

import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_    :						List[Any]    		=      "src/diffusers"
a_    :						Any    		=      "."


# This is to make sure the diffusers module imported is the one in the repo.
a_    :						Union[str, Any]    		=      importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(DIFFUSERS_PATH, "__init__.py"),
    submodule_search_locations=[DIFFUSERS_PATH],
)
a_    :						Union[str, Any]    		=      spec.loader.load_module()


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  Dict	)		->							Optional[Any]:
 '''simple docstring'''


 return line.startswith(_UpperCamelCase	) or len(_UpperCamelCase	) <= 1 or re.search(R'^\s*\)(\s*->.*:|:)\s*$'		,							_UpperCamelCase	) is not None


def        __lowerCAmelCase    (       _UpperCamelCase		:  Tuple	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			object_name.split('.'	)
 SCREAMING_SNAKE_CASE								=			0

 # First let's find the module where our object lives.
 SCREAMING_SNAKE_CASE								=			parts[i]
 while i < len(_UpperCamelCase	) and not os.path.isfile(os.path.join(_UpperCamelCase		,							f"""{module}.py"""	)	):
  i += 1
  if i < len(_UpperCamelCase	):
   SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							parts[i]	)
 if i >= len(_UpperCamelCase	):
  raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}."""	)

 with open(os.path.join(_UpperCamelCase		,							f"""{module}.py"""	)		,							'r'		,							encoding='utf-8'		,							newline='\n'	) as f:
  SCREAMING_SNAKE_CASE								=			f.readlines()

 # Now let's find the class / func in the code!
 SCREAMING_SNAKE_CASE								=			''
 SCREAMING_SNAKE_CASE								=			0
 for name in parts[i + 1 :]:
  while (
      line_index < len(_UpperCamelCase	) and re.search(Rf"""^{indent}(class|def)\s+{name}(\(|\:)"""		,							lines[line_index]	) is None
  ):
   line_index += 1
  indent += "    "
  line_index += 1

 if line_index >= len(_UpperCamelCase	):
  raise ValueError(f""" {object_name} does not match any function or class in {module}."""	)

 # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
 SCREAMING_SNAKE_CASE								=			line_index
 while line_index < len(_UpperCamelCase	) and _should_continue(lines[line_index]		,							_UpperCamelCase	):
  line_index += 1
 # Clean up empty lines at the end (if any).
 while len(lines[line_index - 1]	) <= 1:
  line_index -= 1

 SCREAMING_SNAKE_CASE								=			lines[start_index:line_index]
 return "".join(_UpperCamelCase	)


a_    :						Optional[int]    		=      re.compile(R"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
a_    :						Union[str, Any]    		=      re.compile(R"^\s*(\S+)->(\S+)(\s+.*|$)")
a_    :						Optional[Any]    		=      re.compile(R"<FILL\s+[^>]*>")


def        __lowerCAmelCase    (       _UpperCamelCase		:  Union[str, Any]	)		->							Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			code.split('\n'	)
 SCREAMING_SNAKE_CASE								=			0
 while idx < len(_UpperCamelCase	) and len(lines[idx]	) == 0:
  idx += 1
 if idx < len(_UpperCamelCase	):
  return re.search(R'^(\s*)\S'		,							lines[idx]	).groups()[0]
 return ""


def        __lowerCAmelCase    (       _UpperCamelCase		:  str	)		->							Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			len(get_indent(_UpperCamelCase	)	) > 0
 if has_indent:
  SCREAMING_SNAKE_CASE								=			f"""class Bla:\n{code}"""
 SCREAMING_SNAKE_CASE								=			black.Mode(target_versions={black.TargetVersion.PYaa}		,							line_length=1_19		,							preview=_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			black.format_str(_UpperCamelCase		,							mode=_UpperCamelCase	)
 SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			style_docstrings_in_code(_UpperCamelCase	)
 return result[len('class Bla:\n'	) :] if has_indent else result


def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  Optional[Any]=False	)		->							Optional[Any]:
 '''simple docstring'''


 with open(_UpperCamelCase		,							'r'		,							encoding='utf-8'		,							newline='\n'	) as f:
  SCREAMING_SNAKE_CASE								=			f.readlines()
 SCREAMING_SNAKE_CASE								=			[]
 SCREAMING_SNAKE_CASE								=			0
 # Not a for loop cause `lines` is going to change (if `overwrite=True`).
 while line_index < len(_UpperCamelCase	):
  SCREAMING_SNAKE_CASE								=			_re_copy_warning.search(lines[line_index]	)
  if search is None:
   line_index += 1
   continue

  # There is some copied code here, let's retrieve the original.
  SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			search.groups()
  SCREAMING_SNAKE_CASE								=			find_code_in_diffusers(_UpperCamelCase	)
  SCREAMING_SNAKE_CASE								=			get_indent(_UpperCamelCase	)

  SCREAMING_SNAKE_CASE								=			line_index + 1 if indent == theoretical_indent else line_index + 2
  SCREAMING_SNAKE_CASE								=			theoretical_indent
  SCREAMING_SNAKE_CASE								=			start_index

  # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
  SCREAMING_SNAKE_CASE								=			True
  while line_index < len(_UpperCamelCase	) and should_continue:
   line_index += 1
   if line_index >= len(_UpperCamelCase	):
    break
   SCREAMING_SNAKE_CASE								=			lines[line_index]
   SCREAMING_SNAKE_CASE								=			_should_continue(_UpperCamelCase		,							_UpperCamelCase	) and re.search(f"""^{indent}# End copy"""		,							_UpperCamelCase	) is None
  # Clean up empty lines at the end (if any).
  while len(lines[line_index - 1]	) <= 1:
   line_index -= 1

  SCREAMING_SNAKE_CASE								=			lines[start_index:line_index]
  SCREAMING_SNAKE_CASE								=			''.join(_UpperCamelCase	)

  # Remove any nested `Copied from` comments to avoid circular copies
  SCREAMING_SNAKE_CASE								=			[line for line in theoretical_code.split('\n'	) if _re_copy_warning.search(_UpperCamelCase	) is None]
  SCREAMING_SNAKE_CASE								=			'\n'.join(_UpperCamelCase	)

  # Before comparing, use the `replace_pattern` on the original code.
  if len(_UpperCamelCase	) > 0:
   SCREAMING_SNAKE_CASE								=			replace_pattern.replace('with'		,							''	).split(','	)
   SCREAMING_SNAKE_CASE								=			[_re_replace_pattern.search(_UpperCamelCase	) for p in patterns]
   for pattern in patterns:
    if pattern is None:
     continue
    SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			pattern.groups()
    SCREAMING_SNAKE_CASE								=			re.sub(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)
    if option.strip() == "all-casing":
     SCREAMING_SNAKE_CASE								=			re.sub(obja.lower()		,							obja.lower()		,							_UpperCamelCase	)
     SCREAMING_SNAKE_CASE								=			re.sub(obja.upper()		,							obja.upper()		,							_UpperCamelCase	)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
   SCREAMING_SNAKE_CASE								=			blackify(lines[start_index - 1] + theoretical_code	)
   SCREAMING_SNAKE_CASE								=			theoretical_code[len(lines[start_index - 1]	) :]

  # Test for a diff and act accordingly.
  if observed_code != theoretical_code:
   diffs.append([object_name, start_index]	)
   if overwrite:
    SCREAMING_SNAKE_CASE								=			lines[:start_index] + [theoretical_code] + lines[line_index:]
    SCREAMING_SNAKE_CASE								=			start_index + 1

 if overwrite and len(_UpperCamelCase	) > 0:
  # Warn the user a file has been modified.
  print(f"""Detected changes, rewriting {filename}."""	)
  with open(_UpperCamelCase		,							'w'		,							encoding='utf-8'		,							newline='\n'	) as f:
   f.writelines(_UpperCamelCase	)
 return diffs


def        __lowerCAmelCase    (       _UpperCamelCase		:  bool = False	)		->							Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			glob.glob(os.path.join(_UpperCamelCase		,							'**/*.py'	)		,							recursive=_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			[]
 for filename in all_files:
  SCREAMING_SNAKE_CASE								=			is_copy_consistent(_UpperCamelCase		,							_UpperCamelCase	)
  diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
 if not overwrite and len(_UpperCamelCase	) > 0:
  SCREAMING_SNAKE_CASE								=			'\n'.join(_UpperCamelCase	)
  raise Exception(
      'Found the following copy inconsistencies:\n'
      + diff
      + '\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.'	)


if __name__ == "__main__":
      a_    :						Optional[int]    		=      argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      a_    :						Optional[Any]    		=      parser.parse_args()

      check_copies(args.fix_and_overwrite)




# 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)

