
"""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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						str:


					"""simple docstring"""





					_UpperCamelCase									=    Path('''data_bin'''      )
					_UpperCamelCase									=    FairseqXmodModel.from_pretrained(
					    model_name_or_path=str(Path(__snake_case      ).parent      ),	checkpoint_file=Path(__snake_case      ).name,	_name='''xmod_base''',	arch='''xmod_base''',	task='''multilingual_masked_lm''',	data_name_or_path=str(__snake_case      ),	bpe='''sentencepiece''',	sentencepiece_model=str(Path(__snake_case      ).parent / '''sentencepiece.bpe.model'''      ),	src_dict=str(data_dir / '''dict.txt'''      ),	)
					xmod.eval()  # disable dropout
					print(__snake_case      )

					_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_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:
										_UpperCamelCase									=    xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

					_UpperCamelCase									=    XmodForSequenceClassification(__snake_case      ) if classification_head else XmodForMaskedLM(__snake_case      )
					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(__snake_case      ).unsqueeze(0      )  # batch of size 1
					model.roberta.set_default_language(__snake_case      )

					_UpperCamelCase									=    model(__snake_case      )[0]
					if classification_head:
										_UpperCamelCase									=    xmod.model.classification_heads['''mnli'''](xmod.extract_features(__snake_case      )      )
					else:
										_UpperCamelCase									=    xmod.model(__snake_case,	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(__snake_case,	__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(__snake_case      ).mkdir(parents=__snake_case,	exist_ok=__snake_case      )
					print(F'''Saving model to {pytorch_dump_folder_path}'''      )
					model.save_pretrained(__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
				)










from __future__ import annotations






def 					UpperCamelCase   (      __magic_name__	:				list[int]	)  ->    list[int]:  # This function is recursive
		"""simple docstring"""




		lowercase__	     =						len(__magic_name__	)
		# If the array contains only one element, we return it (it's the stop condition of
		# recursion)
		if array_length <= 1:
				return array
				# Else
		lowercase__	     =						array[0]
		lowercase__	     =						False
		lowercase__	     =						1
		lowercase__	     =						[]
		while not is_found and i < array_length:
				if array[i] < pivot:
						lowercase__	     =						True
						lowercase__	     =						[element for element in array[i:] if element >= array[i]]
						lowercase__	     =						longest_subsequence(__magic_name__	)
						if len(__magic_name__	) > len(__magic_name__	):
								lowercase__	     =						temp_array
				else:
						i += 1

		lowercase__	     =						[element for element in array[1:] if element >= pivot]
		lowercase__	     =						[pivot, *longest_subsequence(__magic_name__	)]
		if len(__magic_name__	) > len(__magic_name__	):
				return temp_array
		else:
				return longest_subseq


if __name__ == "__main__":
	import doctest

	doctest.testmod()

