
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
      from transformers import TvltImageProcessor

if is_speech_available():
      from transformers import TvltFeatureExtractor

from transformers import TvltProcessor





@require_torch
class 				A__						(      unittest.TestCase):
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      List[str]							     = """ZinengTang/tvlt-base"""
       lowerCamelCase						:      Any							     = tempfile.mkdtemp()
      def 		UpperCamelCase__				( self       ,    **__magic_name__ ):
       return TvltImageProcessor.from_pretrained(self.checkpoint       ,    **_UpperCamelCase )
      def 		UpperCamelCase__				( self       ,    **__magic_name__ ):
       return TvltFeatureExtractor.from_pretrained(self.checkpoint       ,    **_UpperCamelCase )
      def 		UpperCamelCase__				( self ):
       shutil.rmtree(self.tmpdirname )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      List[str]							     = self.get_image_processor()
       lowerCamelCase						:      Any							     = self.get_feature_extractor()

       lowerCamelCase						:      List[str]							     = TvltProcessor(image_processor=_UpperCamelCase       ,    feature_extractor=_UpperCamelCase )
       processor.save_pretrained(self.tmpdirname )
       lowerCamelCase						:      int							     = TvltProcessor.from_pretrained(self.tmpdirname )

       self.assertIsInstance(processor.feature_extractor       ,    _UpperCamelCase )
       self.assertIsInstance(processor.image_processor       ,    _UpperCamelCase )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      Union[str, Any]							     = self.get_image_processor()
       lowerCamelCase						:      Dict							     = self.get_feature_extractor()

       lowerCamelCase						:      Tuple							     = TvltProcessor(image_processor=_UpperCamelCase       ,    feature_extractor=_UpperCamelCase )

       lowerCamelCase						:      Any							     = np.ones([1_2_0_0_0] )

       lowerCamelCase						:      List[Any]							     = feature_extractor(_UpperCamelCase       ,    return_tensors="""np""" )
       lowerCamelCase						:      int							     = processor(audio=_UpperCamelCase       ,    return_tensors="""np""" )

       for key in audio_dict.keys():
        self.assertAlmostEqual(audio_dict[key].sum()       ,    input_processor[key].sum()       ,    delta=1e-2 )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      int							     = self.get_image_processor()
       lowerCamelCase						:      Optional[int]							     = self.get_feature_extractor()

       lowerCamelCase						:      Union[str, Any]							     = TvltProcessor(image_processor=_UpperCamelCase       ,    feature_extractor=_UpperCamelCase )

       lowerCamelCase						:      Optional[Any]							     = np.ones([3, 2_2_4, 2_2_4] )

       lowerCamelCase						:      Dict							     = image_processor(_UpperCamelCase       ,    return_tensors="""np""" )
       lowerCamelCase						:      Tuple							     = processor(images=_UpperCamelCase       ,    return_tensors="""np""" )

       for key in image_dict.keys():
        self.assertAlmostEqual(image_dict[key].sum()       ,    input_processor[key].sum()       ,    delta=1e-2 )
      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      List[Any]							     = self.get_image_processor()
       lowerCamelCase						:      Any							     = self.get_feature_extractor()

       lowerCamelCase						:      str							     = TvltProcessor(image_processor=_UpperCamelCase       ,    feature_extractor=_UpperCamelCase )

       lowerCamelCase						:      List[Any]							     = np.ones([1_2_0_0_0] )
       lowerCamelCase						:      Tuple							     = np.ones([3, 2_2_4, 2_2_4] )

       lowerCamelCase						:      List[str]							     = processor(audio=_UpperCamelCase       ,    images=_UpperCamelCase )

       self.assertListEqual(list(inputs.keys() )       ,    ["""audio_values""", """audio_mask""", """pixel_values""", """pixel_mask"""] )

       # test if it raises when no input is passed
       with pytest.raises(_UpperCamelCase ):
        processor()






      def 		UpperCamelCase__				( self ):
       lowerCamelCase						:      str							     = self.get_image_processor()
       lowerCamelCase						:      Any							     = self.get_feature_extractor()

       lowerCamelCase						:      Tuple							     = TvltProcessor(image_processor=_UpperCamelCase       ,    feature_extractor=_UpperCamelCase )

       self.assertListEqual(
           processor.model_input_names       ,    image_processor.model_input_names + feature_extractor.model_input_names       ,    msg="""`processor` and `image_processor`+`feature_extractor` model input names do not match"""       ,    )






# 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__							(							__lowerCAmelCase    :    Dict							):




			"""simple docstring"""


			lowerCAmelCase_         =      dict((re.sub(r"@@$"       ,				""       ,				__lowerCAmelCase							), v) if k.endswith("@@"							) else (re.sub(r"$"       ,				"</w>"       ,				__lowerCAmelCase							), 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   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[Any]							):




			"""simple docstring"""


			assert os.path.exists(__lowerCAmelCase							)
			os.makedirs(__lowerCAmelCase       ,				exist_ok=__lowerCAmelCase							)
			print(F"""Writing results to {pytorch_dump_folder_path}"""							)

			# handle various types of models

			lowerCAmelCase_         =      basename(__lowerCAmelCase							)
			lowerCAmelCase_         =      dirname(__lowerCAmelCase							)

			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(
			    __lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase       ,				archive_map=__lowerCAmelCase       ,				**__lowerCAmelCase							)

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

			lowerCAmelCase_         =      args["source_lang"]
			lowerCAmelCase_         =      args["target_lang"]

			lowerCAmelCase_         =      dirname(__lowerCAmelCase							)
			lowerCAmelCase_         =      basename(__lowerCAmelCase							)

			# dicts
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				F"""dict.{src_lang}.txt"""							)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				F"""dict.{tgt_lang}.txt"""							)

			lowerCAmelCase_         =      Dictionary.load(__lowerCAmelCase							)
			lowerCAmelCase_         =      rewrite_dict_keys(src_dict.indices							)
			lowerCAmelCase_         =      len(__lowerCAmelCase							)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				"vocab-src.json"							)
			print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

			# 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(__lowerCAmelCase							)
			lowerCAmelCase_         =      rewrite_dict_keys(tgt_dict.indices							)
			lowerCAmelCase_         =      len(__lowerCAmelCase							)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				"vocab-tgt.json"							)
			print(F"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

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

			# model config
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				"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(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

			# tokenizer config
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				__lowerCAmelCase							)

			lowerCAmelCase_         =      {
			    "langs": [src_lang, tgt_lang],
			    "model_max_length": 1024,
			    "do_lower_case": do_lower_case,
			}

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

			# 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(__lowerCAmelCase       ,				__lowerCAmelCase							)

			lowerCAmelCase_         =      FSMTConfig.from_pretrained(__lowerCAmelCase							)
			lowerCAmelCase_         =      FSMTForConditionalGeneration(__lowerCAmelCase							)

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

			# save
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				__lowerCAmelCase							)
			print(F"""Generating {pytorch_weights_dump_path}"""							)
			torch.save(__lowerCAmelCase       ,				__lowerCAmelCase							)

			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)



