



'''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()
lowerCamelCase									=							logging.get_logger(__name__)

lowerCamelCase									=							"""Hello, World!"""
lowerCamelCase									=							"""en_XX"""



def 	_A							( _lowerCAmelCase	, _lowerCAmelCase	, _lowerCAmelCase       ):



							"""simple docstring"""



							__lowercase													=Path('data_bin'       )
							__lowercase													=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       )

							__lowercase													=xmod.model.encoder.sentence_encoder
							__lowercase													=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:
														__lowercase													=xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

							__lowercase													=XmodForSequenceClassification(__a       ) if classification_head else XmodForMaskedLM(__a       )
							model.eval()

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

							__lowercase													=xmod_sent_encoder.layernorm_embedding.weight
							__lowercase													=xmod_sent_encoder.layernorm_embedding.bias

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

														# self attention
														__lowercase													=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.'       )

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

														# self-attention output
														__lowercase													=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.'       )
														__lowercase													=xmod_layer.self_attn.out_proj.weight
														__lowercase													=xmod_layer.self_attn.out_proj.bias
														__lowercase													=xmod_layer.self_attn_layer_norm.weight
														__lowercase													=xmod_layer.self_attn_layer_norm.bias

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

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

														# end of layer

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

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

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

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








"""simple docstring"""







import warnings

from .generation import TFGenerationMixin







class 					A_(SCREAMING_SNAKE_CASE_      ):







			"""simple docstring"""



			warnings.warn(
			    """Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
			    """be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."""   ,   SCREAMING_SNAKE_CASE_   ,   )





