





"""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()

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


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	dict((re.sub(r"@@$"  ,	""  ,	_SCREAMING_SNAKE_CASE    ), v) if k.endswith("@@"    ) else (re.sub(r"$"  ,	"</w>"  ,	_SCREAMING_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       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


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

		# handle various types of models

		UpperCamelCase   =	basename(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	dirname(_SCREAMING_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(
		    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	archive_map=_SCREAMING_SNAKE_CASE  ,	**_SCREAMING_SNAKE_CASE    )

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

		UpperCamelCase   =	args["source_lang"]
		UpperCamelCase   =	args["target_lang"]

		UpperCamelCase   =	dirname(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	basename(_SCREAMING_SNAKE_CASE    )

		# dicts
		UpperCamelCase   =	os.path.join(_SCREAMING_SNAKE_CASE  ,	F"dict.{src_lang}.txt"    )
		UpperCamelCase   =	os.path.join(_SCREAMING_SNAKE_CASE  ,	F"dict.{tgt_lang}.txt"    )

		UpperCamelCase   =	Dictionary.load(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	rewrite_dict_keys(src_dict.indices    )
		UpperCamelCase   =	len(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	os.path.join(_SCREAMING_SNAKE_CASE  ,	"vocab-src.json"    )
		print(F"Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"    )
		with open(_SCREAMING_SNAKE_CASE  ,	"w"  ,	encoding="utf-8"    ) as f:
				f.write(json.dumps(_SCREAMING_SNAKE_CASE  ,	ensure_ascii=_SCREAMING_SNAKE_CASE  ,	indent=_SCREAMING_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(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	rewrite_dict_keys(tgt_dict.indices    )
		UpperCamelCase   =	len(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	os.path.join(_SCREAMING_SNAKE_CASE  ,	"vocab-tgt.json"    )
		print(F"Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"    )
		with open(_SCREAMING_SNAKE_CASE  ,	"w"  ,	encoding="utf-8"    ) as f:
				f.write(json.dumps(_SCREAMING_SNAKE_CASE  ,	ensure_ascii=_SCREAMING_SNAKE_CASE  ,	indent=_SCREAMING_SNAKE_CASE    )    )

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

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

		# tokenizer config
		UpperCamelCase   =	os.path.join(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_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(_SCREAMING_SNAKE_CASE  ,	"w"  ,	encoding="utf-8"    ) as f:
				f.write(json.dumps(_SCREAMING_SNAKE_CASE  ,	ensure_ascii=_SCREAMING_SNAKE_CASE  ,	indent=_SCREAMING_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(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	FSMTConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	FSMTForConditionalGeneration(_SCREAMING_SNAKE_CASE    )

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

		# save
		UpperCamelCase   =	os.path.join(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
		print(F"Generating {pytorch_weights_dump_path}"    )
		torch.save(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_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__":
						lowerCAmelCase__     			=			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.'''
						)
						lowerCAmelCase__     			=			parser.parse_args()
						convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)













"""simple docstring"""





import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY





@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class        _lowerCamelCase      (       unittest.TestCase ):
						UpperCAmelCase_ =			MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


						def     snake_case_   (self   ,							__a   ,							__a   ,							__a		) ->   str:
								UpperCamelCase   =	hf_hub_download(
								    repo_id="nateraw/video-demo"   ,							filename="archery.mp4"   ,							repo_type="dataset"		)
								UpperCamelCase   =	VideoClassificationPipeline(model=__a   ,							image_processor=__a   ,							top_k=2		)
								UpperCamelCase   =	[
								    example_video_filepath,
								    "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
								]
								return video_classifier, examples


						def     snake_case_   (self   ,							__a   ,							__a		) ->   str:
								for example in examples:
										UpperCamelCase   =	video_classifier(__a		)

										self.assertEqual(
										    __a   ,							[
										        {"score": ANY(__a		), "label": ANY(__a		)},
										        {"score": ANY(__a		), "label": ANY(__a		)},
										    ]   ,							)


						@require_torch
						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
								UpperCamelCase   =	VideoMAEFeatureExtractor(
								    size={"shortest_edge": 10}   ,							crop_size={"height": 10, "width": 10}		)
								UpperCamelCase   =	pipeline(
								    "video-classification"   ,							model=__a   ,							feature_extractor=__a   ,							frame_sampling_rate=4		)

								UpperCamelCase   =	hf_hub_download(repo_id="nateraw/video-demo"   ,							filename="archery.mp4"   ,							repo_type="dataset"		)
								UpperCamelCase   =	video_classifier(__a   ,							top_k=2		)
								self.assertEqual(
								    nested_simplify(__a   ,							decimals=4		)   ,							[{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}]   ,							)

								UpperCamelCase   =	video_classifier(
								    [
								        video_file_path,
								        video_file_path,
								    ]   ,							top_k=2   ,							)
								self.assertEqual(
								    nested_simplify(__a   ,							decimals=4		)   ,							[
								        [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
								        [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
								    ]   ,							)




						@require_tf
						def     snake_case_   (self		) ->   Optional[Any]:
								pass






