
"""simple docstring"""




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Union[str, Any]					       =   ['''image_processor''', '''tokenizer''']
				__UpperCamelCase      :						Dict					       =   '''LayoutLMv2ImageProcessor'''
				__UpperCamelCase      :						Union[str, Any]					       =   ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=None   ,       **SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																		    """ instead."""   ,       SCREAMING_SNAKE_CASE__   ,       )
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   kwargs.pop("""feature_extractor"""    )

											SCREAMING_SNAKE_CASE__						:       str           =   image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError("""You need to specify an `image_processor`."""    )
											if tokenizer is None:
																		raise ValueError("""You need to specify a `tokenizer`."""    )

											super().__init__(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def __call__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = True   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = 0   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = True   ,       SCREAMING_SNAKE_CASE__ = None   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		BatchEncoding:



											"""simple docstring"""




											if self.image_processor.apply_ocr and (boxes is not None):
																		raise ValueError(
																		    """You cannot provide bounding boxes """
																		    """if you initialized the image processor with apply_ocr set to True."""    )

											if self.image_processor.apply_ocr and (word_labels is not None):
																		raise ValueError(
																		    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""    )

											if return_overflowing_tokens is True and return_offsets_mapping is False:
																		raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""    )

											# first, apply the image processor
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.image_processor(images=SCREAMING_SNAKE_CASE__   ,       return_tensors=SCREAMING_SNAKE_CASE__    )

											# second, apply the tokenizer
											if text is not None and self.image_processor.apply_ocr and text_pair is None:
																		if isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																									SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
																		SCREAMING_SNAKE_CASE__						:       Dict           =   features["""words"""]

											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.tokenizer(
											    text=text if text is not None else features["""words"""]   ,       text_pair=text_pair if text_pair is not None else None   ,       boxes=boxes if boxes is not None else features["""boxes"""]   ,       word_labels=SCREAMING_SNAKE_CASE__   ,       add_special_tokens=SCREAMING_SNAKE_CASE__   ,       padding=SCREAMING_SNAKE_CASE__   ,       truncation=SCREAMING_SNAKE_CASE__   ,       max_length=SCREAMING_SNAKE_CASE__   ,       stride=SCREAMING_SNAKE_CASE__   ,       pad_to_multiple_of=SCREAMING_SNAKE_CASE__   ,       return_token_type_ids=SCREAMING_SNAKE_CASE__   ,       return_attention_mask=SCREAMING_SNAKE_CASE__   ,       return_overflowing_tokens=SCREAMING_SNAKE_CASE__   ,       return_special_tokens_mask=SCREAMING_SNAKE_CASE__   ,       return_offsets_mapping=SCREAMING_SNAKE_CASE__   ,       return_length=SCREAMING_SNAKE_CASE__   ,       verbose=SCREAMING_SNAKE_CASE__   ,       return_tensors=SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__   ,       )

											# add pixel values
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   features.pop("""pixel_values"""    )
											if return_overflowing_tokens is True:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   self.get_overflowing_images(SCREAMING_SNAKE_CASE__   ,       encoded_inputs["""overflow_to_sample_mapping"""]    )
											SCREAMING_SNAKE_CASE__						:       Any           =   images

											return encoded_inputs



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   []
											for sample_idx in overflow_to_sample_mapping:
																		images_with_overflow.append(images[sample_idx]    )

											if len(SCREAMING_SNAKE_CASE__    ) != len(SCREAMING_SNAKE_CASE__    ):
																		raise ValueError(
																		    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
																		    F''' {len(SCREAMING_SNAKE_CASE__    )} and {len(SCREAMING_SNAKE_CASE__    )}'''    )

											return images_with_overflow



				def 	__magic_name__    (self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       *SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				@property
				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											return ["input_ids", "bbox", "attention_mask", "image"]



				@property
				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,       SCREAMING_SNAKE_CASE__   ,       )
											return self.image_processor_class



				@property
				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											warnings.warn(
											    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,       SCREAMING_SNAKE_CASE__   ,       )
											return self.image_processor






"""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()
UpperCAmelCase__      :    Union[str, Any]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    List[str]    							= 'Hello world! cécé herlolip'

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       int           =   FairseqRobertaModel.from_pretrained(_snake_case						)
							roberta.eval()  # disable dropout
							SCREAMING_SNAKE_CASE__						:       Any           =   roberta.model.encoder.sentence_encoder
							SCREAMING_SNAKE_CASE__						:       Any           =   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:
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

							print("""Our RoBERTa config:"""   ,_snake_case						)

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

							# Now let's copy all the weights.
							# Embeddings
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_sent_encoder.embed_tokens.weight
							SCREAMING_SNAKE_CASE__						:       int           =   roberta_sent_encoder.embed_positions.weight
							SCREAMING_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.

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   roberta_sent_encoder.layer_norm.weight
							SCREAMING_SNAKE_CASE__						:       Dict           =   roberta_sent_encoder.layer_norm.bias

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

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

														# self attention
														SCREAMING_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)						)
														)

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

														# self-attention output
														SCREAMING_SNAKE_CASE__						:       BertSelfOutput           =   layer.attention.output
														assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   roberta_layer.self_attn.out_proj.weight
														SCREAMING_SNAKE_CASE__						:       List[str]           =   roberta_layer.self_attn.out_proj.bias

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

														# intermediate
														SCREAMING_SNAKE_CASE__						:       BertIntermediate           =   layer.intermediate
														assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta_layer.fca.bias

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

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

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

							SCREAMING_SNAKE_CASE__						:       Tuple           =   model(_snake_case						)[0]
							if classification_head:
														SCREAMING_SNAKE_CASE__						:       Dict           =   roberta.model.classification_heads["""mnli"""](roberta.extract_features(_snake_case						)						)
							else:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   roberta.model(_snake_case						)[0]
							print(our_output.shape   ,their_output.shape						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.max(torch.abs(our_output - their_output						)						).item()
							print(f'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
							SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.allclose(_snake_case   ,_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(_snake_case						).mkdir(parents=_snake_case   ,exist_ok=_snake_case						)
							print(f'''Saving model to {pytorch_dump_folder_path}'''						)
							model.save_pretrained(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__      :    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.'
					)
					UpperCAmelCase__      :    Any    							= parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)




