




'''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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   Union[str, Any]										=      ['''pixel_values''']


       def __init__( self     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = 1 / 255     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	None:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	size if size is not None else {'''height''': 224, '''width''': 224}
            __snake_case            =	get_size_dict(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
            __snake_case            =	get_size_dict(__SCREAMING_SNAKE_CASE     ,	default_to_square=__SCREAMING_SNAKE_CASE     ,	param_name='''crop_size'''	)

            __snake_case            =	do_resize
            __snake_case            =	do_rescale
            __snake_case            =	do_normalize
            __snake_case            =	do_center_crop
            __snake_case            =	crop_size
            __snake_case            =	size
            __snake_case            =	resample
            __snake_case            =	rescale_factor
            __snake_case            =	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
            __snake_case            =	image_std if image_std is not None else IMAGENET_DEFAULT_STD


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR     ,	__SCREAMING_SNAKE_CASE = None     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	np.ndarray:



            '''simple docstring'''




            __snake_case            =	get_size_dict(__SCREAMING_SNAKE_CASE	)

            if "shortest_edge" in size:
                 __snake_case            =	get_resize_output_image_size(__SCREAMING_SNAKE_CASE     ,	size=size['''shortest_edge''']     ,	default_to_square=__SCREAMING_SNAKE_CASE	)
                 # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
            elif "height" in size and "width" in size:
                 __snake_case            =	(size['''height'''], size['''width'''])
            else:
                 raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''	)
            return resize(__SCREAMING_SNAKE_CASE     ,	size=__SCREAMING_SNAKE_CASE     ,	resample=__SCREAMING_SNAKE_CASE     ,	data_format=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	np.ndarray:



            '''simple docstring'''




            __snake_case            =	get_size_dict(__SCREAMING_SNAKE_CASE	)
            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(__SCREAMING_SNAKE_CASE     ,	size=(size['''height'''], size['''width'''])     ,	data_format=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	**__SCREAMING_SNAKE_CASE	)   ->	np.ndarray:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	np.ndarray:



            '''simple docstring'''




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




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	BatchFeature:



            '''simple docstring'''




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

            __snake_case            =	size if size is not None else self.size
            __snake_case            =	get_size_dict(__SCREAMING_SNAKE_CASE	)

            if not is_batched(__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	[images]

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

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

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

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

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

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

            __snake_case            =	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) for image in images]

            __snake_case            =	{'''pixel_values''': images}
            return BatchFeature(data=__SCREAMING_SNAKE_CASE     ,	tensor_type=__SCREAMING_SNAKE_CASE	)










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

lowerCAmelCase   : Tuple             =				"""Hello world! cécé herlolip"""




def 	_A  (       A							,A							,A							)  ->				str:
	lowercase						:		Optional[Any]   					=		FairseqRobertaModel.from_pretrained(A							)
	roberta.eval()  # disable dropout
	lowercase						:		int   					=		roberta.model.encoder.sentence_encoder
	lowercase						:		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_1_4							,type_vocab_size=1							,layer_norm_eps=1e-5							,)
	if classification_head:
		lowercase						:		Tuple   					=		roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

	print("Our RoBERTa config:"							,A							)

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

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

	lowercase						:		Dict   					=		roberta_sent_encoder.layer_norm.weight
	lowercase						:		str   					=		roberta_sent_encoder.layer_norm.bias

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

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

		# self attention
		lowercase						:		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)							)
		)

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

		# self-attention output
		lowercase						:		BertSelfOutput   					=		layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		lowercase						:		Tuple   					=		roberta_layer.self_attn.out_proj.weight
		lowercase						:		str   					=		roberta_layer.self_attn.out_proj.bias

		# this one is final layer norm
		lowercase						:		List[Any]   					=		roberta_layer.final_layer_norm.weight
		lowercase						:		Optional[Any]   					=		roberta_layer.final_layer_norm.bias

		# intermediate
		lowercase						:		BertIntermediate   					=		layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		lowercase						:		Optional[int]   					=		roberta_layer.fca.weight
		lowercase						:		Dict   					=		roberta_layer.fca.bias

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

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

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

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


