


'''simple docstring'''
from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFConvBertForMaskedLM,
					    TFConvBertForMultipleChoice,
					    TFConvBertForQuestionAnswering,
					    TFConvBertForSequenceClassification,
					    TFConvBertForTokenClassification,
					    TFConvBertModel,
					)




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_3							,      a_=7							,      a_=True							,      a_=True							,      a_=True							,      a_=True							,      a_=9_9							,      a_=3_2							,      a_=2							,      a_=4							,      a_=3_7							,      a_="gelu"							,      a_=0.1							,      a_=0.1							,      a_=5_1_2							,      a_=1_6							,      a_=2							,      a_=0.02							,      a_=3							,      a_=4							,      a_=None							,      )    ->  Union[str, Any]:
					lowercase						:		Optional[int]   					=		parent
					lowercase						:		Optional[Any]   					=		1_3
					lowercase						:		List[Any]   					=		7
					lowercase						:		Tuple   					=		True
					lowercase						:		List[str]   					=		True
					lowercase						:		List[str]   					=		True
					lowercase						:		int   					=		True
					lowercase						:		int   					=		9_9
					lowercase						:		Dict   					=		3_8_4
					lowercase						:		int   					=		2
					lowercase						:		Optional[int]   					=		4
					lowercase						:		Dict   					=		3_7
					lowercase						:		List[Any]   					=		"gelu"
					lowercase						:		Optional[Any]   					=		0.1
					lowercase						:		Optional[int]   					=		0.1
					lowercase						:		Optional[int]   					=		5_1_2
					lowercase						:		str   					=		1_6
					lowercase						:		List[str]   					=		2
					lowercase						:		Optional[int]   					=		0.02
					lowercase						:		Union[str, Any]   					=		3
					lowercase						:		Optional[Any]   					=		4
					lowercase						:		Any   					=		1_2_8
					lowercase						:		List[Any]   					=		2
					lowercase						:		List[Any]   					=		9
					lowercase						:		Any   					=		1
					lowercase						:		Tuple   					=		None

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Optional[int]   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size							)

					lowercase						:		Any   					=		None
					if self.use_input_mask:
						lowercase						:		Union[str, Any]   					=		random_attention_mask([self.batch_size, self.seq_length]							)

					lowercase						:		Optional[Any]   					=		None
					if self.use_token_type_ids:
						lowercase						:		List[Any]   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.type_vocab_size							)

					lowercase						:		Optional[Any]   					=		None
					lowercase						:		Optional[Any]   					=		None
					lowercase						:		Optional[Any]   					=		None
					if self.use_labels:
						lowercase						:		Union[str, Any]   					=		ids_tensor([self.batch_size]							,      self.type_sequence_label_size							)
						lowercase						:		Optional[Any]   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.num_labels							)
						lowercase						:		Optional[Any]   					=		ids_tensor([self.batch_size]							,      self.num_choices							)

					lowercase						:		Dict   					=		ConvBertConfig(
					    vocab_size=self.vocab_size							,      hidden_size=self.hidden_size							,      num_hidden_layers=self.num_hidden_layers							,      num_attention_heads=self.num_attention_heads							,      intermediate_size=self.intermediate_size							,      hidden_act=self.hidden_act							,      hidden_dropout_prob=self.hidden_dropout_prob							,      attention_probs_dropout_prob=self.attention_probs_dropout_prob							,      max_position_embeddings=self.max_position_embeddings							,      type_vocab_size=self.type_vocab_size							,      initializer_range=self.initializer_range							,      return_dict=a_							,      )

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							)    ->  List[str]:
					lowercase						:		List[Any]   					=		TFConvBertModel(config=a_							)
					lowercase						:		Tuple   					=		{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

					lowercase						:		Tuple   					=		[input_ids, input_mask]
					lowercase						:		List[Any]   					=		model(a_							)

					lowercase						:		List[Any]   					=		model(a_							)

					self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.seq_length, self.hidden_size)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							)    ->  Optional[int]:
					lowercase						:		Optional[int]   					=		TFConvBertForMaskedLM(config=a_							)
					lowercase						:		List[Any]   					=		{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "token_type_ids": token_type_ids,
					}
					lowercase						:		int   					=		model(a_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.seq_length, self.vocab_size)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							)    ->  Any:
					lowercase						:		List[str]   					=		self.num_labels
					lowercase						:		str   					=		TFConvBertForSequenceClassification(config=a_							)
					lowercase						:		List[Any]   					=		{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "token_type_ids": token_type_ids,
					}

					lowercase						:		str   					=		model(a_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_labels)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							)    ->  str:
					lowercase						:		List[Any]   					=		self.num_choices
					lowercase						:		Dict   					=		TFConvBertForMultipleChoice(config=a_							)
					lowercase						:		List[Any]   					=		tf.tile(tf.expand_dims(a_							,      1							)							,      (1, self.num_choices, 1)							)
					lowercase						:		Tuple   					=		tf.tile(tf.expand_dims(a_							,      1							)							,      (1, self.num_choices, 1)							)
					lowercase						:		Optional[int]   					=		tf.tile(tf.expand_dims(a_							,      1							)							,      (1, self.num_choices, 1)							)
					lowercase						:		Union[str, Any]   					=		{
					    "input_ids": multiple_choice_inputs_ids,
					    "attention_mask": multiple_choice_input_mask,
					    "token_type_ids": multiple_choice_token_type_ids,
					}
					lowercase						:		Dict   					=		model(a_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_choices)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							)    ->  List[Any]:
					lowercase						:		Optional[int]   					=		self.num_labels
					lowercase						:		Optional[int]   					=		TFConvBertForTokenClassification(config=a_							)
					lowercase						:		Optional[int]   					=		{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "token_type_ids": token_type_ids,
					}
					lowercase						:		List[str]   					=		model(a_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.seq_length, self.num_labels)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							)    ->  List[Any]:
					lowercase						:		Optional[int]   					=		TFConvBertForQuestionAnswering(config=a_							)
					lowercase						:		int   					=		{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "token_type_ids": token_type_ids,
					}

					lowercase						:		Dict   					=		model(a_							)
					self.parent.assertEqual(result.start_logits.shape							,      (self.batch_size, self.seq_length)							)
					self.parent.assertEqual(result.end_logits.shape							,      (self.batch_size, self.seq_length)							)


				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		List[str]   					=		self.prepare_config_and_inputs()
					(
					    (
					    lowercase
					)					,		(
					    lowercase
					)					,		(
					    lowercase
					)					,		(
					    lowercase
					)					,		(
					    lowercase
					)					,		(
					    lowercase
					)					,		(
					    lowercase
					)					,		
					)						:		Dict   					=		config_and_inputs
					lowercase						:		int   					=		{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
					return config, inputs_dict




@require_tf
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(
				    (
				        TFConvBertModel,
				        TFConvBertForMaskedLM,
				        TFConvBertForQuestionAnswering,
				        TFConvBertForSequenceClassification,
				        TFConvBertForTokenClassification,
				        TFConvBertForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				_snake_case						       =				(
				    {
				        '''feature-extraction''': TFConvBertModel,
				        '''fill-mask''': TFConvBertForMaskedLM,
				        '''question-answering''': TFConvBertForQuestionAnswering,
				        '''text-classification''': TFConvBertForSequenceClassification,
				        '''token-classification''': TFConvBertForTokenClassification,
				        '''zero-shot''': TFConvBertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False

				def 	a__		(     self							)    ->  int:
					lowercase						:		Dict   					=		TFConvBertModelTester(self							)
					lowercase						:		Union[str, Any]   					=		ConfigTester(self							,      config_class=a_							,      hidden_size=3_7							)

				def 	a__		(     self							)    ->  Optional[Any]:
					self.config_tester.run_common_tests()

				def 	a__		(     self							)    ->  str:
					lowercase						:		List[Any]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*a_							)

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		Optional[int]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*a_							)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Tuple   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*a_							)

				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		str   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*a_							)

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		List[str]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*a_							)

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		str   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*a_							)

				@slow
				def 	a__		(     self							)    ->  List[str]:
					lowercase					,		lowercase						:		Tuple   					=		self.model_tester.prepare_config_and_inputs_for_common()
					lowercase						:		int   					=		True
					lowercase						:		Any   					=		True

					if hasattr(a_							,      "use_cache"							):
						lowercase						:		Any   					=		True

					lowercase						:		Dict   					=		getattr(self.model_tester							,      "encoder_seq_length"							,      self.model_tester.seq_length							)
					lowercase						:		Tuple   					=		getattr(self.model_tester							,      "key_length"							,      a_							)

					for model_class in self.all_model_classes:
						lowercase						:		Dict   					=		self._prepare_for_class(a_							,      a_							)
						lowercase						:		Optional[Any]   					=		model_class(a_							)
						lowercase						:		Optional[int]   					=		len(model(a_							)							)

						with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(a_							,      saved_model=a_							)
							lowercase						:		int   					=		os.path.join(a_							,      "saved_model"							,      "1"							)
							lowercase						:		Any   					=		tf.keras.models.load_model(a_							)
							lowercase						:		List[Any]   					=		model(a_							)

							if self.is_encoder_decoder:
								lowercase						:		Optional[Any]   					=		outputs["encoder_hidden_states"]
								lowercase						:		Optional[int]   					=		outputs["encoder_attentions"]
							else:
								lowercase						:		Union[str, Any]   					=		outputs["hidden_states"]
								lowercase						:		Any   					=		outputs["attentions"]

							self.assertEqual(len(a_							)							,      a_							)

							lowercase						:		Optional[Any]   					=		getattr(
							    self.model_tester							,      "expected_num_hidden_layers"							,      self.model_tester.num_hidden_layers + 1							)

							self.assertEqual(len(a_							)							,      a_							)
							self.assertListEqual(
							    list(output_hidden_states[0].shape[-2:]							)							,      [self.model_tester.seq_length, self.model_tester.hidden_size]							,      )

							self.assertEqual(len(a_							)							,      self.model_tester.num_hidden_layers							)
							self.assertListEqual(
							    list(output_attentions[0].shape[-3:]							)							,      [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]							,      )

				@slow
				def 	a__		(     self							)    ->  int:
					lowercase						:		List[str]   					=		TFConvBertModel.from_pretrained("YituTech/conv-bert-base"							)
					self.assertIsNotNone(a_							)


				def 	a__		(     self							)    ->  Optional[int]:
					lowercase					,		lowercase						:		Dict   					=		self.model_tester.prepare_config_and_inputs_for_common()
					lowercase						:		Tuple   					=		True
					lowercase						:		Tuple   					=		getattr(self.model_tester							,      "decoder_seq_length"							,      self.model_tester.seq_length							)
					lowercase						:		Dict   					=		getattr(self.model_tester							,      "encoder_seq_length"							,      self.model_tester.seq_length							)
					lowercase						:		Optional[int]   					=		getattr(self.model_tester							,      "key_length"							,      a_							)
					lowercase						:		List[Any]   					=		getattr(self.model_tester							,      "key_length"							,      a_							)

					def check_decoder_attentions_output(a_							):
						lowercase						:		int   					=		len(a_							)
						self.assertEqual(out_len % 2							,      0							)
						lowercase						:		List[Any]   					=		outputs.decoder_attentions
						self.assertEqual(len(a_							)							,      self.model_tester.num_hidden_layers							)
						self.assertListEqual(
						    list(decoder_attentions[0].shape[-3:]							)							,      [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]							,      )

					def check_encoder_attentions_output(a_							):
						lowercase						:		Union[str, Any]   					=		[
						    t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
						]
						self.assertEqual(len(a_							)							,      self.model_tester.num_hidden_layers							)
						self.assertListEqual(
						    list(attentions[0].shape[-3:]							)							,      [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]							,      )

					for model_class in self.all_model_classes:
						lowercase						:		List[str]   					=		True
						lowercase						:		List[str]   					=		False
						lowercase						:		Optional[int]   					=		model_class(a_							)
						lowercase						:		int   					=		model(self._prepare_for_class(a_							,      a_							)							)
						lowercase						:		Optional[Any]   					=		len(a_							)
						self.assertEqual(config.output_hidden_states							,      a_							)
						check_encoder_attentions_output(a_							)

						if self.is_encoder_decoder:
							lowercase						:		List[str]   					=		model_class(a_							)
							lowercase						:		Optional[int]   					=		model(self._prepare_for_class(a_							,      a_							)							)
							self.assertEqual(config.output_hidden_states							,      a_							)
							check_decoder_attentions_output(a_							)

						# Check that output attentions can also be changed via the config
						del inputs_dict["output_attentions"]
						lowercase						:		Optional[Any]   					=		True
						lowercase						:		str   					=		model_class(a_							)
						lowercase						:		Union[str, Any]   					=		model(self._prepare_for_class(a_							,      a_							)							)
						self.assertEqual(config.output_hidden_states							,      a_							)
						check_encoder_attentions_output(a_							)

						# Check attention is always last and order is fine
						lowercase						:		Optional[int]   					=		True
						lowercase						:		str   					=		True
						lowercase						:		Optional[Any]   					=		model_class(a_							)
						lowercase						:		Any   					=		model(self._prepare_for_class(a_							,      a_							)							)

						self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)							,      len(a_							)							)
						self.assertEqual(model.config.output_hidden_states							,      a_							)
						check_encoder_attentions_output(a_							)




@require_tf
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				@slow
				def 	a__		(     self							)    ->  List[Any]:
					lowercase						:		Optional[Any]   					=		TFConvBertModel.from_pretrained("YituTech/conv-bert-base"							)
					lowercase						:		List[str]   					=		tf.constant([[0, 1, 2, 3, 4, 5]]							)
					lowercase						:		Any   					=		model(a_							)[0]

					lowercase						:		List[str]   					=		[1, 6, 7_6_8]
					self.assertEqual(output.shape							,      a_							)

					lowercase						:		Dict   					=		tf.constant(
					    [
					        [
					            [-0.03_47_54_93, -0.4_68_60_34, -0.30_63_88_32],
					            [0.22_63_72_48, -0.26_98_86_46, -0.7_42_34_24],
					            [0.10_32_48_68, -0.45_01_35_08, -0.58_28_07_84],
					        ]
					    ]							)
					tf.debugging.assert_near(output[:, :3, :3]							,      a_							,      atol=1e-4							)






'''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   : Optional[int]             =				logging.get_logger(__name__)

lowerCAmelCase   : Tuple             =				"""Hello world! cécé herlolip"""




def 	_A  (       A							,A							,A							)  ->				str:
	lowercase						:		Optional[Any]   					=		FairseqRobertaModel.from_pretrained(A							)
	roberta.eval()  # disable dropout
	lowercase						:		int   					=		roberta.model.encoder.sentence_encoder
	lowercase						:		Tuple   					=		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=5_1_4							,type_vocab_size=1							,layer_norm_eps=1e-5							,)
	if classification_head:
		lowercase						:		Tuple   					=		roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

	print("Our RoBERTa config:"							,A							)

	lowercase						:		Union[str, Any]   					=		XLMRobertaXLForSequenceClassification(A							) if classification_head else XLMRobertaXLForMaskedLM(A							)
	model.eval()

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

	lowercase						:		Dict   					=		roberta_sent_encoder.layer_norm.weight
	lowercase						:		str   					=		roberta_sent_encoder.layer_norm.bias

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

		lowercase						:		RobertaAttention   					=		layer.attention
		lowercase						:		str   					=		roberta_layer.self_attn_layer_norm.weight
		lowercase						:		List[str]   					=		roberta_layer.self_attn_layer_norm.bias

		# self attention
		lowercase						:		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)							)
		)

		lowercase						:		str   					=		roberta_layer.self_attn.q_proj.weight
		lowercase						:		List[str]   					=		roberta_layer.self_attn.q_proj.bias
		lowercase						:		Union[str, Any]   					=		roberta_layer.self_attn.k_proj.weight
		lowercase						:		List[Any]   					=		roberta_layer.self_attn.k_proj.bias
		lowercase						:		Dict   					=		roberta_layer.self_attn.v_proj.weight
		lowercase						:		Tuple   					=		roberta_layer.self_attn.v_proj.bias

		# self-attention output
		lowercase						:		BertSelfOutput   					=		layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		lowercase						:		Tuple   					=		roberta_layer.self_attn.out_proj.weight
		lowercase						:		str   					=		roberta_layer.self_attn.out_proj.bias

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

		# intermediate
		lowercase						:		BertIntermediate   					=		layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		lowercase						:		Optional[int]   					=		roberta_layer.fca.weight
		lowercase						:		Dict   					=		roberta_layer.fca.bias

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

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

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

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

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


if __name__ == "__main__":
					lowerCAmelCase   : 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."""
					)
					lowerCAmelCase   : Optional[Any]             =				parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)


