


# 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()

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







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







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							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

							__snake_case	:    List[str]											=     basename(lowerCamelCase__			)
							__snake_case	:    Tuple											=     dirname(lowerCamelCase__			)

							__snake_case	:    List[Any]											=     fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
							__snake_case	:    List[Any]											=     cls.hub_models()
							__snake_case	:    List[Any]											=     {"bpe": "fastbpe", "tokenizer": "moses"}
							__snake_case	:    int											=     "."
							# 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}'			)
							__snake_case	:    Optional[int]											=     hub_utils.from_pretrained(
							    lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			archive_map=lowerCamelCase__							,			**lowerCamelCase__			)

							__snake_case	:    List[Any]											=     vars(chkpt["args"]["model"]			)

							__snake_case	:    Tuple											=     args["source_lang"]
							__snake_case	:    Optional[int]											=     args["target_lang"]

							__snake_case	:    List[Any]											=     dirname(lowerCamelCase__			)
							__snake_case	:    Any											=     basename(lowerCamelCase__			)

							# dicts
							__snake_case	:    int											=     os.path.join(lowerCamelCase__							,			F'dict.{src_lang}.txt'			)
							__snake_case	:    int											=     os.path.join(lowerCamelCase__							,			F'dict.{tgt_lang}.txt'			)

							__snake_case	:    Any											=     Dictionary.load(lowerCamelCase__			)
							__snake_case	:    Dict											=     rewrite_dict_keys(src_dict.indices			)
							__snake_case	:    str											=     len(lowerCamelCase__			)
							__snake_case	:    str											=     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
							__snake_case	:    int											=     True
							for k in src_vocab.keys():
														if not k.islower():
																					__snake_case	:    Any											=     False
																					break

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

							__snake_case	:    List[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": 0.0_2,
							    "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
							__snake_case	:    Dict											=     5
							__snake_case	:    str											=     False
							if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
														__snake_case	:    List[Any]											=     best_score_hparams[model_dir]["length_penalty"]
							else:
														__snake_case	:    List[str]											=     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
							__snake_case	:    List[Any]											=     os.path.join(lowerCamelCase__							,			lowerCamelCase__			)

							__snake_case	:    int											=     {
							    "langs": [src_lang, tgt_lang],
							    "model_max_length": 1_0_2_4,
							    "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
							__snake_case	:    Tuple											=     chkpt["models"][0]
							__snake_case	:    str											=     model.state_dict()

							# rename keys to start with 'model.'
							__snake_case	:    Optional[Any]											=     OrderedDict(("model." + k, v) for k, v in model_state_dict.items()			)

							# remove unneeded keys
							__snake_case	:    Tuple											=     [
							    "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__			)

							__snake_case	:    List[str]											=     FSMTConfig.from_pretrained(lowerCamelCase__			)
							__snake_case	:    List[str]											=     FSMTForConditionalGeneration(lowerCamelCase__			)

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

							# save
							__snake_case	:    Union[str, Any]											=     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__":
	_snake_case  :	Dict			      =       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."
	)
	_snake_case  :	Tuple			      =       parser.parse_args()
	convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
_snake_case  :	Dict			      =       {"LayoutLMv2Config", "LayoutLMv3Config"}

@is_pipeline_test
class        a	(unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Union[str, Any]  						=						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							__UpperCAmelCase					:							Optional[int]  						=						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														__UpperCAmelCase					:							Optional[int]  						=						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														__UpperCAmelCase					:							Any  						=						{
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}

							@require_torch
							def 					__snake_case      (							self :						str       )			->					str:
														__snake_case	:    Dict											=     pipeline(
														    task="text-classification"					,			model="hf-internal-testing/tiny-random-distilbert"					,			framework="pt"       )

														__snake_case	:    Optional[Any]											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

														__snake_case	:    Any											=     text_classifier("This is great !"					,			top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]       )

														__snake_case	:    int											=     text_classifier(["This is great !", "This is bad"]					,			top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														    ]					,			)

														__snake_case	:    List[str]											=     text_classifier("This is great !"					,			top_k=1       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

														# Legacy behavior
														__snake_case	:    Dict											=     text_classifier("This is great !"					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

														__snake_case	:    Any											=     text_classifier("This is great !"					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]       )

														__snake_case	:    Tuple											=     text_classifier(["This is great !", "Something else"]					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														    ]					,			)

														__snake_case	:    Tuple											=     text_classifier(["This is great !", "Something else"]					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[
														        {"label": "LABEL_0", "score": 0.5_04},
														        {"label": "LABEL_0", "score": 0.5_04},
														    ]					,			)

							@require_torch
							def 					__snake_case      (							self :						Optional[int]       )			->					List[Any]:
														import torch

														__snake_case	:    Dict											=     pipeline(
														    task="text-classification"					,			model="hf-internal-testing/tiny-random-distilbert"					,			framework="pt"					,			device=torch.device("cpu"       )					,			)

														__snake_case	:    Optional[int]											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

							@require_tf
							def 					__snake_case      (							self :						Any       )			->					Tuple:
														__snake_case	:    List[Any]											=     pipeline(
														    task="text-classification"					,			model="hf-internal-testing/tiny-random-distilbert"					,			framework="tf"       )

														__snake_case	:    str											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

							@slow
							@require_torch
							def 					__snake_case      (							self :						int       )			->					int:
														__snake_case	:    Dict											=     pipeline("text-classification"       )

														__snake_case	:    Dict											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 1.0}]       )
														__snake_case	:    int											=     text_classifier("This is bad !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "NEGATIVE", "score": 1.0}]       )
														__snake_case	:    str											=     text_classifier("Birds are a type of animal"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 0.9_88}]       )

							@slow
							@require_tf
							def 					__snake_case      (							self :						List[Any]       )			->					str:
														__snake_case	:    Optional[Any]											=     pipeline("text-classification"					,			framework="tf"       )

														__snake_case	:    Any											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 1.0}]       )
														__snake_case	:    Optional[int]											=     text_classifier("This is bad !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "NEGATIVE", "score": 1.0}]       )
														__snake_case	:    Any											=     text_classifier("Birds are a type of animal"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 0.9_88}]       )

							def 					__snake_case      (							self :						str					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						Any					,			lowerCamelCase :						Union[str, Any]       )			->					List[Any]:
														__snake_case	:    Union[str, Any]											=     TextClassificationPipeline(model=lowerCamelCase					,			tokenizer=lowerCamelCase       )
														return text_classifier, ["HuggingFace is in", "This is another test"]






							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						Tuple       )			->					str:
														__snake_case	:    Tuple											=     text_classifier.model
														# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
														__snake_case	:    Any											=     "HuggingFace is in"
														__snake_case	:    Tuple											=     text_classifier(lowerCamelCase       )

														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}]       )
														self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )

														__snake_case	:    Union[str, Any]											=     ["HuggingFace is in ", "Paris is in France"]
														__snake_case	:    Union[str, Any]											=     text_classifier(lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}, {"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}]					,			)
														self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )
														self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()       )

														# Forcing to get all results with `top_k=None`
														# This is NOT the legacy format
														__snake_case	:    str											=     text_classifier(lowerCamelCase					,			top_k=lowerCamelCase       )
														__snake_case	:    int											=     len(model.config.idalabel.values()       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}] * N, [{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}] * N]					,			)

														__snake_case	:    Optional[int]											=     {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
														__snake_case	:    Optional[Any]											=     text_classifier(lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}					,			)
														self.assertTrue(outputs["label"] in model.config.idalabel.values()       )

														# This might be used a text pair, but tokenizer + pipe interaction
														# makes it hard to understand that it's not using the pair properly
														# https://github.com/huggingface/transformers/issues/17305
														# We disabled this usage instead as it was outputting wrong outputs.
														__snake_case	:    Any											=     [["HuggingFace is in ", "Paris is in France"]]
														with self.assertRaises(lowerCamelCase       ):
																					text_classifier(lowerCamelCase       )

														# This used to be valid for doing text pairs
														# We're keeping it working because of backward compatibility
														__snake_case	:    List[Any]											=     text_classifier([[["HuggingFace is in ", "Paris is in France"]]]       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}]					,			)
														self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )

