
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)






def   lowerCamelCase__							(							__lowerCAmelCase    :    Any							):




			"""simple docstring"""


			lowerCAmelCase_         =      torch.exp(__lowerCAmelCase							)
			lowerCAmelCase_         =      torch.sum(__lowerCAmelCase       ,				dim=1							)  # sum of exp(x_i)
			lowerCAmelCase_         =      torch.sum(x * exp_x       ,				dim=1							)  # sum of x_i * exp(x_i)
			return torch.log(__lowerCAmelCase							) - B / A





class       _lowerCAmelCase   (      nn.Module			):




				def __init__(   self							,							_UpperCamelCase	)							-> Optional[int]:
							super().__init__()
							lowerCAmelCase_         =      config.output_attentions
							lowerCAmelCase_         =      config.output_hidden_states
							lowerCAmelCase_         =      nn.ModuleList([BertLayer(_UpperCamelCase	) for _ in range(config.num_hidden_layers	)]	)
							lowerCAmelCase_         =      nn.ModuleList([BertHighway(_UpperCamelCase	) for _ in range(config.num_hidden_layers	)]	)

							lowerCAmelCase_         =      [-1 for _ in range(config.num_hidden_layers	)]




				def 	__a		(   self							,							_UpperCamelCase	)							-> int:
							if (type(_UpperCamelCase	) is float) or (type(_UpperCamelCase	) is int):
										for i in range(len(self.early_exit_entropy	)	):
													lowerCAmelCase_         =      x
							else:
										lowerCAmelCase_         =      x




				def 	__a		(   self							,							_UpperCamelCase	)							-> Optional[Any]:
							lowerCAmelCase_         =      pooler.state_dict()
							for highway in self.highway:
										for name, param in highway.pooler.state_dict().items():
													param.copy_(loaded_model[name]	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							)							-> List[str]:
							lowerCAmelCase_         =      ()
							lowerCAmelCase_         =      ()
							lowerCAmelCase_         =      ()
							for i, layer_module in enumerate(self.layer	):
										if self.output_hidden_states:
													lowerCAmelCase_         =      all_hidden_states + (hidden_states,)

										lowerCAmelCase_         =      layer_module(
										    _UpperCamelCase							,							_UpperCamelCase							,							head_mask[i]							,							_UpperCamelCase							,							_UpperCamelCase	)
										lowerCAmelCase_         =      layer_outputs[0]

										if self.output_attentions:
													lowerCAmelCase_         =      all_attentions + (layer_outputs[1],)

										lowerCAmelCase_         =      (hidden_states,)
										if self.output_hidden_states:
													lowerCAmelCase_         =      current_outputs + (all_hidden_states,)
										if self.output_attentions:
													lowerCAmelCase_         =      current_outputs + (all_attentions,)

										lowerCAmelCase_         =      self.highway[i](_UpperCamelCase	)
										# logits, pooled_output

										if not self.training:
													lowerCAmelCase_         =      highway_exit[0]
													lowerCAmelCase_         =      entropy(_UpperCamelCase	)
													lowerCAmelCase_         =      highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
													lowerCAmelCase_         =      all_highway_exits + (highway_exit,)

													if highway_entropy < self.early_exit_entropy[i]:
																lowerCAmelCase_         =      (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
																raise HighwayException(_UpperCamelCase							,							i + 1	)
										else:
													lowerCAmelCase_         =      all_highway_exits + (highway_exit,)

        # Add last layer
							if self.output_hidden_states:
										lowerCAmelCase_         =      all_hidden_states + (hidden_states,)

							lowerCAmelCase_         =      (hidden_states,)
							if self.output_hidden_states:
										lowerCAmelCase_         =      outputs + (all_hidden_states,)
							if self.output_attentions:
										lowerCAmelCase_         =      outputs + (all_attentions,)

							lowerCAmelCase_         =      outputs + (all_highway_exits,)
							return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits





@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''   , __a   , )
class       _lowerCAmelCase   (      __a			):




				def __init__(   self							,							_UpperCamelCase	)							-> Optional[Any]:
							super().__init__(_UpperCamelCase	)
							lowerCAmelCase_         =      config

							lowerCAmelCase_         =      BertEmbeddings(_UpperCamelCase	)
							lowerCAmelCase_         =      DeeBertEncoder(_UpperCamelCase	)
							lowerCAmelCase_         =      BertPooler(_UpperCamelCase	)

							self.init_weights()




				def 	__a		(   self	)							-> Optional[Any]:
							self.encoder.init_highway_pooler(self.pooler	)




				def 	__a		(   self	)							-> Dict:
							return self.embeddings.word_embeddings




				def 	__a		(   self							,							_UpperCamelCase	)							-> Union[str, Any]:
							lowerCAmelCase_         =      value




				def 	__a		(   self							,							_UpperCamelCase	)							-> List[str]:
							for layer, heads in heads_to_prune.items():
										self.encoder.layer[layer].attention.prune_heads(_UpperCamelCase	)




				@add_start_docstrings_to_model_forward(_UpperCamelCase	)
				def 	__a		(   self							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							)							-> Tuple:
							if input_ids is not None and inputs_embeds is not None:
										raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"	)
							elif input_ids is not None:
										lowerCAmelCase_         =      input_ids.size()
							elif inputs_embeds is not None:
										lowerCAmelCase_         =      inputs_embeds.size()[:-1]
							else:
										raise ValueError("You have to specify either input_ids or inputs_embeds"	)

							lowerCAmelCase_         =      input_ids.device if input_ids is not None else inputs_embeds.device

							if attention_mask is None:
										lowerCAmelCase_         =      torch.ones(_UpperCamelCase							,							device=_UpperCamelCase	)
							if encoder_attention_mask is None:
										lowerCAmelCase_         =      torch.ones(_UpperCamelCase							,							device=_UpperCamelCase	)
							if token_type_ids is None:
										lowerCAmelCase_         =      torch.zeros(_UpperCamelCase							,							dtype=torch.long							,							device=_UpperCamelCase	)

							# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
							# ourselves in which case we just need to make it broadcastable to all heads.
							lowerCAmelCase_         =      self.get_extended_attention_mask(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)

							# If a 2D ou 3D attention mask is provided for the cross-attention
							# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
							if encoder_attention_mask.dim() == 3:
										lowerCAmelCase_         =      encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.dim() == 2:
										lowerCAmelCase_         =      encoder_attention_mask[:, None, None, :]

							lowerCAmelCase_         =      encoder_extended_attention_mask.to(
							    dtype=next(self.parameters()	).dtype	)  # fp16 compatibility
							lowerCAmelCase_         =      (1.0 - encoder_extended_attention_mask) * -10000.0

							# Prepare head mask if needed
							# 1.0 in head_mask indicate we keep the head
							# attention_probs has shape bsz x n_heads x N x N
							# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
							# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
							lowerCAmelCase_         =      self.get_head_mask(_UpperCamelCase							,							self.config.num_hidden_layers	)

							lowerCAmelCase_         =      self.embeddings(
							    input_ids=_UpperCamelCase							,							position_ids=_UpperCamelCase							,							token_type_ids=_UpperCamelCase							,							inputs_embeds=_UpperCamelCase	)
							lowerCAmelCase_         =      self.encoder(
							    _UpperCamelCase							,							attention_mask=_UpperCamelCase							,							head_mask=_UpperCamelCase							,							encoder_hidden_states=_UpperCamelCase							,							encoder_attention_mask=_UpperCamelCase							,							)
							lowerCAmelCase_         =      encoder_outputs[0]
							lowerCAmelCase_         =      self.pooler(_UpperCamelCase	)

							lowerCAmelCase_         =      (
							    sequence_output,
							    pooled_output,
							) + encoder_outputs[
							    1:
							]  # add hidden_states and attentions if they are here
							return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits





class       _lowerCAmelCase   (      __a			):




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase	)							-> List[Any]:
							lowerCAmelCase_         =      message
							lowerCAmelCase_         =      exit_layer  # start from 1!





class       _lowerCAmelCase   (      nn.Module			):




				def __init__(   self							,							_UpperCamelCase	)							-> str:
							super().__init__()
							lowerCAmelCase_         =      BertPooler(_UpperCamelCase	)
							lowerCAmelCase_         =      nn.Dropout(config.hidden_dropout_prob	)
							lowerCAmelCase_         =      nn.Linear(config.hidden_size							,							config.num_labels	)




				def 	__a		(   self							,							_UpperCamelCase	)							-> Union[str, Any]:
							# Pooler
							lowerCAmelCase_         =      encoder_outputs[0]
							lowerCAmelCase_         =      self.pooler(_UpperCamelCase	)
							# "return" pooler_output

							# BertModel
							lowerCAmelCase_         =      (pooler_input, pooler_output) + encoder_outputs[1:]
							# "return" bmodel_output

							# Dropout and classification
							lowerCAmelCase_         =      bmodel_output[1]

							lowerCAmelCase_         =      self.dropout(_UpperCamelCase	)
							lowerCAmelCase_         =      self.classifier(_UpperCamelCase	)

							return logits, pooled_output





@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''   , __a   , )
class       _lowerCAmelCase   (      __a			):




				def __init__(   self							,							_UpperCamelCase	)							-> Optional[Any]:
							super().__init__(_UpperCamelCase	)
							lowerCAmelCase_         =      config.num_labels
							lowerCAmelCase_         =      config.num_hidden_layers

							lowerCAmelCase_         =      DeeBertModel(_UpperCamelCase	)
							lowerCAmelCase_         =      nn.Dropout(config.hidden_dropout_prob	)
							lowerCAmelCase_         =      nn.Linear(config.hidden_size							,							self.config.num_labels	)

							self.init_weights()




				@add_start_docstrings_to_model_forward(_UpperCamelCase	)
				def 	__a		(   self							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=None							,							_UpperCamelCase=-1							,							_UpperCamelCase=False							,							)							-> Optional[int]:
							lowerCAmelCase_         =      self.num_layers
							try:
										lowerCAmelCase_         =      self.bert(
										    _UpperCamelCase							,							attention_mask=_UpperCamelCase							,							token_type_ids=_UpperCamelCase							,							position_ids=_UpperCamelCase							,							head_mask=_UpperCamelCase							,							inputs_embeds=_UpperCamelCase							,							)
										# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

										lowerCAmelCase_         =      outputs[1]

										lowerCAmelCase_         =      self.dropout(_UpperCamelCase	)
										lowerCAmelCase_         =      self.classifier(_UpperCamelCase	)
										lowerCAmelCase_         =      (logits,) + outputs[2:]  # add hidden states and attention if they are here
							except HighwayException as e:
										lowerCAmelCase_         =      e.message
										lowerCAmelCase_         =      e.exit_layer
										lowerCAmelCase_         =      outputs[0]

							if not self.training:
										lowerCAmelCase_         =      entropy(_UpperCamelCase	)
										lowerCAmelCase_         =      []
										lowerCAmelCase_         =      []
							if labels is not None:
										if self.num_labels == 1:
													#  We are doing regression
													lowerCAmelCase_         =      MSELoss()
													lowerCAmelCase_         =      loss_fct(logits.view(-1	)							,							labels.view(-1	)	)
										else:
													lowerCAmelCase_         =      CrossEntropyLoss()
													lowerCAmelCase_         =      loss_fct(logits.view(-1							,							self.num_labels	)							,							labels.view(-1	)	)

										# work with highway exits
										lowerCAmelCase_         =      []
										for highway_exit in outputs[-1]:
													lowerCAmelCase_         =      highway_exit[0]
													if not self.training:
																highway_logits_all.append(_UpperCamelCase	)
																highway_entropy.append(highway_exit[2]	)
													if self.num_labels == 1:
																#  We are doing regression
																lowerCAmelCase_         =      MSELoss()
																lowerCAmelCase_         =      loss_fct(highway_logits.view(-1	)							,							labels.view(-1	)	)
													else:
																lowerCAmelCase_         =      CrossEntropyLoss()
																lowerCAmelCase_         =      loss_fct(highway_logits.view(-1							,							self.num_labels	)							,							labels.view(-1	)	)
													highway_losses.append(_UpperCamelCase	)

										if train_highway:
													lowerCAmelCase_         =      (sum(highway_losses[:-1]	),) + outputs
													# exclude the final highway, of course
										else:
													lowerCAmelCase_         =      (loss,) + outputs
							if not self.training:
										lowerCAmelCase_         =      outputs + ((original_entropy, highway_entropy), exit_layer)
										if output_layer >= 0:
													lowerCAmelCase_         =      (
													    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
													)  # use the highway of the last layer

							return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)





# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_A  =	2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
_A  =	{
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
_A  =	{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
			_A  =	"facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
			_A  =	"allenai"






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict							):




			"""simple docstring"""


			lowerCAmelCase_         =      dict((re.sub(r"@@$"       ,				""       ,				__lowerCAmelCase							), v) if k.endswith("@@"							) else (re.sub(r"$"       ,				"</w>"       ,				__lowerCAmelCase							), v) for k, v in d.items()							)
			lowerCAmelCase_         =      "<s> <pad> </s> <unk>".split()
			# restore the special tokens
			for k in keep_keys:
						del da[F"""{k}</w>"""]
						lowerCAmelCase_         =      d[k]  # restore
			return da






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[Any]							):




			"""simple docstring"""


			assert os.path.exists(__lowerCAmelCase							)
			os.makedirs(__lowerCAmelCase       ,				exist_ok=__lowerCAmelCase							)
			print(F"""Writing results to {pytorch_dump_folder_path}"""							)

			# handle various types of models

			lowerCAmelCase_         =      basename(__lowerCAmelCase							)
			lowerCAmelCase_         =      dirname(__lowerCAmelCase							)

			lowerCAmelCase_         =      fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
			lowerCAmelCase_         =      cls.hub_models()
			lowerCAmelCase_         =      {"bpe": "fastbpe", "tokenizer": "moses"}
			lowerCAmelCase_         =      "."
			# note: since the model dump is old, fairseq has upgraded its model some
			# time later, and it does a whole lot of rewrites and splits on the saved
			# weights, therefore we can't use torch.load() directly on the model file.
			# see: upgrade_state_dict(state_dict) in fairseq_model.py
			print(F"""using checkpoint {checkpoint_file}"""							)
			lowerCAmelCase_         =      hub_utils.from_pretrained(
			    __lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase       ,				archive_map=__lowerCAmelCase       ,				**__lowerCAmelCase							)

			lowerCAmelCase_         =      vars(chkpt["args"]["model"]							)

			lowerCAmelCase_         =      args["source_lang"]
			lowerCAmelCase_         =      args["target_lang"]

			lowerCAmelCase_         =      dirname(__lowerCAmelCase							)
			lowerCAmelCase_         =      basename(__lowerCAmelCase							)

			# dicts
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				F"""dict.{src_lang}.txt"""							)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				F"""dict.{tgt_lang}.txt"""							)

			lowerCAmelCase_         =      Dictionary.load(__lowerCAmelCase							)
			lowerCAmelCase_         =      rewrite_dict_keys(src_dict.indices							)
			lowerCAmelCase_         =      len(__lowerCAmelCase							)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				"vocab-src.json"							)
			print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

			# detect whether this is a do_lower_case situation, which can be derived by checking whether we
			# have at least one uppercase letter in the source vocab
			lowerCAmelCase_         =      True
			for k in src_vocab.keys():
						if not k.islower():
									lowerCAmelCase_         =      False
									break

			lowerCAmelCase_         =      Dictionary.load(__lowerCAmelCase							)
			lowerCAmelCase_         =      rewrite_dict_keys(tgt_dict.indices							)
			lowerCAmelCase_         =      len(__lowerCAmelCase							)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				"vocab-tgt.json"							)
			print(F"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

			# merges_file (bpecodes)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				VOCAB_FILES_NAMES["merges_file"]							)
			for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
						lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				__lowerCAmelCase							)
						if os.path.exists(__lowerCAmelCase							):
									break
			with open(__lowerCAmelCase       ,				encoding="utf-8"							) as fin:
						lowerCAmelCase_         =      fin.read()
			lowerCAmelCase_         =      re.sub(r" \d+$"       ,				""       ,				__lowerCAmelCase       ,				0       ,				re.M							)  # remove frequency number
			print(F"""Generating {merges_file}"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as fout:
						fout.write(__lowerCAmelCase							)

			# model config
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				"config.json"							)

			# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
			# may have to modify the tokenizer if a different type is used by a future model
			assert args["bpe"] == "fastbpe", F"""need to extend tokenizer to support bpe={args['bpe']}"""
			assert args["tokenizer"] == "moses", F"""need to extend tokenizer to support bpe={args['tokenizer']}"""

			lowerCAmelCase_         =      {
			    "architectures": ["FSMTForConditionalGeneration"],
			    "model_type": "fsmt",
			    "activation_dropout": args["activation_dropout"],
			    "activation_function": "relu",
			    "attention_dropout": args["attention_dropout"],
			    "d_model": args["decoder_embed_dim"],
			    "dropout": args["dropout"],
			    "init_std": 0.02,
			    "max_position_embeddings": args["max_source_positions"],
			    "num_hidden_layers": args["encoder_layers"],
			    "src_vocab_size": src_vocab_size,
			    "tgt_vocab_size": tgt_vocab_size,
			    "langs": [src_lang, tgt_lang],
			    "encoder_attention_heads": args["encoder_attention_heads"],
			    "encoder_ffn_dim": args["encoder_ffn_embed_dim"],
			    "encoder_layerdrop": args["encoder_layerdrop"],
			    "encoder_layers": args["encoder_layers"],
			    "decoder_attention_heads": args["decoder_attention_heads"],
			    "decoder_ffn_dim": args["decoder_ffn_embed_dim"],
			    "decoder_layerdrop": args["decoder_layerdrop"],
			    "decoder_layers": args["decoder_layers"],
			    "bos_token_id": 0,
			    "pad_token_id": 1,
			    "eos_token_id": 2,
			    "is_encoder_decoder": True,
			    "scale_embedding": not args["no_scale_embedding"],
			    "tie_word_embeddings": args["share_all_embeddings"],
			}

			# good hparam defaults to start with
			lowerCAmelCase_         =      5
			lowerCAmelCase_         =      False
			if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
						lowerCAmelCase_         =      best_score_hparams[model_dir]["length_penalty"]
			else:
						lowerCAmelCase_         =      1.0

			print(F"""Generating {fsmt_model_config_file}"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

			# tokenizer config
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				__lowerCAmelCase							)

			lowerCAmelCase_         =      {
			    "langs": [src_lang, tgt_lang],
			    "model_max_length": 1024,
			    "do_lower_case": do_lower_case,
			}

			print(F"""Generating {fsmt_tokenizer_config_file}"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

			# model
			lowerCAmelCase_         =      chkpt["models"][0]
			lowerCAmelCase_         =      model.state_dict()

			# rename keys to start with 'model.'
			lowerCAmelCase_         =      OrderedDict(("model." + k, v) for k, v in model_state_dict.items()							)

			# remove unneeded keys
			lowerCAmelCase_         =      [
			    "model.model",
			    "model.encoder.version",
			    "model.decoder.version",
			    "model.encoder_embed_tokens.weight",
			    "model.decoder_embed_tokens.weight",
			    "model.encoder.embed_positions._float_tensor",
			    "model.decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						model_state_dict.pop(__lowerCAmelCase       ,				__lowerCAmelCase							)

			lowerCAmelCase_         =      FSMTConfig.from_pretrained(__lowerCAmelCase							)
			lowerCAmelCase_         =      FSMTForConditionalGeneration(__lowerCAmelCase							)

			# check that it loads ok
			model_new.load_state_dict(__lowerCAmelCase       ,				strict=__lowerCAmelCase							)

			# save
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				__lowerCAmelCase							)
			print(F"""Generating {pytorch_weights_dump_path}"""							)
			torch.save(__lowerCAmelCase       ,				__lowerCAmelCase							)

			print("Conversion is done!"							)
			print("\nLast step is to upload the files to s3"							)
			print(F"""cd {data_root}"""							)
			print(F"""transformers-cli upload {model_dir}"""							)


if __name__ == "__main__":
			_A  =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--fsmt_checkpoint_path",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
			        " bpecodes, etc."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			_A  =	parser.parse_args()
			convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)



