



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

							__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:'  ,   _lowerCAmelCase					)

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

							__lowercase       	=model(_lowerCAmelCase					)[0]
							if classification_head:
														__lowercase       	=xmod.model.classification_heads['mnli'](xmod.extract_features(_lowerCAmelCase					)					)
							else:
														__lowercase       	=xmod.model(_lowerCAmelCase  ,   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(_lowerCAmelCase  ,   _lowerCAmelCase  ,   atol=1e-3					)
							print('Do both models output the same tensors?'  ,   '🔥' if success else '💩'					)
							if not success:
														raise Exception('Something went wRoNg'					)

							Path(_lowerCAmelCase					).mkdir(parents=_lowerCAmelCase  ,   exist_ok=_lowerCAmelCase					)
							print(f"""Saving model to {pytorch_dump_folder_path}"""					)
							model.save_pretrained(_lowerCAmelCase					)


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'''



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCamelCase	 =       """Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=_ask_options(
							    'In which compute environment are you running?'  ,   ['This machine', 'AWS (Amazon SageMaker)']  ,   _convert_compute_environment  ,   )
							if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
														__lowercase       	=get_sagemaker_input()
							else:
														__lowercase       	=get_cluster_input()
							return config



def   _A       (   _lowerCAmelCase=None					):



							"""simple docstring"""



							if subparsers is not None:
														__lowercase       	=subparsers.add_parser('config'  ,   description=_lowerCAmelCase					)
							else:
														__lowercase       	=argparse.ArgumentParser('Accelerate config command'  ,   description=_lowerCAmelCase					)

							parser.add_argument(
							    '--config_file'  ,   default=_lowerCAmelCase  ,   help=(
							        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
							        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
							        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
							        'with \'huggingface\'.'
							    )  ,   )

							if subparsers is not None:
														parser.set_defaults(func=_lowerCAmelCase					)
							return parser



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=get_user_input()
							if args.config_file is not None:
														__lowercase       	=args.config_file
							else:
														if not os.path.isdir(_lowerCAmelCase					):
																					os.makedirs(_lowerCAmelCase					)
														__lowercase       	=default_yaml_config_file

							if config_file.endswith('.json'					):
														config.to_json_file(_lowerCAmelCase					)
							else:
														config.to_yaml_file(_lowerCAmelCase					)
							print(f"""accelerate configuration saved at {config_file}"""					)



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=config_command_parser()
							__lowercase       	=parser.parse_args()
							config_command(_lowerCAmelCase					)


if __name__ == "__main__":
		main()



