

import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


a_    :						Any 		=      logging.get_logger(__name__)

a_    :						Union[str, Any] 		=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

a_    :						Union[str, Any] 		=      {
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
a_    :						Tuple 		=      {
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
a_    :						Any 		=      {
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

a_    :						str 		=      {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
a_    :						int 		=      {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
a_    :						Optional[Any] 		=      {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


a_    :						Any 		=      {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
a_    :						Optional[int] 		=      {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
a_    :						int 		=      {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
    __UpperCamelCase         =DPRContextEncoderTokenizer




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
    __UpperCamelCase         =DPRQuestionEncoderTokenizer


a_    :						Tuple 		=      collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

a_    :						Any 		=      collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


a_    :						int 		=      R"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "




@add_start_docstrings(SCREAMING_SNAKE_CASE   )
class      UpperCamelCase :

    def __call__(			self :      Union[str, Any]							,    snake_case__ :      List[Any]							,    snake_case__ :      Optional[str] = None							,    snake_case__ :      Optional[str] = None							,    snake_case__ :      Union[bool, str] = False							,    snake_case__ :      Union[bool, str] = False							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      Optional[Union[str, TensorType]] = None							,    snake_case__ :      Optional[bool] = None							,    **snake_case__ :      str							,    ):

     """simple docstring"""






     if titles is None and texts is None:
      return super().__call__(
          snake_case__							,    padding=snake_case__							,    truncation=snake_case__							,    max_length=snake_case__							,    return_tensors=snake_case__							,    return_attention_mask=snake_case__							,    **snake_case__							,    )
     elif titles is None or texts is None:
      SCREAMING_SNAKE_CASE								=			titles if texts is None else texts
      return super().__call__(
          snake_case__							,    snake_case__							,    padding=snake_case__							,    truncation=snake_case__							,    max_length=snake_case__							,    return_tensors=snake_case__							,    return_attention_mask=snake_case__							,    **snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			titles if not isinstance(snake_case__							,    snake_case__    ) else [titles]
     SCREAMING_SNAKE_CASE								=			texts if not isinstance(snake_case__							,    snake_case__    ) else [texts]
     SCREAMING_SNAKE_CASE								=			len(snake_case__    )
     SCREAMING_SNAKE_CASE								=			questions if not isinstance(snake_case__							,    snake_case__    ) else [questions] * n_passages
     assert len(snake_case__    ) == len(
         snake_case__    ), F"""There should be as many titles than texts but got {len(snake_case__    )} titles and {len(snake_case__    )} texts."""
     SCREAMING_SNAKE_CASE								=			super().__call__(snake_case__							,    snake_case__							,    padding=snake_case__							,    truncation=snake_case__    )['input_ids']
     SCREAMING_SNAKE_CASE								=			super().__call__(snake_case__							,    add_special_tokens=snake_case__							,    padding=snake_case__							,    truncation=snake_case__    )['input_ids']
     SCREAMING_SNAKE_CASE								=			{
         'input_ids': [
             (encoded_question_and_title + encoded_text)[:max_length]
             if max_length is not None and truncation
             else encoded_question_and_title + encoded_text
             for encoded_question_and_title, encoded_text in zip(snake_case__							,    snake_case__    )
         ]
     }
     if return_attention_mask is not False:
      SCREAMING_SNAKE_CASE								=			[]
      for input_ids in encoded_inputs["input_ids"]:
       attention_mask.append([int(input_id != self.pad_token_id    ) for input_id in input_ids]    )
      SCREAMING_SNAKE_CASE								=			attention_mask
     return self.pad(snake_case__							,    padding=snake_case__							,    max_length=snake_case__							,    return_tensors=snake_case__    )

    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      BatchEncoding							,    snake_case__ :      DPRReaderOutput							,    snake_case__ :      int = 1_6							,    snake_case__ :      int = 6_4							,    snake_case__ :      int = 4							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			reader_input['input_ids']
     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			reader_output[:3]
     SCREAMING_SNAKE_CASE								=			len(snake_case__    )
     SCREAMING_SNAKE_CASE								=			sorted(range(snake_case__    )							,    reverse=snake_case__							,    key=relevance_logits.__getitem__    )
     SCREAMING_SNAKE_CASE								=			[]
     for doc_id in sorted_docs:
      SCREAMING_SNAKE_CASE								=			list(input_ids[doc_id]    )
      # assuming question & title information is at the beginning of the sequence
      SCREAMING_SNAKE_CASE								=			sequence_ids.index(self.sep_token_id							,    2    ) + 1  # second sep id
      if sequence_ids[-1] == self.pad_token_id:
       SCREAMING_SNAKE_CASE								=			sequence_ids.index(self.pad_token_id    )
      else:
       SCREAMING_SNAKE_CASE								=			len(snake_case__    )

      SCREAMING_SNAKE_CASE								=			self._get_best_spans(
          start_logits=start_logits[doc_id][passage_offset:sequence_len]							,    end_logits=end_logits[doc_id][passage_offset:sequence_len]							,    max_answer_length=snake_case__							,    top_spans=snake_case__							,    )
      for start_index, end_index in best_spans:
       start_index += passage_offset
       end_index += passage_offset
       nbest_spans_predictions.append(
           DPRSpanPrediction(
               span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]							,    relevance_score=relevance_logits[doc_id]							,    doc_id=snake_case__							,    start_index=snake_case__							,    end_index=snake_case__							,    text=self.decode(sequence_ids[start_index : end_index + 1]    )							,    )    )
      if len(snake_case__    ) >= num_spans:
       break
     return nbest_spans_predictions[:num_spans]




    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      List[int]							,    snake_case__ :      List[int]							,    snake_case__ :      int							,    snake_case__ :      int							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[]
     for start_index, start_score in enumerate(snake_case__    ):
      for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]    ):
       scores.append(((start_index, start_index + answer_length), start_score + end_score)    )
     SCREAMING_SNAKE_CASE								=			sorted(snake_case__							,    key=lambda snake_case__    : x[1]							,    reverse=snake_case__    )
     SCREAMING_SNAKE_CASE								=			[]
     for (start_index, end_index), score in scores:
      assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
      SCREAMING_SNAKE_CASE								=			end_index - start_index + 1
      assert length <= max_answer_length, F"""Span is too long: {length} > {max_answer_length}"""
      if any(
          start_index <= prev_start_index <= prev_end_index <= end_index
          or prev_start_index <= start_index <= end_index <= prev_end_index
          for (prev_start_index, prev_end_index) in chosen_span_intervals    ):
       continue
      chosen_span_intervals.append((start_index, end_index)    )

      if len(snake_case__    ) == top_spans:
       break
     return chosen_span_intervals




@add_end_docstrings(SCREAMING_SNAKE_CASE   )
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =READER_PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =READER_PRETRAINED_INIT_CONFIGURATION
    __UpperCamelCase         =["input_ids", "attention_mask"]
    __UpperCamelCase         =DPRReaderTokenizer




# 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_    :						str    		=      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_    :						int    		=      {
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.1_5},
    "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_    :						str    		=      {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
      a_    :						List[Any]    		=      "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_    :						Any    		=      "allenai"


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]	)		->							Any:
 '''simple docstring'''


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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  List[str]	)		->							Dict:
 '''simple docstring'''


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

 # handle various types of models

 SCREAMING_SNAKE_CASE								=			basename(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			dirname(_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
 SCREAMING_SNAKE_CASE								=			cls.hub_models()
 SCREAMING_SNAKE_CASE								=			{'bpe': 'fastbpe', 'tokenizer': 'moses'}
 SCREAMING_SNAKE_CASE								=			'.'
 # 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}"""	)
 SCREAMING_SNAKE_CASE								=			hub_utils.from_pretrained(
     _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							archive_map=_UpperCamelCase		,							**_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			vars(chkpt['args']['model']	)

 SCREAMING_SNAKE_CASE								=			args['source_lang']
 SCREAMING_SNAKE_CASE								=			args['target_lang']

 SCREAMING_SNAKE_CASE								=			dirname(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			basename(_UpperCamelCase	)

 # dicts
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							f"""dict.{src_lang}.txt"""	)
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							f"""dict.{tgt_lang}.txt"""	)

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

 # 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
 SCREAMING_SNAKE_CASE								=			True
 for k in src_vocab.keys():
  if not k.islower():
   SCREAMING_SNAKE_CASE								=			False
   break

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

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

 # model config
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							'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']}"""

 SCREAMING_SNAKE_CASE								=			{
     '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
 SCREAMING_SNAKE_CASE								=			5
 SCREAMING_SNAKE_CASE								=			False
 if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
  SCREAMING_SNAKE_CASE								=			best_score_hparams[model_dir]['length_penalty']
 else:
  SCREAMING_SNAKE_CASE								=			1.0

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

 # tokenizer config
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			{
     'langs': [src_lang, tgt_lang],
     'model_max_length': 10_24,
     'do_lower_case': do_lower_case,
 }

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

 # model
 SCREAMING_SNAKE_CASE								=			chkpt['models'][0]
 SCREAMING_SNAKE_CASE								=			model.state_dict()

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

 # remove unneeded keys
 SCREAMING_SNAKE_CASE								=			[
     '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(_UpperCamelCase		,							_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			FSMTConfig.from_pretrained(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			FSMTForConditionalGeneration(_UpperCamelCase	)

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

 # save
 SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							_UpperCamelCase	)
 print(f"""Generating {pytorch_weights_dump_path}"""	)
 torch.save(_UpperCamelCase		,							_UpperCamelCase	)

 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_    :						List[str]    		=      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_    :						int    		=      parser.parse_args()
      convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)

