

"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a					:List[str]    = logging.get_logger(__name__)


if is_vision_available():
 import PIL

class      __a							(lowercase__):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[str]       = ['pixel_values']

     def __init__(						self	,    _a = True	,    _a = None	,    _a = PILImageResampling.BICUBIC	,    _a = True	,    _a = None	,    _a = True	,    _a = 1 / 255	,    _a = True	,    _a = None	,    _a = None	,    _a = True	,    **_a	,    )  ->      Union[str, Any]:


           """simple docstring"""


           super().__init__(**_a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	size if size is not None else {"""shortest_edge""": 224}
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	get_size_dict(_a	,    default_to_square=_a  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	get_size_dict(_a	,    default_to_square=_a	,    param_name="""crop_size"""  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	do_resize
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	resample
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	do_center_crop
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	crop_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	do_rescale
           SCREAMING_SNAKE_CASE__   :      str      		=	rescale_factor
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	do_normalize
           SCREAMING_SNAKE_CASE__   :      int      		=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	image_std if image_std is not None else OPENAI_CLIP_STD
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	do_convert_rgb

     def 					_a			(						self	,    _a	,    _a	,    _a = PILImageResampling.BICUBIC	,    _a = None	,    **_a	,    )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	get_size_dict(_a	,    default_to_square=_a  )
           if "shortest_edge" not in size:
                 raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''  )
           SCREAMING_SNAKE_CASE__   :      str      		=	get_resize_output_image_size(_a	,    size=size["""shortest_edge"""]	,    default_to_square=_a  )
           return resize(_a	,    size=_a	,    resample=_a	,    data_format=_a	,    **_a  )

     def 					_a			(						self	,    _a	,    _a	,    _a = None	,    **_a	,    )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	get_size_dict(_a  )
           if "height" not in size or "width" not in size:
                 raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''  )
           return center_crop(_a	,    size=(size["""height"""], size["""width"""])	,    data_format=_a	,    **_a  )

     def 					_a			(						self	,    _a	,    _a	,    _a = None	,    **_a	,    )  ->      Optional[int]:


           """simple docstring"""


           return rescale(_a	,    scale=_a	,    data_format=_a	,    **_a  )

     def 					_a			(						self	,    _a	,    _a	,    _a	,    _a = None	,    **_a	,    )  ->      Tuple:


           """simple docstring"""


           return normalize(_a	,    mean=_a	,    std=_a	,    data_format=_a	,    **_a  )





     def 					_a			(						self	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = ChannelDimension.FIRST	,    **_a	,    )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	do_resize if do_resize is not None else self.do_resize
           SCREAMING_SNAKE_CASE__   :      Dict      		=	size if size is not None else self.size
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	get_size_dict(_a	,    param_name="""size"""	,    default_to_square=_a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	resample if resample is not None else self.resample
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	do_center_crop if do_center_crop is not None else self.do_center_crop
           SCREAMING_SNAKE_CASE__   :      str      		=	crop_size if crop_size is not None else self.crop_size
           SCREAMING_SNAKE_CASE__   :      Dict      		=	get_size_dict(_a	,    param_name="""crop_size"""	,    default_to_square=_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	do_rescale if do_rescale is not None else self.do_rescale
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	rescale_factor if rescale_factor is not None else self.rescale_factor
           SCREAMING_SNAKE_CASE__   :      str      		=	do_normalize if do_normalize is not None else self.do_normalize
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	image_mean if image_mean is not None else self.image_mean
           SCREAMING_SNAKE_CASE__   :      Dict      		=	image_std if image_std is not None else self.image_std
           SCREAMING_SNAKE_CASE__   :      int      		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           SCREAMING_SNAKE_CASE__   :      int      		=	make_list_of_images(_a  )

           if not valid_images(_a  ):
                 raise ValueError(
                     """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                     """torch.Tensor, tf.Tensor or jax.ndarray."""  )

           if do_resize and size is None:
                 raise ValueError("""Size must be specified if do_resize is True."""  )

           if do_center_crop and crop_size is None:
                 raise ValueError("""Crop size must be specified if do_center_crop is True."""  )

           if do_rescale and rescale_factor is None:
                 raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

           if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError("""Image mean and std must be specified if do_normalize is True."""  )

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	[convert_to_rgb(_a  ) for image in images]

           # All transformations expect numpy arrays.
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	[to_numpy_array(_a  ) for image in images]

           if do_resize:
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	[self.resize(image=_a	,    size=_a	,    resample=_a  ) for image in images]

           if do_center_crop:
                 SCREAMING_SNAKE_CASE__   :      int      		=	[self.center_crop(image=_a	,    size=_a  ) for image in images]

           if do_rescale:
                 SCREAMING_SNAKE_CASE__   :      int      		=	[self.rescale(image=_a	,    scale=_a  ) for image in images]

           if do_normalize:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	[self.normalize(image=_a	,    mean=_a	,    std=_a  ) for image in images]

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[to_channel_dimension_format(_a	,    _a  ) for image in images]

           SCREAMING_SNAKE_CASE__   :      Any      		=	{"""pixel_values""": images}
           return BatchFeature(data=_a	,    tensor_type=_a  )







"""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					: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": 1.15},
    "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					:Dict    = {}
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					:str    = "allenai"




def  _lowercase					(    __lowerCAmelCase							)       ->				Any:
      # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
      # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
      SCREAMING_SNAKE_CASE__   :      str      		=	dict((re.sub(r"""@@$"""  ,					""""""  ,					__lowerCAmelCase							), v) if k.endswith("""@@"""							) else (re.sub(r"""$"""  ,					"""</w>"""  ,					__lowerCAmelCase							), v) for k, v in d.items()							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""<s> <pad> </s> <unk>""".split()
      # restore the special tokens
      for k in keep_keys:
            del da[F'''{k}</w>''']
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	d[k]  # restore
      return da




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Optional[int]:
      # prep
      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

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	basename(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	dirname(__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      int      		=	fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	cls.hub_models()
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"""."""
      # 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__   :      Union[str, Any]      		=	hub_utils.from_pretrained(
          __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					archive_map=__lowerCAmelCase  ,					**__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vars(chkpt["""args"""]["""model"""]							)

      SCREAMING_SNAKE_CASE__   :      Any      		=	args["""source_lang"""]
      SCREAMING_SNAKE_CASE__   :      Any      		=	args["""target_lang"""]

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	dirname(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	basename(__lowerCAmelCase							)

      # dicts
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	os.path.join(__lowerCAmelCase  ,					F'''dict.{src_lang}.txt'''							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	os.path.join(__lowerCAmelCase  ,					F'''dict.{tgt_lang}.txt'''							)

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	Dictionary.load(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	rewrite_dict_keys(src_dict.indices							)
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	len(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      int      		=	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
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	True
      for k in src_vocab.keys():
            if not k.islower():
                  SCREAMING_SNAKE_CASE__   :      Tuple      		=	False
                  break

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	Dictionary.load(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	rewrite_dict_keys(tgt_dict.indices							)
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	len(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	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)
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	os.path.join(__lowerCAmelCase  ,					VOCAB_FILES_NAMES["""merges_file"""]							)
      for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)
            if os.path.exists(__lowerCAmelCase							):
                  break
      with open(__lowerCAmelCase  ,					encoding="""utf-8"""							) as fin:
            SCREAMING_SNAKE_CASE__   :      Any      		=	fin.read()
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	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
      SCREAMING_SNAKE_CASE__   :      Dict      		=	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']}'''

      SCREAMING_SNAKE_CASE__   :      str      		=	{
          """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__   :      Tuple      		=	5
      SCREAMING_SNAKE_CASE__   :      str      		=	False
      if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	best_score_hparams[model_dir]["""length_penalty"""]
      else:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	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
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Tuple      		=	{
          """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
      SCREAMING_SNAKE_CASE__   :      Dict      		=	chkpt["""models"""][0]
      SCREAMING_SNAKE_CASE__   :      int      		=	model.state_dict()

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

      # remove unneeded keys
      SCREAMING_SNAKE_CASE__   :      str      		=	[
          """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							)

      SCREAMING_SNAKE_CASE__   :      Dict      		=	FSMTConfig.from_pretrained(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FSMTForConditionalGeneration(__lowerCAmelCase							)

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

      # save
      SCREAMING_SNAKE_CASE__   :      int      		=	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					:Optional[Any]    = 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)




