

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

__UpperCamelCase  :      Optional[Any]							      =		'''Hello world! cécé herlolip'''

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Optional[Any]  				=	FairseqRobertaModel.from_pretrained(A_ )
	roberta.eval()  # disable dropout
	lowerCAmelCase__	:						str  				=	roberta.model.encoder.sentence_encoder
	lowerCAmelCase__	:						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_14						,     type_vocab_size=1						,     layer_norm_eps=1e-5						,     )
	if classification_head:
		lowerCAmelCase__	:						Optional[Any]  				=	roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

	print('''Our RoBERTa config:'''						,     A_ )

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

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

	lowerCAmelCase__	:						Union[str, Any]  				=	roberta_sent_encoder.layer_norm.weight
	lowerCAmelCase__	:						List[Any]  				=	roberta_sent_encoder.layer_norm.bias

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

		lowerCAmelCase__	:						RobertaAttention  				=	layer.attention
		lowerCAmelCase__	:						Tuple  				=	roberta_layer.self_attn_layer_norm.weight
		lowerCAmelCase__	:						int  				=	roberta_layer.self_attn_layer_norm.bias

		# self attention
		lowerCAmelCase__	:						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) )
		)

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

		# self-attention output
		lowerCAmelCase__	:						BertSelfOutput  				=	layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		lowerCAmelCase__	:						List[Any]  				=	roberta_layer.self_attn.out_proj.weight
		lowerCAmelCase__	:						Dict  				=	roberta_layer.self_attn.out_proj.bias

		# this one is final layer norm
		lowerCAmelCase__	:						Any  				=	roberta_layer.final_layer_norm.weight
		lowerCAmelCase__	:						str  				=	roberta_layer.final_layer_norm.bias

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

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

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

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

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







"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__UpperCamelCase  :      Optional[Any]							      =		logging.get_logger(__name__)


if is_vision_available():
							import PIL







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       ["pixel_values"]







					def __init__(       self			:				str   ,lowercase_			:				bool = True   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				PILImageResampling = PILImageResampling.BICUBIC   ,lowercase_			:				bool = True   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				bool = True   ,lowercase_			:				Union[int, float] = 1 / 2_5_5   ,lowercase_			:				bool = True   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				bool = True   ,**lowercase_			:				Optional[Any]   ,):
						super().__init__(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	size if size is not None else {'''shortest_edge''': 2_2_4}
						lowerCAmelCase__	:						Tuple  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						lowerCAmelCase__	:						Optional[Any]  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_   ,param_name='''crop_size'''		)

						lowerCAmelCase__	:						Dict  				=	do_resize
						lowerCAmelCase__	:						Optional[int]  				=	size
						lowerCAmelCase__	:						Dict  				=	resample
						lowerCAmelCase__	:						Optional[Any]  				=	do_center_crop
						lowerCAmelCase__	:						Dict  				=	crop_size
						lowerCAmelCase__	:						Tuple  				=	do_rescale
						lowerCAmelCase__	:						str  				=	rescale_factor
						lowerCAmelCase__	:						List[str]  				=	do_normalize
						lowerCAmelCase__	:						Tuple  				=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						lowerCAmelCase__	:						Optional[Any]  				=	image_std if image_std is not None else OPENAI_CLIP_STD
						lowerCAmelCase__	:						Optional[Any]  				=	do_convert_rgb







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				np.ndarray   ,lowercase_			:				Dict[str, int]   ,lowercase_			:				PILImageResampling = PILImageResampling.BICUBIC   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Optional[Any]   ,):
						lowerCAmelCase__	:						Dict  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_		)
						if "shortest_edge" not in size:
							raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'		)
						lowerCAmelCase__	:						Optional[Any]  				=	get_resize_output_image_size(lowercase_   ,size=size['''shortest_edge''']   ,default_to_square=lowercase_		)
						return resize(lowercase_   ,size=lowercase_   ,resample=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				np.ndarray   ,lowercase_			:				Dict[str, int]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				str   ,):
						lowerCAmelCase__	:						List[str]  				=	get_size_dict(lowercase_		)
						if "height" not in size or "width" not in size:
							raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'		)
						return center_crop(lowercase_   ,size=(size['''height'''], size['''width'''])   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				np.ndarray   ,lowercase_			:				Union[int, float]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Union[str, Any]   ,):
						return rescale(lowercase_   ,scale=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				np.ndarray   ,lowercase_			:				Union[float, List[float]]   ,lowercase_			:				Union[float, List[float]]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				int   ,):
						return normalize(lowercase_   ,mean=lowercase_   ,std=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				ImageInput   ,lowercase_			:				bool = None   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				PILImageResampling = None   ,lowercase_			:				bool = None   ,lowercase_			:				int = None   ,lowercase_			:				bool = None   ,lowercase_			:				float = None   ,lowercase_			:				bool = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				bool = None   ,lowercase_			:				Optional[Union[str, TensorType]] = None   ,lowercase_			:				Optional[ChannelDimension] = ChannelDimension.FIRST   ,**lowercase_			:				List[Any]   ,):
						lowerCAmelCase__	:						Optional[int]  				=	do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__	:						Optional[int]  				=	size if size is not None else self.size
						lowerCAmelCase__	:						Union[str, Any]  				=	get_size_dict(lowercase_   ,param_name='''size'''   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	resample if resample is not None else self.resample
						lowerCAmelCase__	:						Optional[Any]  				=	do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCAmelCase__	:						Optional[int]  				=	crop_size if crop_size is not None else self.crop_size
						lowerCAmelCase__	:						Dict  				=	get_size_dict(lowercase_   ,param_name='''crop_size'''   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						int  				=	do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__	:						List[str]  				=	rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__	:						Optional[int]  				=	do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__	:						str  				=	image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__	:						str  				=	image_std if image_std is not None else self.image_std
						lowerCAmelCase__	:						int  				=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						lowerCAmelCase__	:						Any  				=	make_list_of_images(lowercase_		)

						if not valid_images(lowercase_		):
							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.'''		)

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
							lowerCAmelCase__	:						Tuple  				=	[convert_to_rgb(lowercase_		) for image in images]

						# All transformations expect numpy arrays.
						lowerCAmelCase__	:						Optional[int]  				=	[to_numpy_array(lowercase_		) for image in images]

						if do_resize:
							lowerCAmelCase__	:						Optional[int]  				=	[self.resize(image=lowercase_   ,size=lowercase_   ,resample=lowercase_		) for image in images]

						if do_center_crop:
							lowerCAmelCase__	:						Tuple  				=	[self.center_crop(image=lowercase_   ,size=lowercase_		) for image in images]

						if do_rescale:
							lowerCAmelCase__	:						Tuple  				=	[self.rescale(image=lowercase_   ,scale=lowercase_		) for image in images]

						if do_normalize:
							lowerCAmelCase__	:						Union[str, Any]  				=	[self.normalize(image=lowercase_   ,mean=lowercase_   ,std=lowercase_		) for image in images]

						lowerCAmelCase__	:						Optional[Any]  				=	[to_channel_dimension_format(lowercase_   ,lowercase_		) for image in images]

						lowerCAmelCase__	:						List[Any]  				=	{'''pixel_values''': images}
						return BatchFeature(data=lowercase_   ,tensor_type=lowercase_		)




