

"""simple docstring"""


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

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

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

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

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

		__lowerCAmelCase:	List[str]   				=      xmod_sent_encoder.layernorm_embedding.weight
		__lowerCAmelCase:	int   				=      xmod_sent_encoder.layernorm_embedding.bias

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

				# self attention
				__lowerCAmelCase:	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:	int   				=      xmod_layer.self_attn.q_proj.weight
				__lowerCAmelCase:	Tuple   				=      xmod_layer.self_attn.q_proj.bias
				__lowerCAmelCase:	Dict   				=      xmod_layer.self_attn.k_proj.weight
				__lowerCAmelCase:	Any   				=      xmod_layer.self_attn.k_proj.bias
				__lowerCAmelCase:	Tuple   				=      xmod_layer.self_attn.v_proj.weight
				__lowerCAmelCase:	str   				=      xmod_layer.self_attn.v_proj.bias

				# self-attention output
				__lowerCAmelCase:	List[str]   				=      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:	List[Any]   				=      xmod_layer.self_attn.out_proj.bias
				__lowerCAmelCase:	Optional[int]   				=      xmod_layer.self_attn_layer_norm.weight
				__lowerCAmelCase:	List[Any]   				=      xmod_layer.self_attn_layer_norm.bias

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

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

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

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

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

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


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
			)





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A						       =      {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Swinv2ForImageClassification",
			    "Swinv2ForMaskedImageModeling",
			    "Swinv2Model",
			    "Swinv2PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			__A						       =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


