


"""simple docstring"""







import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
		raise Exception('''requires fairseq >= 1.0.0a''')

logging.set_verbosity_info()
a__    :							Any            =					logging.get_logger(__name__)

a__    :							List[str]            =					'''Hello world! cécé herlolip'''




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




						'''simple docstring'''


						__SCREAMING_SNAKE_CASE						       =    FairseqRobertaModel.from_pretrained(lowerCAmelCase_       )
						roberta.eval()  # disable dropout
						__SCREAMING_SNAKE_CASE						       =    roberta.model.encoder.sentence_encoder
						__SCREAMING_SNAKE_CASE						       =    XLMRobertaConfig(
						    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings  ,						hidden_size=roberta.cfg.model.encoder_embed_dim  ,						num_hidden_layers=roberta.cfg.model.encoder_layers  ,						num_attention_heads=roberta.cfg.model.encoder_attention_heads  ,						intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim  ,						max_position_embeddings=514  ,						type_vocab_size=1  ,						layer_norm_eps=1E-5  ,						)
						if classification_head:
												__SCREAMING_SNAKE_CASE						       =    roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

						print("Our RoBERTa config:"  ,						lowerCAmelCase_       )

						__SCREAMING_SNAKE_CASE						       =    XLMRobertaXLForSequenceClassification(lowerCAmelCase_       ) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase_       )
						model.eval()

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

						__SCREAMING_SNAKE_CASE						       =    roberta_sent_encoder.layer_norm.weight
						__SCREAMING_SNAKE_CASE						       =    roberta_sent_encoder.layer_norm.bias

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

												__SCREAMING_SNAKE_CASE						       =    layer.attention
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.self_attn_layer_norm.weight
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.self_attn_layer_norm.bias

												# self attention
												__SCREAMING_SNAKE_CASE						       =    layer.attention.self
												assert (
												    roberta_layer.self_attn.k_proj.weight.data.shape
												    == roberta_layer.self_attn.q_proj.weight.data.shape
												    == roberta_layer.self_attn.v_proj.weight.data.shape
												    == torch.Size((config.hidden_size, config.hidden_size)       )
												)

												__SCREAMING_SNAKE_CASE						       =    roberta_layer.self_attn.q_proj.weight
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.self_attn.q_proj.bias
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.self_attn.k_proj.weight
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.self_attn.k_proj.bias
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.self_attn.v_proj.weight
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.self_attn.v_proj.bias

												# self-attention output
												__SCREAMING_SNAKE_CASE						       =    layer.attention.output
												assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.self_attn.out_proj.weight
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.self_attn.out_proj.bias

												# this one is final layer norm
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.final_layer_norm.weight
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.final_layer_norm.bias

												# intermediate
												__SCREAMING_SNAKE_CASE						       =    layer.intermediate
												assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.fca.weight
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.fca.bias

												# output
												__SCREAMING_SNAKE_CASE						       =    layer.output
												assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.fca.weight
												__SCREAMING_SNAKE_CASE						       =    roberta_layer.fca.bias
												# end of layer

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

						# Let's check that we get the same results.
						__SCREAMING_SNAKE_CASE						       =    roberta.encode(lowerCAmelCase_       ).unsqueeze(0       )  # batch of size 1

						__SCREAMING_SNAKE_CASE						       =    model(lowerCAmelCase_       )[0]
						if classification_head:
												__SCREAMING_SNAKE_CASE						       =    roberta.model.classification_heads["mnli"](roberta.extract_features(lowerCAmelCase_       )       )
						else:
												__SCREAMING_SNAKE_CASE						       =    roberta.model(lowerCAmelCase_       )[0]
						print(our_output.shape  ,						their_output.shape       )
						__SCREAMING_SNAKE_CASE						       =    torch.max(torch.abs(our_output - their_output       )       ).item()
						print(f"""max_absolute_diff = {max_absolute_diff}"""       )  # ~ 1e-7
						__SCREAMING_SNAKE_CASE						       =    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"       )

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


if __name__ == "__main__":
		a__    :							Any            =					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--roberta_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__    :							List[Any]            =					parser.parse_args()
		convert_xlm_roberta_xl_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)




"""simple docstring"""







from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__    :							Tuple            =					logging.get_logger(__name__)

a__    :							List[Any]            =					{
    '''snap-research/efficientformer-l1-300''': (
        '''https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'''
    ),
}




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Any    =      "efficientformer"




	def __init__(			self      :    Any      ,      UpperCAmelCase__      :    List[int] = [3, 2, 6, 4]      ,      UpperCAmelCase__      :    List[int] = [4_8, 9_6, 2_2_4, 4_4_8]      ,      UpperCAmelCase__      :    List[bool] = [True, True, True, True]      ,      UpperCAmelCase__      :    int = 4_4_8      ,      UpperCAmelCase__      :    int = 3_2      ,      UpperCAmelCase__      :    int = 4      ,      UpperCAmelCase__      :    int = 7      ,      UpperCAmelCase__      :    int = 5      ,      UpperCAmelCase__      :    int = 8      ,      UpperCAmelCase__      :    int = 4      ,      UpperCAmelCase__      :    float = 0.0      ,      UpperCAmelCase__      :    int = 1_6      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 3      ,      UpperCAmelCase__      :    int = 2      ,      UpperCAmelCase__      :    int = 1      ,      UpperCAmelCase__      :    float = 0.0      ,      UpperCAmelCase__      :    int = 1      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    float = 1E-5      ,      UpperCAmelCase__      :    str = "gelu"      ,      UpperCAmelCase__      :    float = 0.02      ,      UpperCAmelCase__      :    float = 1E-12      ,      UpperCAmelCase__      :    int = 2_2_4      ,      UpperCAmelCase__      :    float = 1E-05      ,      **UpperCAmelCase__      :    Tuple      ,      )	->     None:
						super().__init__(**UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    hidden_act
						__SCREAMING_SNAKE_CASE						       =    hidden_dropout_prob
						__SCREAMING_SNAKE_CASE						       =    hidden_sizes
						__SCREAMING_SNAKE_CASE						       =    num_hidden_layers
						__SCREAMING_SNAKE_CASE						       =    num_attention_heads
						__SCREAMING_SNAKE_CASE						       =    initializer_range
						__SCREAMING_SNAKE_CASE						       =    layer_norm_eps
						__SCREAMING_SNAKE_CASE						       =    patch_size
						__SCREAMING_SNAKE_CASE						       =    num_channels
						__SCREAMING_SNAKE_CASE						       =    depths
						__SCREAMING_SNAKE_CASE						       =    mlp_expansion_ratio
						__SCREAMING_SNAKE_CASE						       =    downsamples
						__SCREAMING_SNAKE_CASE						       =    dim
						__SCREAMING_SNAKE_CASE						       =    key_dim
						__SCREAMING_SNAKE_CASE						       =    attention_ratio
						__SCREAMING_SNAKE_CASE						       =    resolution
						__SCREAMING_SNAKE_CASE						       =    pool_size
						__SCREAMING_SNAKE_CASE						       =    downsample_patch_size
						__SCREAMING_SNAKE_CASE						       =    downsample_stride
						__SCREAMING_SNAKE_CASE						       =    downsample_pad
						__SCREAMING_SNAKE_CASE						       =    drop_path_rate
						__SCREAMING_SNAKE_CASE						       =    num_metaad_blocks
						__SCREAMING_SNAKE_CASE						       =    distillation
						__SCREAMING_SNAKE_CASE						       =    use_layer_scale
						__SCREAMING_SNAKE_CASE						       =    layer_scale_init_value
						__SCREAMING_SNAKE_CASE						       =    image_size
						__SCREAMING_SNAKE_CASE						       =    batch_norm_eps
