



import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser





def 		a__      (		_UpperCamelCase      :			List[str]=None			,_UpperCamelCase      :			Optional[Any]=None				):
   return field(default_factory=lambda: default			,metadata=_UpperCamelCase				)



@dataclass
class 			__lowerCAmelCase  :
 lowerCAmelCase__							       =	field(
     metadata={"""help""": """The csv file to plot."""}	,						)
 lowerCAmelCase__							       =	field(
     default=lowerCAmelCase__	,						metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}	,						)
 lowerCAmelCase__							       =	field(
     default=lowerCAmelCase__	,						metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}	,						)
 lowerCAmelCase__							       =	field(
     default=lowerCAmelCase__	,						metadata={"""help""": """Disable logarithmic scale when plotting"""}	,						)
 lowerCAmelCase__							       =	field(
     default=lowerCAmelCase__	,						metadata={
         """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
     }	,						)
 lowerCAmelCase__							       =	field(
     default=lowerCAmelCase__	,						metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}	,						)
 lowerCAmelCase__							       =	list_field(
     default=lowerCAmelCase__	,						metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""} )





def 		a__      (		_UpperCamelCase      :			Dict				):
   try:
      int(_UpperCamelCase				)
      return True
   except ValueError:
      return False





def 		a__      (		_UpperCamelCase      :			Optional[Any]				):
   try:
      float(_UpperCamelCase				)
      return True
   except ValueError:
      return False



class 			__lowerCAmelCase  :




 def __init__(    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     args
    __lowerCamelCase    				=     defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

    with open(self.args.csv_file						,					newline=''''''					) as csv_file:
       __lowerCamelCase    				=     csv.DictReader(__UpperCAmelCase					)
       for row in reader:
          __lowerCamelCase    				=     row['''model''']
          self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']					)					)
          self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']					)					)
          if can_convert_to_int(row['''result''']					):
             # value is not None
             __lowerCamelCase    				=     int(row['''result''']					)
          elif can_convert_to_float(row['''result''']					):
             # value is not None
             __lowerCamelCase    				=     float(row['''result''']					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase				,__lowerCamelCase    				=     plt.subplots()
    __lowerCamelCase    				=     '''Time usage''' if self.args.is_time else '''Memory usage'''
    __lowerCamelCase    				=     title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

    if not self.args.no_log_scale:
       # set logarithm scales
       ax.set_xscale('''log'''					)
       ax.set_yscale('''log'''					)

    for axis in [ax.xaxis, ax.yaxis]:
       axis.set_major_formatter(ScalarFormatter()					)

    for model_name_idx, model_name in enumerate(self.result_dict.keys()					):
       __lowerCamelCase    				=     sorted(set(self.result_dict[model_name]['''bsz''']					)					)
       __lowerCamelCase    				=     sorted(set(self.result_dict[model_name]['''seq_len''']					)					)
       __lowerCamelCase    				=     self.result_dict[model_name]['''result''']

       ((__lowerCamelCase)				,(__lowerCamelCase))    				=     (
           (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
       )

       __lowerCamelCase    				=     (
           model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
       )

       for inner_loop_value in inner_loop_array:
          if self.args.plot_along_batch:
             __lowerCamelCase    				=     np.asarray(
                 [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]						,					dtype=__UpperCAmelCase						,					)
          else:
             __lowerCamelCase    				=     np.asarray(
                 [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]						,					dtype=np.floataa						,					)

          ((__lowerCamelCase)				,(__lowerCamelCase))    				=     (
              ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
          )

          __lowerCamelCase    				=     np.asarray(__UpperCAmelCase						,					__UpperCAmelCase					)[: len(__UpperCAmelCase					)]
          plt.scatter(
              __UpperCAmelCase						,					__UpperCAmelCase						,					label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""					)
          plt.plot(__UpperCAmelCase						,					__UpperCAmelCase						,					'''--'''					)

       title_str += F""" {label_model_name} vs."""

    __lowerCamelCase    				=     title_str[:-4]
    __lowerCamelCase    				=     '''Time in s''' if self.args.is_time else '''Memory in MB'''

    # plot
    plt.title(__UpperCAmelCase					)
    plt.xlabel(__UpperCAmelCase					)
    plt.ylabel(__UpperCAmelCase					)
    plt.legend()

    if self.args.figure_png_file is not None:
       plt.savefig(self.args.figure_png_file					)
    else:
       plt.show()





def 		a__      (		):
   __lowerCamelCase    				=     HfArgumentParser(_UpperCamelCase				)
   __lowerCamelCase    				=     parser.parse_args_into_dataclasses()[0]
   __lowerCamelCase    				=     Plot(args=_UpperCamelCase				)
   plot.plot()


if __name__ == "__main__":
  main()











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_								=       logging.get_logger(__name__)

a_								=       """Hello, World!"""
a_								=       """en_XX"""





def 		a__      (		_UpperCamelCase      :			str			,_UpperCamelCase      :			str			,_UpperCamelCase      :			bool				):
   __lowerCamelCase    				=     Path('''data_bin'''				)
   __lowerCamelCase    				=     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				)

   __lowerCamelCase    				=     xmod.model.encoder.sentence_encoder
   __lowerCamelCase    				=     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:
      __lowerCamelCase    				=     xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

   __lowerCamelCase    				=     XmodForSequenceClassification(_UpperCamelCase				) if classification_head else XmodForMaskedLM(_UpperCamelCase				)
   model.eval()

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

   __lowerCamelCase    				=     xmod_sent_encoder.layernorm_embedding.weight
   __lowerCamelCase    				=     xmod_sent_encoder.layernorm_embedding.bias

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

      # self attention
      __lowerCamelCase    				=     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    				=     xmod_layer.self_attn.q_proj.weight
      __lowerCamelCase    				=     xmod_layer.self_attn.q_proj.bias
      __lowerCamelCase    				=     xmod_layer.self_attn.k_proj.weight
      __lowerCamelCase    				=     xmod_layer.self_attn.k_proj.bias
      __lowerCamelCase    				=     xmod_layer.self_attn.v_proj.weight
      __lowerCamelCase    				=     xmod_layer.self_attn.v_proj.bias

      # self-attention output
      __lowerCamelCase    				=     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    				=     xmod_layer.self_attn.out_proj.weight
      __lowerCamelCase    				=     xmod_layer.self_attn.out_proj.bias
      __lowerCamelCase    				=     xmod_layer.self_attn_layer_norm.weight
      __lowerCamelCase    				=     xmod_layer.self_attn_layer_norm.bias

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

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

      # end of layer

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

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

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

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






