

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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							bool      )			->      List[str]:




				'''simple docstring'''





				A__        =			Path("data_bin"      )
				A__        =			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_      )

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

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

				A__        =			XmodForSequenceClassification(SCREAMING_SNAKE_CASE_      ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE_      )
				model.eval()

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

				A__        =			xmod_sent_encoder.layernorm_embedding.weight
				A__        =			xmod_sent_encoder.layernorm_embedding.bias

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

								# self attention
								A__        =			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__        =			xmod_layer.self_attn.q_proj.weight
								A__        =			xmod_layer.self_attn.q_proj.bias
								A__        =			xmod_layer.self_attn.k_proj.weight
								A__        =			xmod_layer.self_attn.k_proj.bias
								A__        =			xmod_layer.self_attn.v_proj.weight
								A__        =			xmod_layer.self_attn.v_proj.bias

								# self-attention output
								A__        =			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__        =			xmod_layer.self_attn.out_proj.weight
								A__        =			xmod_layer.self_attn.out_proj.bias
								A__        =			xmod_layer.self_attn_layer_norm.weight
								A__        =			xmod_layer.self_attn_layer_norm.bias

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

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

								# end of layer

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

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

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

				A__        =			model(SCREAMING_SNAKE_CASE_      )[0]
				if classification_head:
								A__        =			xmod.model.classification_heads["mnli"](xmod.extract_features(SCREAMING_SNAKE_CASE_      )      )
				else:
								A__        =			xmod.model(SCREAMING_SNAKE_CASE_	,       lang_id=[SAMPLE_LANGUAGE]      )[0]
				print(our_output.shape	,       their_output.shape      )
				A__        =			torch.max(torch.abs(our_output - their_output      )      ).item()
				print(F'max_absolute_diff = {max_absolute_diff}'      )  # ~ 1e-7
				A__        =			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__":
	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 warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


lowerCAmelCase__	   = logging.get_logger(__name__)

class 					a__						(					snake_case			):
			"""simple docstring"""







			def __init__(    self      ,      *lowercase      ,      **lowercase       )  ->						None:
							'''simple docstring'''






							warnings.warn(
							    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
							    " use CLIPImageProcessor instead."      ,      lowercase      ,      )
							super().__init__(*lowercase      ,      **lowercase       )

