
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  :  int				):




			"""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  :  int    ,							__lowerCAmelCase  :  Tuple				):




			"""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)





"""simple docstring"""



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFResNetForImageClassification, TFResNetModel
 from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class       lowerCAmelCase__				:







     def __init__(							self			:			Dict    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			Optional[int]=3    ,      _lowerCamelCase			:			List[str]=32    ,      _lowerCamelCase			:			Optional[int]=3    ,      _lowerCamelCase			:			Dict=10    ,      _lowerCamelCase			:			Tuple=[10, 20, 30, 40]    ,      _lowerCamelCase			:			int=[1, 1, 2, 1]    ,      _lowerCamelCase			:			int=True    ,      _lowerCamelCase			:			Optional[int]=True    ,      _lowerCamelCase			:			Optional[int]="relu"    ,      _lowerCamelCase			:			List[Any]=3    ,      _lowerCamelCase			:			Dict=None    ,      ):
       _snake_case					     =       parent
       _snake_case					     =       batch_size
       _snake_case					     =       image_size
       _snake_case					     =       num_channels
       _snake_case					     =       embeddings_size
       _snake_case					     =       hidden_sizes
       _snake_case					     =       depths
       _snake_case					     =       is_training
       _snake_case					     =       use_labels
       _snake_case					     =       hidden_act
       _snake_case					     =       num_labels
       _snake_case					     =       scope
       _snake_case					     =       len(_lowerCamelCase			)







     def      lowercase	(							self			:			Optional[int]			):
       _snake_case					     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

       _snake_case					     =       None
       if self.use_labels:
         _snake_case					     =       ids_tensor([self.batch_size]    ,      self.num_labels			)

       _snake_case					     =       self.get_config()

       return config, pixel_values, labels







     def      lowercase	(							self			:			Tuple			):
       return ResNetConfig(
           num_channels=self.num_channels    ,      embeddings_size=self.embeddings_size    ,      hidden_sizes=self.hidden_sizes    ,      depths=self.depths    ,      hidden_act=self.hidden_act    ,      num_labels=self.num_labels    ,      image_size=self.image_size    ,      )







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			List[Any]			):
       _snake_case					     =       TFResNetModel(config=_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase			)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,      )







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       self.num_labels
       _snake_case					     =       TFResNetForImageClassification(_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase    ,      labels=_lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_labels)			)







     def      lowercase	(							self			:			Tuple			):
       _snake_case					     =       self.prepare_config_and_inputs()
       _snake_case     ,					_snake_case     ,					_snake_case					     =       config_and_inputs
       _snake_case					     =       {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_tf
class       lowerCAmelCase__				(						A_ ,	A_ ,	unittest.TestCase	):
     __a						=					(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     __a						=					(
         {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     __a						=					False
     __a						=					False
     __a						=					False
     __a						=					False
     __a						=					False







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       TFResNetModelTester(self			)
       _snake_case					     =       ConfigTester(self    ,      config_class=_lowerCamelCase    ,      has_text_modality=_lowerCamelCase			)







     def      lowercase	(							self			:			Tuple			):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()







     def      lowercase	(							self			:			List[Any]			):
       return







     @unittest.skip(reason='''ResNet does not use inputs_embeds'''			)
     def      lowercase	(							self			:			Any			):
       pass







     @unittest.skip(reason='''ResNet does not support input and output embeddings'''			)
     def      lowercase	(							self			:			List[str]			):
       pass







     def      lowercase	(							self			:			int			):
       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _snake_case					     =       model_class(_lowerCamelCase			)
         _snake_case					     =       inspect.signature(model.call			)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _snake_case					     =       [*signature.parameters.keys()]

         _snake_case					     =       ['''pixel_values''']
         self.assertListEqual(arg_names[:1]    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       def check_hidden_states_output(_lowerCamelCase			:			int    ,      _lowerCamelCase			:			List[Any]    ,      _lowerCamelCase			:			str			):
         _snake_case					     =       model_class(_lowerCamelCase			)
         _snake_case					     =       model(**self._prepare_for_class(_lowerCamelCase    ,      _lowerCamelCase			)			)

         _snake_case					     =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         _snake_case					     =       self.model_tester.num_stages
         self.assertEqual(len(_lowerCamelCase			)    ,      expected_num_stages + 1			)

         # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]			)    ,      [self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,      )

       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()
       _snake_case					     =       ['''basic''', '''bottleneck''']
       for model_class in self.all_model_classes:
         for layer_type in layers_type:
           _snake_case					     =       layer_type
           _snake_case					     =       True
           check_hidden_states_output(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _snake_case					     =       True

           check_hidden_states_output(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase			)







     @slow
     def      lowercase	(							self			:			List[str]			):
       for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case					     =       TFResNetModel.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)






def 					_UpperCAmelCase      ( )     ->					Union[str, Any]:
  _snake_case					     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
  return image

@require_tf
@require_vision
class       lowerCAmelCase__				(						unittest.TestCase	):







     @cached_property
     def      lowercase	(							self			:			Dict			):
       return (
           AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
           if is_vision_available()
           else None
       )







     @slow
     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

       _snake_case					     =       self.default_image_processor
       _snake_case					     =       prepare_img()
       _snake_case					     =       image_processor(images=_lowerCamelCase    ,      return_tensors='''tf'''			)

       # forward pass
       _snake_case					     =       model(**_lowerCamelCase			)

       # verify the logits
       _snake_case					     =       tf.TensorShape((1, 1000)			)
       self.assertEqual(outputs.logits.shape    ,      _lowerCamelCase			)

       _snake_case					     =       tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]			)

       self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()    ,      _lowerCamelCase    ,      atol=1e-4			)			)



