





'''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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Dict      =   ['''pixel_values''']

     def __init__(					self						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = PILImageResampling.BILINEAR						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = True						,   lowerCamelCase = 1 / 255						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       super().__init__(**lowerCamelCase	)
       _snake_case     		=				size if size is not None else {"shortest_edge": 256}
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				crop_size if crop_size is not None else {"height": 224, "width": 224}
       _snake_case     		=				get_size_dict(lowerCamelCase	)
       _snake_case     		=				do_resize
       _snake_case     		=				size
       _snake_case     		=				resample
       _snake_case     		=				do_center_crop
       _snake_case     		=				crop_size
       _snake_case     		=				do_rescale
       _snake_case     		=				rescale_factor
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       _snake_case     		=				image_std if image_std is not None else IMAGENET_STANDARD_STD

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = PILImageResampling.BICUBIC						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       if "shortest_edge" not in size:
         raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''	)
       _snake_case     		=				get_resize_output_image_size(lowerCamelCase						,   size=size["shortest_edge"]						,   default_to_square=lowerCamelCase	)
       return resize(lowerCamelCase						,   size=lowerCamelCase						,   resample=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase	)
       return center_crop(lowerCamelCase						,   size=(size["height"], size["width"])						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase	):
       return rescale(lowerCamelCase						,   scale=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return normalize(lowerCamelCase						,   mean=lowerCamelCase						,   std=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = ChannelDimension.FIRST						,   **lowerCamelCase						,   ):
       _snake_case     		=				do_resize if do_resize is not None else self.do_resize
       _snake_case     		=				size if size is not None else self.size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				resample if resample is not None else self.resample
       _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(lowerCamelCase	)
       _snake_case     		=				do_rescale if do_rescale is not None else self.do_rescale
       _snake_case     		=				rescale_factor if rescale_factor is not None else self.rescale_factor
       _snake_case     		=				do_normalize if do_normalize is not None else self.do_normalize
       _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     		=				make_list_of_images(lowerCamelCase	)

       if not valid_images(lowerCamelCase	):
         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."	)

       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.
       _snake_case     		=				[to_numpy_array(lowerCamelCase	) for image in images]

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

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

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

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

       _snake_case     		=				[to_channel_dimension_format(lowerCamelCase						,   lowerCamelCase	) for image in images]

       _snake_case     		=				{"pixel_values": images}
       return BatchFeature(data=lowerCamelCase						,   tensor_type=lowerCamelCase	)









'''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
 )


