

# 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()

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

def 			lowerCamelCase__			(       _a):
	# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
	# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							dict((re.sub(r"@@$"				,      ""				,      _a), v) if k.endswith("@@") else (re.sub(r"$"				,      "</w>"				,      _a), v) for k, v in d.items())
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							"<s> <pad> </s> <unk>".split()
	# restore the special tokens
	for k in keep_keys:
		del da[f"{k}</w>"]
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							d[k]  # restore
	return da

def 			lowerCamelCase__			(       _a				,      _a):
	# prep
	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

	SCREAMING_SNAKE_CASE :   Dict					  =							basename(_a)
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							dirname(_a)

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

	SCREAMING_SNAKE_CASE :   int					  =							vars(chkpt["args"]["model"])

	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							args["source_lang"]
	SCREAMING_SNAKE_CASE :   List[Any]					  =							args["target_lang"]

	SCREAMING_SNAKE_CASE :   Any					  =							dirname(_a)
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							basename(_a)

	# dicts
	SCREAMING_SNAKE_CASE :   Tuple					  =							os.path.join(_a				,      f"dict.{src_lang}.txt")
	SCREAMING_SNAKE_CASE :   Dict					  =							os.path.join(_a				,      f"dict.{tgt_lang}.txt")

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

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

	SCREAMING_SNAKE_CASE :   int					  =							{
	    "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
	SCREAMING_SNAKE_CASE :   List[Any]					  =							5
	SCREAMING_SNAKE_CASE :   List[str]					  =							False
	if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
		SCREAMING_SNAKE_CASE :   Tuple					  =							best_score_hparams[model_dir]["length_penalty"]
	else:
		SCREAMING_SNAKE_CASE :   int					  =							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
	SCREAMING_SNAKE_CASE :   int					  =							os.path.join(_a				,      _a)

	SCREAMING_SNAKE_CASE :   Dict					  =							{
	    "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
	SCREAMING_SNAKE_CASE :   Tuple					  =							chkpt["models"][0]
	SCREAMING_SNAKE_CASE :   List[Any]					  =							model.state_dict()

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

	# remove unneeded keys
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							[
	    "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)

	SCREAMING_SNAKE_CASE :   Any					  =							FSMTConfig.from_pretrained(_a)
	SCREAMING_SNAKE_CASE :   int					  =							FSMTForConditionalGeneration(_a)

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

	# save
	SCREAMING_SNAKE_CASE :   Tuple					  =							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__":
			a_      =     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.'
			)
			a_      =     parser.parse_args()
			convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






@slow
@require_torch_gpu
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"						)
				SCREAMING_SNAKE_CASE :   str					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_euler"						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							sd_pipe([prompt]   ,   generator=a   ,   guidance_scale=9.0   ,   num_inference_steps=20   ,   output_type="np"						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							output.images

				SCREAMING_SNAKE_CASE :   Any					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_euler"						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe([prompt]   ,   generator=a   ,   guidance_scale=9.0   ,   num_inference_steps=20   ,   output_type="np"						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							output.images

				SCREAMING_SNAKE_CASE :   Tuple					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-1






			def 				__UpperCamelCase			(      self				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_dpmpp_2m"						)

				SCREAMING_SNAKE_CASE :   str					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   str					  =							sd_pipe(
				    [prompt]   ,   generator=a   ,   guidance_scale=7.5   ,   num_inference_steps=15   ,   output_type="np"   ,   use_karras_sigmas=a   ,   )

				SCREAMING_SNAKE_CASE :   str					  =							output.images

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2