


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()
_snake_case  :	List[Any]			      =       logging.get_logger(__name__)

_snake_case  :	Any			      =       "Hello world! cécé herlolip"







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    List[str]											=     FairseqRobertaModel.from_pretrained(__lowerCamelCase			)
							roberta.eval()  # disable dropout
							__snake_case	:    Optional[int]											=     roberta.model.encoder.sentence_encoder
							__snake_case	:    Optional[int]											=     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:
														__snake_case	:    str											=     roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

							print("Our RoBERTa config:"							,			__lowerCamelCase			)

							__snake_case	:    Optional[int]											=     XLMRobertaXLForSequenceClassification(__lowerCamelCase			) if classification_head else XLMRobertaXLForMaskedLM(__lowerCamelCase			)
							model.eval()

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

							__snake_case	:    Any											=     roberta_sent_encoder.layer_norm.weight
							__snake_case	:    int											=     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[Any]											=     roberta_layer.self_attn_layer_norm.weight
														__snake_case	:    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	:    List[str]											=     roberta_layer.self_attn.q_proj.weight
														__snake_case	:    int											=     roberta_layer.self_attn.q_proj.bias
														__snake_case	:    List[str]											=     roberta_layer.self_attn.k_proj.weight
														__snake_case	:    Tuple											=     roberta_layer.self_attn.k_proj.bias
														__snake_case	:    Any											=     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	:    Optional[Any]											=     roberta_layer.self_attn.out_proj.weight
														__snake_case	:    List[Any]											=     roberta_layer.self_attn.out_proj.bias

														# this one is final layer norm
														__snake_case	:    Tuple											=     roberta_layer.final_layer_norm.weight
														__snake_case	:    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	:    Union[str, Any]											=     roberta_layer.fca.weight
														__snake_case	:    Optional[Any]											=     roberta_layer.fca.bias

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

							if classification_head:
														__snake_case	:    str											=     roberta.model.classification_heads["mnli"].dense.weight
														__snake_case	:    List[Any]											=     roberta.model.classification_heads["mnli"].dense.bias
														__snake_case	:    List[str]											=     roberta.model.classification_heads["mnli"].out_proj.weight
														__snake_case	:    Dict											=     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	:    str											=     roberta.model.encoder.lm_head.layer_norm.bias
														__snake_case	:    List[Any]											=     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(__lowerCamelCase			).unsqueeze(0			)  # batch of size 1

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





import unittest

from transformers import DonutProcessor


_snake_case  :	Dict			      =       "naver-clova-ix/donut-base"

class        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Dict       )			->					int:
														__snake_case	:    Any											=     DonutProcessor.from_pretrained(lowerCamelCase       )






							def 					__snake_case      (							self :						str       )			->					List[Any]:
														__snake_case	:    Union[str, Any]											=     {
														    "name": "John Doe",
														    "age": "99",
														    "city": "Atlanta",
														    "state": "GA",
														    "zip": "30301",
														    "phone": "123-4567",
														    "nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
														}

														__snake_case	:    Union[str, Any]											=     (
														    "<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
														    "<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
														    "<s_nicknames><s_nickname>Johnny</s_nickname>"
														    "<sep/><s_nickname>JD</s_nickname></s_nicknames>"
														)
														__snake_case	:    Tuple											=     self.processor.tokenajson(lowerCamelCase       )

														self.assertDictEqual(lowerCamelCase					,			lowerCamelCase       )

