

'''simple docstring'''



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "LayoutLMv2ImageProcessor"
 lowerCAmelCase__       = ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")
 def __init__(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					List[str]:


      """simple docstring"""




      if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
           __SCREAMING_SNAKE_CASE      = kwargs.pop("""feature_extractor"""     )

      __SCREAMING_SNAKE_CASE      = image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
           raise ValueError("""You need to specify an `image_processor`."""     )
      if tokenizer is None:
           raise ValueError("""You need to specify a `tokenizer`."""     )

      super().__init__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Union[List[List[int]], List[List[List[int]]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[List[int], List[List[int]]]] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Union[bool, str, PaddingStrategy] = False   ,				__SCREAMING_SNAKE_CASE   :			Union[bool, str, TruncationStrategy] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[int] = None   ,				__SCREAMING_SNAKE_CASE   :			int = 0   ,				__SCREAMING_SNAKE_CASE   :			Optional[int] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[bool] = None   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, TensorType]] = None   ,				**__SCREAMING_SNAKE_CASE   :			Any   ,				)    ->					BatchEncoding:


      """simple docstring"""




      if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               """You cannot provide bounding boxes """
               """if you initialized the image processor with apply_ocr set to True."""     )

      if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""     )

      if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""     )

      # first, apply the image processor
      __SCREAMING_SNAKE_CASE      = self.image_processor(images=__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE     )

      # second, apply the tokenizer
      if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
                __SCREAMING_SNAKE_CASE      = [text]  # add batch dimension (as the image processor always adds a batch dimension)
           __SCREAMING_SNAKE_CASE      = features["""words"""]

      __SCREAMING_SNAKE_CASE      = self.tokenizer(
          text=text if text is not None else features["""words"""]   ,				text_pair=text_pair if text_pair is not None else None   ,				boxes=boxes if boxes is not None else features["""boxes"""]   ,				word_labels=__SCREAMING_SNAKE_CASE   ,				add_special_tokens=__SCREAMING_SNAKE_CASE   ,				padding=__SCREAMING_SNAKE_CASE   ,				truncation=__SCREAMING_SNAKE_CASE   ,				max_length=__SCREAMING_SNAKE_CASE   ,				stride=__SCREAMING_SNAKE_CASE   ,				pad_to_multiple_of=__SCREAMING_SNAKE_CASE   ,				return_token_type_ids=__SCREAMING_SNAKE_CASE   ,				return_attention_mask=__SCREAMING_SNAKE_CASE   ,				return_overflowing_tokens=__SCREAMING_SNAKE_CASE   ,				return_special_tokens_mask=__SCREAMING_SNAKE_CASE   ,				return_offsets_mapping=__SCREAMING_SNAKE_CASE   ,				return_length=__SCREAMING_SNAKE_CASE   ,				verbose=__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE   ,				)

      # add pixel values
      __SCREAMING_SNAKE_CASE      = features.pop("""pixel_values"""     )
      if return_overflowing_tokens is True:
           __SCREAMING_SNAKE_CASE      = self.get_overflowing_images(__SCREAMING_SNAKE_CASE   ,				encoded_inputs["""overflow_to_sample_mapping"""]     )
      __SCREAMING_SNAKE_CASE      = images

      return encoded_inputs
 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = []
      for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]     )

      if len(__SCREAMING_SNAKE_CASE     ) != len(__SCREAMING_SNAKE_CASE     ):
           raise ValueError(
               """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
               f' {len(__SCREAMING_SNAKE_CASE     )} and {len(__SCREAMING_SNAKE_CASE     )}'     )

      return images_with_overflow
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				*__SCREAMING_SNAKE_CASE   :			str   ,				**__SCREAMING_SNAKE_CASE   :			List[str]     )    ->					List[str]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				*__SCREAMING_SNAKE_CASE   :			Dict   ,				**__SCREAMING_SNAKE_CASE   :			Any     )    ->					Union[str, Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 @property
 def  UpperCAmelCase__						(						self   :			int     )    ->					Optional[int]:


      """simple docstring"""




      return ["input_ids", "bbox", "attention_mask", "image"]
 @property
 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[Any]:


      """simple docstring"""




      warnings.warn(
          """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
      return self.image_processor_class


 @property
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Any:


      """simple docstring"""




      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
      return self.image_processor









'''simple docstring'''



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
  raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
  raise Exception('requires fairseq < v2')

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

UpperCAmelCase					:			Tuple       =	'Hello, World!'
UpperCAmelCase					:			Any       =	'en_XX'







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = Path("""data_bin"""		)
     __SCREAMING_SNAKE_CASE      = FairseqXmodModel.from_pretrained(
         model_name_or_path=str(Path(a__		).parent		) ,       checkpoint_file=Path(a__		).name ,       _name="""xmod_base""" ,       arch="""xmod_base""" ,       task="""multilingual_masked_lm""" ,       data_name_or_path=str(a__		) ,       bpe="""sentencepiece""" ,       sentencepiece_model=str(Path(a__		).parent / """sentencepiece.bpe.model"""		) ,       src_dict=str(data_dir / """dict.txt"""		) ,       )
     xmod.eval()  # disable dropout
     print(a__		)

     __SCREAMING_SNAKE_CASE      = xmod.model.encoder.sentence_encoder
     __SCREAMING_SNAKE_CASE      = XmodConfig(
         vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings ,       hidden_size=xmod.cfg.model.encoder_embed_dim ,       num_hidden_layers=xmod.cfg.model.encoder_layers ,       num_attention_heads=xmod.cfg.model.encoder_attention_heads ,       intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim ,       max_position_embeddings=5_14 ,       type_vocab_size=1 ,       layer_norm_eps=1E-5 ,       pre_norm=xmod.cfg.model.encoder_normalize_before ,       adapter_reduction_factor=getattr(xmod.cfg.model ,       """bottleneck""" ,       2		) ,       adapter_layer_norm=xmod.cfg.model.adapter_layer_norm ,       adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm ,       ln_before_adapter=xmod.cfg.model.ln_before_adapter ,       languages=xmod.cfg.model.languages ,       )
     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

     print("""Our X-MOD config:""" ,       a__		)

     __SCREAMING_SNAKE_CASE      = XmodForSequenceClassification(a__		) if classification_head else XmodForMaskedLM(a__		)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.embed_tokens.weight
     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.embed_positions.weight
     __SCREAMING_SNAKE_CASE      = torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c xmod doesn't use them.

     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layernorm_embedding.weight
     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers		):
          # Encoder: start of layer
          __SCREAMING_SNAKE_CASE      = model.roberta.encoder.layer[i]
          __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layers[i]

          # self attention
          __SCREAMING_SNAKE_CASE      = layer.attention.self
          if not (
              xmod_layer.self_attn.k_proj.weight.data.shape
              == xmod_layer.self_attn.q_proj.weight.data.shape
              == xmod_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)		)
          ):
               raise AssertionError("""Dimensions of self-attention weights do not match."""		)

          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.q_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.q_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.k_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.k_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.v_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.v_proj.bias

          # self-attention output
          __SCREAMING_SNAKE_CASE      = layer.attention.output
          if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
               raise AssertionError("""Dimensions of self-attention output weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.out_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.out_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn_layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn_layer_norm.bias

          # intermediate
          __SCREAMING_SNAKE_CASE      = layer.intermediate
          if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("""Dimensions of intermediate weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.bias

          # output
          __SCREAMING_SNAKE_CASE      = layer.output
          if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("""Dimensions of feed-forward weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.final_layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.final_layer_norm.bias
          if bert_output.adapter_layer_norm is not None:
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_layer_norm.weight
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_layer_norm.bias

          if sorted(bert_output.adapter_modules.keys()		) != sorted(xmod_layer.adapter_modules.keys()		):
               raise AssertionError("""Lists of language adapters do not match."""		)
          for lang_code, adapter in xmod_layer.adapter_modules.items():
               __SCREAMING_SNAKE_CASE      = bert_output.adapter_modules[lang_code]
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_modules[lang_code]
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.weight
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.bias
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.weight
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.bias

          # end of layer

     if xmod_sent_encoder.layer_norm is not None:
          __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layer_norm.bias

     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].dense.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].dense.bias
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].out_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].out_proj.bias
     else:
          # LM Head
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.dense.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.dense.bias
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.layer_norm.bias
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     __SCREAMING_SNAKE_CASE      = xmod.encode(a__		).unsqueeze(0		)  # batch of size 1
     model.roberta.set_default_language(a__		)

     __SCREAMING_SNAKE_CASE      = model(a__		)[0]
     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""](xmod.extract_features(a__		)		)
     else:
          __SCREAMING_SNAKE_CASE      = xmod.model(a__ ,       lang_id=[SAMPLE_LANGUAGE]		)[0]
     print(our_output.shape ,       their_output.shape		)
     __SCREAMING_SNAKE_CASE      = torch.max(torch.abs(our_output - their_output		)		).item()
     print(F'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
     __SCREAMING_SNAKE_CASE      = 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"""		)

     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					:			Tuple       =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--xmod_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					:			Optional[int]       =	parser.parse_args()
  convert_xmod_checkpoint_to_pytorch(
      args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )






