






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        =			logging.get_logger(__name__)

A        =			'Hello world! cécé herlolip'



def 					_lowerCamelCase(			lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		str    ,    lowerCAmelCase__				:		bool      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	str													=   FairseqRobertaModel.from_pretrained(lowerCAmelCase__      )
				roberta.eval()  # disable dropout
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   roberta.model.encoder.sentence_encoder
				SCREAMING_SNAKE_CASE_   :	Any													=   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_   :	Any													=   roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

				print('Our RoBERTa config:'    ,    lowerCAmelCase__      )

				SCREAMING_SNAKE_CASE_   :	List[Any]													=   XLMRobertaXLForSequenceClassification(lowerCAmelCase__      ) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase__      )
				model.eval()

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

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

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

								SCREAMING_SNAKE_CASE_   :	RobertaAttention													=   layer.attention
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   roberta_layer.self_attn_layer_norm.weight
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   roberta_layer.self_attn_layer_norm.bias

								# self attention
								SCREAMING_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)      )
								)

								SCREAMING_SNAKE_CASE_   :	List[str]													=   roberta_layer.self_attn.q_proj.weight
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   roberta_layer.self_attn.q_proj.bias
								SCREAMING_SNAKE_CASE_   :	Any													=   roberta_layer.self_attn.k_proj.weight
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   roberta_layer.self_attn.k_proj.bias
								SCREAMING_SNAKE_CASE_   :	Any													=   roberta_layer.self_attn.v_proj.weight
								SCREAMING_SNAKE_CASE_   :	int													=   roberta_layer.self_attn.v_proj.bias

								# self-attention output
								SCREAMING_SNAKE_CASE_   :	BertSelfOutput													=   layer.attention.output
								assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   roberta_layer.self_attn.out_proj.weight
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   roberta_layer.self_attn.out_proj.bias

								# this one is final layer norm
								SCREAMING_SNAKE_CASE_   :	str													=   roberta_layer.final_layer_norm.weight
								SCREAMING_SNAKE_CASE_   :	List[Any]													=   roberta_layer.final_layer_norm.bias

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

								# output
								SCREAMING_SNAKE_CASE_   :	BertOutput													=   layer.output
								assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
								SCREAMING_SNAKE_CASE_   :	Dict													=   roberta_layer.fca.weight
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   roberta_layer.fca.bias
								# end of layer

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

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

				SCREAMING_SNAKE_CASE_   :	Any													=   model(lowerCAmelCase__      )[0]
				if classification_head:
								SCREAMING_SNAKE_CASE_   :	Optional[int]													=   roberta.model.classification_heads['mnli'](roberta.extract_features(lowerCAmelCase__      )      )
				else:
								SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   roberta.model(lowerCAmelCase__      )[0]
				print(our_output.shape    ,    their_output.shape      )
				SCREAMING_SNAKE_CASE_   :	Any													=   torch.max(torch.abs(our_output - their_output      )      ).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''      )  # ~ 1e-7
				SCREAMING_SNAKE_CASE_   :	List[Any]													=   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        =			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        =			parser.parse_args()
							convert_xlm_roberta_xl_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tempfile.mkdtemp()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    '[PAD]',
						    '[MASK]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						SCREAMING_SNAKE_CASE_   :	Dict													=   os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['vocab_file']       )
						with open(self.vocab_file ,    'w' ,    encoding='utf-8'       ) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   {
						    'do_resize': True,
						    'size': 20,
						    'do_center_crop': True,
						    'crop_size': 18,
						    'do_normalize': True,
						    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
						    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
						}
						SCREAMING_SNAKE_CASE_   :	str													=   os.path.join(self.tmpdirname ,    UpperCamelCase__       )
						with open(self.image_processor_file ,    'w' ,    encoding='utf-8'       ) as fp:
										json.dump(UpperCamelCase__ ,    UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return BertTokenizer.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return BertTokenizerFast.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self       ):
						shutil.rmtree(self.tmpdirname       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   [np.random.randint(255 ,    size=(3, 30, 400) ,    dtype=np.uinta       )]
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [Image.fromarray(np.moveaxis(UpperCamelCase__ ,    0 ,    -1       )       ) for x in image_inputs]
						return image_inputs


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.get_tokenizer()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )
						processor_slow.save_pretrained(self.tmpdirname       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor.from_pretrained(self.tmpdirname ,    use_fast=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )
						processor_fast.save_pretrained(self.tmpdirname       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor.from_pretrained(self.tmpdirname       )

						self.assertEqual(processor_slow.tokenizer.get_vocab() ,    tokenizer_slow.get_vocab()       )
						self.assertEqual(processor_fast.tokenizer.get_vocab() ,    tokenizer_fast.get_vocab()       )
						self.assertEqual(tokenizer_slow.get_vocab() ,    tokenizer_fast.get_vocab()       )
						self.assertIsInstance(processor_slow.tokenizer ,    UpperCamelCase__       )
						self.assertIsInstance(processor_fast.tokenizer ,    UpperCamelCase__       )

						self.assertEqual(processor_slow.image_processor.to_json_string() ,    image_processor.to_json_string()       )
						self.assertEqual(processor_fast.image_processor.to_json_string() ,    image_processor.to_json_string()       )
						self.assertIsInstance(processor_slow.image_processor ,    UpperCamelCase__       )
						self.assertIsInstance(processor_fast.image_processor ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=self.get_tokenizer() ,    image_processor=self.get_image_processor()       )
						processor.save_pretrained(self.tmpdirname       )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_tokenizer(bos_token='(BOS)' ,    eos_token='(EOS)'       )
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_image_processor(do_normalize=UpperCamelCase__ ,    padding_value=1.0       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   AlignProcessor.from_pretrained(
						    self.tmpdirname ,    bos_token='(BOS)' ,    eos_token='(EOS)' ,    do_normalize=UpperCamelCase__ ,    padding_value=1.0       )

						self.assertEqual(processor.tokenizer.get_vocab() ,    tokenizer_add_kwargs.get_vocab()       )
						self.assertIsInstance(processor.tokenizer ,    UpperCamelCase__       )

						self.assertEqual(processor.image_processor.to_json_string() ,    image_processor_add_kwargs.to_json_string()       )
						self.assertIsInstance(processor.image_processor ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	str													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	str													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	int													=   image_processor(UpperCamelCase__ ,    return_tensors='np'       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   processor(images=UpperCamelCase__ ,    return_tensors='np'       )

						for key in input_image_proc.keys():
										self.assertAlmostEqual(input_image_proc[key].sum() ,    input_processor[key].sum() ,    delta=1E-2       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   'lower newer'

						SCREAMING_SNAKE_CASE_   :	Tuple													=   processor(text=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	int													=   tokenizer(UpperCamelCase__ ,    padding='max_length' ,    max_length=64       )

						for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key] ,    encoded_processor[key]       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	str													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	str													=   'lower newer'
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   processor(text=UpperCamelCase__ ,    images=UpperCamelCase__       )

						self.assertListEqual(list(inputs.keys()       ) ,    ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']       )

						# test if it raises when no input is passed
						with pytest.raises(UpperCamelCase__       ):
										processor()


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						SCREAMING_SNAKE_CASE_   :	List[str]													=   processor.batch_decode(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer.batch_decode(UpperCamelCase__       )

						self.assertListEqual(UpperCamelCase__ ,    UpperCamelCase__       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	str													=   'lower newer'
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   processor(text=UpperCamelCase__ ,    images=UpperCamelCase__       )

						self.assertListEqual(list(inputs.keys()       ) ,    processor.model_input_names       )