





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

_A					:				List[Any]				=			'''Hello, World!'''
_A					:				Dict				=			'''en_XX'''


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			Path('''data_bin'''	)
    SCREAMING_SNAKE_CASE__											=			FairseqXmodModel.from_pretrained(
        model_name_or_path=str(Path(UpperCAmelCase_	).parent	) ,							checkpoint_file=Path(UpperCAmelCase_	).name ,							_name='''xmod_base''' ,							arch='''xmod_base''' ,							task='''multilingual_masked_lm''' ,							data_name_or_path=str(UpperCAmelCase_	) ,							bpe='''sentencepiece''' ,							sentencepiece_model=str(Path(UpperCAmelCase_	).parent / '''sentencepiece.bpe.model'''	) ,							src_dict=str(data_dir / '''dict.txt'''	) ,							)
    xmod.eval()  # disable dropout
    print(UpperCAmelCase_	)

    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_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:
        SCREAMING_SNAKE_CASE__											=			xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

    print('''Our X-MOD config:''' ,							UpperCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			XmodForSequenceClassification(UpperCAmelCase_	) if classification_head else XmodForMaskedLM(UpperCAmelCase_	)
    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(UpperCAmelCase_	).unsqueeze(0	)  # batch of size 1
    model.roberta.set_default_language(UpperCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			model(UpperCAmelCase_	)[0]
    if classification_head:
        SCREAMING_SNAKE_CASE__											=			xmod.model.classification_heads['''mnli'''](xmod.extract_features(UpperCAmelCase_	)	)
    else:
        SCREAMING_SNAKE_CASE__											=			xmod.model(UpperCAmelCase_ ,							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(UpperCAmelCase_ ,							UpperCAmelCase_ ,							atol=1e-3	)
    print('''Do both models output the same tensors?''' ,							'''🔥''' if success else '''💩'''	)
    if not success:
        raise Exception('''Something went wRoNg'''	)

    Path(UpperCAmelCase_	).mkdir(parents=UpperCAmelCase_ ,							exist_ok=UpperCAmelCase_	)
    print(f'''Saving model to {pytorch_dump_folder_path}'''	)
    model.save_pretrained(UpperCAmelCase_	)


if __name__ == "__main__":
  _A					:				Union[str, Any]				=			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."""
  )
  _A					:				Union[str, Any]				=			parser.parse_args()
  convert_xmod_checkpoint_to_pytorch(
      args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )







'''simple docstring'''




a__					:			Optional[Any]          =     '''Alexander Joslin'''

import operator as op

from .stack import Stack




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	snake_case__	       =   {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

	snake_case__	       =   Stack()
	snake_case__	       =   Stack()

	for i in equation:
		if i.isdigit():
			# RULE 1
			operand_stack.push(int(UpperCAmelCase_							)							)
		elif i in operators:
			# RULE 2
			operator_stack.push(UpperCAmelCase_							)
		elif i == ")":
			# RULE 4
			snake_case__	       =   operator_stack.peek()
			operator_stack.pop()
			snake_case__	       =   operand_stack.peek()
			operand_stack.pop()
			snake_case__	       =   operand_stack.peek()
			operand_stack.pop()

			snake_case__	       =   operators[opr](UpperCAmelCase_					,				UpperCAmelCase_							)
			operand_stack.push(UpperCAmelCase_							)

    # RULE 5
	return operand_stack.peek()


if __name__ == "__main__":
			a__					:			Any          =     '''(5 + ((4 * 2) * (2 + 3)))'''
			# answer = 45
			print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



