

'''simple docstring'''




# 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					:			List[Any]							=    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					:			Dict							=    {
    # 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					:			str							=    {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  _a					:			List[str]							=    """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					:			Optional[Any]							=    """allenai"""


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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : Dict    ):
  assert os.path.exists(lowercase_    )
  os.makedirs(lowercase_							, exist_ok=lowercase_    )
  print(f'''Writing results to {pytorch_dump_folder_path}'''    )

  # handle various types of models

  UpperCAmelCase    				=						basename(lowercase_    )
  UpperCAmelCase    				=						dirname(lowercase_    )

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

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

  UpperCAmelCase    				=						args["""source_lang"""]
  UpperCAmelCase    				=						args["""target_lang"""]

  UpperCAmelCase    				=						dirname(lowercase_    )
  UpperCAmelCase    				=						basename(lowercase_    )

  # dicts
  UpperCAmelCase    				=						os.path.join(lowercase_							, f'''dict.{src_lang}.txt'''    )
  UpperCAmelCase    				=						os.path.join(lowercase_							, f'''dict.{tgt_lang}.txt'''    )

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

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

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

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

  # model config
  UpperCAmelCase    				=						os.path.join(lowercase_							, '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']}'''

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

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

  # tokenizer config
  UpperCAmelCase    				=						os.path.join(lowercase_							, lowercase_    )

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

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

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

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

  # remove unneeded keys
  UpperCAmelCase    				=						[
      """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(lowercase_							, lowercase_    )

  UpperCAmelCase    				=						FSMTConfig.from_pretrained(lowercase_    )
  UpperCAmelCase    				=						FSMTForConditionalGeneration(lowercase_    )

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

  # save
  UpperCAmelCase    				=						os.path.join(lowercase_							, lowercase_    )
  print(f'''Generating {pytorch_weights_dump_path}'''    )
  torch.save(lowercase_							, lowercase_    )

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




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  int	   =      (DDPMScheduler,)


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   {
       """num_train_timesteps""": 10_00,
       """beta_start""": 0.0_0_0_1,
       """beta_end""": 0.0_2,
       """beta_schedule""": """linear""",
       """variance_type""": """fixed_small""",
       """clip_sample""": True,
   }

   config.update(**SCREAMING_SNAKE_CASE_)
   return config


 def   lowercase__  (       self):


   '''simple docstring'''




   for timesteps in [1, 5, 1_00, 10_00]:
     self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]       ,	[0.0_0_2, 0.0_2, 0.2, 2]):
     self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,	beta_end=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for variance in ["fixed_small", "fixed_large", "other"]:
     self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_)
   for threshold in [0.5, 1.0, 2.0]:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
       self.check_over_configs(
           thresholding=SCREAMING_SNAKE_CASE_       ,	prediction_type=SCREAMING_SNAKE_CASE_       ,	sample_max_value=SCREAMING_SNAKE_CASE_       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   for prediction_type in ["epsilon", "sample", "v_prediction"]:
     self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for t in [0, 5_00, 9_99]:
     self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   self.scheduler_classes[0]
   lowercase__	: Union[str, Any]     		=   self.get_scheduler_config()
   lowercase__	: List[Any]     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0_9_7_9)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.0_2)) < 1E-5


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.scheduler_classes[0]
   lowercase__	: str     		=   self.get_scheduler_config()
   lowercase__	: Tuple     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   len(SCREAMING_SNAKE_CASE_)

   lowercase__	: Any     		=   self.dummy_model()
   lowercase__	: List[Any]     		=   self.dummy_sample_deter
   lowercase__	: str     		=   torch.manual_seed(0)

   for t in reversed(range(SCREAMING_SNAKE_CASE_)):
     # 1. predict noise residual
     lowercase__	: Dict     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 2. predict previous mean of sample x_t-1
     lowercase__	: List[str]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_).prev_sample

     # if t > 0:
     #     noise = self.dummy_sample_deter
     #     variance = scheduler.get_variance(t) ** (0.5) * noise
     #
     # sample = pred_prev_sample + variance
     lowercase__	: str     		=   pred_prev_sample

   lowercase__	: Optional[int]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: Optional[Any]     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 2_5_8.9_6_0_6) < 1E-2
   assert abs(result_mean.item() - 0.3_3_7_2) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   self.scheduler_classes[0]
   lowercase__	: Tuple     		=   self.get_scheduler_config(prediction_type="""v_prediction""")
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   len(SCREAMING_SNAKE_CASE_)

   lowercase__	: Tuple     		=   self.dummy_model()
   lowercase__	: Union[str, Any]     		=   self.dummy_sample_deter
   lowercase__	: int     		=   torch.manual_seed(0)

   for t in reversed(range(SCREAMING_SNAKE_CASE_)):
     # 1. predict noise residual
     lowercase__	: List[Any]     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 2. predict previous mean of sample x_t-1
     lowercase__	: int     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_).prev_sample

     # if t > 0:
     #     noise = self.dummy_sample_deter
     #     variance = scheduler.get_variance(t) ** (0.5) * noise
     #
     # sample = pred_prev_sample + variance
     lowercase__	: Tuple     		=   pred_prev_sample

   lowercase__	: Union[str, Any]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: int     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 2_0_2.0_2_9_6) < 1E-2
   assert abs(result_mean.item() - 0.2_6_3_1) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.scheduler_classes[0]
   lowercase__	: int     		=   self.get_scheduler_config()
   lowercase__	: str     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   [1_00, 87, 50, 1, 0]

   scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   scheduler.timesteps

   for i, timestep in enumerate(SCREAMING_SNAKE_CASE_):
     if i == len(SCREAMING_SNAKE_CASE_) - 1:
       lowercase__	: Optional[int]     		=   -1
     else:
       lowercase__	: Tuple     		=   timesteps[i + 1]

     lowercase__	: Any     		=   scheduler.previous_timestep(SCREAMING_SNAKE_CASE_)
     lowercase__	: int     		=   prev_t.item()

     self.assertEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.scheduler_classes[0]
   lowercase__	: List[Any]     		=   self.get_scheduler_config()
   lowercase__	: int     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   [1_00, 87, 50, 51, 0]

   with self.assertRaises(SCREAMING_SNAKE_CASE_       ,	msg="""`custom_timesteps` must be in descending order."""):
     scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   self.scheduler_classes[0]
   lowercase__	: List[Any]     		=   self.get_scheduler_config()
   lowercase__	: int     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   [1_00, 87, 50, 1, 0]
   lowercase__	: Union[str, Any]     		=   len(SCREAMING_SNAKE_CASE_)

   with self.assertRaises(SCREAMING_SNAKE_CASE_       ,	msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""):
     scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE_       ,	timesteps=SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.scheduler_classes[0]
   lowercase__	: int     		=   self.get_scheduler_config()
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: str     		=   [scheduler.config.num_train_timesteps]

   with self.assertRaises(
       SCREAMING_SNAKE_CASE_       ,	msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""       ,	):
     scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)






