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					:					Optional[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
_a					:					str=			{
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.1_5},
    "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					:					Optional[int]=			{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
	_a					:					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",
]:
	_a					:					List[Any]=			"allenai"
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[int]  )							->      Any:

	'''simple docstring'''





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

	'''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

	__snake_case      :							Dict						=			basename(_a  )
	__snake_case      :							Dict						=			dirname(_a  )

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

	__snake_case      :							Optional[int]						=			vars(chkpt['args']['model']  )

	__snake_case      :							List[Any]						=			args['source_lang']
	__snake_case      :							List[str]						=			args['target_lang']

	__snake_case      :							List[str]						=			dirname(_a  )
	__snake_case      :							List[str]						=			basename(_a  )

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

	__snake_case      :							Optional[Any]						=			Dictionary.load(_a  )
	__snake_case      :							List[str]						=			rewrite_dict_keys(src_dict.indices  )
	__snake_case      :							List[str]						=			len(_a  )
	__snake_case      :							Optional[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
	__snake_case      :							List[Any]						=			True
	for k in src_vocab.keys():
		if not k.islower():
			__snake_case      :							Union[str, Any]						=			False
			break

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

	__snake_case      :							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': 0.02,
	    '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
	__snake_case      :							List[str]						=			5
	__snake_case      :							List[str]						=			False
	if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
		__snake_case      :							int						=			best_score_hparams[model_dir]['length_penalty']
	else:
		__snake_case      :							List[str]						=			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
	__snake_case      :							Union[str, Any]						=			os.path.join(_a      ,							_a  )

	__snake_case      :							List[str]						=			{
	    '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(_a      ,							'w'      ,							encoding='utf-8'  ) as f:
		f.write(json.dumps(_a      ,							ensure_ascii=_a      ,							indent=_a  )  )

	# model
	__snake_case      :							Optional[int]						=			chkpt['models'][0]
	__snake_case      :							Tuple						=			model.state_dict()

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

	# remove unneeded keys
	__snake_case      :							Dict						=			[
	    '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  )

	__snake_case      :							Optional[Any]						=			FSMTConfig.from_pretrained(_a  )
	__snake_case      :							str						=			FSMTForConditionalGeneration(_a  )

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

	# save
	__snake_case      :							List[str]						=			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					:					List[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."
	)
	_a					:					Union[str, Any]=			parser.parse_args()
	convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_a					:					List[str]=			False

class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				List[str])   ->     List[Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



				@property
				def  _lowercase					(self    :				Optional[Any])   ->     Union[str, Any]:
					return 12



				@property
				def  _lowercase					(self    :				Dict)   ->     Union[str, Any]:
					return 12



				@property
				def  _lowercase					(self    :				int)   ->     Tuple:
					return 32



				@property
				def  _lowercase					(self    :				Optional[int])   ->     Dict:
					torch.manual_seed(0)
					__snake_case      :							Any						=			VQModel(
					    block_out_channels=[32, 64]     ,  in_channels=3     ,  out_channels=3     ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']     ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']     ,  latent_channels=3     ,  num_vq_embeddings=self.num_embed     ,  vq_embed_dim=3     ,  )
					return model



				@property
				def  _lowercase					(self    :				List[Any])   ->     Optional[int]:
					__snake_case      :							Dict						=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
					return tokenizer



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[int]:
					torch.manual_seed(0)
					__snake_case      :							Any						=			CLIPTextConfig(
					    bos_token_id=0     ,  eos_token_id=2     ,  hidden_size=self.text_embedder_hidden_size     ,  intermediate_size=37     ,  layer_norm_eps=1E-05     ,  num_attention_heads=4     ,  num_hidden_layers=5     ,  pad_token_id=1     ,  vocab_size=10_00     ,  )
					return CLIPTextModel(_A)



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
					torch.manual_seed(0)

					__snake_case      :							Any						=			12
					__snake_case      :							int						=			12

					__snake_case      :							List[Any]						=			{
					    'attention_bias': True,
					    'cross_attention_dim': 32,
					    'attention_head_dim': height * width,
					    'num_attention_heads': 1,
					    'num_vector_embeds': self.num_embed,
					    'num_embeds_ada_norm': self.num_embeds_ada_norm,
					    'norm_num_groups': 32,
					    'sample_size': width,
					    'activation_fn': 'geglu-approximate',
					}

					__snake_case      :							Union[str, Any]						=			TransformeraDModel(**_A)
					return model



				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
					__snake_case      :							Tuple						=			'cpu'

					__snake_case      :							List[str]						=			self.dummy_vqvae
					__snake_case      :							str						=			self.dummy_text_encoder
					__snake_case      :							Optional[Any]						=			self.dummy_tokenizer
					__snake_case      :							Dict						=			self.dummy_transformer
					__snake_case      :							Optional[int]						=			VQDiffusionScheduler(self.num_embed)
					__snake_case      :							List[Any]						=			LearnedClassifierFreeSamplingEmbeddings(learnable=_A)

					__snake_case      :							List[Any]						=			VQDiffusionPipeline(
					    vqvae=_A     ,  text_encoder=_A     ,  tokenizer=_A     ,  transformer=_A     ,  scheduler=_A     ,  learned_classifier_free_sampling_embeddings=_A     ,  )
					__snake_case      :							List[Any]						=			pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Optional[Any]						=			'teddy bear playing in the pool'

					__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Union[str, Any]						=			pipe([prompt]     ,  generator=_A     ,  num_inference_steps=2     ,  output_type='np')
					__snake_case      :							Optional[int]						=			output.images

					__snake_case      :							int						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Tuple						=			pipe(
					    [prompt]     ,  generator=_A     ,  output_type='np'     ,  return_dict=_A     ,  num_inference_steps=2)[0]

					__snake_case      :							str						=			image[0, -3:, -3:, -1]
					__snake_case      :							Union[str, Any]						=			image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 24, 24, 3)

					__snake_case      :							str						=			np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992])

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






				def  _lowercase					(self    :				Tuple)   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			'cpu'

					__snake_case      :							Optional[int]						=			self.dummy_vqvae
					__snake_case      :							List[str]						=			self.dummy_text_encoder
					__snake_case      :							Optional[int]						=			self.dummy_tokenizer
					__snake_case      :							Optional[Any]						=			self.dummy_transformer
					__snake_case      :							Union[str, Any]						=			VQDiffusionScheduler(self.num_embed)
					__snake_case      :							Optional[int]						=			LearnedClassifierFreeSamplingEmbeddings(
					    learnable=_A     ,  hidden_size=self.text_embedder_hidden_size     ,  length=tokenizer.model_max_length)

					__snake_case      :							Union[str, Any]						=			VQDiffusionPipeline(
					    vqvae=_A     ,  text_encoder=_A     ,  tokenizer=_A     ,  transformer=_A     ,  scheduler=_A     ,  learned_classifier_free_sampling_embeddings=_A     ,  )
					__snake_case      :							Union[str, Any]						=			pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Union[str, Any]						=			'teddy bear playing in the pool'

					__snake_case      :							Optional[int]						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Tuple						=			pipe([prompt]     ,  generator=_A     ,  num_inference_steps=2     ,  output_type='np')
					__snake_case      :							Optional[Any]						=			output.images

					__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Dict						=			pipe(
					    [prompt]     ,  generator=_A     ,  output_type='np'     ,  return_dict=_A     ,  num_inference_steps=2)[0]

					__snake_case      :							int						=			image[0, -3:, -3:, -1]
					__snake_case      :							Tuple						=			image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 24, 24, 3)

					__snake_case      :							Optional[Any]						=			np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988])

					assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2







@slow
@require_torch_gpu
class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				Any)   ->     Union[str, Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






				def  _lowercase					(self    :				Tuple)   ->     Optional[int]:
					__snake_case      :							List[Any]						=			load_numpy(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy')

					__snake_case      :							Union[str, Any]						=			VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq')
					__snake_case      :							Tuple						=			pipeline.to(_A)
					pipeline.set_progress_bar_config(disable=_A)

					# requires GPU generator for gumbel softmax
					# don't use GPU generator in tests though
					__snake_case      :							Optional[int]						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							int						=			pipeline(
					    'teddy bear playing in the pool'     ,  num_images_per_prompt=1     ,  generator=_A     ,  output_type='np'     ,  )

					__snake_case      :							int						=			output.images[0]

					assert image.shape == (2_56, 2_56, 3)
					assert np.abs(expected_image - image).max() < 2.0





