






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()
__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						'Hello, World!'
__UpperCAmelCase								=						'en_XX'




def      lowercase__      (     __snake_case    :							str     ,       __snake_case    :							str     ,       __snake_case    :							bool  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Any            =						Path('data_bin'  )
					UpperCAmelCase_    :      Any            =						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_    :      Dict            =						xmod.model.encoder.sentence_encoder
					UpperCAmelCase_    :      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=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:
										UpperCAmelCase_    :      Optional[Any]            =						xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

					UpperCAmelCase_    :      int            =						XmodForSequenceClassification(__snake_case  ) if classification_head else XmodForMaskedLM(__snake_case  )
					model.eval()

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

					UpperCAmelCase_    :      List[str]            =						xmod_sent_encoder.layernorm_embedding.weight
					UpperCAmelCase_    :      Dict            =						xmod_sent_encoder.layernorm_embedding.bias

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

										# self attention
										UpperCAmelCase_    :      Dict            =						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_    :      Optional[int]            =						xmod_layer.self_attn.q_proj.weight
										UpperCAmelCase_    :      Optional[int]            =						xmod_layer.self_attn.q_proj.bias
										UpperCAmelCase_    :      Tuple            =						xmod_layer.self_attn.k_proj.weight
										UpperCAmelCase_    :      Optional[Any]            =						xmod_layer.self_attn.k_proj.bias
										UpperCAmelCase_    :      Union[str, Any]            =						xmod_layer.self_attn.v_proj.weight
										UpperCAmelCase_    :      List[str]            =						xmod_layer.self_attn.v_proj.bias

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

										# intermediate
										UpperCAmelCase_    :      Optional[Any]            =						layer.intermediate
										if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
															raise AssertionError('Dimensions of intermediate weights do not match.'  )
										UpperCAmelCase_    :      str            =						xmod_layer.fca.weight
										UpperCAmelCase_    :      str            =						xmod_layer.fca.bias

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

										# end of layer

					if xmod_sent_encoder.layer_norm is not None:
										UpperCAmelCase_    :      str            =						xmod_sent_encoder.layer_norm.weight
										UpperCAmelCase_    :      List[Any]            =						xmod_sent_encoder.layer_norm.bias

					if classification_head:
										UpperCAmelCase_    :      List[Any]            =						xmod.model.classification_heads['mnli'].dense.weight
										UpperCAmelCase_    :      str            =						xmod.model.classification_heads['mnli'].dense.bias
										UpperCAmelCase_    :      int            =						xmod.model.classification_heads['mnli'].out_proj.weight
										UpperCAmelCase_    :      str            =						xmod.model.classification_heads['mnli'].out_proj.bias
					else:
										# LM Head
										UpperCAmelCase_    :      List[Any]            =						xmod.model.encoder.lm_head.dense.weight
										UpperCAmelCase_    :      Optional[Any]            =						xmod.model.encoder.lm_head.dense.bias
										UpperCAmelCase_    :      List[str]            =						xmod.model.encoder.lm_head.layer_norm.weight
										UpperCAmelCase_    :      Any            =						xmod.model.encoder.lm_head.layer_norm.bias
										UpperCAmelCase_    :      Optional[Any]            =						xmod.model.encoder.lm_head.weight
										UpperCAmelCase_    :      str            =						xmod.model.encoder.lm_head.bias

					# Let's check that we get the same results.
					UpperCAmelCase_    :      List[Any]            =						xmod.encode(__snake_case  ).unsqueeze(0  )  # batch of size 1
					model.roberta.set_default_language(__snake_case  )

					UpperCAmelCase_    :      Any            =						model(__snake_case  )[0]
					if classification_head:
										UpperCAmelCase_    :      int            =						xmod.model.classification_heads['mnli'](xmod.extract_features(__snake_case  )  )
					else:
										UpperCAmelCase_    :      Optional[Any]            =						xmod.model(__snake_case     ,       lang_id=[SAMPLE_LANGUAGE]  )[0]
					print(our_output.shape     ,       their_output.shape  )
					UpperCAmelCase_    :      int            =						torch.max(torch.abs(our_output - their_output  )  ).item()
					print(F"max_absolute_diff = {max_absolute_diff}"  )  # ~ 1e-7
					UpperCAmelCase_    :      Union[str, Any]            =						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__":
		__UpperCAmelCase								=						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.'
		)
		__UpperCAmelCase								=						parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)











'''simple docstring'''

import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch

class 	__magic_name__						(    _UpperCamelCase   ):







							@require_torch
							def 							__lowercase			(							self					:       Tuple		):
									# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
									# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
									# while running an external program

									# python one-liner segments

									# this must be loaded before socket.socket is monkey-patched
									_a     :      Optional[int]        =							'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

									_a     :      List[str]        =							'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

									_a     :      Tuple        =							'\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

									# Force fetching the files so that we can use the cache
									_a     :      List[Any]        =							'hf-internal-testing/tiny-random-bert'
									BertConfig.from_pretrained(_UpperCAmelCase		)
									BertModel.from_pretrained(_UpperCAmelCase		)
									BertTokenizer.from_pretrained(_UpperCAmelCase		)
									pipeline(task='fill-mask'	,model=_UpperCAmelCase		)

									# baseline - just load from_pretrained with normal network
									_a     :      Optional[int]        =							[sys.executable, '-c', '\n'.join([load, run, mock]		)]

									# should succeed
									_a     :      Tuple        =							self.get_env()
									# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
									_a     :      int        =							'1'
									_a     :      List[Any]        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)







							@require_torch
							def 							__lowercase			(							self					:       Any		):
									# python one-liner segments
									# this must be loaded before socket.socket is monkey-patched
									_a     :      Dict        =							'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

									_a     :      Optional[int]        =							'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

									_a     :      Optional[Any]        =							'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

									# Force fetching the files so that we can use the cache
									_a     :      int        =							'hf-internal-testing/tiny-random-bert'
									BertConfig.from_pretrained(_UpperCAmelCase		)
									BertModel.from_pretrained(_UpperCAmelCase		)
									BertTokenizer.from_pretrained(_UpperCAmelCase		)
									pipeline(task='fill-mask'	,model=_UpperCAmelCase		)

									# baseline - just load from_pretrained with normal network
									_a     :      Optional[int]        =							[sys.executable, '-c', '\n'.join([load, run, mock]		)]

									# should succeed
									_a     :      str        =							self.get_env()
									_a     :      Optional[Any]        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)







							@require_torch
							def 							__lowercase			(							self					:       List[str]		):
									# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
									# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
									# while running an external program

									# python one-liner segments

									# this must be loaded before socket.socket is monkey-patched
									_a     :      Union[str, Any]        =							'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

									_a     :      Optional[Any]        =							'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

									_a     :      str        =							'\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

									# baseline - just load from_pretrained with normal network
									_a     :      Optional[Any]        =							[sys.executable, '-c', '\n'.join([load, run]		)]

									# should succeed
									_a     :      Dict        =							self.get_env()
									_a     :      int        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)

									# next emulate no network
									_a     :      List[Any]        =							[sys.executable, '-c', '\n'.join([load, mock, run]		)]

									# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
									# env["TRANSFORMERS_OFFLINE"] = "0"
									# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
									# self.assertEqual(result.returncode, 1, result.stderr)

									# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
									_a     :      int        =							'1'
									_a     :      Any        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)







							@require_torch
							def 							__lowercase			(							self					:       int		):
									_a     :      Optional[Any]        =							'\nfrom transformers import pipeline\n        '
									_a     :      str        =							'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

									_a     :      List[str]        =							'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
									_a     :      List[Any]        =							self.get_env()
									_a     :      Dict        =							'1'
									_a     :      Dict        =							[sys.executable, '-c', '\n'.join([load, mock, run]		)]
									_a     :      str        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,1	,result.stderr		)
									self.assertIn(
									    'You cannot infer task automatically within `pipeline` when using offline mode'	,result.stderr.decode().replace('\n'	,''		)	,)







							@require_torch
							def 							__lowercase			(							self					:       int		):
									_a     :      Optional[int]        =							'\nfrom transformers import AutoModel\n        '
									_a     :      List[Any]        =							'\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

									# baseline - just load from_pretrained with normal network
									_a     :      Union[str, Any]        =							[sys.executable, '-c', '\n'.join([load, run]		)]

									# should succeed
									_a     :      Tuple        =							self.get_env()
									_a     :      List[str]        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)

									# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
									_a     :      Optional[Any]        =							'1'
									_a     :      Any        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)




