
"""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()

_SCREAMING_SNAKE_CASE	       =      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
_SCREAMING_SNAKE_CASE	       =      {
    # 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
_SCREAMING_SNAKE_CASE	       =      {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
		_SCREAMING_SNAKE_CASE	       =      """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",
]:
		_SCREAMING_SNAKE_CASE	       =      """allenai"""
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[int]:







							"""simple docstring"""


							__snake_case     			=       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() )
							__snake_case     			=       "<s> <pad> </s> <unk>".split()
							# restore the special tokens
							for k in keep_keys:
														del da[F'''{k}</w>''']
														__snake_case     			=       d[k]  # restore
							return da
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Dict:







							"""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

							__snake_case     			=       basename(SCREAMING_SNAKE_CASE )
							__snake_case     			=       dirname(SCREAMING_SNAKE_CASE )

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

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

							__snake_case     			=       args["source_lang"]
							__snake_case     			=       args["target_lang"]

							__snake_case     			=       dirname(SCREAMING_SNAKE_CASE )
							__snake_case     			=       basename(SCREAMING_SNAKE_CASE )

							# dicts
							__snake_case     			=       os.path.join(SCREAMING_SNAKE_CASE							,    F'''dict.{src_lang}.txt''' )
							__snake_case     			=       os.path.join(SCREAMING_SNAKE_CASE							,    F'''dict.{tgt_lang}.txt''' )

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

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

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

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

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

							# remove unneeded keys
							__snake_case     			=       [
							    "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 )

							__snake_case     			=       FSMTConfig.from_pretrained(SCREAMING_SNAKE_CASE )
							__snake_case     			=       FSMTForConditionalGeneration(SCREAMING_SNAKE_CASE )

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

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


"""simple docstring"""







from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
		from ..tf_utils import stable_softmax

_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)







@add_end_docstrings(lowercase__   )
class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Tuple							,						**snake_case_   :							str    ):
												super().__init__(**snake_case_    )

												requires_backends(self							,						"vision"    )
												self.check_model_type(
												    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
												    if self.framework == "tf"
												    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING    )





					def __call__(				self   :							Optional[Any]							,						snake_case_   :							Union[str, List[str], "Image", List["Image"]]							,						**snake_case_   :							Optional[int]    ):
												return super().__call__(snake_case_							,						**snake_case_    )





					def 							lowerCAmelCase      (				self   :							Tuple							,						**snake_case_   :							Any    ):
												__snake_case     			=       {}
												if "candidate_labels" in kwargs:
																			__snake_case     			=       kwargs["candidate_labels"]
												if "hypothesis_template" in kwargs:
																			__snake_case     			=       kwargs["hypothesis_template"]

												return preprocess_params, {}, {}





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							List[str]							,						snake_case_   :							List[str]=None							,						snake_case_   :							int="This is a photo of {}."    ):
												__snake_case     			=       load_image(snake_case_    )
												__snake_case     			=       self.image_processor(images=[image]							,						return_tensors=self.framework    )
												__snake_case     			=       candidate_labels
												__snake_case     			=       [hypothesis_template.format(snake_case_    ) for x in candidate_labels]
												__snake_case     			=       self.tokenizer(snake_case_							,						return_tensors=self.framework							,						padding=snake_case_    )
												__snake_case     			=       [text_inputs]
												return inputs





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Dict    ):
												__snake_case     			=       model_inputs.pop("candidate_labels"    )
												__snake_case     			=       model_inputs.pop("text_inputs"    )
												if isinstance(text_inputs[0]							,						snake_case_    ):
																			__snake_case     			=       text_inputs[0]
												else:
																			# Batching case.
																			__snake_case     			=       text_inputs[0][0]

												__snake_case     			=       self.model(**snake_case_							,						**snake_case_    )

												__snake_case     			=       {
												    "candidate_labels": candidate_labels,
												    "logits": outputs.logits_per_image,
												}
												return model_outputs





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							Any    ):
												__snake_case     			=       model_outputs.pop("candidate_labels"    )
												__snake_case     			=       model_outputs["logits"][0]
												if self.framework == "pt":
																			__snake_case     			=       logits.softmax(dim=-1    ).squeeze(-1    )
																			__snake_case     			=       probs.tolist()
																			if not isinstance(snake_case_							,						snake_case_    ):
																										__snake_case     			=       [scores]
												elif self.framework == "tf":
																			__snake_case     			=       stable_softmax(snake_case_							,						axis=-1    )
																			__snake_case     			=       probs.numpy().tolist()
												else:
																			raise ValueError(F'''Unsupported framework: {self.framework}'''    )

												__snake_case     			=       [
												    {"score": score, "label": candidate_label}
												    for score, candidate_label in sorted(zip(snake_case_							,						snake_case_    )							,						key=lambda snake_case_    : -x[0]    )
												]
												return result
