


"""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()
lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   '''Hello, World!'''
lowerCAmelCase__      				=   '''en_XX'''
def 					a__			(				SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    bool				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							Path("data_bin"				)
  lowerCAmelCase     :     Union[str, Any]								=							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				)

  lowerCAmelCase     :     List[str]								=							xmod.model.encoder.sentence_encoder
  lowerCAmelCase     :     List[Any]								=							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_1_4				,      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:
    lowerCAmelCase     :     List[str]								=							xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

  lowerCAmelCase     :     List[str]								=							XmodForSequenceClassification(SCREAMING_SNAKE_CASE				) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE				)
  model.eval()

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

  lowerCAmelCase     :     Union[str, Any]								=							xmod_sent_encoder.layernorm_embedding.weight
  lowerCAmelCase     :     List[str]								=							xmod_sent_encoder.layernorm_embedding.bias

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

    # self attention
    lowerCAmelCase     :     List[str]								=							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."				)

    lowerCAmelCase     :     Optional[Any]								=							xmod_layer.self_attn.q_proj.weight
    lowerCAmelCase     :     Dict								=							xmod_layer.self_attn.q_proj.bias
    lowerCAmelCase     :     str								=							xmod_layer.self_attn.k_proj.weight
    lowerCAmelCase     :     List[Any]								=							xmod_layer.self_attn.k_proj.bias
    lowerCAmelCase     :     List[str]								=							xmod_layer.self_attn.v_proj.weight
    lowerCAmelCase     :     int								=							xmod_layer.self_attn.v_proj.bias

    # self-attention output
    lowerCAmelCase     :     Optional[int]								=							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."				)
    lowerCAmelCase     :     Optional[int]								=							xmod_layer.self_attn.out_proj.weight
    lowerCAmelCase     :     Dict								=							xmod_layer.self_attn.out_proj.bias
    lowerCAmelCase     :     List[Any]								=							xmod_layer.self_attn_layer_norm.weight
    lowerCAmelCase     :     Optional[Any]								=							xmod_layer.self_attn_layer_norm.bias

    # intermediate
    lowerCAmelCase     :     Dict								=							layer.intermediate
    if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("Dimensions of intermediate weights do not match."				)
    lowerCAmelCase     :     int								=							xmod_layer.fca.weight
    lowerCAmelCase     :     Union[str, Any]								=							xmod_layer.fca.bias

    # output
    lowerCAmelCase     :     List[Any]								=							layer.output
    if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("Dimensions of feed-forward weights do not match."				)
    lowerCAmelCase     :     Optional[int]								=							xmod_layer.fca.weight
    lowerCAmelCase     :     str								=							xmod_layer.fca.bias
    lowerCAmelCase     :     List[Any]								=							xmod_layer.final_layer_norm.weight
    lowerCAmelCase     :     int								=							xmod_layer.final_layer_norm.bias
    if bert_output.adapter_layer_norm is not None:
      lowerCAmelCase     :     Union[str, Any]								=							xmod_layer.adapter_layer_norm.weight
      lowerCAmelCase     :     Any								=							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():
      lowerCAmelCase     :     Any								=							bert_output.adapter_modules[lang_code]
      lowerCAmelCase     :     Optional[int]								=							xmod_layer.adapter_modules[lang_code]
      lowerCAmelCase     :     int								=							from_adapter.fca.weight
      lowerCAmelCase     :     str								=							from_adapter.fca.bias
      lowerCAmelCase     :     List[Any]								=							from_adapter.fca.weight
      lowerCAmelCase     :     Union[str, Any]								=							from_adapter.fca.bias

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    lowerCAmelCase     :     Any								=							xmod_sent_encoder.layer_norm.weight
    lowerCAmelCase     :     Dict								=							xmod_sent_encoder.layer_norm.bias

  if classification_head:
    lowerCAmelCase     :     Any								=							xmod.model.classification_heads["mnli"].dense.weight
    lowerCAmelCase     :     Dict								=							xmod.model.classification_heads["mnli"].dense.bias
    lowerCAmelCase     :     List[Any]								=							xmod.model.classification_heads["mnli"].out_proj.weight
    lowerCAmelCase     :     int								=							xmod.model.classification_heads["mnli"].out_proj.bias
  else:
    # LM Head
    lowerCAmelCase     :     List[str]								=							xmod.model.encoder.lm_head.dense.weight
    lowerCAmelCase     :     Optional[int]								=							xmod.model.encoder.lm_head.dense.bias
    lowerCAmelCase     :     List[str]								=							xmod.model.encoder.lm_head.layer_norm.weight
    lowerCAmelCase     :     Optional[Any]								=							xmod.model.encoder.lm_head.layer_norm.bias
    lowerCAmelCase     :     List[Any]								=							xmod.model.encoder.lm_head.weight
    lowerCAmelCase     :     Any								=							xmod.model.encoder.lm_head.bias

  # Let's check that we get the same results.
  lowerCAmelCase     :     List[str]								=							xmod.encode(SCREAMING_SNAKE_CASE				).unsqueeze(0				)  # batch of size 1
  model.roberta.set_default_language(SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     Optional[Any]								=							model(SCREAMING_SNAKE_CASE				)[0]
  if classification_head:
    lowerCAmelCase     :     Optional[Any]								=							xmod.model.classification_heads["mnli"](xmod.extract_features(SCREAMING_SNAKE_CASE				)				)
  else:
    lowerCAmelCase     :     Optional[int]								=							xmod.model(SCREAMING_SNAKE_CASE				,      lang_id=[SAMPLE_LANGUAGE]				)[0]
  print(our_output.shape				,      their_output.shape				)
  lowerCAmelCase     :     Union[str, Any]								=							torch.max(torch.abs(our_output - their_output				)				).item()
  print(f"""max_absolute_diff = {max_absolute_diff}"""				)  # ~ 1e-7
  lowerCAmelCase     :     Union[str, Any]								=							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__":
     lowerCAmelCase__      				=   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.'''
     )
     lowerCAmelCase__      				=   parser.parse_args()
     convert_xmod_checkpoint_to_pytorch(
         args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )













"""simple docstring"""



import argparse
import os
import re

import packaging.version


__A		:       List[str]  				=		'''examples/'''
__A		:       int  				=		{
    '''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
    '''init''': (re.compile(R'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
    '''setup''': (re.compile(R'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), R'''\1version="VERSION",'''),
    '''doc''': (re.compile(R'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
__A		:       Dict  				=		{
    '''init''': '''src/transformers/__init__.py''',
    '''setup''': '''setup.py''',
}
__A		:       Optional[int]  				=		'''README.md'''



def 						lowercase    (				__snake_case				: int		,					__snake_case				: Any		,					__snake_case				: int	):
		with open(__snake_case		,					'''r'''		,					encoding='''utf-8'''		,					newline='''\n'''	) as f:
				lowercase_ : int							       =   f.read()
		lowercase_       ,					lowercase_ : List[str]							       =   REPLACE_PATTERNS[pattern]
		lowercase_ : Union[str, Any]							       =   replace.replace('''VERSION'''		,					__snake_case	)
		lowercase_ : Optional[Any]							       =   re_pattern.sub(__snake_case		,					__snake_case	)
		with open(__snake_case		,					'''w'''		,					encoding='''utf-8'''		,					newline='''\n'''	) as f:
				f.write(__snake_case	)



def 						lowercase    (				__snake_case				: int	):
		for folder, directories, fnames in os.walk(__snake_case	):
				# Removing some of the folders with non-actively maintained examples from the walk
				if "research_projects" in directories:
						directories.remove('''research_projects'''	)
				if "legacy" in directories:
						directories.remove('''legacy'''	)
				for fname in fnames:
						if fname.endswith('''.py'''	):
								update_version_in_file(os.path.join(__snake_case		,					__snake_case	)		,					__snake_case		,					pattern='''examples'''	)



def 						lowercase    (				__snake_case				: Optional[Any]		,					__snake_case				: Optional[Any]=False	):
		for pattern, fname in REPLACE_FILES.items():
				update_version_in_file(__snake_case		,					__snake_case		,					__snake_case	)
		if not patch:
				update_version_in_examples(__snake_case	)



def 						lowercase    (				):
		lowercase_ : Union[str, Any]							       =   '''🤗 Transformers currently provides the following architectures'''
		lowercase_ : Union[str, Any]							       =   '''1. Want to contribute a new model?'''
		with open(__snake_case		,					'''r'''		,					encoding='''utf-8'''		,					newline='''\n'''	) as f:
				lowercase_ : List[str]							       =   f.readlines()

		# Find the start of the list.
		lowercase_ : Optional[Any]							       =   0
		while not lines[start_index].startswith(_start_prompt	):
				start_index += 1
		start_index += 1

		lowercase_ : str							       =   start_index
		# Update the lines in the model list.
		while not lines[index].startswith(_end_prompt	):
				if lines[index].startswith('''1.'''	):
						lowercase_ : str							       =   lines[index].replace(
						    '''https://huggingface.co/docs/transformers/main/model_doc'''		,					'''https://huggingface.co/docs/transformers/model_doc'''		,					)
				index += 1

		with open(__snake_case		,					'''w'''		,					encoding='''utf-8'''		,					newline='''\n'''	) as f:
				f.writelines(__snake_case	)



def 						lowercase    (				):
		with open(REPLACE_FILES['''init''']		,					'''r'''	) as f:
				lowercase_ : List[Any]							       =   f.read()
		lowercase_ : List[str]							       =   REPLACE_PATTERNS['''init'''][0].search(__snake_case	).groups()[0]
		return packaging.version.parse(__snake_case	)



def 						lowercase    (				__snake_case				: Optional[Any]=False	):
		lowercase_ : str							       =   get_version()
		if patch and default_version.is_devrelease:
				raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''	)
		if default_version.is_devrelease:
				lowercase_ : Optional[Any]							       =   default_version.base_version
		elif patch:
				lowercase_ : Optional[int]							       =   F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
		else:
				lowercase_ : Optional[int]							       =   F'''{default_version.major}.{default_version.minor + 1}.0'''

		# Now let's ask nicely if that's the right one.
		lowercase_ : int							       =   input(F'''Which version are you releasing? [{default_version}]'''	)
		if len(__snake_case	) == 0:
				lowercase_ : Dict							       =   default_version

		print(F'''Updating version to {version}.'''	)
		global_version_update(__snake_case		,					patch=__snake_case	)
		if not patch:
				print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''	)
				clean_main_ref_in_model_list()



def 						lowercase    (				):
		lowercase_ : List[Any]							       =   get_version()
		lowercase_ : List[str]							       =   F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
		lowercase_ : Any							       =   current_version.base_version

		# Check with the user we got that right.
		lowercase_ : Tuple							       =   input(F'''Which version are we developing now? [{dev_version}]'''	)
		if len(__snake_case	) == 0:
				lowercase_ : str							       =   dev_version

		print(F'''Updating version to {version}.'''	)
		global_version_update(__snake_case	)
		print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''	)
		clean_main_ref_in_model_list()


if __name__ == "__main__":
						__A		:       int  				=		argparse.ArgumentParser()
						parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
						parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
						__A		:       Any  				=		parser.parse_args()
						if not args.post_release:
												pre_release_work(patch=args.patch)
						elif args.patch:
												print('''Nothing to do after a patch :-)''')
						else:
												post_release_work()



