




"""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 RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
		raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
_lowerCamelCase	:      List[str]   =							logging.get_logger(__name__)

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

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Dict						 =		FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__						)
	roberta.eval()  # disable dropout
	A_	:					Optional[Any]						 =		roberta.model.encoder.sentence_encoder
	A_	:					Optional[int]						 =		RobertaConfig(
	    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings	,   hidden_size=roberta.args.encoder_embed_dim	,   num_hidden_layers=roberta.args.encoder_layers	,   num_attention_heads=roberta.args.encoder_attention_heads	,   intermediate_size=roberta.args.encoder_ffn_embed_dim	,   max_position_embeddings=514	,   type_vocab_size=1	,   layer_norm_eps=1E-5	,   )
	if classification_head:
		A_	:					Optional[Any]						 =		roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
	print('''Our BERT config:'''	,   SCREAMING_SNAKE_CASE__						)

	A_	:					Optional[Any]						 =		RobertaForSequenceClassification(SCREAMING_SNAKE_CASE__						) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE__						)
	model.eval()

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

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

		# self attention
		A_	:					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)						)
		)

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

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

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

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

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

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

	A_	:					Optional[int]						 =		model(SCREAMING_SNAKE_CASE__						)[0]
	if classification_head:
		A_	:					List[Any]						 =		roberta.model.classification_heads['''mnli'''](roberta.extract_features(SCREAMING_SNAKE_CASE__						)						)
	else:
		A_	:					Tuple						 =		roberta.model(SCREAMING_SNAKE_CASE__						)[0]
	print(our_output.shape	,   their_output.shape						)
	A_	:					Tuple						 =		torch.max(torch.abs(our_output - their_output						)						).item()
	print(f"""max_absolute_diff = {max_absolute_diff}"""						)  # ~ 1e-7
	A_	:					List[Any]						 =		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__":
		_lowerCamelCase	:      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.'
		)
		_lowerCamelCase	:      Dict   =							parser.parse_args()
		convert_roberta_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


snake_case_  :							Any            =	logging.get_logger(__name__)


if is_vision_available():
					import PIL


class        __a   (lowerCamelCase  ):
							__a       : Any          =							["pixel_values"]

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







													"""simple docstring"""
													super().__init__(**__magic_name__ )
													UpperCAmelCase_      :   List[Any]      =	size if size is not None else {'''shortest_edge''': 2_24}
													UpperCAmelCase_      :   List[str]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   int      =	crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
													UpperCAmelCase_      :   Union[str, Any]      =	get_size_dict(__magic_name__ )

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

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







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   Any      =	get_resize_output_image_size(
													    __magic_name__							,  size=(size['''height'''], size['''width'''])							,  default_to_square=__magic_name__ )
													return resize(__magic_name__							,  size=__magic_name__							,  resample=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )

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







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	get_size_dict(__magic_name__ )
													return center_crop(__magic_name__							,  size=(size['''height'''], size['''width'''])							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Union[int, float]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Any							,  )       ->							Union[str, Any]:







													"""simple docstring"""
													return rescale(__magic_name__							,  scale=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )

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







													"""simple docstring"""
													return normalize(__magic_name__							,  mean=__magic_name__							,  std=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )



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







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_      :   Dict      =	size if size is not None else self.size
													UpperCAmelCase_      :   Union[str, Any]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   str      =	resample if resample is not None else self.resample
													UpperCAmelCase_      :   Tuple      =	do_center_crop if do_center_crop is not None else self.do_center_crop
													UpperCAmelCase_      :   List[str]      =	crop_size if crop_size is not None else self.crop_size
													UpperCAmelCase_      :   Optional[Any]      =	get_size_dict(__magic_name__ )
													UpperCAmelCase_      :   str      =	do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_      :   Tuple      =	rescale_factor if rescale_factor is not None else self.rescale_factor
													UpperCAmelCase_      :   List[Any]      =	do_normalize if do_normalize is not None else self.do_normalize
													UpperCAmelCase_      :   Tuple      =	image_mean if image_mean is not None else self.image_mean
													UpperCAmelCase_      :   int      =	image_std if image_std is not None else self.image_std
													UpperCAmelCase_      :   Optional[Any]      =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													UpperCAmelCase_      :   Any      =	make_list_of_images(__magic_name__ )

													if not valid_images(__magic_name__ ):
																			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:
																			UpperCAmelCase_      :   Optional[int]      =	[convert_to_rgb(__magic_name__ ) for image in images]

													# All transformations expect numpy arrays.
													UpperCAmelCase_      :   Optional[Any]      =	[to_numpy_array(__magic_name__ ) for image in images]

													if do_resize:
																			UpperCAmelCase_      :   Dict      =	[self.resize(image=__magic_name__							,  size=__magic_name__							,  resample=__magic_name__ ) for image in images]

													if do_center_crop:
																			UpperCAmelCase_      :   Union[str, Any]      =	[self.center_crop(image=__magic_name__							,  size=__magic_name__ ) for image in images]

													if do_rescale:
																			UpperCAmelCase_      :   Dict      =	[self.rescale(image=__magic_name__							,  scale=__magic_name__ ) for image in images]

													if do_normalize:
																			UpperCAmelCase_      :   Optional[int]      =	[self.normalize(image=__magic_name__							,  mean=__magic_name__							,  std=__magic_name__ ) for image in images]

													UpperCAmelCase_      :   Optional[Any]      =	[to_channel_dimension_format(__magic_name__							,  __magic_name__ ) for image in images]

													UpperCAmelCase_      :   int      =	{'''pixel_values''': images}
													return BatchFeature(data=__magic_name__							,  tensor_type=__magic_name__ )





