



'''simple docstring'''

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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							)      ->						Dict:







	"""simple docstring"""
	lowerCAmelCase   =     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()							)
	lowerCAmelCase   =     """<s> <pad> </s> <unk>""".split()
	# restore the special tokens
	for k in keep_keys:
		del da[f'{k}</w>']
		lowerCAmelCase   =     d[k]  # restore
	return da






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						Optional[int]:







	"""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

	lowerCAmelCase   =     basename(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     dirname(_SCREAMING_SNAKE_CASE							)

	lowerCAmelCase   =     fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
	lowerCAmelCase   =     cls.hub_models()
	lowerCAmelCase   =     {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
	lowerCAmelCase   =     """."""
	# 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}'							)
	lowerCAmelCase   =     hub_utils.from_pretrained(
	    _SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		archive_map=_SCREAMING_SNAKE_CASE							,		**_SCREAMING_SNAKE_CASE							)

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

	lowerCAmelCase   =     args["""source_lang"""]
	lowerCAmelCase   =     args["""target_lang"""]

	lowerCAmelCase   =     dirname(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     basename(_SCREAMING_SNAKE_CASE							)

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

	lowerCAmelCase   =     Dictionary.load(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     rewrite_dict_keys(src_dict.indices							)
	lowerCAmelCase   =     len(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     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
	lowerCAmelCase   =     True
	for k in src_vocab.keys():
		if not k.islower():
			lowerCAmelCase   =     False
			break

	lowerCAmelCase   =     Dictionary.load(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     rewrite_dict_keys(tgt_dict.indices							)
	lowerCAmelCase   =     len(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     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)
	lowerCAmelCase   =     os.path.join(_SCREAMING_SNAKE_CASE							,		VOCAB_FILES_NAMES["""merges_file"""]							)
	for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
		lowerCAmelCase   =     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:
		lowerCAmelCase   =     fin.read()
	lowerCAmelCase   =     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
	lowerCAmelCase   =     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"]}'

	lowerCAmelCase   =     {
	    """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
	lowerCAmelCase   =     5
	lowerCAmelCase   =     False
	if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
		lowerCAmelCase   =     best_score_hparams[model_dir]["""length_penalty"""]
	else:
		lowerCAmelCase   =     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
	lowerCAmelCase   =     os.path.join(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	lowerCAmelCase   =     {
	    """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
	lowerCAmelCase   =     chkpt["""models"""][0]
	lowerCAmelCase   =     model.state_dict()

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

	# remove unneeded keys
	lowerCAmelCase   =     [
	    """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							)

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

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

	# save
	lowerCAmelCase   =     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__":
				UpperCAmelCase     		=   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     		=   parser.parse_args()
				convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




'''simple docstring'''

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		["image_processor", "tokenizer"]
						UpperCAmelCase							:   Tuple									=		"BlipImageProcessor"
						UpperCAmelCase							:   Union[str, Any]									=		("BertTokenizer", "BertTokenizerFast")


						def __init__( self     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     False
							super().__init__(A_     ,	A_				)
							lowerCAmelCase   =     self.image_processor


						def __call__( self     ,	A_ = None     ,	A_ = None     ,	A_ = True     ,	A_ = False     ,	A_ = None     ,	A_ = None     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = True     ,	A_ = None     ,	**A_     ,	)     ->     BatchEncoding:
							if images is None and text is None:
								raise ValueError("""You have to specify either images or text."""				)

							# Get only text
							if images is None:
								lowerCAmelCase   =     self.tokenizer
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
								return text_encoding

							# add pixel_values
							lowerCAmelCase   =     self.image_processor(A_     ,	return_tensors=A_				)

							if text is not None:
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
							else:
								lowerCAmelCase   =     None

							if text_encoding is not None:
								encoding_image_processor.update(A_				)

							return encoding_image_processor


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Union[str, Any]:
							return self.tokenizer.batch_decode(*A_     ,	**A_				)


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Tuple:
							return self.tokenizer.decode(*A_     ,	**A_				)




						@property
						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.tokenizer.model_input_names
							lowerCAmelCase   =     self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)