




"""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()

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







def   _snake_case    (       lowercase__	):
				# (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}
				_lowerCamelCase  :    Union[str, Any]					=					dict((re.sub(r'@@$'							,					''							,					lowercase__	), v) if k.endswith('@@'	) else (re.sub(r'$'							,					'</w>'							,					lowercase__	), v) for k, v in d.items()	)
				_lowerCamelCase  :    List[str]					=					'<s> <pad> </s> <unk>'.split()
				# restore the special tokens
				for k in keep_keys:
								del da[f'''{k}</w>''']
								_lowerCamelCase  :    Optional[Any]					=					d[k]  # restore
				return da







def   _snake_case    (       lowercase__							,					lowercase__	):
				# prep
				assert os.path.exists(lowercase__	)
				os.makedirs(lowercase__							,					exist_ok=lowercase__	)
				print(f'''Writing results to {pytorch_dump_folder_path}'''	)

				# handle various types of models

				_lowerCamelCase  :    int					=					basename(lowercase__	)
				_lowerCamelCase  :    Tuple					=					dirname(lowercase__	)

				_lowerCamelCase  :    List[str]					=					fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
				_lowerCamelCase  :    List[Any]					=					cls.hub_models()
				_lowerCamelCase  :    Tuple					=					{'bpe': 'fastbpe', 'tokenizer': 'moses'}
				_lowerCamelCase  :    Dict					=					'.'
				# 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  :    List[Any]					=					hub_utils.from_pretrained(
				    lowercase__							,					lowercase__							,					lowercase__							,					archive_map=lowercase__							,					**lowercase__	)

				_lowerCamelCase  :    Any					=					vars(chkpt['args']['model']	)

				_lowerCamelCase  :    Optional[int]					=					args['source_lang']
				_lowerCamelCase  :    Optional[int]					=					args['target_lang']

				_lowerCamelCase  :    List[Any]					=					dirname(lowercase__	)
				_lowerCamelCase  :    Union[str, Any]					=					basename(lowercase__	)

				# dicts
				_lowerCamelCase  :    Tuple					=					os.path.join(lowercase__							,					f'''dict.{src_lang}.txt'''	)
				_lowerCamelCase  :    Dict					=					os.path.join(lowercase__							,					f'''dict.{tgt_lang}.txt'''	)

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

				# 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  :    Tuple					=					True
				for k in src_vocab.keys():
								if not k.islower():
												_lowerCamelCase  :    Optional[Any]					=					False
												break

				_lowerCamelCase  :    int					=					Dictionary.load(lowercase__	)
				_lowerCamelCase  :    Optional[Any]					=					rewrite_dict_keys(tgt_dict.indices	)
				_lowerCamelCase  :    Any					=					len(lowercase__	)
				_lowerCamelCase  :    int					=					os.path.join(lowercase__							,					'vocab-tgt.json'	)
				print(f'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''	)
				with open(lowercase__							,					'w'							,					encoding='utf-8'	) as f:
								f.write(json.dumps(lowercase__							,					ensure_ascii=lowercase__							,					indent=lowercase__	)	)

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

				# model config
				_lowerCamelCase  :    Union[str, Any]					=					os.path.join(lowercase__							,					'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  :    Any					=					{
				    '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': 0.0_2,
				    '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  :    Optional[Any]					=					5
				_lowerCamelCase  :    Optional[Any]					=					False
				if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
								_lowerCamelCase  :    List[Any]					=					best_score_hparams[model_dir]['length_penalty']
				else:
								_lowerCamelCase  :    Union[str, Any]					=					1.0

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

				# tokenizer config
				_lowerCamelCase  :    Any					=					os.path.join(lowercase__							,					lowercase__	)

				_lowerCamelCase  :    Tuple					=					{
				    'langs': [src_lang, tgt_lang],
				    'model_max_length': 1024,
				    'do_lower_case': do_lower_case,
				}

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

				# model
				_lowerCamelCase  :    int					=					chkpt['models'][0]
				_lowerCamelCase  :    Union[str, Any]					=					model.state_dict()

				# rename keys to start with 'model.'
				_lowerCamelCase  :    Union[str, Any]					=					OrderedDict(('model.' + k, v) for k, v in model_state_dict.items()	)

				# remove unneeded keys
				_lowerCamelCase  :    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(lowercase__							,					lowercase__	)

				_lowerCamelCase  :    List[Any]					=					FSMTConfig.from_pretrained(lowercase__	)
				_lowerCamelCase  :    Optional[int]					=					FSMTForConditionalGeneration(lowercase__	)

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

				# save
				_lowerCamelCase  :    Tuple					=					os.path.join(lowercase__							,					lowercase__	)
				print(f'''Generating {pytorch_weights_dump_path}'''	)
				torch.save(lowercase__							,					lowercase__	)

				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__":
			lowercase__  		=		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."""
			)
			lowercase__  		=		parser.parse_args()
			convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""






from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
			import torch

if is_tf_available():
			import tensorflow as tf






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    ["""image_processor"""]
			lowerCamelCase__   =    """SamImageProcessor"""
			def __init__(						self						,   lowercase						):
							super().__init__(lowercase						)
							_lowerCamelCase  :    str					=					self.image_processor
							_lowerCamelCase  :    Optional[int]					=					-10
							_lowerCamelCase  :    Optional[Any]					=					self.image_processor.size['longest_edge']
			def __call__(						self						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   lowercase = None						,   **lowercase						,   ):
							_lowerCamelCase  :    int					=					self.image_processor(
							    lowercase						,   return_tensors=lowercase						,   **lowercase						,   )

							# pop arguments that are not used in the foward but used nevertheless
							_lowerCamelCase  :    int					=					encoding_image_processor['original_sizes']

							if hasattr(lowercase						,   'numpy'						):  # Checks if Torch or TF tensor
											_lowerCamelCase  :    Tuple					=					original_sizes.numpy()

							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    Optional[Any]					=					self._check_and_preprocess_points(
							    input_points=lowercase						,   input_labels=lowercase						,   input_boxes=lowercase						,   )

							_lowerCamelCase  :    Tuple					=					self._normalize_and_convert(
							    lowercase						,   lowercase						,   input_points=lowercase						,   input_labels=lowercase						,   input_boxes=lowercase						,   return_tensors=lowercase						,   )

							return encoding_image_processor
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   lowercase="pt"						,   ):
							if input_points is not None:
											if len(lowercase						) != len(lowercase						):
															_lowerCamelCase  :    str					=					[
															    self._normalize_coordinates(self.target_size						,   lowercase						,   original_sizes[0]						) for point in input_points
															]
											else:
															_lowerCamelCase  :    Optional[Any]					=					[
															    self._normalize_coordinates(self.target_size						,   lowercase						,   lowercase						)
															    for point, original_size in zip(lowercase						,   lowercase						)
															]
											# check that all arrays have the same shape
											if not all(point.shape == input_points[0].shape for point in input_points						):
															if input_labels is not None:
																			_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					self._pad_points_and_labels(lowercase						,   lowercase						)

											_lowerCamelCase  :    Optional[int]					=					np.array(lowercase						)

							if input_labels is not None:
											_lowerCamelCase  :    List[Any]					=					np.array(lowercase						)

							if input_boxes is not None:
											if len(lowercase						) != len(lowercase						):
															_lowerCamelCase  :    Optional[int]					=					[
															    self._normalize_coordinates(self.target_size						,   lowercase						,   original_sizes[0]						,   is_bounding_box=lowercase						)
															    for box in input_boxes
															]
											else:
															_lowerCamelCase  :    Optional[int]					=					[
															    self._normalize_coordinates(self.target_size						,   lowercase						,   lowercase						,   is_bounding_box=lowercase						)
															    for box, original_size in zip(lowercase						,   lowercase						)
															]
											_lowerCamelCase  :    Any					=					np.array(lowercase						)

							if input_boxes is not None:
											if return_tensors == "pt":
															_lowerCamelCase  :    List[str]					=					torch.from_numpy(lowercase						)
															# boxes batch size of 1 by default
															_lowerCamelCase  :    str					=					input_boxes.unsqueeze(1						) if len(input_boxes.shape						) != 3 else input_boxes
											elif return_tensors == "tf":
															_lowerCamelCase  :    int					=					tf.convert_to_tensor(lowercase						)
															# boxes batch size of 1 by default
															_lowerCamelCase  :    Union[str, Any]					=					tf.expand_dims(lowercase						,   1						) if len(input_boxes.shape						) != 3 else input_boxes
											encoding_image_processor.update({'input_boxes': input_boxes}						)
							if input_points is not None:
											if return_tensors == "pt":
															_lowerCamelCase  :    Optional[Any]					=					torch.from_numpy(lowercase						)
															# point batch size of 1 by default
															_lowerCamelCase  :    Union[str, Any]					=					input_points.unsqueeze(1						) if len(input_points.shape						) != 4 else input_points
											elif return_tensors == "tf":
															_lowerCamelCase  :    List[str]					=					tf.convert_to_tensor(lowercase						)
															# point batch size of 1 by default
															_lowerCamelCase  :    Union[str, Any]					=					tf.expand_dims(lowercase						,   1						) if len(input_points.shape						) != 4 else input_points
											encoding_image_processor.update({'input_points': input_points}						)
							if input_labels is not None:
											if return_tensors == "pt":
															_lowerCamelCase  :    Union[str, Any]					=					torch.from_numpy(lowercase						)
															# point batch size of 1 by default
															_lowerCamelCase  :    Optional[Any]					=					input_labels.unsqueeze(1						) if len(input_labels.shape						) != 3 else input_labels
											elif return_tensors == "tf":
															_lowerCamelCase  :    Dict					=					tf.convert_to_tensor(lowercase						)
															# point batch size of 1 by default
															_lowerCamelCase  :    Any					=					tf.expand_dims(lowercase						,   1						) if len(input_labels.shape						) != 3 else input_labels
											encoding_image_processor.update({'input_labels': input_labels}						)

							return encoding_image_processor
			def       A_					(						self						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    Optional[int]					=					max([point.shape[0] for point in input_points]						)
							_lowerCamelCase  :    Optional[int]					=					[]
							for i, point in enumerate(lowercase						):
											if point.shape[0] != expected_nb_points:
															_lowerCamelCase  :    Tuple					=					np.concatenate(
															    [point, np.zeros((expected_nb_points - point.shape[0], 2)						) + self.point_pad_value]						,   axis=0						)
															_lowerCamelCase  :    Optional[Any]					=					np.append(input_labels[i]						,   [self.point_pad_value]						)
											processed_input_points.append(lowercase						)
							_lowerCamelCase  :    Any					=					processed_input_points
							return input_points, input_labels
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase=False						):
							_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					original_size
							_lowerCamelCase,				_lowerCamelCase  :    Optional[int]					=					self.image_processor._get_preprocess_shape(lowercase						,   longest_edge=lowercase						)
							_lowerCamelCase  :    Tuple					=					deepcopy(lowercase						).astype(lowercase						)

							if is_bounding_box:
											_lowerCamelCase  :    List[str]					=					coords.reshape(-1						,   2						,   2						)

							_lowerCamelCase  :    Optional[Any]					=					coords[..., 0] * (new_w / old_w)
							_lowerCamelCase  :    List[Any]					=					coords[..., 1] * (new_h / old_h)

							if is_bounding_box:
											_lowerCamelCase  :    Union[str, Any]					=					coords.reshape(-1						,   4						)

							return coords
			def       A_					(						self						,   lowercase=None						,   lowercase=None						,   lowercase=None						,   ):
							if input_points is not None:
											if hasattr(lowercase						,   'numpy'						):  # Checks for TF or Torch tensor
															_lowerCamelCase  :    int					=					input_points.numpy().tolist()

											if not isinstance(lowercase						,   lowercase						) or not isinstance(input_points[0]						,   lowercase						):
															raise ValueError('Input points must be a list of list of floating points.'						)
											_lowerCamelCase  :    Optional[int]					=					[np.array(lowercase						) for input_point in input_points]
							else:
											_lowerCamelCase  :    Optional[Any]					=					None

							if input_labels is not None:
											if hasattr(lowercase						,   'numpy'						):
															_lowerCamelCase  :    Any					=					input_labels.numpy().tolist()

											if not isinstance(lowercase						,   lowercase						) or not isinstance(input_labels[0]						,   lowercase						):
															raise ValueError('Input labels must be a list of list integers.'						)
											_lowerCamelCase  :    List[str]					=					[np.array(lowercase						) for label in input_labels]
							else:
											_lowerCamelCase  :    List[str]					=					None

							if input_boxes is not None:
											if hasattr(lowercase						,   'numpy'						):
															_lowerCamelCase  :    Optional[int]					=					input_boxes.numpy().tolist()

											if (
											    not isinstance(lowercase						,   lowercase						)
											    or not isinstance(input_boxes[0]						,   lowercase						)
											    or not isinstance(input_boxes[0][0]						,   lowercase						)
											):
															raise ValueError('Input boxes must be a list of list of list of floating points.'						)
											_lowerCamelCase  :    int					=					[np.array(lowercase						).astype(np.floataa						) for box in input_boxes]
							else:
											_lowerCamelCase  :    str					=					None

							return input_points, input_labels, input_boxes
			@property
			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					self.image_processor.model_input_names
							return list(dict.fromkeys(lowercase						)						)






			def       A_					(						self						,   *lowercase						,   **lowercase						):
							return self.image_processor.post_process_masks(*lowercase						,   **lowercase						)