




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()
__a          =			logging.get_logger(__name__)

__a          =			'Hello world! cécé herlolip'







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Dict       =      FairseqRobertaModel.from_pretrained(_lowercase				)
     roberta.eval()  # disable dropout
     UpperCAmelCase_      :							str       =      roberta.model.encoder.sentence_encoder
     UpperCAmelCase_      :							int       =      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_      :							Dict       =      roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

     UpperCAmelCase_      :							List[Any]       =      XLMRobertaXLForSequenceClassification(_lowercase				) if classification_head else XLMRobertaXLForMaskedLM(_lowercase				)
     model.eval()

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

     UpperCAmelCase_      :							Union[str, Any]       =      roberta_sent_encoder.layer_norm.weight
     UpperCAmelCase_      :							List[str]       =      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_      :							Union[str, Any]       =      roberta_layer.self_attn_layer_norm.weight
          UpperCAmelCase_      :							Optional[Any]       =      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_      :							str       =      roberta_layer.self_attn.q_proj.weight
          UpperCAmelCase_      :							Dict       =      roberta_layer.self_attn.q_proj.bias
          UpperCAmelCase_      :							Optional[int]       =      roberta_layer.self_attn.k_proj.weight
          UpperCAmelCase_      :							str       =      roberta_layer.self_attn.k_proj.bias
          UpperCAmelCase_      :							Any       =      roberta_layer.self_attn.v_proj.weight
          UpperCAmelCase_      :							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_      :							Tuple       =      roberta_layer.self_attn.out_proj.weight
          UpperCAmelCase_      :							Any       =      roberta_layer.self_attn.out_proj.bias

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

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

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

     if classification_head:
          UpperCAmelCase_      :							List[Any]       =      roberta.model.classification_heads['''mnli'''].dense.weight
          UpperCAmelCase_      :							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_      :							Optional[Any]       =      roberta.model.encoder.lm_head.dense.weight
          UpperCAmelCase_      :							Optional[int]       =      roberta.model.encoder.lm_head.dense.bias
          UpperCAmelCase_      :							List[Any]       =      roberta.model.encoder.lm_head.layer_norm.weight
          UpperCAmelCase_      :							Optional[int]       =      roberta.model.encoder.lm_head.layer_norm.bias
          UpperCAmelCase_      :							str       =      roberta.model.encoder.lm_head.weight
          UpperCAmelCase_      :							Any       =      roberta.model.encoder.lm_head.bias

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

     UpperCAmelCase_      :							str       =      model(_lowercase				)[0]
     if classification_head:
          UpperCAmelCase_      :							Union[str, Any]       =      roberta.model.classification_heads['''mnli'''](roberta.extract_features(_lowercase				)				)
     else:
          UpperCAmelCase_      :							Dict       =      roberta.model(_lowercase				)[0]
     print(our_output.shape			,						their_output.shape				)
     UpperCAmelCase_      :							int       =      torch.max(torch.abs(our_output - their_output				)				).item()
     print(f'''max_absolute_diff = {max_absolute_diff}'''				)  # ~ 1e-7
     UpperCAmelCase_      :							Optional[int]       =      torch.allclose(_lowercase			,						_lowercase			,						atol=1E-3				)
     print('''Do both models output the same tensors?'''			,						'''🔥''' if success else '''💩'''				)
     if not success:
          raise Exception('''Something went wRoNg'''				)

     pathlib.Path(_lowercase				).mkdir(parents=_lowercase			,						exist_ok=_lowercase				)
     print(f'''Saving model to {pytorch_dump_folder_path}'''				)
     model.save_pretrained(_lowercase				)


if __name__ == "__main__":
     __a          =			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.'
     )
     __a          =			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, 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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


__a          =			logging.get_logger(__name__)







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if isinstance(_lowercase			,						(list, tuple)				) and isinstance(videos[0]			,						(list, tuple)				) and is_valid_image(videos[0][0]				):
          return videos

     elif isinstance(_lowercase			,						(list, tuple)				) and is_valid_image(videos[0]				):
          return [videos]

     elif is_valid_image(_lowercase				):
          return [[videos]]

     raise ValueError(f'''Could not make batched video from {videos}'''				)



class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				['''pixel_values''']





    def __init__(				self       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = 1 / 255       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		None:
         super().__init__(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      size if size is not None else {'''shortest_edge''': 224}
         UpperCAmelCase_      :							Any       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         UpperCAmelCase_      :							List[str]       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,param_name='''crop_size'''  )

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





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		np.ndarray:
         UpperCAmelCase_      :							Optional[int]       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         if "shortest_edge" in size:
              UpperCAmelCase_      :							Dict       =      get_resize_output_image_size(_SCREAMING_SNAKE_CASE       ,size['''shortest_edge''']       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         elif "height" in size and "width" in size:
              UpperCAmelCase_      :							Tuple       =      (size['''height'''], size['''width'''])
         else:
              raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. 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 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		np.ndarray:
         UpperCAmelCase_      :							str       =      get_size_dict(_SCREAMING_SNAKE_CASE  )
         if "height" not in size or "width" not in size:
              raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''  )
         return center_crop(_SCREAMING_SNAKE_CASE       ,size=(size['''height'''], size['''width'''])       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		Dict:
         return rescale(_SCREAMING_SNAKE_CASE       ,scale=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		np.ndarray:
         return normalize(_SCREAMING_SNAKE_CASE       ,mean=_SCREAMING_SNAKE_CASE       ,std=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				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 = ChannelDimension.FIRST       ,)			->		np.ndarray:
         if do_resize and size is None or resample is None:
              raise ValueError('''Size and resample 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_      :							Any       =      to_numpy_array(_SCREAMING_SNAKE_CASE  )

         if do_resize:
              UpperCAmelCase_      :							Union[str, Any]       =      self.resize(image=_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE       ,resample=_SCREAMING_SNAKE_CASE  )

         if do_center_crop:
              UpperCAmelCase_      :							Optional[int]       =      self.center_crop(_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE  )

         if do_rescale:
              UpperCAmelCase_      :							str       =      self.rescale(image=_SCREAMING_SNAKE_CASE       ,scale=_SCREAMING_SNAKE_CASE  )

         if do_normalize:
              UpperCAmelCase_      :							List[Any]       =      self.normalize(image=_SCREAMING_SNAKE_CASE       ,mean=_SCREAMING_SNAKE_CASE       ,std=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[int]       =      to_channel_dimension_format(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
         return image





    def 							a__				(				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       ,)			->		PIL.Image.Image:
         UpperCAmelCase_      :							Dict       =      do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_      :							int       =      resample if resample is not None else self.resample
         UpperCAmelCase_      :							List[str]       =      do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_      :							Optional[Any]       =      do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_      :							List[Any]       =      rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_      :							Tuple       =      do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_      :							Optional[int]       =      image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_      :							Optional[int]       =      image_std if image_std is not None else self.image_std

         UpperCAmelCase_      :							List[str]       =      size if size is not None else self.size
         UpperCAmelCase_      :							Optional[int]       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_      :							Any       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,param_name='''crop_size'''  )

         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.'''  )

         UpperCAmelCase_      :							List[Any]       =      make_batched(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							str       =      [
             [
                 self._preprocess_image(
                     image=_SCREAMING_SNAKE_CASE       ,do_resize=_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE       ,resample=_SCREAMING_SNAKE_CASE       ,do_center_crop=_SCREAMING_SNAKE_CASE       ,crop_size=_SCREAMING_SNAKE_CASE       ,do_rescale=_SCREAMING_SNAKE_CASE       ,rescale_factor=_SCREAMING_SNAKE_CASE       ,do_normalize=_SCREAMING_SNAKE_CASE       ,image_mean=_SCREAMING_SNAKE_CASE       ,image_std=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,)
                 for img in video
             ]
             for video in videos
         ]

         UpperCAmelCase_      :							Any       =      {'''pixel_values''': videos}
         return BatchFeature(data=_SCREAMING_SNAKE_CASE       ,tensor_type=_SCREAMING_SNAKE_CASE  )