0]
			print(our_output.shape						,			their_output.shape)
			lowerCamelCase						:List[str]						  =     torch.max(torch.abs(our_output - their_output)).item()
			print(F"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
			lowerCamelCase						:str						  =     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__":
			A__           =							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."""
			)
			A__           =							parser.parse_args()
			convert_xmod_checkpoint_to_pytorch(
			    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



