




"""simple docstring"""

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": 1.1_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					=			{}
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 						_snake_case     (					_snake_case							:					str							)    ->					List[Any]:






      '''simple docstring'''


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






def 						_snake_case     (					_snake_case							:					Tuple				,  _snake_case							:					Tuple							)    ->					Union[str, Any]:






      '''simple docstring'''


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

      # handle various types of models

      _A					   =   basename(__A							)
      _A					   =   dirname(__A							)

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

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

      _A					   =   args['source_lang']
      _A					   =   args['target_lang']

      _A					   =   dirname(__A							)
      _A					   =   basename(__A							)

      # dicts
      _A					   =   os.path.join(__A				,  F'''dict.{src_lang}.txt'''							)
      _A					   =   os.path.join(__A				,  F'''dict.{tgt_lang}.txt'''							)

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

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

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

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

      # model config
      _A					   =   os.path.join(__A				,  '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"]}'''

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

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

      # tokenizer config
      _A					   =   os.path.join(__A				,  __A							)

      _A					   =   {
          '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(__A				,  'w'				,  encoding='utf-8'							) as f:
            f.write(json.dumps(__A				,  ensure_ascii=__A				,  indent=__A							)							)

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

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

      # remove unneeded keys
      _A					   =   [
          '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(__A				,  __A							)

      _A					   =   FSMTConfig.from_pretrained(__A							)
      _A					   =   FSMTForConditionalGeneration(__A							)

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

      # save
      _A					   =   os.path.join(__A				,  __A							)
      print(F'''Generating {pytorch_weights_dump_path}'''							)
      torch.save(__A				,  __A							)

      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)



'''simple docstring'''







import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch


class 						__UpperCAmelCase      (					_lowerCamelCase						):


							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

								_snake_case   						=		'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

								_snake_case   						=		'\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

								# Force fetching the files so that we can use the cache
								_snake_case   						=		'hf-internal-testing/tiny-random-bert'
								BertConfig.from_pretrained(lowerCAmelCase_  )
								BertModel.from_pretrained(lowerCAmelCase_  )
								BertTokenizer.from_pretrained(lowerCAmelCase_  )
								pipeline(task='fill-mask'    ,   model=lowerCAmelCase_  )

								# baseline - just load from_pretrained with normal network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, run, mock]  )]

								# should succeed
								_snake_case   						=		self.get_env()
								# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
								_snake_case   						=		'1'
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )


							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

								_snake_case   						=		'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

								_snake_case   						=		'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

								# Force fetching the files so that we can use the cache
								_snake_case   						=		'hf-internal-testing/tiny-random-bert'
								BertConfig.from_pretrained(lowerCAmelCase_  )
								BertModel.from_pretrained(lowerCAmelCase_  )
								BertTokenizer.from_pretrained(lowerCAmelCase_  )
								pipeline(task='fill-mask'    ,   model=lowerCAmelCase_  )

								# baseline - just load from_pretrained with normal network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, run, mock]  )]

								# should succeed
								_snake_case   						=		self.get_env()
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )


							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

								_snake_case   						=		'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

								_snake_case   						=		'\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

								# baseline - just load from_pretrained with normal network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, run]  )]

								# should succeed
								_snake_case   						=		self.get_env()
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )

								# next emulate no network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, mock, run]  )]

								# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
								# env["TRANSFORMERS_OFFLINE"] = "0"
								# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
								# self.assertEqual(result.returncode, 1, result.stderr)

								# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
								_snake_case   						=		'1'
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )


							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import pipeline\n        '
								_snake_case   						=		'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

								_snake_case   						=		'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
								_snake_case   						=		self.get_env()
								_snake_case   						=		'1'
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, mock, run]  )]
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   1    ,   result.stderr  )
								self.assertIn(
								    'You cannot infer task automatically within `pipeline` when using offline mode'    ,   result.stderr.decode().replace('\n'    ,   ''  )    ,   )




							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import AutoModel\n        '
								_snake_case   						=		'\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

								# baseline - just load from_pretrained with normal network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, run]  )]

								# should succeed
								_snake_case   						=		self.get_env()
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )

								# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
								_snake_case   						=		'1'
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )

