




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()
SCREAMING_SNAKE_CASE__     :     Dict		    =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       "Hello world! cécé herlolip"

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							List[Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE )
			roberta.eval()  # disable dropout
			SCREAMING_SNAKE_CASE_								=						roberta.model.encoder.sentence_encoder
			SCREAMING_SNAKE_CASE_								=						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_14       ,    type_vocab_size=1       ,    layer_norm_eps=1E-5       ,    )
			if classification_head:
						SCREAMING_SNAKE_CASE_								=						roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

			print('Our RoBERTa config:'       ,    SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE_								=						XLMRobertaXLForSequenceClassification(SCREAMING_SNAKE_CASE ) if classification_head else XLMRobertaXLForMaskedLM(SCREAMING_SNAKE_CASE )
			model.eval()

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

			SCREAMING_SNAKE_CASE_								=						roberta_sent_encoder.layer_norm.weight
			SCREAMING_SNAKE_CASE_								=						roberta_sent_encoder.layer_norm.bias

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

						SCREAMING_SNAKE_CASE_								=						layer.attention
						SCREAMING_SNAKE_CASE_								=						roberta_layer.self_attn_layer_norm.weight
						SCREAMING_SNAKE_CASE_								=						roberta_layer.self_attn_layer_norm.bias

						# self attention
						SCREAMING_SNAKE_CASE_								=						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_								=						roberta_layer.self_attn.q_proj.weight
						SCREAMING_SNAKE_CASE_								=						roberta_layer.self_attn.q_proj.bias
						SCREAMING_SNAKE_CASE_								=						roberta_layer.self_attn.k_proj.weight
						SCREAMING_SNAKE_CASE_								=						roberta_layer.self_attn.k_proj.bias
						SCREAMING_SNAKE_CASE_								=						roberta_layer.self_attn.v_proj.weight
						SCREAMING_SNAKE_CASE_								=						roberta_layer.self_attn.v_proj.bias

						# self-attention output
						SCREAMING_SNAKE_CASE_								=						layer.attention.output
						assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
						SCREAMING_SNAKE_CASE_								=						roberta_layer.self_attn.out_proj.weight
						SCREAMING_SNAKE_CASE_								=						roberta_layer.self_attn.out_proj.bias

						# this one is final layer norm
						SCREAMING_SNAKE_CASE_								=						roberta_layer.final_layer_norm.weight
						SCREAMING_SNAKE_CASE_								=						roberta_layer.final_layer_norm.bias

						# intermediate
						SCREAMING_SNAKE_CASE_								=						layer.intermediate
						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
						SCREAMING_SNAKE_CASE_								=						roberta_layer.fca.weight
						SCREAMING_SNAKE_CASE_								=						roberta_layer.fca.bias

						# output
						SCREAMING_SNAKE_CASE_								=						layer.output
						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
						SCREAMING_SNAKE_CASE_								=						roberta_layer.fca.weight
						SCREAMING_SNAKE_CASE_								=						roberta_layer.fca.bias
						# end of layer

			if classification_head:
						SCREAMING_SNAKE_CASE_								=						roberta.model.classification_heads['mnli'].dense.weight
						SCREAMING_SNAKE_CASE_								=						roberta.model.classification_heads['mnli'].dense.bias
						SCREAMING_SNAKE_CASE_								=						roberta.model.classification_heads['mnli'].out_proj.weight
						SCREAMING_SNAKE_CASE_								=						roberta.model.classification_heads['mnli'].out_proj.bias
			else:
						# LM Head
						SCREAMING_SNAKE_CASE_								=						roberta.model.encoder.lm_head.dense.weight
						SCREAMING_SNAKE_CASE_								=						roberta.model.encoder.lm_head.dense.bias
						SCREAMING_SNAKE_CASE_								=						roberta.model.encoder.lm_head.layer_norm.weight
						SCREAMING_SNAKE_CASE_								=						roberta.model.encoder.lm_head.layer_norm.bias
						SCREAMING_SNAKE_CASE_								=						roberta.model.encoder.lm_head.weight
						SCREAMING_SNAKE_CASE_								=						roberta.model.encoder.lm_head.bias

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

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

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


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     Optional[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."
				)
				SCREAMING_SNAKE_CASE__     :     Dict		    =       parser.parse_args()
				convert_xlm_roberta_xl_checkpoint_to_pytorch(
				    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
				import tensorflow as tf

				from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class       a_       (     unittest.TestCase     ):





	@slow
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'      )
				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('google/mt5-small'      )

				SCREAMING_SNAKE_CASE_								=						tokenizer('Hello there'      ,     return_tensors='tf'      ).input_ids
				SCREAMING_SNAKE_CASE_								=						tokenizer('Hi I am'      ,     return_tensors='tf'      ).input_ids

				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     labels=SCREAMING_SNAKE_CASE      ).loss
				SCREAMING_SNAKE_CASE_								=						-tf.math.reduce_mean(SCREAMING_SNAKE_CASE      ).numpy()

				SCREAMING_SNAKE_CASE_								=						-2_1.2_2_8_1_6_8
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 2e-4      )
