






'''simple docstring'''




import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		__lowerCamelCase   :					int   							=					'''platform'''
		import jax
		import jax.numpy as jnp
		import numpy as np

		from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel







@require_flax
class    A_	:
							"""simple docstring"""




							a__   		=	PegasusConfig
							a__   		=	{}
							a__   		=	'gelu'

							def __init__(		self			:Dict       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:int=13       ,							lowerCAmelCase__			:str=7       ,							lowerCAmelCase__			:Dict=True       ,							lowerCAmelCase__			:Any=False       ,							lowerCAmelCase__			:Dict=99       ,							lowerCAmelCase__			:int=32       ,							lowerCAmelCase__			:Dict=5       ,							lowerCAmelCase__			:List[str]=4       ,							lowerCAmelCase__			:Any=37       ,							lowerCAmelCase__			:List[Any]=0.1       ,							lowerCAmelCase__			:List[str]=0.1       ,							lowerCAmelCase__			:Any=20       ,							lowerCAmelCase__			:Union[str, Any]=2       ,							lowerCAmelCase__			:Dict=1       ,							lowerCAmelCase__			:Tuple=0       ,							)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       parent
										snake_case_				:					int   =       batch_size
										snake_case_				:					List[Any]   =       seq_length
										snake_case_				:					int   =       is_training
										snake_case_				:					Optional[int]   =       use_labels
										snake_case_				:					List[str]   =       vocab_size
										snake_case_				:					int   =       hidden_size
										snake_case_				:					List[Any]   =       num_hidden_layers
										snake_case_				:					Tuple   =       num_attention_heads
										snake_case_				:					Any   =       intermediate_size

										snake_case_				:					Optional[Any]   =       hidden_dropout_prob
										snake_case_				:					Optional[int]   =       attention_probs_dropout_prob
										snake_case_				:					Union[str, Any]   =       max_position_embeddings
										snake_case_				:					Union[str, Any]   =       eos_token_id
										snake_case_				:					int   =       pad_token_id
										snake_case_				:					Optional[int]   =       bos_token_id

							def 							_A	(		self			:Dict				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       ids_tensor([self.batch_size, self.seq_length - 1]       ,							self.vocab_size				).clip(3       ,							self.vocab_size				)
										snake_case_				:					Any   =       np.expand_dims(np.array([self.eos_token_id] * self.batch_size				)       ,							1				)
										snake_case_				:					int   =       np.concatenate([input_ids, eos_tensor]       ,							axis=1				)

										snake_case_				:					int   =       ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size				)

										snake_case_				:					Dict   =       self.config_cls(
										    vocab_size=self.vocab_size       ,							d_model=self.hidden_size       ,							encoder_layers=self.num_hidden_layers       ,							decoder_layers=self.num_hidden_layers       ,							encoder_attention_heads=self.num_attention_heads       ,							decoder_attention_heads=self.num_attention_heads       ,							encoder_ffn_dim=self.intermediate_size       ,							decoder_ffn_dim=self.intermediate_size       ,							dropout=self.hidden_dropout_prob       ,							attention_dropout=self.attention_probs_dropout_prob       ,							max_position_embeddings=self.max_position_embeddings       ,							eos_token_ids=[2]       ,							bos_token_id=self.bos_token_id       ,							pad_token_id=self.pad_token_id       ,							decoder_start_token_id=self.pad_token_id       ,							**self.config_updates       ,							)
										snake_case_				:					List[Any]   =       prepare_pegasus_inputs_dict(_a       ,							_a       ,							_a				)
										return config, inputs_dict

							def 							_A	(		self			:Tuple       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:str				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       20
										snake_case_				:					List[str]   =       model_class_name(_a				)

										snake_case_				:					List[str]   =       model.encode(inputs_dict["input_ids"]				)

										snake_case_,	snake_case_				:					List[str]   =       (
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

										snake_case_				:					List[Any]   =       model.init_cache(decoder_input_ids.shape[0]       ,							_a       ,							_a				)
										snake_case_				:					Any   =       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,							dtype="i4"				)

										snake_case_				:					Dict   =       jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]       ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,							)
										snake_case_				:					str   =       model.decode(
										    decoder_input_ids[:, :-1]       ,							_a       ,							decoder_attention_mask=_a       ,							past_key_values=_a       ,							decoder_position_ids=_a       ,							)

										snake_case_				:					Optional[Any]   =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,							dtype="i4"				)
										snake_case_				:					Optional[int]   =       model.decode(
										    decoder_input_ids[:, -1:]       ,							_a       ,							decoder_attention_mask=_a       ,							past_key_values=outputs_cache.past_key_values       ,							decoder_position_ids=_a       ,							)

										snake_case_				:					str   =       model.decode(_a       ,							_a				)

										snake_case_				:					Dict   =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
										self.parent.assertTrue(diff < 1E-3       ,							msg=F'''Max diff is {diff}'''				)






							def 							_A	(		self			:Union[str, Any]       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:Optional[int]				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       20
										snake_case_				:					List[str]   =       model_class_name(_a				)

										snake_case_				:					Any   =       model.encode(inputs_dict["input_ids"]				)

										snake_case_,	snake_case_				:					str   =       (
										    inputs_dict["decoder_input_ids"],
										    inputs_dict["decoder_attention_mask"],
										)

										snake_case_				:					List[Any]   =       jnp.concatenate(
										    [
										        decoder_attention_mask,
										        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
										    ]       ,							axis=-1       ,							)

										snake_case_				:					Any   =       model.init_cache(decoder_input_ids.shape[0]       ,							_a       ,							_a				)
										snake_case_				:					List[str]   =       jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]       ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,							)

										snake_case_				:					Any   =       model.decode(
										    decoder_input_ids[:, :-1]       ,							_a       ,							decoder_attention_mask=_a       ,							past_key_values=_a       ,							decoder_position_ids=_a       ,							)
										snake_case_				:					str   =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,							dtype="i4"				)
										snake_case_				:					int   =       model.decode(
										    decoder_input_ids[:, -1:]       ,							_a       ,							past_key_values=outputs_cache.past_key_values       ,							decoder_attention_mask=_a       ,							decoder_position_ids=_a       ,							)

										snake_case_				:					Tuple   =       model.decode(_a       ,							_a       ,							decoder_attention_mask=_a				)

										snake_case_				:					Union[str, Any]   =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
										self.parent.assertTrue(diff < 1E-3       ,							msg=F'''Max diff is {diff}'''				)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__=None     ,__magic_name__=None     ,)->    Dict:



			"""simple docstring"""



			if attention_mask is None:
						snake_case_				:					int   =       np.not_equal(lowerCAmelCase__     ,config.pad_token_id						).astype(np.inta						)
			if decoder_attention_mask is None:
						snake_case_				:					str   =       np.concatenate(
						    [
						        np.ones(decoder_input_ids[:, :1].shape     ,dtype=np.inta						),
						        np.not_equal(decoder_input_ids[:, 1:]     ,config.pad_token_id						).astype(np.inta						),
						    ]     ,axis=-1     ,)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			}







@require_flax
class    A_	(__SCREAMING_SNAKE_CASE						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	(
							    (
							        FlaxPegasusForConditionalGeneration,
							        FlaxPegasusModel,
							    )
							    if is_flax_available()
							    else ()
							)
							a__   		=	(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
							a__   		=	True
							a__   		=	False
							a__   		=	False
							a__   		=	False

							def 							_A	(		self			:Any				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					int   =       FlaxPegasusModelTester(self				)
										snake_case_				:					List[str]   =       ConfigTester(self       ,							config_class=_a				)

							def 							_A	(		self			:Dict				)				->  List[Any]:


										'''simple docstring'''




										self.config_tester.run_common_tests()

							def 							_A	(		self			:int				)				->  str:


										'''simple docstring'''




										snake_case_,	snake_case_				:					Optional[int]   =       self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(_a       ,							_a       ,							_a				)

							def 							_A	(		self			:int				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_,	snake_case_				:					Optional[Any]   =       self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward_with_attn_mask(_a       ,							_a       ,							_a				)

							def 							_A	(		self			:Any				)				->  int:


										'''simple docstring'''




										snake_case_,	snake_case_				:					Union[str, Any]   =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__				):
																snake_case_				:					int   =       self._prepare_for_class(_a       ,							_a				)
																snake_case_				:					Union[str, Any]   =       model_class(_a				)

																@jax.jit
																def encode_jitted(lowerCAmelCase__			:Dict       ,							lowerCAmelCase__			:Tuple=None       ,							**lowerCAmelCase__			:List[str]				):
																			return model.encode(input_ids=_a       ,							attention_mask=_a				)

																with self.subTest("JIT Enabled"				):
																			snake_case_				:					Union[str, Any]   =       encode_jitted(**_a				).to_tuple()

																with self.subTest("JIT Disabled"				):
																			with jax.disable_jit():
																						snake_case_				:					Tuple   =       encode_jitted(**_a				).to_tuple()

																self.assertEqual(len(_a				)       ,							len(_a				)				)
																for jitted_output, output in zip(_a       ,							_a				):
																			self.assertEqual(jitted_output.shape       ,							output.shape				)

							def 							_A	(		self			:Optional[int]				)				->  List[str]:


										'''simple docstring'''




										snake_case_,	snake_case_				:					int   =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__				):
																snake_case_				:					Optional[Any]   =       model_class(_a				)
																snake_case_				:					Dict   =       model.encode(inputs_dict["input_ids"]       ,							inputs_dict["attention_mask"]				)

																snake_case_				:					Optional[int]   =       {
																    "decoder_input_ids": inputs_dict["decoder_input_ids"],
																    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
																    "encoder_outputs": encoder_outputs,
																}

																@jax.jit
																def decode_jitted(lowerCAmelCase__			:Union[str, Any]       ,							lowerCAmelCase__			:List[str]       ,							lowerCAmelCase__			:Optional[Any]				):
																			return model.decode(
																			    decoder_input_ids=_a       ,							decoder_attention_mask=_a       ,							encoder_outputs=_a       ,							)

																with self.subTest("JIT Enabled"				):
																			snake_case_				:					Any   =       decode_jitted(**_a				).to_tuple()

																with self.subTest("JIT Disabled"				):
																			with jax.disable_jit():
																						snake_case_				:					Union[str, Any]   =       decode_jitted(**_a				).to_tuple()

																self.assertEqual(len(_a				)       ,							len(_a				)				)
																for jitted_output, output in zip(_a       ,							_a				):
																			self.assertEqual(jitted_output.shape       ,							output.shape				)

							@slow
							def 							_A	(		self			:List[str]				)				->  Union[str, Any]:


										'''simple docstring'''




										for model_class_name in self.all_model_classes:
													snake_case_				:					List[Any]   =       model_class_name.from_pretrained("google/pegasus-large"       ,							from_pt=_a				)
													snake_case_				:					int   =       np.ones((1, 1)				)
													snake_case_				:					List[str]   =       model(_a				)
													self.assertIsNotNone(_a				)






							@slow
							def 							_A	(		self			:int				)				->  str:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"				)
										snake_case_				:					Tuple   =       PegasusTokenizer.from_pretrained("google/pegasus-xsum"				)

										snake_case_				:					Tuple   =       [
										    " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
										    " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning \'Oh I think you\'re nominated\'\", said Dappy.\"And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around.\"At the end of the day we\'re grateful to be where we are in our careers.\"If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
										]

										snake_case_				:					Tuple   =       [
										    "California\'s largest electricity provider has turned off power to hundreds of thousands of customers.",
										    "Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.",
										]

										snake_case_				:					List[str]   =       tokenizer(_a       ,							return_tensors="np"       ,							truncation=_a       ,							max_length=512       ,							padding=_a				)
										snake_case_				:					Optional[int]   =       model.generate(**_a       ,							num_beams=2				).sequences
										snake_case_				:					Union[str, Any]   =       tokenizer.batch_decode(_a       ,							skip_special_tokens=_a				)
										assert tgt_text == decoded












'''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
		)




