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()
__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					'''Hello world! cécé herlolip'''



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[int]			  =		FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE	)
    roberta.eval()  # disable dropout
    __UpperCamelCase     :Optional[Any]			  =		roberta.model.encoder.sentence_encoder
    __UpperCamelCase     :Dict			  =		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     :Union[str, Any]			  =		roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

    __UpperCamelCase     :Tuple			  =		XLMRobertaXLForSequenceClassification(SCREAMING_SNAKE_CASE	) if classification_head else XLMRobertaXLForMaskedLM(SCREAMING_SNAKE_CASE	)
    model.eval()

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

    __UpperCamelCase     :List[Any]			  =		roberta_sent_encoder.layer_norm.weight
    __UpperCamelCase     :Dict			  =		roberta_sent_encoder.layer_norm.bias

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

        __UpperCamelCase     :RobertaAttention			  =		layer.attention
        __UpperCamelCase     :Optional[int]			  =		roberta_layer.self_attn_layer_norm.weight
        __UpperCamelCase     :Tuple			  =		roberta_layer.self_attn_layer_norm.bias

        # self attention
        __UpperCamelCase     :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)	)
        )

        __UpperCamelCase     :List[str]			  =		roberta_layer.self_attn.q_proj.weight
        __UpperCamelCase     :Optional[Any]			  =		roberta_layer.self_attn.q_proj.bias
        __UpperCamelCase     :Any			  =		roberta_layer.self_attn.k_proj.weight
        __UpperCamelCase     :List[Any]			  =		roberta_layer.self_attn.k_proj.bias
        __UpperCamelCase     :Optional[Any]			  =		roberta_layer.self_attn.v_proj.weight
        __UpperCamelCase     :Optional[int]			  =		roberta_layer.self_attn.v_proj.bias

        # self-attention output
        __UpperCamelCase     :BertSelfOutput			  =		layer.attention.output
        assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
        __UpperCamelCase     :Dict			  =		roberta_layer.self_attn.out_proj.weight
        __UpperCamelCase     :int			  =		roberta_layer.self_attn.out_proj.bias

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

        # intermediate
        __UpperCamelCase     :BertIntermediate			  =		layer.intermediate
        assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
        __UpperCamelCase     :str			  =		roberta_layer.fca.weight
        __UpperCamelCase     :int			  =		roberta_layer.fca.bias

        # output
        __UpperCamelCase     :BertOutput			  =		layer.output
        assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
        __UpperCamelCase     :int			  =		roberta_layer.fca.weight
        __UpperCamelCase     :Tuple			  =		roberta_layer.fca.bias
        # end of layer

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

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

    __UpperCamelCase     :Optional[int]			  =		model(SCREAMING_SNAKE_CASE	)[0]
    if classification_head:
        __UpperCamelCase     :int			  =		roberta.model.classification_heads['''mnli'''](roberta.extract_features(SCREAMING_SNAKE_CASE	)	)
    else:
        __UpperCamelCase     :str			  =		roberta.model(SCREAMING_SNAKE_CASE	)[0]
    print(our_output.shape  ,			their_output.shape	)
    __UpperCamelCase     :Any			  =		torch.max(torch.abs(our_output - their_output	)	).item()
    print(f"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
    __UpperCamelCase     :List[str]			  =		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__":
    __lowercase							 =					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.'''
    )
    __lowercase							 =					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 List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase							 =					logging.get_logger(__name__)


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      ["""pixel_values"""]







    def __init__(	self , __lowercase = True , __lowercase = 32 , __lowercase=PILImageResampling.BILINEAR , __lowercase = True , **__lowercase , )       ->	None:
        __UpperCamelCase     :Optional[int]			  =		do_resize
        __UpperCamelCase     :Any			  =		do_rescale
        __UpperCamelCase     :str			  =		size_divisor
        __UpperCamelCase     :Dict			  =		resample
        super().__init__(**__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		get_image_size(__lowercase)
        # Rounds the height and width down to the closest multiple of size_divisor
        __UpperCamelCase     :List[Any]			  =		height // size_divisor * size_divisor
        __UpperCamelCase     :List[str]			  =		width // size_divisor * size_divisor
        __UpperCamelCase     :str			  =		resize(__lowercase , (new_h, new_w) , resample=__lowercase , data_format=__lowercase , **__lowercase)
        return image







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        return rescale(image=__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = None , __lowercase=None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , )       ->	BatchFeature:
        __UpperCamelCase     :Union[str, Any]			  =		do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase     :Tuple			  =		do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase     :List[str]			  =		size_divisor if size_divisor is not None else self.size_divisor
        __UpperCamelCase     :List[Any]			  =		resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
            raise ValueError('''size_divisor is required for resizing''')

        __UpperCamelCase     :List[Any]			  =		make_list_of_images(__lowercase)

        if not valid_images(__lowercase):
            raise ValueError('''Invalid image(s)''')

        # All transformations expect numpy arrays.
        __UpperCamelCase     :Optional[Any]			  =		[to_numpy_array(__lowercase) for img in images]

        if do_resize:
            __UpperCamelCase     :List[str]			  =		[self.resize(__lowercase , size_divisor=__lowercase , resample=__lowercase) for image in images]

        if do_rescale:
            __UpperCamelCase     :Dict			  =		[self.rescale(__lowercase , scale=1 / 255) for image in images]

        __UpperCamelCase     :str			  =		[to_channel_dimension_format(__lowercase , __lowercase) for image in images]

        __UpperCamelCase     :int			  =		{'''pixel_values''': images}
        return BatchFeature(data=__lowercase , tensor_type=__lowercase)





