
"""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()

UpperCamelCase_					:						Tuple				= 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_					:						Optional[Any]				= {
    # 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
UpperCamelCase_					:						Union[str, Any]				= {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
		UpperCamelCase_					:						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",
]:
		UpperCamelCase_					:						int				= '''allenai'''
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						dict((re.sub(R"@@$"				,   ""				,   __a				), v) if k.endswith("@@"				) else (re.sub(R"$"				,   "</w>"				,   __a				), 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 							A_     (__a				,   __a				):

					'''simple docstring'''





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

					# handle various types of models

					A_				 =						basename(__a				)
					A_				 =						dirname(__a				)

					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(
					    __a				,   __a				,   __a				,   archive_map=__a				,   **__a				)

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

					A_				 =						args["source_lang"]
					A_				 =						args["target_lang"]

					A_				 =						dirname(__a				)
					A_				 =						basename(__a				)

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

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

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

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

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

					# tokenizer config
					A_				 =						os.path.join(__a				,   __a				)

					A_				 =						{
					    "langs": [src_lang, tgt_lang],
					    "model_max_length": 1024,
					    "do_lower_case": do_lower_case,
					}

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

					# 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(__a				,   __a				)

					A_				 =						FSMTConfig.from_pretrained(__a				)
					A_				 =						FSMTForConditionalGeneration(__a				)

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

					# save
					A_				 =						os.path.join(__a				,   __a				)
					print(f'Generating {pytorch_weights_dump_path}'				)
					torch.save(__a				,   __a				)

					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_					:						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.'''
		)
		UpperCamelCase_					:						str				= parser.parse_args()
		convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""





import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
		from PIL import Image
else:



		class __lowerCAmelCase				:




					"""simple docstring"""







					@staticmethod
					def      lowerCamelCase__     (				*_snake_case						:					int       ,   **_snake_case						:					Optional[Any]	)      -> str:







										"""simple docstring"""
										pass
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						hashlib.mda(image.tobytes()				)
					return m.hexdigest()[:10]
def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						np.array(__a				)
					A_				 =						npimg.shape
					return {"hash": hashimage(__a				), "shape": shape}




@is_pipeline_test
@require_vision
@require_torch
class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])      )
			snake_case	      =				dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()      ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])      )




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					List[str]       ,   _snake_case						:					Dict       ,   _snake_case						:					int	)      -> str:







								"""simple docstring"""
								A_				 =						MaskGenerationPipeline(model=_snake_case       ,   image_processor=_snake_case	)
								return image_segmenter, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					int       ,   _snake_case						:					Optional[Any]	)      -> Dict:







								"""simple docstring"""
								pass




			@require_tf
			@unittest.skip("Image segmentation not implemented in TF"	)
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Tuple:







								"""simple docstring"""
								pass




			@slow
			@require_torch
			def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







								"""simple docstring"""
								A_				 =						pipeline("mask-generation"       ,   model="facebook/sam-vit-huge"	)

								A_				 =						image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"       ,   points_per_batch=256	)

								# Shortening by hashing
								A_				 =						[]
								for i, o in enumerate(outputs["masks"]	):
													new_outupt += [{"mask": mask_to_test_readable(_snake_case	), "scores": outputs["scores"][i]}]

								# fmt: off
								self.assertEqual(
								    nested_simplify(_snake_case       ,   decimals=4	)       ,   [
								        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
								        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1},
								        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
								        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
								        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
								        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9_9_6_7},
								        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_9_3},
								        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9_9_0_9},
								        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9_8_7_9},
								        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9_8_3_4},
								        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9_7_1_6},
								        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9_6_1_2},
								        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9_5_9_9},
								        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9_5_5_2},
								        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9_5_3_2},
								        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9_5_1_6},
								        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9_4_9_9},
								        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9_4_8_3},
								        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9_4_6_4},
								        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_4_3},
								        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_4_3},
								        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.9_4_0_8},
								        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9_3_3_5},
								        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9_3_2_6},
								        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9_2_6_2},
								        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8_9_9_9},
								        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8_9_8_6},
								        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8_9_8_4},
								        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8_8_7_3},
								        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8_8_7_1}
								    ]       ,   )
								# fmt: on






			@require_torch
			@slow
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						"facebook/sam-vit-huge"
								A_				 =						pipeline("mask-generation"       ,   model=_snake_case	)

								A_				 =						image_segmenter(
								    "http://images.cocodataset.org/val2017/000000039769.jpg"       ,   pred_iou_thresh=1       ,   points_per_batch=256	)

								# Shortening by hashing
								A_				 =						[]
								for i, o in enumerate(outputs["masks"]	):
													new_outupt += [{"mask": mask_to_test_readable(_snake_case	), "scores": outputs["scores"][i]}]

								self.assertEqual(
								    nested_simplify(_snake_case       ,   decimals=4	)       ,   [
								        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_4_4_4},
								        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_2_1_0},
								        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_1_6_7},
								        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_1_3_2},
								        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_0_5_3},
								    ]       ,   )




