



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__     = logging.get_logger(__name__)

UpperCAmelCase__     = 'Hello world! cécé herlolip'





def 							A			(  _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Any					)       ->				Dict:







			'''simple docstring'''
			_UpperCAmelCase	 =     FairseqRobertaModel.from_pretrained(lowerCAmelCase_					)
			roberta.eval()  # disable dropout
			_UpperCAmelCase	 =     roberta.model.encoder.sentence_encoder
			_UpperCAmelCase	 =     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:
						_UpperCAmelCase	 =     roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

			print('Our RoBERTa config:'							,     lowerCAmelCase_					)

			_UpperCAmelCase	 =     XLMRobertaXLForSequenceClassification(lowerCAmelCase_					) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase_					)
			model.eval()

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

			_UpperCAmelCase	 =     roberta_sent_encoder.layer_norm.weight
			_UpperCAmelCase	 =     roberta_sent_encoder.layer_norm.bias

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

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

						# self attention
						_UpperCAmelCase	 =     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)					)
						)

						_UpperCAmelCase	 =     roberta_layer.self_attn.q_proj.weight
						_UpperCAmelCase	 =     roberta_layer.self_attn.q_proj.bias
						_UpperCAmelCase	 =     roberta_layer.self_attn.k_proj.weight
						_UpperCAmelCase	 =     roberta_layer.self_attn.k_proj.bias
						_UpperCAmelCase	 =     roberta_layer.self_attn.v_proj.weight
						_UpperCAmelCase	 =     roberta_layer.self_attn.v_proj.bias

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

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

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

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

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

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

			_UpperCAmelCase	 =     model(lowerCAmelCase_					)[0]
			if classification_head:
						_UpperCAmelCase	 =     roberta.model.classification_heads['''mnli'''](roberta.extract_features(lowerCAmelCase_					)					)
			else:
						_UpperCAmelCase	 =     roberta.model(lowerCAmelCase_					)[0]
			print(our_output.shape							,     their_output.shape					)
			_UpperCAmelCase	 =     torch.max(torch.abs(our_output - their_output					)					).item()
			print(F"max_absolute_diff = {max_absolute_diff}"					)  # ~ 1e-7
			_UpperCAmelCase	 =     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__":
				UpperCAmelCase__     = 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__     = parser.parse_args()
				convert_xlm_roberta_xl_checkpoint_to_pytorch(
				    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)





from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
							import torch


UpperCAmelCase__     = logging.get_logger(__name__)







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  ['''pixel_values''']



			def __init__(		self     :						Any				,	A     :						bool = True				,	A     :						Optional[Dict[str, int]] = None				,	A     :						PILImageResampling = PILImageResampling.BILINEAR				,	A     :						bool = True				,	A     :						Dict[str, int] = None				,	A     :						bool = True				,	A     :						Union[int, float] = 1 / 2_55				,	A     :						bool = True				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[float, List[float]]] = None				,	**A     :						Union[str, Any]				,	)     ->					None:



						"""simple docstring"""






						super().__init__(**A)
						_UpperCAmelCase	 =     size if size is not None else {'shortest_edge': 2_56}
						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						_UpperCAmelCase	 =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
						_UpperCAmelCase	 =     get_size_dict(A				,	param_name='crop_size')
						_UpperCAmelCase	 =     do_resize
						_UpperCAmelCase	 =     size
						_UpperCAmelCase	 =     resample
						_UpperCAmelCase	 =     do_center_crop
						_UpperCAmelCase	 =     crop_size
						_UpperCAmelCase	 =     do_rescale
						_UpperCAmelCase	 =     rescale_factor
						_UpperCAmelCase	 =     do_normalize
						_UpperCAmelCase	 =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_UpperCAmelCase	 =     image_std if image_std is not None else IMAGENET_STANDARD_STD



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						np.ndarray				,	A     :						Dict[str, int]				,	A     :						PILImageResampling = PILImageResampling.BICUBIC				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						List[str]				,	)     ->					np.ndarray:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						if "shortest_edge" not in size:
									raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
						_UpperCAmelCase	 =     get_resize_output_image_size(A				,	size=size['shortest_edge']				,	default_to_square=A)
						return resize(A				,	size=A				,	resample=A				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						Any				,	A     :						np.ndarray				,	A     :						Dict[str, int]				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Union[str, Any]				,	)     ->					np.ndarray:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_size_dict(A)
						if "height" not in size or "width" not in size:
									raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}")
						return center_crop(A				,	size=(size['height'], size['width'])				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						Any				,	A     :						np.ndarray				,	A     :						float				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Dict)     ->					np.ndarray:



						"""simple docstring"""






						return rescale(A				,	scale=A				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						int				,	A     :						np.ndarray				,	A     :						Union[float, List[float]]				,	A     :						Union[float, List[float]]				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Dict				,	)     ->					np.ndarray:



						"""simple docstring"""






						return normalize(A				,	mean=A				,	std=A				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						Union[str, Any]				,	A     :						ImageInput				,	A     :						Optional[bool] = None				,	A     :						Dict[str, int] = None				,	A     :						PILImageResampling = None				,	A     :						bool = None				,	A     :						Dict[str, int] = None				,	A     :						Optional[bool] = None				,	A     :						Optional[float] = None				,	A     :						Optional[bool] = None				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[str, TensorType]] = None				,	A     :						Union[str, ChannelDimension] = ChannelDimension.FIRST				,	**A     :						int				,	)     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     do_resize if do_resize is not None else self.do_resize
						_UpperCAmelCase	 =     size if size is not None else self.size
						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						_UpperCAmelCase	 =     resample if resample is not None else self.resample
						_UpperCAmelCase	 =     do_center_crop if do_center_crop is not None else self.do_center_crop
						_UpperCAmelCase	 =     crop_size if crop_size is not None else self.crop_size
						_UpperCAmelCase	 =     get_size_dict(A				,	param_name='crop_size')
						_UpperCAmelCase	 =     do_rescale if do_rescale is not None else self.do_rescale
						_UpperCAmelCase	 =     rescale_factor if rescale_factor is not None else self.rescale_factor
						_UpperCAmelCase	 =     do_normalize if do_normalize is not None else self.do_normalize
						_UpperCAmelCase	 =     image_mean if image_mean is not None else self.image_mean
						_UpperCAmelCase	 =     image_std if image_std is not None else self.image_std

						_UpperCAmelCase	 =     make_list_of_images(A)

						if not valid_images(A):
									raise ValueError(
									    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
									    'torch.Tensor, tf.Tensor or jax.ndarray.')

						if do_resize and size is None:
									raise ValueError('Size must be specified if do_resize is True.')

						if do_center_crop and crop_size is None:
									raise ValueError('Crop size must be specified if do_center_crop is True.')

						if do_rescale and rescale_factor is None:
									raise ValueError('Rescale factor must be specified if do_rescale is True.')

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError('Image mean and std must be specified if do_normalize is True.')

						# All transformations expect numpy arrays.
						_UpperCAmelCase	 =     [to_numpy_array(A) for image in images]

						if do_resize:
									_UpperCAmelCase	 =     [self.resize(image=A				,	size=A				,	resample=A) for image in images]

						if do_center_crop:
									_UpperCAmelCase	 =     [self.center_crop(image=A				,	size=A) for image in images]

						if do_rescale:
									_UpperCAmelCase	 =     [self.rescale(image=A				,	scale=A) for image in images]

						if do_normalize:
									_UpperCAmelCase	 =     [self.normalize(image=A				,	mean=A				,	std=A) for image in images]

						_UpperCAmelCase	 =     [to_channel_dimension_format(A				,	A) for image in images]

						_UpperCAmelCase	 =     {'pixel_values': images}
						return BatchFeature(data=A				,	tensor_type=A)



			def 					_lowerCamelCase (		self     :						str				,	A     :						Any				,	A     :						List[Tuple] = None)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
									if len(A) != len(A):
												raise ValueError(
												    'Make sure that you pass in as many target sizes as the batch dimension of the logits')

									if is_torch_tensor(A):
												_UpperCAmelCase	 =     target_sizes.numpy()

									_UpperCAmelCase	 =     []

									for idx in range(len(A)):
												_UpperCAmelCase	 =     torch.nn.functional.interpolate(
												    logits[idx].unsqueeze(dim=0)				,	size=target_sizes[idx]				,	mode='bilinear'				,	align_corners=A)
												_UpperCAmelCase	 =     resized_logits[0].argmax(dim=0)
												semantic_segmentation.append(A)
						else:
									_UpperCAmelCase	 =     logits.argmax(dim=1)
									_UpperCAmelCase	 =     [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

						return semantic_segmentation
