





'''simple docstring'''



import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=sys.maxsize	):
       _snake_case     		=				'''bilinear'''
       _snake_case     		=				max_size
       _snake_case     		=				short_edge_length






     def __call__(					self						,   lowerCamelCase	):
       _snake_case     		=				[]
       for img in imgs:
         _snake_case     		=				img.shape[:2]
         # later: provide list and randomly choose index for resize
         _snake_case     		=				np.random.randint(self.short_edge_length[0]						,   self.short_edge_length[1] + 1	)
         if size == 0:
           return img
         _snake_case     		=				size * 1.0 / min(lowerCamelCase						,   lowerCamelCase	)
         if h < w:
           _snake_case     		=				size, scale * w
         else:
           _snake_case     		=				scale * h, size
         if max(lowerCamelCase						,   lowerCamelCase	) > self.max_size:
           _snake_case     		=				self.max_size * 1.0 / max(lowerCamelCase						,   lowerCamelCase	)
           _snake_case     		=				newh * scale
           _snake_case     		=				neww * scale
         _snake_case     		=				int(neww + 0.5	)
         _snake_case     		=				int(newh + 0.5	)

         if img.dtype == np.uinta:
           _snake_case     		=				Image.fromarray(lowerCamelCase	)
           _snake_case     		=				pil_image.resize((neww, newh)						,   PILImageResampling.BILINEAR	)
           _snake_case     		=				np.asarray(lowerCamelCase	)
         else:
           _snake_case     		=				img.permute(2						,   0						,   1	).unsqueeze(0	)  # 3, 0, 1)  # hw(c) -> nchw
           _snake_case     		=				nn.functional.interpolate(
               lowerCamelCase						,   (newh, neww)						,   mode=self.interp_method						,   align_corners=lowerCamelCase	).squeeze(0	)
         img_augs.append(lowerCamelCase	)

       return img_augs







class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase	):
       _snake_case     		=				ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,   cfg.INPUT.MAX_SIZE_TEST	)
       _snake_case     		=				cfg.INPUT.FORMAT
       _snake_case     		=				cfg.SIZE_DIVISIBILITY
       _snake_case     		=				cfg.PAD_VALUE
       _snake_case     		=				cfg.INPUT.MAX_SIZE_TEST
       _snake_case     		=				cfg.MODEL.DEVICE
       _snake_case     		=				torch.tensor(cfg.MODEL.PIXEL_STD	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)						,   1						,   1	)
       _snake_case     		=				torch.tensor(cfg.MODEL.PIXEL_MEAN	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)						,   1						,   1	)
       _snake_case     		=				lambda lowerCamelCase	: (x - self.pixel_mean) / self.pixel_std

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				tuple(max(lowerCamelCase	) for s in zip(*[img.shape for img in images]	)	)
       _snake_case     		=				[im.shape[-2:] for im in images]
       _snake_case     		=				[
           nn.functional.pad(
               lowerCamelCase						,   [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,   value=self.pad_value						,   )
           for size, im in zip(lowerCamelCase						,   lowerCamelCase	)
       ]

       return torch.stack(lowerCamelCase	), torch.tensor(lowerCamelCase	)






     def __call__(					self						,   lowerCamelCase						,   lowerCamelCase=False	):

       with torch.no_grad():
         if not isinstance(lowerCamelCase						,   lowerCamelCase	):
           _snake_case     		=				[images]
         if single_image:
           assert len(lowerCamelCase	) == 1
         for i in range(len(lowerCamelCase	)	):
           if isinstance(images[i]						,   torch.Tensor	):
             images.insert(lowerCamelCase						,   images.pop(lowerCamelCase	).to(self.device	).float()	)
           elif not isinstance(images[i]						,   torch.Tensor	):
             images.insert(
                 lowerCamelCase						,   torch.as_tensor(img_tensorize(images.pop(lowerCamelCase	)						,   input_format=self.input_format	)	)
                 .to(self.device	)
                 .float()						,   )
               # resize smallest edge
         _snake_case     		=				torch.tensor([im.shape[:2] for im in images]	)
         _snake_case     		=				self.aug(lowerCamelCase	)
         # transpose images and convert to torch tensors
         # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
         # now normalize before pad to avoid useless arithmetic
         _snake_case     		=				[self.normalizer(lowerCamelCase	) for x in images]
         # now pad them to do the following operations
         _snake_case     		=				self.pad(lowerCamelCase	)
         # Normalize

         if self.size_divisibility > 0:
           raise NotImplementedError()
         # pad
         _snake_case     		=				torch.true_divide(lowerCamelCase						,   lowerCamelCase	)
         if single_image:
           return images[0], sizes[0], scales_yx[0]
         else:
           return images, sizes, scales_yx







def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  assert torch.isfinite(__A       ).all(), "Box tensor contains infinite or NaN!"
  _snake_case     		=				box_size
  tensor[:, 0].clamp_(min=0					,	max=__A       )
  tensor[:, 1].clamp_(min=0					,	max=__A       )
  tensor[:, 2].clamp_(min=0					,	max=__A       )
  tensor[:, 3].clamp_(min=0					,	max=__A       )









'''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()
__magic_name__   :						Union[str, Any]    =			logging.get_logger(__name__)

__magic_name__   :						Tuple    =			"""Hello, World!"""
__magic_name__   :						Union[str, Any]    =			"""en_XX"""




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

  _snake_case     		=				xmod.model.encoder.sentence_encoder
  _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:
    _snake_case     		=				xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

  _snake_case     		=				XmodForSequenceClassification(SCREAMING_SNAKE_CASE__       ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE__       )
  model.eval()

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

  _snake_case     		=				xmod_sent_encoder.layernorm_embedding.weight
  _snake_case     		=				xmod_sent_encoder.layernorm_embedding.bias

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

    # self attention
    _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."       )

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

    # self-attention output
    _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."       )
    _snake_case     		=				xmod_layer.self_attn.out_proj.weight
    _snake_case     		=				xmod_layer.self_attn.out_proj.bias
    _snake_case     		=				xmod_layer.self_attn_layer_norm.weight
    _snake_case     		=				xmod_layer.self_attn_layer_norm.bias

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

    # output
    _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."       )
    _snake_case     		=				xmod_layer.fca.weight
    _snake_case     		=				xmod_layer.fca.bias
    _snake_case     		=				xmod_layer.final_layer_norm.weight
    _snake_case     		=				xmod_layer.final_layer_norm.bias
    if bert_output.adapter_layer_norm is not None:
      _snake_case     		=				xmod_layer.adapter_layer_norm.weight
      _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():
      _snake_case     		=				bert_output.adapter_modules[lang_code]
      _snake_case     		=				xmod_layer.adapter_modules[lang_code]
      _snake_case     		=				from_adapter.fca.weight
      _snake_case     		=				from_adapter.fca.bias
      _snake_case     		=				from_adapter.fca.weight
      _snake_case     		=				from_adapter.fca.bias

    # end of layer

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

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

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

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

  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__":
 __magic_name__   :						str    =			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."""
 )
 __magic_name__   :						List[Any]    =			parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )


