




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()

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

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
					a     =				dict((re.sub(r"@@$"		, ""		, __snake_case), v) if k.endswith("@@") else (re.sub(r"$"		, "</w>"		, __snake_case), 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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					int:
					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

					a     =				basename(__snake_case)
					a     =				dirname(__snake_case)

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

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

					a     =				args["source_lang"]
					a     =				args["target_lang"]

					a     =				dirname(__snake_case)
					a     =				basename(__snake_case)

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

					a     =				Dictionary.load(__snake_case)
					a     =				rewrite_dict_keys(src_dict.indices)
					a     =				len(__snake_case)
					a     =				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
					a     =				True
					for k in src_vocab.keys():
										if not k.islower():
															a     =				False
															break

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

					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(__snake_case		, "w"		, encoding="utf-8") as f:
										f.write(json.dumps(__snake_case		, ensure_ascii=__snake_case		, indent=__snake_case))

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

					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(__snake_case		, "w"		, encoding="utf-8") as f:
										f.write(json.dumps(__snake_case		, ensure_ascii=__snake_case		, indent=__snake_case))

					# 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(__snake_case		, __snake_case)

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

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

					# save
					a     =				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__":
			lowercase__				:      Union[str, 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."
			)
			lowercase__				:      Any	   = parser.parse_args()
			convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase__				:      Optional[Any]	   = logging.get_logger(__name__)

lowercase__				:      str	   = {
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}




class       a__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     """table-transformer"""
     a		:      Tuple		 =     ["""past_key_values"""]
     a		:      int		 =     {
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }







     def __init__(     self  ,		A=True  ,		A=None  ,		A=3  ,		A=100  ,		A=6  ,		A=2048  ,		A=8  ,		A=6  ,		A=2048  ,		A=8  ,		A=0.0  ,		A=0.0  ,		A=True  ,		A="relu"  ,		A=256  ,		A=0.1  ,		A=0.0  ,		A=0.0  ,		A=0.0_2  ,		A=1.0  ,		A=False  ,		A="sine"  ,		A="resnet50"  ,		A=True  ,		A=False  ,		A=1  ,		A=5  ,		A=2  ,		A=1  ,		A=1  ,		A=5  ,		A=2  ,		A=0.1  ,		**A  ,		)  ->   List[str]:



          '''simple docstring'''



          if backbone_config is not None and use_timm_backbone:
               raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."			)

          if not use_timm_backbone:
               if backbone_config is None:
                    logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."			)
                    a     =				CONFIG_MAPPING["resnet"](out_features=["stage4"]			)
               elif isinstance(A  ,		A			):
                    a     =				backbone_config.get("model_type"			)
                    a     =				CONFIG_MAPPING[backbone_model_type]
                    a     =				config_class.from_dict(A			)
               # set timm attributes to None
               a  ,				a  ,				a     =				None, None, None

          a     =				use_timm_backbone
          a     =				backbone_config
          a     =				num_channels
          a     =				num_queries
          a     =				d_model
          a     =				encoder_ffn_dim
          a     =				encoder_layers
          a     =				encoder_attention_heads
          a     =				decoder_ffn_dim
          a     =				decoder_layers
          a     =				decoder_attention_heads
          a     =				dropout
          a     =				attention_dropout
          a     =				activation_dropout
          a     =				activation_function
          a     =				init_std
          a     =				init_xavier_std
          a     =				encoder_layerdrop
          a     =				decoder_layerdrop
          a     =				encoder_layers
          a     =				auxiliary_loss
          a     =				position_embedding_type
          a     =				backbone
          a     =				use_pretrained_backbone
          a     =				dilation
          # Hungarian matcher
          a     =				class_cost
          a     =				bbox_cost
          a     =				giou_cost
          # Loss coefficients
          a     =				mask_loss_coefficient
          a     =				dice_loss_coefficient
          a     =				bbox_loss_coefficient
          a     =				giou_loss_coefficient
          a     =				eos_coefficient
          super().__init__(is_encoder_decoder=A  ,		**A			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.encoder_attention_heads







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return self.d_model







class       a__	( UpperCamelCase__				):
     a		:      Any		 =     version.parse("""1.11"""				)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Mapping[str, Mapping[int, str]]:



          '''simple docstring'''



          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ("pixel_mask", {0: "batch"}),
              ]			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   float:



          '''simple docstring'''



          return 1e-5







     @property
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          return 12

