





'''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   : str             =			logging.get_logger(__name__)

A   : str             =			'Hello world! cécé herlolip'





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__						)   ->Any:
						_snake_case:   Any  					=		FairseqRobertaModel.from_pretrained(lowercase__						)
						roberta.eval()  # disable dropout
						_snake_case:   List[Any]  					=		roberta.model.encoder.sentence_encoder
						_snake_case:   Dict  					=		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:   Optional[Any]  					=		roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

						print('Our RoBERTa config:'				,  lowercase__						)

						_snake_case:   str  					=		XLMRobertaXLForSequenceClassification(lowercase__						) if classification_head else XLMRobertaXLForMaskedLM(lowercase__						)
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						_snake_case:   List[str]  					=		roberta_sent_encoder.embed_tokens.weight
						_snake_case:   Tuple  					=		roberta_sent_encoder.embed_positions.weight
						_snake_case:   Dict  					=		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:   List[Any]  					=		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:   List[str]  					=		roberta_layer.self_attn_layer_norm.weight
												_snake_case:   Union[str, Any]  					=		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:   Union[str, Any]  					=		roberta_layer.self_attn.q_proj.weight
												_snake_case:   List[str]  					=		roberta_layer.self_attn.q_proj.bias
												_snake_case:   Tuple  					=		roberta_layer.self_attn.k_proj.weight
												_snake_case:   Dict  					=		roberta_layer.self_attn.k_proj.bias
												_snake_case:   str  					=		roberta_layer.self_attn.v_proj.weight
												_snake_case:   List[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:   int  					=		roberta_layer.self_attn.out_proj.weight
												_snake_case:   List[str]  					=		roberta_layer.self_attn.out_proj.bias

												# this one is final layer norm
												_snake_case:   Optional[int]  					=		roberta_layer.final_layer_norm.weight
												_snake_case:   Optional[int]  					=		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:   Optional[int]  					=		roberta_layer.fca.bias

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

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

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

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

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


if __name__ == "__main__":
		A   : Union[str, 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   : Dict             =			parser.parse_args()
		convert_xlm_roberta_xl_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)












'''simple docstring'''






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


A   : Tuple             =			['bert-base-uncased', 'bert-base-cased']
A   : str             =			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():







		class 			lowerCamelCase					(						tf.keras.Model  ):


							def __init__(					self	:  Union[str, Any]       ,     __snake_case	:  List[Any]       ):





													'''simple docstring'''





													super().__init__()
													_snake_case:   List[Any]  					=		tokenizer
													_snake_case:   str  					=		AutoConfig.from_pretrained(__snake_case       )
													_snake_case:   List[Any]  					=		TFAutoModel.from_config(__snake_case       )




							def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     __snake_case	:  List[str]       ):





													'''simple docstring'''





													_snake_case:   Optional[int]  					=		self.tokenizer(__snake_case       )
													_snake_case:   Tuple  					=		self.bert(**__snake_case       )
													return out["pooler_output"]







@require_tf
@require_tensorflow_text
class 			lowerCamelCase					(						unittest.TestCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											super().setUp()

											_snake_case:   List[Any]  					=		[
											    BertTokenizer.from_pretrained(__snake_case       ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
											]  # repeat for when fast_bert_tokenizer=false
											_snake_case:   List[Any]  					=		[TFBertTokenizer.from_pretrained(__snake_case       ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
											    TFBertTokenizer.from_pretrained(__snake_case       ,     use_fast_bert_tokenizer=__snake_case       )
											    for checkpoint in TOKENIZER_CHECKPOINTS
											]
											assert len(self.tokenizers       ) == len(self.tf_tokenizers       )

											_snake_case:   int  					=		[
											    'This is a straightforward English test sentence.',
											    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
											    'Now we\'re going to add some Chinese: 一 二 三 一二三',
											    'And some much more rare Chinese: 齉 堃 齉堃',
											    'Je vais aussi écrire en français pour tester les accents',
											    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
											]
											_snake_case:   str  					=		list(zip(self.test_sentences       ,     self.test_sentences[::-1]       )       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											for tokenizer, tf_tokenizer in zip(self.tokenizers       ,     self.tf_tokenizers       ):
																	for test_inputs in (self.test_sentences, self.paired_sentences):
																							_snake_case:   List[Any]  					=		tokenizer(__snake_case       ,     return_tensors='tf'       ,     padding='longest'       )
																							_snake_case:   Optional[Any]  					=		tf_tokenizer(__snake_case       )

																							for key in python_outputs.keys():
																													self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape       )       )
																													self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]       ,     tf.intaa       ) == tf_outputs[key]       )       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   str  					=		tf_tokenizer(self.paired_sentences       )
																	_snake_case:   Any  					=		tf_tokenizer(
																	    text=[sentence[0] for sentence in self.paired_sentences]       ,     text_pair=[sentence[1] for sentence in self.paired_sentences]       ,     )
																	for key in merged_outputs.keys():
																							self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]       ,     tf.intaa       ) == separated_outputs[key]       )       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   List[str]  					=		tf.function(__snake_case       )
																	for test_inputs in (self.test_sentences, self.paired_sentences):
																							_snake_case:   Union[str, Any]  					=		tf.constant(__snake_case       )
																							_snake_case:   Any  					=		compiled_tokenizer(__snake_case       )
																							_snake_case:   Union[str, Any]  					=		tf_tokenizer(__snake_case       )

																							for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]       )       )




					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   Optional[Any]  					=		ModelToSave(tokenizer=__snake_case       )
																	_snake_case:   Tuple  					=		tf.convert_to_tensor(self.test_sentences       )
																	_snake_case:   List[str]  					=		model(__snake_case       )  # Build model with some sample inputs
																	with TemporaryDirectory() as tempdir:
																							_snake_case:   List[str]  					=		Path(__snake_case       ) / 'saved.model'
																							model.save(__snake_case       )
																							_snake_case:   int  					=		tf.keras.models.load_model(__snake_case       )
																	_snake_case:   int  					=		loaded_model(__snake_case       )
																	# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																	self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output       )       )       ,     1e-5       )





