
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()
lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							"Hello, World!"
lowerCamelCase__	=							"en_XX"


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	Path("""data_bin""" )
		_UpperCamelCase						    =	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 )

		_UpperCamelCase						    =	xmod.model.encoder.sentence_encoder
		_UpperCamelCase						    =	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:
				_UpperCamelCase						    =	xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

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

		_UpperCamelCase						    =	XmodForSequenceClassification(lowerCAmelCase ) if classification_head else XmodForMaskedLM(lowerCAmelCase )
		model.eval()

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

		_UpperCamelCase						    =	xmod_sent_encoder.layernorm_embedding.weight
		_UpperCamelCase						    =	xmod_sent_encoder.layernorm_embedding.bias

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

				# self attention
				_UpperCamelCase						    =	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.""" )

				_UpperCamelCase						    =	xmod_layer.self_attn.q_proj.weight
				_UpperCamelCase						    =	xmod_layer.self_attn.q_proj.bias
				_UpperCamelCase						    =	xmod_layer.self_attn.k_proj.weight
				_UpperCamelCase						    =	xmod_layer.self_attn.k_proj.bias
				_UpperCamelCase						    =	xmod_layer.self_attn.v_proj.weight
				_UpperCamelCase						    =	xmod_layer.self_attn.v_proj.bias

				# self-attention output
				_UpperCamelCase						    =	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.""" )
				_UpperCamelCase						    =	xmod_layer.self_attn.out_proj.weight
				_UpperCamelCase						    =	xmod_layer.self_attn.out_proj.bias
				_UpperCamelCase						    =	xmod_layer.self_attn_layer_norm.weight
				_UpperCamelCase						    =	xmod_layer.self_attn_layer_norm.bias

				# intermediate
				_UpperCamelCase						    =	layer.intermediate
				if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
						raise AssertionError("""Dimensions of intermediate weights do not match.""" )
				_UpperCamelCase						    =	xmod_layer.fca.weight
				_UpperCamelCase						    =	xmod_layer.fca.bias

				# output
				_UpperCamelCase						    =	layer.output
				if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
						raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
				_UpperCamelCase						    =	xmod_layer.fca.weight
				_UpperCamelCase						    =	xmod_layer.fca.bias
				_UpperCamelCase						    =	xmod_layer.final_layer_norm.weight
				_UpperCamelCase						    =	xmod_layer.final_layer_norm.bias
				if bert_output.adapter_layer_norm is not None:
						_UpperCamelCase						    =	xmod_layer.adapter_layer_norm.weight
						_UpperCamelCase						    =	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():
						_UpperCamelCase						    =	bert_output.adapter_modules[lang_code]
						_UpperCamelCase						    =	xmod_layer.adapter_modules[lang_code]
						_UpperCamelCase						    =	from_adapter.fca.weight
						_UpperCamelCase						    =	from_adapter.fca.bias
						_UpperCamelCase						    =	from_adapter.fca.weight
						_UpperCamelCase						    =	from_adapter.fca.bias

				# end of layer

		if xmod_sent_encoder.layer_norm is not None:
				_UpperCamelCase						    =	xmod_sent_encoder.layer_norm.weight
				_UpperCamelCase						    =	xmod_sent_encoder.layer_norm.bias

		if classification_head:
				_UpperCamelCase						    =	xmod.model.classification_heads["""mnli"""].dense.weight
				_UpperCamelCase						    =	xmod.model.classification_heads["""mnli"""].dense.bias
				_UpperCamelCase						    =	xmod.model.classification_heads["""mnli"""].out_proj.weight
				_UpperCamelCase						    =	xmod.model.classification_heads["""mnli"""].out_proj.bias
		else:
				# LM Head
				_UpperCamelCase						    =	xmod.model.encoder.lm_head.dense.weight
				_UpperCamelCase						    =	xmod.model.encoder.lm_head.dense.bias
				_UpperCamelCase						    =	xmod.model.encoder.lm_head.layer_norm.weight
				_UpperCamelCase						    =	xmod.model.encoder.lm_head.layer_norm.bias
				_UpperCamelCase						    =	xmod.model.encoder.lm_head.weight
				_UpperCamelCase						    =	xmod.model.encoder.lm_head.bias

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

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





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: int	=		"deta"
						UpperCamelCase_							: Union[str, Any]	=		{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}

						def __init__(			self       ,							a=None       ,							a=9_00       ,							a=20_48       ,							a=6       ,							a=20_48       ,							a=8       ,							a=6       ,							a=10_24       ,							a=8       ,							a=0.0       ,							a=True       ,							a="relu"       ,							a=2_56       ,							a=0.1       ,							a=0.0       ,							a=0.0       ,							a=0.02       ,							a=1.0       ,							a=True       ,							a=False       ,							a="sine"       ,							a=5       ,							a=4       ,							a=4       ,							a=True       ,							a=3_00       ,							a=True       ,							a=True       ,							a=1       ,							a=5       ,							a=2       ,							a=1       ,							a=1       ,							a=5       ,							a=2       ,							a=0.1       ,							a=0.25       ,							**a       ,							)					->      Dict:


								'''simple docstring'''

								if backbone_config is None:
										logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""				)
										_UpperCamelCase						    =	CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]				)
								else:
										if isinstance(a       ,							a				):
												_UpperCamelCase						    =	backbone_config.pop("""model_type"""				)
												_UpperCamelCase						    =	CONFIG_MAPPING[backbone_model_type]
												_UpperCamelCase						    =	config_class.from_dict(a				)

								_UpperCamelCase						    =	backbone_config
								_UpperCamelCase						    =	num_queries
								_UpperCamelCase						    =	max_position_embeddings
								_UpperCamelCase						    =	d_model
								_UpperCamelCase						    =	encoder_ffn_dim
								_UpperCamelCase						    =	encoder_layers
								_UpperCamelCase						    =	encoder_attention_heads
								_UpperCamelCase						    =	decoder_ffn_dim
								_UpperCamelCase						    =	decoder_layers
								_UpperCamelCase						    =	decoder_attention_heads
								_UpperCamelCase						    =	dropout
								_UpperCamelCase						    =	attention_dropout
								_UpperCamelCase						    =	activation_dropout
								_UpperCamelCase						    =	activation_function
								_UpperCamelCase						    =	init_std
								_UpperCamelCase						    =	init_xavier_std
								_UpperCamelCase						    =	encoder_layerdrop
								_UpperCamelCase						    =	auxiliary_loss
								_UpperCamelCase						    =	position_embedding_type
								# deformable attributes
								_UpperCamelCase						    =	num_feature_levels
								_UpperCamelCase						    =	encoder_n_points
								_UpperCamelCase						    =	decoder_n_points
								_UpperCamelCase						    =	two_stage
								_UpperCamelCase						    =	two_stage_num_proposals
								_UpperCamelCase						    =	with_box_refine
								_UpperCamelCase						    =	assign_first_stage
								if two_stage is True and with_box_refine is False:
										raise ValueError("""If two_stage is True, with_box_refine must be True."""				)
								# Hungarian matcher
								_UpperCamelCase						    =	class_cost
								_UpperCamelCase						    =	bbox_cost
								_UpperCamelCase						    =	giou_cost
								# Loss coefficients
								_UpperCamelCase						    =	mask_loss_coefficient
								_UpperCamelCase						    =	dice_loss_coefficient
								_UpperCamelCase						    =	bbox_loss_coefficient
								_UpperCamelCase						    =	giou_loss_coefficient
								_UpperCamelCase						    =	eos_coefficient
								_UpperCamelCase						    =	focal_alpha
								super().__init__(is_encoder_decoder=a       ,							**a				)

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.encoder_attention_heads

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.d_model







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	copy.deepcopy(self.__dict__				)
								_UpperCamelCase						    =	self.backbone_config.to_dict()
								_UpperCamelCase						    =	self.__class__.model_type
								return output



