

"""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													=logging.get_logger(__name__)

UpperCAmelCase													="Hello, World!"
UpperCAmelCase													="en_XX"



def   _A					(	_a	:						str     ,   _a	:						str     ,   _a	:						bool				):



      """simple docstring"""






      A				  =		Path("""data_bin"""				)
      A				  =		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				)

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

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

      A				  =		XmodForSequenceClassification(_a				) if classification_head else XmodForMaskedLM(_a				)
      model.eval()

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

      A				  =		xmod_sent_encoder.layernorm_embedding.weight
      A				  =		xmod_sent_encoder.layernorm_embedding.bias

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

            # self attention
            A				  =		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."""				)

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

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

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

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

            # end of layer

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

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

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

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







"""simple docstring"""


from __future__ import annotations

from collections.abc import Sequence
from typing import Literal



def   _A					(	_a	:						str     ,   _a	:						str				):



      """simple docstring"""






      A				  =		list(_a				)
      A				  =		list(_a				)
      A				  =		0
      for i in range(len(_a				)				):
            if lista[i] != lista[i]:
                  count += 1
                  A				  =		"""_"""
      if count > 1:
            return False
      else:
            return "".join(_a				)



def   _A					(	_a	:						list[str]				):



      """simple docstring"""






      A				  =		[]
      while True:
            A				  =		["""$"""] * len(_a				)
            A				  =		[]
            for i in range(len(_a				)				):
                  for j in range(i + 1     ,   len(_a				)				):
                        A				  =		compare_string(binary[i]     ,   binary[j]				)
                        if k is False:
                              A				  =		"""*"""
                              A				  =		"""*"""
                              temp.append("""X"""				)
            for i in range(len(_a				)				):
                  if checka[i] == "$":
                        pi.append(binary[i]				)
            if len(_a				) == 0:
                  return pi
            A				  =		list(set(_a				)				)



def   _A					(	_a	:						int     ,   _a	:						Sequence[float]				):



      """simple docstring"""






      A				  =		[]
      for minterm in minterms:
            A				  =		""""""
            for _ in range(_a				):
                  A				  =		str(minterm % 2				) + string
                  minterm //= 2
            temp.append(_a				)
      return temp



def   _A					(	_a	:						str     ,   _a	:						str     ,   _a	:						int				):



      """simple docstring"""






      A				  =		list(_a				)
      A				  =		list(_a				)
      A				  =		0
      for i in range(len(_a				)				):
            if lista[i] != lista[i]:
                  count_n += 1
      return count_n == count



def   _A					(	_a	:						list[list[int]]     ,   _a	:						list[str]				):



      """simple docstring"""






      A				  =		[]
      A				  =		[0] * len(_a				)
      for i in range(len(chart[0]				)				):
            A				  =		0
            A				  =		-1
            for j in range(len(_a				)				):
                  if chart[j][i] == 1:
                        count += 1
                        A				  =		j
            if count == 1:
                  A				  =		1
      for i in range(len(_a				)				):
            if select[i] == 1:
                  for j in range(len(chart[0]				)				):
                        if chart[i][j] == 1:
                              for k in range(len(_a				)				):
                                    A				  =		0
                  temp.append(prime_implicants[i]				)
      while True:
            A				  =		0
            A				  =		-1
            A				  =		0
            for i in range(len(_a				)				):
                  A				  =		chart[i].count(1				)
                  if count_n > max_n:
                        A				  =		count_n
                        A				  =		i

            if max_n == 0:
                  return temp

            temp.append(prime_implicants[rem]				)

            for i in range(len(chart[0]				)				):
                  if chart[rem][i] == 1:
                        for j in range(len(_a				)				):
                              A				  =		0



def   _A					(	_a	:						list[str]     ,   _a	:						list[str]				):



      """simple docstring"""






      A				  =		[[0 for x in range(len(_a				)				)] for x in range(len(_a				)				)]
      for i in range(len(_a				)				):
            A				  =		prime_implicants[i].count("""_"""				)
            for j in range(len(_a				)				):
                  if is_for_table(prime_implicants[i]     ,   binary[j]     ,   _a				):
                        A				  =		1

      return chart



def   _A					(	):



      """simple docstring"""






      A				  =		int(input("""Enter the no. of variables\n"""				)				)
      A				  =		[
          float(_a				)
          for x in input(
              """Enter the decimal representation of Minterms 'Spaces Separated'\n"""				).split()
      ]
      A				  =		decimal_to_binary(_a     ,   _a				)

      A				  =		check(_a				)
      print("""Prime Implicants are:"""				)
      print(_a				)
      A				  =		prime_implicant_chart(_a     ,   _a				)

      A				  =		selection(_a     ,   _a				)
      print("""Essential Prime Implicants are:"""				)
      print(_a				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




