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()

UpperCamelCase__    							= 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
UpperCamelCase__    							= {
    # 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
UpperCamelCase__    							= {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
						UpperCamelCase__    							= """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",
]:
						UpperCamelCase__    							= """allenai"""




def        lowerCAmelCase_			(					__A							)					->    str:







						'''simple docstring'''


						UpperCAmelCase__								=  dict((re.sub(r"@@$",  "",  __snake_case							), v) if k.endswith("@@"							) else (re.sub(r"$",  "</w>",  __snake_case							), 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_			(					__A,  __A							)					->    int:







						'''simple docstring'''


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

						# handle various types of models

						UpperCAmelCase__								=  basename(__snake_case							)
						UpperCAmelCase__								=  dirname(__snake_case							)

						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(
						    __snake_case,  __snake_case,  __snake_case,  archive_map=__snake_case,  **__snake_case							)

						UpperCAmelCase__								=  vars(chkpt["args"]["model"]							)

						UpperCAmelCase__								=  args["source_lang"]
						UpperCAmelCase__								=  args["target_lang"]

						UpperCAmelCase__								=  dirname(__snake_case							)
						UpperCAmelCase__								=  basename(__snake_case							)

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

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

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

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

						# model config
						UpperCAmelCase__								=  os.path.join(__snake_case,  "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(__snake_case,  "w",  encoding="utf-8"							) as f:
												f.write(json.dumps(__snake_case,  ensure_ascii=__snake_case,  indent=__snake_case							)							)

						# tokenizer config
						UpperCAmelCase__								=  os.path.join(__snake_case,  __snake_case							)

						UpperCAmelCase__								=  {
						    "langs": [src_lang, tgt_lang],
						    "model_max_length": 1_024,
						    "do_lower_case": do_lower_case,
						}

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

						# 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(__snake_case,  __snake_case							)

						UpperCAmelCase__								=  FSMTConfig.from_pretrained(__snake_case							)
						UpperCAmelCase__								=  FSMTForConditionalGeneration(__snake_case							)

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

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

						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__":
						UpperCamelCase__    							= 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.'
						)
						UpperCamelCase__    							= parser.parse_args()
						convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__    							= logging.get_logger(__name__)




def        lowerCAmelCase_			(					__A,  __A=False							)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  []
      for i in range(config.num_hidden_layers							):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""deit.encoder.layer.{i}.layernorm_before.weight""")							)
            rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")							)
            rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")							)
            rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")							)
            rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")							)
            rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")							)
            rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")							)
            rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")							)
            rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")							)
            rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")							)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("cls_token", "deit.embeddings.cls_token"),
              ("dist_token", "deit.embeddings.distillation_token"),
              ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
              ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
              ("pos_embed", "deit.embeddings.position_embeddings"),
          ]							)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ("norm.weight", "layernorm.weight"),
                    ("norm.bias", "layernorm.bias"),
                    ("pre_logits.fc.weight", "pooler.dense.weight"),
                    ("pre_logits.fc.bias", "pooler.dense.bias"),
                ]							)

            # if just the base model, we should remove "deit" from all keys that start with "deit"
            UpperCAmelCase__								=  [(pair[0], pair[1][4:]) if pair[1].startswith("deit"							) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ("norm.weight", "deit.layernorm.weight"),
                    ("norm.bias", "deit.layernorm.bias"),
                    ("head.weight", "cls_classifier.weight"),
                    ("head.bias", "cls_classifier.bias"),
                    ("head_dist.weight", "distillation_classifier.weight"),
                    ("head_dist.bias", "distillation_classifier.bias"),
                ]							)

      return rename_keys




def        lowerCAmelCase_			(					__A,  __A,  __A=False							)					->    Tuple:







      '''simple docstring'''


      for i in range(config.num_hidden_layers							):
            if base_model:
                  UpperCAmelCase__								=  ""
            else:
                  UpperCAmelCase__								=  "deit."
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            UpperCAmelCase__								=  state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""							)
            UpperCAmelCase__								=  state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""							)
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase__								=  in_proj_weight[
                : config.hidden_size, :
            ]
            UpperCAmelCase__								=  in_proj_bias[: config.hidden_size]
            UpperCAmelCase__								=  in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            UpperCAmelCase__								=  in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            UpperCAmelCase__								=  in_proj_weight[
                -config.hidden_size :, :
            ]
            UpperCAmelCase__								=  in_proj_bias[-config.hidden_size :]




def        lowerCAmelCase_			(					__A,  __A,  __A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  dct.pop(__A							)
      UpperCAmelCase__								=  val




def        lowerCAmelCase_			(					)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  "http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase__								=  Image.open(requests.get(__A,  stream=__A							).raw							)
      return im




@torch.no_grad()
def        lowerCAmelCase_			(					__A,  __A							)					->    Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  DeiTConfig()
      # all deit models have fine-tuned heads
      UpperCAmelCase__								=  False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      UpperCAmelCase__								=  1_000
      UpperCAmelCase__								=  "huggingface/label-files"
      UpperCAmelCase__								=  "imagenet-1k-id2label.json"
      UpperCAmelCase__								=  json.load(open(hf_hub_download(__A,  __A,  repo_type="dataset"							),  "r"							)							)
      UpperCAmelCase__								=  {int(__A							): v for k, v in idalabel.items()}
      UpperCAmelCase__								=  idalabel
      UpperCAmelCase__								=  {v: k for k, v in idalabel.items()}
      UpperCAmelCase__								=  int(deit_name[-6:-4]							)
      UpperCAmelCase__								=  int(deit_name[-3:]							)
      # size of the architecture
      if deit_name[9:].startswith("tiny"							):
            UpperCAmelCase__								=  192
            UpperCAmelCase__								=  768
            UpperCAmelCase__								=  12
            UpperCAmelCase__								=  3
      elif deit_name[9:].startswith("small"							):
            UpperCAmelCase__								=  384
            UpperCAmelCase__								=  1_536
            UpperCAmelCase__								=  12
            UpperCAmelCase__								=  6
      if deit_name[9:].startswith("base"							):
            pass
      elif deit_name[4:].startswith("large"							):
            UpperCAmelCase__								=  1_024
            UpperCAmelCase__								=  4_096
            UpperCAmelCase__								=  24
            UpperCAmelCase__								=  16

      # load original model from timm
      UpperCAmelCase__								=  timm.create_model(__A,  pretrained=__A							)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      UpperCAmelCase__								=  timm_model.state_dict()
      UpperCAmelCase__								=  create_rename_keys(__A,  __A							)
      for src, dest in rename_keys:
            rename_key(__A,  __A,  __A							)
      read_in_q_k_v(__A,  __A,  __A							)

      # load HuggingFace model
      UpperCAmelCase__								=  DeiTForImageClassificationWithTeacher(__A							).eval()
      model.load_state_dict(__A							)

      # Check outputs on an image, prepared by DeiTImageProcessor
      UpperCAmelCase__								=  int(
          (256 / 224) * config.image_size							)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      UpperCAmelCase__								=  DeiTImageProcessor(size=__A,  crop_size=config.image_size							)
      UpperCAmelCase__								=  image_processor(images=prepare_img(),  return_tensors="pt"							)
      UpperCAmelCase__								=  encoding["pixel_values"]
      UpperCAmelCase__								=  model(__A							)

      UpperCAmelCase__								=  timm_model(__A							)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(__A,  outputs.logits,  atol=1e-3							)

      Path(__A							).mkdir(exist_ok=__A							)
      print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}"""							)
      model.save_pretrained(__A							)
      print(f"""Saving image processor to {pytorch_dump_folder_path}"""							)
      image_processor.save_pretrained(__A							)


if __name__ == "__main__":
      UpperCamelCase__    							= argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--deit_name',
          default='vit_deit_base_distilled_patch16_224',
          type=str,
          help='Name of the DeiT timm model you\'d like to convert.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
      )

      UpperCamelCase__    							= parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



