
"""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()
_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					'''Hello, World!'''
_lowercase				 =					'''en_XX'''






def    _snake_case					(		snake_case__ :  str						,   snake_case__ :  str						,   snake_case__ :  bool  ):
	A     = Path('data_bin'  )
	A     = 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__  )

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

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

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

	A     = model(snake_case__  )[0]
	if classification_head:
		A     = xmod.model.classification_heads['mnli'](xmod.extract_features(snake_case__  )  )
	else:
		A     = xmod.model(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(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__":
		_lowercase				 =					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.'''
		)
		_lowercase				 =					parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)

"""simple docstring"""


import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     BioGptTokenizer
						_lowerCamelCase:     Tuple   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							A     = [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    'w</w>',
							    'r</w>',
							    't</w>',
							    'lo',
							    'low',
							    'er</w>',
							    'low</w>',
							    'lowest</w>',
							    'newer</w>',
							    'wider</w>',
							    '<unk>',
							]
							A     = dict(zip(A_	,range(len(A_   )   )   )   )
							A     = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']   )
							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['merges_file']   )
							with open(self.vocab_file	,'w'   ) as fp:
								fp.write(json.dumps(A_   )   )
							with open(self.merges_file	,'w'   ) as fp:
								fp.write('\n'.join(A_   )   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   Tuple   )  -> int:
							A     = 'lower newer'
							A     = 'lower newer'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							A     = BioGptTokenizer(self.vocab_file	,self.merges_file   )

							A     = 'lower'
							A     = ['low', 'er</w>']
							A     = tokenizer.tokenize(A_   )
							self.assertListEqual(A_	,A_   )

							A     = tokens + ['<unk>']
							A     = [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )	,A_   )




						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> Optional[Any]:
							A     = BioGptTokenizer.from_pretrained('microsoft/biogpt'   )

							A     = tokenizer.encode('sequence builders'	,add_special_tokens=A_   )
							A     = tokenizer.encode('multi-sequence build'	,add_special_tokens=A_   )

							A     = tokenizer.build_inputs_with_special_tokens(A_   )
							A     = tokenizer.build_inputs_with_special_tokens(A_	,A_   )

							self.assertTrue(encoded_sentence == [2] + text   )
							self.assertTrue(encoded_pair == [2] + text + [2] + text_a   )