






'''simple docstring'''




import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase   :					Optional[Any]   							=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase   :					Optional[Any]   							=					[]
for i in range(6):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
		)
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
		rename_keys.append(
		    (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
		)
		rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
		rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
		)
		rename_keys.append(
		    (
		        f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
		        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
		    )
		)
		rename_keys.append(
		    (
		        f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
		        f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
		    )
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.encoder.norm.weight''', '''encoder.layernorm.weight'''),
        ('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



			snake_case_				:					Dict   =       state_dict.pop(__magic_name__						)
			snake_case_				:					List[Any]   =       val

def 	__UpperCAmelCase       ( __magic_name__						)->    List[str]:



			"""simple docstring"""



			snake_case_				:					str   =       OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									snake_case_				:					Any   =       key.replace("backbone.0.body"     ,"backbone.conv_encoder.model"						)
									snake_case_				:					List[Any]   =       value
						else:
									snake_case_				:					Dict   =       value

			return new_state_dict

def 	__UpperCAmelCase       ( __magic_name__						)->    List[Any]:



			"""simple docstring"""



			snake_case_				:					str   =       ""

			# first: transformer encoder
			for i in range(6						):
						# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
						snake_case_				:					Optional[int]   =       state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''						)
						snake_case_				:					List[Any]   =       state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''						)
						# next, add query, keys and values (in that order) to the state dict
						snake_case_				:					Tuple   =       in_proj_weight[:256, :]
						snake_case_				:					str   =       in_proj_bias[:256]
						snake_case_				:					Dict   =       in_proj_weight[256:512, :]
						snake_case_				:					Optional[Any]   =       in_proj_bias[256:512]
						snake_case_				:					Optional[int]   =       in_proj_weight[-256:, :]
						snake_case_				:					Tuple   =       in_proj_bias[-256:]
			# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
			for i in range(6						):
						# read in weights + bias of input projection layer of self-attention
						snake_case_				:					Tuple   =       state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''						)
						snake_case_				:					Any   =       state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''						)
						# next, add query, keys and values (in that order) to the state dict
						snake_case_				:					int   =       in_proj_weight[:256, :]
						snake_case_				:					List[Any]   =       in_proj_bias[:256]
						snake_case_				:					List[Any]   =       in_proj_weight[256:512, :]
						snake_case_				:					List[str]   =       in_proj_bias[256:512]
						snake_case_				:					Dict   =       in_proj_weight[-256:, :]
						snake_case_				:					int   =       in_proj_bias[-256:]
						# read in weights + bias of input projection layer of cross-attention
						snake_case_				:					Optional[int]   =       state_dict.pop(
						    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''						)
						snake_case_				:					Union[str, Any]   =       state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''						)
						# next, add query, keys and values (in that order) of cross-attention to the state dict
						snake_case_				:					int   =       in_proj_weight_cross_attn[:256, :]
						snake_case_				:					Optional[Any]   =       in_proj_bias_cross_attn[:256]
						snake_case_				:					Any   =       in_proj_weight_cross_attn[256:512, :]
						snake_case_				:					Optional[int]   =       in_proj_bias_cross_attn[256:512]
						snake_case_				:					Optional[Any]   =       in_proj_weight_cross_attn[-256:, :]
						snake_case_				:					Any   =       in_proj_bias_cross_attn[-256:]

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Tuple:



			"""simple docstring"""



			snake_case_,	snake_case_				:					Optional[int]   =       image.size
			snake_case_				:					Dict   =       max(__magic_name__     ,__magic_name__						)
			snake_case_				:					Optional[int]   =       800 if "detection" in checkpoint_url else 1000
			snake_case_				:					Optional[int]   =       target_max_size / current_max_size
			snake_case_				:					str   =       image.resize((int(round(scale * width						)						), int(round(scale * height						)						))						)

			return resized_image

def 	__UpperCAmelCase       ( __magic_name__						)->    Tuple:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       F.to_tensor(__magic_name__						)
			snake_case_				:					List[Any]   =       F.normalize(__magic_name__     ,mean=[0.485, 0.456, 0.406]     ,std=[0.229, 0.224, 0.225]						)
			return image

@torch.no_grad()
def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Tuple:



			"""simple docstring"""



			logger.info("Converting model..."						)

			# load original state dict
			snake_case_				:					str   =       torch.hub.load_state_dict_from_url(__magic_name__     ,map_location="cpu"						)
			# rename keys
			for src, dest in rename_keys:
						rename_key(__magic_name__     ,__magic_name__     ,__magic_name__						)
			snake_case_				:					Dict   =       rename_backbone_keys(__magic_name__						)
			# query, key and value matrices need special treatment
			read_in_q_k_v(__magic_name__						)
			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
			snake_case_				:					Tuple   =       "model."
			for key in state_dict.copy().keys():
						if not key.startswith("class_labels_classifier"						) and not key.startswith("bbox_predictor"						):
									snake_case_				:					str   =       state_dict.pop(__magic_name__						)
									snake_case_				:					int   =       val
    # create HuggingFace model and load state dict
			snake_case_				:					List[str]   =       TableTransformerConfig(
			    backbone="resnet18"     ,mask_loss_coefficient=1     ,dice_loss_coefficient=1     ,ce_loss_coefficient=1     ,bbox_loss_coefficient=5     ,giou_loss_coefficient=2     ,eos_coefficient=0.4     ,class_cost=1     ,bbox_cost=5     ,giou_cost=2     ,)

			if "detection" in checkpoint_url:
						snake_case_				:					str   =       15
						snake_case_				:					Tuple   =       2
						snake_case_				:					Any   =       {0: "table", 1: "table rotated"}
						snake_case_				:					List[str]   =       idalabel
						snake_case_				:					Optional[int]   =       {v: k for k, v in idalabel.items()}
			else:
						snake_case_				:					int   =       125
						snake_case_				:					List[Any]   =       6
						snake_case_				:					str   =       {
						    0: "table",
						    1: "table column",
						    2: "table row",
						    3: "table column header",
						    4: "table projected row header",
						    5: "table spanning cell",
						}
						snake_case_				:					Any   =       idalabel
						snake_case_				:					int   =       {v: k for k, v in idalabel.items()}

			snake_case_				:					List[str]   =       DetrImageProcessor(
			    format="coco_detection"     ,max_size=800 if "detection" in checkpoint_url else 1000						)
			snake_case_				:					Optional[Any]   =       TableTransformerForObjectDetection(__magic_name__						)
			model.load_state_dict(__magic_name__						)
			model.eval()

			# verify our conversion
			snake_case_				:					Dict   =       "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
			snake_case_				:					List[str]   =       hf_hub_download(repo_id="nielsr/example-pdf"     ,repo_type="dataset"     ,filename=__magic_name__						)
			snake_case_				:					Any   =       Image.open(__magic_name__						).convert("RGB"						)
			snake_case_				:					Optional[int]   =       normalize(resize(__magic_name__     ,__magic_name__						)						).unsqueeze(0						)

			snake_case_				:					Optional[Any]   =       model(__magic_name__						)

			if "detection" in checkpoint_url:
						snake_case_				:					Optional[Any]   =       (1, 15, 3)
						snake_case_				:					Union[str, Any]   =       torch.tensor(
						    [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]						)
						snake_case_				:					int   =       torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]						)

			else:
						snake_case_				:					Any   =       (1, 125, 7)
						snake_case_				:					Dict   =       torch.tensor(
						    [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]						)
						snake_case_				:					Optional[Any]   =       torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]						)

			assert outputs.logits.shape == expected_shape
			assert torch.allclose(outputs.logits[0, :3, :3]     ,__magic_name__     ,atol=1E-4						)
			assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,__magic_name__     ,atol=1E-4						)
			print("Looks ok!"						)

			if pytorch_dump_folder_path is not None:
						# Save model and image processor
						logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''						)
						Path(__magic_name__						).mkdir(exist_ok=__magic_name__						)
						model.save_pretrained(__magic_name__						)
						image_processor.save_pretrained(__magic_name__						)

			if push_to_hub:
						# Push model to HF hub
						logger.info("Pushing model to the hub..."						)
						snake_case_				:					str   =       (
						    "microsoft/table-transformer-detection"
						    if "detection" in checkpoint_url
						    else "microsoft/table-transformer-structure-recognition"
						)
						model.push_to_hub(__magic_name__						)
						image_processor.push_to_hub(__magic_name__						)


if __name__ == "__main__":
		__lowerCamelCase   :					List[str]   							=					argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
		    type=str,
		    choices=[
		        '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
		        '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
		    ],
		    help='''URL of the Table Transformer checkpoint you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)
		__lowerCamelCase   :					Dict   							=					parser.parse_args()
		convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












'''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()
__lowerCamelCase   :					Union[str, Any]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					Union[str, Any]   							=					'''Hello world! cécé herlolip'''

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					str   =       FairseqRobertaModel.from_pretrained(__magic_name__						)
			roberta.eval()  # disable dropout
			snake_case_				:					Dict   =       roberta.model.encoder.sentence_encoder
			snake_case_				:					List[str]   =       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:
						snake_case_				:					List[str]   =       roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

			print("Our RoBERTa config:"     ,__magic_name__						)

			snake_case_				:					List[str]   =       XLMRobertaXLForSequenceClassification(__magic_name__						) if classification_head else XLMRobertaXLForMaskedLM(__magic_name__						)
			model.eval()

			# Now let's copy all the weights.
			# Embeddings
			snake_case_				:					List[Any]   =       roberta_sent_encoder.embed_tokens.weight
			snake_case_				:					int   =       roberta_sent_encoder.embed_positions.weight
			snake_case_				:					Union[str, Any]   =       torch.zeros_like(
			    model.roberta.embeddings.token_type_embeddings.weight						)  # just zero them out b/c RoBERTa doesn't use them.

			snake_case_				:					Union[str, Any]   =       roberta_sent_encoder.layer_norm.weight
			snake_case_				:					str   =       roberta_sent_encoder.layer_norm.bias

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

						snake_case_				:					RobertaAttention   =       layer.attention
						snake_case_				:					Dict   =       roberta_layer.self_attn_layer_norm.weight
						snake_case_				:					Dict   =       roberta_layer.self_attn_layer_norm.bias

						# self attention
						snake_case_				:					BertSelfAttention   =       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)						)
						)

						snake_case_				:					Dict   =       roberta_layer.self_attn.q_proj.weight
						snake_case_				:					Any   =       roberta_layer.self_attn.q_proj.bias
						snake_case_				:					Optional[Any]   =       roberta_layer.self_attn.k_proj.weight
						snake_case_				:					Optional[Any]   =       roberta_layer.self_attn.k_proj.bias
						snake_case_				:					Optional[int]   =       roberta_layer.self_attn.v_proj.weight
						snake_case_				:					Any   =       roberta_layer.self_attn.v_proj.bias

						# self-attention output
						snake_case_				:					BertSelfOutput   =       layer.attention.output
						assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
						snake_case_				:					List[str]   =       roberta_layer.self_attn.out_proj.weight
						snake_case_				:					Optional[int]   =       roberta_layer.self_attn.out_proj.bias

						# this one is final layer norm
						snake_case_				:					int   =       roberta_layer.final_layer_norm.weight
						snake_case_				:					Union[str, Any]   =       roberta_layer.final_layer_norm.bias

						# intermediate
						snake_case_				:					BertIntermediate   =       layer.intermediate
						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
						snake_case_				:					List[str]   =       roberta_layer.fca.weight
						snake_case_				:					List[Any]   =       roberta_layer.fca.bias

						# output
						snake_case_				:					BertOutput   =       layer.output
						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
						snake_case_				:					Any   =       roberta_layer.fca.weight
						snake_case_				:					Any   =       roberta_layer.fca.bias
						# end of layer

			if classification_head:
						snake_case_				:					int   =       roberta.model.classification_heads["mnli"].dense.weight
						snake_case_				:					Union[str, Any]   =       roberta.model.classification_heads["mnli"].dense.bias
						snake_case_				:					Tuple   =       roberta.model.classification_heads["mnli"].out_proj.weight
						snake_case_				:					str   =       roberta.model.classification_heads["mnli"].out_proj.bias
			else:
						# LM Head
						snake_case_				:					Optional[Any]   =       roberta.model.encoder.lm_head.dense.weight
						snake_case_				:					int   =       roberta.model.encoder.lm_head.dense.bias
						snake_case_				:					Optional[Any]   =       roberta.model.encoder.lm_head.layer_norm.weight
						snake_case_				:					Optional[int]   =       roberta.model.encoder.lm_head.layer_norm.bias
						snake_case_				:					int   =       roberta.model.encoder.lm_head.weight
						snake_case_				:					List[str]   =       roberta.model.encoder.lm_head.bias

			# Let's check that we get the same results.
			snake_case_				:					torch.Tensor   =       roberta.encode(__magic_name__						).unsqueeze(0						)  # batch of size 1

			snake_case_				:					Union[str, Any]   =       model(__magic_name__						)[0]
			if classification_head:
						snake_case_				:					Optional[Any]   =       roberta.model.classification_heads["mnli"](roberta.extract_features(__magic_name__						)						)
			else:
						snake_case_				:					List[str]   =       roberta.model(__magic_name__						)[0]
			print(our_output.shape     ,their_output.shape						)
			snake_case_				:					str   =       torch.max(torch.abs(our_output - their_output						)						).item()
			print(F'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
			snake_case_				:					Any   =       torch.allclose(__magic_name__     ,__magic_name__     ,atol=1E-3						)
			print("Do both models output the same tensors?"     ,"🔥" if success else "💩"						)
			if not success:
						raise Exception("Something went wRoNg"						)

			pathlib.Path(__magic_name__						).mkdir(parents=__magic_name__     ,exist_ok=__magic_name__						)
			print(F'''Saving model to {pytorch_dump_folder_path}'''						)
			model.save_pretrained(__magic_name__						)


if __name__ == "__main__":
		__lowerCamelCase   :					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.'''
		)
		__lowerCamelCase   :					Tuple   							=					parser.parse_args()
		convert_xlm_roberta_xl_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)




