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

SCREAMING_SNAKE_CASE_					     =	'''Hello, World!'''
SCREAMING_SNAKE_CASE_					     =	'''en_XX'''

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  Path("""data_bin"""						)
						__lowerCAmelCase     =  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						)

						__lowerCAmelCase     =  xmod.model.encoder.sentence_encoder
						__lowerCAmelCase     =  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:
												__lowerCAmelCase     =  xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

						print("""Our X-MOD config:""" ,    _lowerCAmelCase						)

						__lowerCAmelCase     =  XmodForSequenceClassification(_lowerCAmelCase						) if classification_head else XmodForMaskedLM(_lowerCAmelCase						)
						model.eval()

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

						__lowerCAmelCase     =  xmod_sent_encoder.layernorm_embedding.weight
						__lowerCAmelCase     =  xmod_sent_encoder.layernorm_embedding.bias

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

												# self attention
												__lowerCAmelCase     =  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."""						)

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

												# self-attention output
												__lowerCAmelCase     =  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."""						)
												__lowerCAmelCase     =  xmod_layer.self_attn.out_proj.weight
												__lowerCAmelCase     =  xmod_layer.self_attn.out_proj.bias
												__lowerCAmelCase     =  xmod_layer.self_attn_layer_norm.weight
												__lowerCAmelCase     =  xmod_layer.self_attn_layer_norm.bias

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

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

												# end of layer

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

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

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

						__lowerCAmelCase     =  model(_lowerCAmelCase						)[0]
						if classification_head:
												__lowerCAmelCase     =  xmod.model.classification_heads["""mnli"""](xmod.extract_features(_lowerCAmelCase						)						)
						else:
												__lowerCAmelCase     =  xmod.model(_lowerCAmelCase ,    lang_id=[SAMPLE_LANGUAGE]						)[0]
						print(our_output.shape ,    their_output.shape						)
						__lowerCAmelCase     =  torch.max(torch.abs(our_output - their_output						)						).item()
						print(f"""max_absolute_diff = {max_absolute_diff}"""						)  # ~ 1e-7
						__lowerCAmelCase     =  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__":
			SCREAMING_SNAKE_CASE_					     =	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.'''
			)
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			convert_xmod_checkpoint_to_pytorch(
			    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)


"""simple docstring"""
from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''efficientnet'''




							def __init__( self    ,				snake_case_ = 3    ,				snake_case_ = 600    ,				snake_case_ = 2.0    ,				snake_case_ = 3.1    ,				snake_case_ = 8    ,				snake_case_ = [3, 3, 5, 3, 5, 5, 3]    ,				snake_case_ = [32, 16, 24, 40, 80, 112, 192]    ,				snake_case_ = [16, 24, 40, 80, 112, 192, 320]    ,				snake_case_ = []    ,				snake_case_ = [1, 2, 2, 2, 1, 2, 1]    ,				snake_case_ = [1, 2, 2, 3, 3, 4, 1]    ,				snake_case_ = [1, 6, 6, 6, 6, 6, 6]    ,				snake_case_ = 0.25    ,				snake_case_ = "swish"    ,				snake_case_ = 2_560    ,				snake_case_ = "mean"    ,				snake_case_ = 0.02    ,				snake_case_ = 0.001    ,				snake_case_ = 0.99    ,				snake_case_ = 0.5    ,				snake_case_ = 0.2    ,				**snake_case_    ,				)       ->  List[str]:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  width_coefficient
													__lowerCAmelCase     =  depth_coefficient
													__lowerCAmelCase     =  depth_divisor
													__lowerCAmelCase     =  kernel_sizes
													__lowerCAmelCase     =  in_channels
													__lowerCAmelCase     =  out_channels
													__lowerCAmelCase     =  depthwise_padding
													__lowerCAmelCase     =  strides
													__lowerCAmelCase     =  num_block_repeats
													__lowerCAmelCase     =  expand_ratios
													__lowerCAmelCase     =  squeeze_expansion_ratio
													__lowerCAmelCase     =  hidden_act
													__lowerCAmelCase     =  hidden_dim
													__lowerCAmelCase     =  pooling_type
													__lowerCAmelCase     =  initializer_range
													__lowerCAmelCase     =  batch_norm_eps
													__lowerCAmelCase     =  batch_norm_momentum
													__lowerCAmelCase     =  dropout_rate
													__lowerCAmelCase     =  drop_connect_rate
													__lowerCAmelCase     =  sum(snake_case_				) * 4
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		version.parse('''1.11''' )




							@property
							def    A__						( self				)       ->  Mapping[str, Mapping[int, str]]:
													return OrderedDict(
													    [
													        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
													    ]				)




							@property
							def    A__						( self				)       ->  float:
													return 1e-5
