



import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import UperNetForSemanticSegmentation
  from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor







class 		snake_case						:



   '''simple docstring'''






   def __init__(					self							:			Optional[int]				,		lowerCAmelCase							:			Union[str, Any]				,		lowerCAmelCase							:			Tuple=13				,		lowerCAmelCase							:			Optional[int]=32				,		lowerCAmelCase							:			Optional[Any]=3				,		lowerCAmelCase							:			int=4				,		lowerCAmelCase							:			str=[10, 20, 30, 40]				,		lowerCAmelCase							:			Any=[2, 2, 3, 2]				,		lowerCAmelCase							:			Optional[Any]=True				,		lowerCAmelCase							:			Union[str, Any]=True				,		lowerCAmelCase							:			str=37				,		lowerCAmelCase							:			Dict="gelu"				,		lowerCAmelCase							:			Union[str, Any]=10				,		lowerCAmelCase							:			List[str]=0.02				,		lowerCAmelCase							:			Tuple=["stage2", "stage3", "stage4"]				,		lowerCAmelCase							:			List[Any]=3				,		lowerCAmelCase							:			Optional[Any]=None				,		)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  Any					=					parent
    _snake_case    :  str					=					batch_size
    _snake_case    :  Tuple					=					image_size
    _snake_case    :  List[Any]					=					num_channels
    _snake_case    :  Optional[Any]					=					num_stages
    _snake_case    :  List[Any]					=					hidden_sizes
    _snake_case    :  Optional[Any]					=					depths
    _snake_case    :  List[Any]					=					is_training
    _snake_case    :  Any					=					use_labels
    _snake_case    :  Any					=					intermediate_size
    _snake_case    :  Optional[int]					=					hidden_act
    _snake_case    :  List[str]					=					type_sequence_label_size
    _snake_case    :  Optional[Any]					=					initializer_range
    _snake_case    :  Optional[int]					=					out_features
    _snake_case    :  Optional[Any]					=					num_labels
    _snake_case    :  Optional[int]					=					scope
    _snake_case    :  Tuple					=					num_stages

   def      UpperCamelCase_  (					self							:			Tuple)				->				Any:

    """simple docstring"""







    _snake_case    :  List[str]					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

    _snake_case    :  str					=					None
    if self.use_labels:
     _snake_case    :  List[Any]					=					ids_tensor([self.batch_size]				,		self.type_sequence_label_size)

    _snake_case    :  Optional[int]					=					self.get_config()

    return config, pixel_values, labels

   def      UpperCamelCase_  (					self							:			str)				->				Dict:

    """simple docstring"""







    return ConvNextConfig(
        num_channels=self.num_channels				,		num_stages=self.num_stages				,		hidden_sizes=self.hidden_sizes				,		depths=self.depths				,		is_training=self.is_training				,		intermediate_size=self.intermediate_size				,		hidden_act=self.hidden_act				,		out_features=self.out_features				,		)

   def      UpperCamelCase_  (					self							:			Optional[Any])				->				Dict:

    """simple docstring"""







    return UperNetConfig(
        backbone_config=self.get_backbone_config()				,		hidden_size=512				,		pool_scales=[1, 2, 3, 6]				,		use_auxiliary_head=lowerCAmelCase				,		auxiliary_loss_weight=0.4				,		auxiliary_in_channels=40				,		auxiliary_channels=256				,		auxiliary_num_convs=1				,		auxiliary_concat_input=lowerCAmelCase				,		loss_ignore_index=255				,		num_labels=self.num_labels				,		)

   def      UpperCamelCase_  (					self							:			Union[str, Any]				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Dict)				->				str:

    """simple docstring"""







    _snake_case    :  Dict					=					UperNetForSemanticSegmentation(config=lowerCAmelCase)
    model.to(lowerCAmelCase)
    model.eval()
    _snake_case    :  Optional[int]					=					model(lowerCAmelCase)
    self.parent.assertEqual(
        result.logits.shape				,		(self.batch_size, self.num_labels, self.image_size, self.image_size))


   def      UpperCamelCase_  (					self							:			List[str])				->				List[Any]:

    """simple docstring"""







    _snake_case    :  Dict					=					self.prepare_config_and_inputs()
    (
        (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    
    )    :  Union[str, Any]					=					config_and_inputs
    _snake_case    :  Dict					=					{"""pixel_values""": pixel_values}
    return config, inputs_dict







@require_torch
class 		snake_case						(	SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   snake_case_					: Dict          =		(UperNetForSemanticSegmentation,) if is_torch_available() else ()
   snake_case_					: List[str]          =		{"""image-segmentation""": UperNetForSemanticSegmentation} if is_torch_available() else {}
   snake_case_					: Optional[Any]          =		False
   snake_case_					: Union[str, Any]          =		False
   snake_case_					: Optional[int]          =		False
   snake_case_					: Optional[int]          =		False
   snake_case_					: int          =		False
   snake_case_					: int          =		False

   def      UpperCamelCase_  (					self							:			str)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  str					=					UperNetModelTester(self)
    _snake_case    :  str					=					ConfigTester(self				,		config_class=lowerCAmelCase				,		has_text_modality=lowerCAmelCase				,		hidden_size=37)

   def      UpperCamelCase_  (					self							:			Optional[Any])				->				Any:

    """simple docstring"""







    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      UpperCamelCase_  (					self							:			Any)				->				Dict:

    """simple docstring"""







    return

   def      UpperCamelCase_  (					self							:			Tuple)				->				Tuple:

    """simple docstring"""







    _snake_case	,    _snake_case    :  List[Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     _snake_case    :  int					=					model_class(lowerCAmelCase)
     _snake_case    :  List[str]					=					inspect.signature(model.forward)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     _snake_case    :  Union[str, Any]					=					[*signature.parameters.keys()]

     _snake_case    :  Optional[Any]					=					["""pixel_values"""]
     self.assertListEqual(arg_names[:1]				,		lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  str					=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase)

   @unittest.skip(reason="""UperNet does not use inputs_embeds""")
   def      UpperCamelCase_  (					self							:			Optional[Any])				->				Dict:

    """simple docstring"""







    pass

   @unittest.skip(reason="""UperNet does not support input and output embeddings""")
   def      UpperCamelCase_  (					self							:			Optional[int])				->				int:

    """simple docstring"""







    pass

   @unittest.skip(reason="""UperNet does not have a base model""")
   def      UpperCamelCase_  (					self							:			str)				->				Tuple:

    """simple docstring"""







    pass

   @unittest.skip(reason="""UperNet does not have a base model""")
   def      UpperCamelCase_  (					self							:			List[str])				->				Any:

    """simple docstring"""







    pass

   @require_torch_multi_gpu
   @unittest.skip(reason="""UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`""")
   def      UpperCamelCase_  (					self							:			Tuple)				->				List[Any]:

    """simple docstring"""







    pass

   @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
   def      UpperCamelCase_  (					self							:			List[Any])				->				Tuple:

    """simple docstring"""







    pass

   def      UpperCamelCase_  (					self							:			Optional[int])				->				List[str]:

    """simple docstring"""







    def check_hidden_states_output(lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			Union[str, Any]):
     _snake_case    :  Optional[Any]					=					model_class(lowerCAmelCase)
     model.to(lowerCAmelCase)
     model.eval()

     with torch.no_grad():
      _snake_case    :  int					=					model(**self._prepare_for_class(lowerCAmelCase				,		lowerCAmelCase))

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

     _snake_case    :  str					=					self.model_tester.num_stages
     self.assertEqual(len(lowerCAmelCase)				,		expected_num_stages + 1)

     # ConvNext'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    :  Dict					=					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     _snake_case    :  List[str]					=					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    :  Any					=					True

     check_hidden_states_output(lowerCAmelCase				,		lowerCAmelCase				,		lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Optional[int])				->				str:

    """simple docstring"""







    _snake_case	,    _snake_case    :  List[str]					=					self.model_tester.prepare_config_and_inputs_for_common()

    _snake_case    :  Dict					=					_config_zero_init(lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					_config_zero_init(configs_no_init.backbone_config)
    for model_class in self.all_model_classes:
     _snake_case    :  List[str]					=					model_class(config=lowerCAmelCase)
     for name, param in model.named_parameters():
      if param.requires_grad:
       self.assertIn(
           ((param.data.mean() * 1E9).round() / 1E9).item()				,		[0.0, 1.0]				,		msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				,		)

   @unittest.skip(reason="""UperNet does not have tied weights""")
   def      UpperCamelCase_  (					self							:			Optional[Any])				->				int:

    """simple docstring"""







    pass


   @slow
   def      UpperCamelCase_  (					self							:			Dict)				->				Any:

    """simple docstring"""







    for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case    :  Optional[Any]					=					UperNetForSemanticSegmentation.from_pretrained(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)



def       lowercase       (   )  ->     List[Any]:
 _snake_case    :  Any					=					hf_hub_download(
     repo_id="""hf-internal-testing/fixtures_ade20k"""				,       repo_type="""dataset"""				,       filename="""ADE_val_00000001.jpg"""				)
 _snake_case    :  Tuple					=					Image.open(SCREAMING_SNAKE_CASE__				).convert("""RGB"""				)
 return image







@require_torch
@require_vision
@slow
class 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






   def      UpperCamelCase_  (					self							:			Optional[int])				->				Any:

    """simple docstring"""







    _snake_case    :  Dict					=					AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny""")
    _snake_case    :  Tuple					=					UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny""").to(lowerCAmelCase)

    _snake_case    :  Optional[Any]					=					prepare_img()
    _snake_case    :  Any					=					processor(images=lowerCAmelCase				,		return_tensors="""pt""").to(lowerCAmelCase)

    with torch.no_grad():
     _snake_case    :  Union[str, Any]					=					model(**lowerCAmelCase)

    _snake_case    :  Any					=					torch.Size((1, model.config.num_labels, 512, 512))
    self.assertEqual(outputs.logits.shape				,		lowerCAmelCase)

    _snake_case    :  Any					=					torch.tensor(
        [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]).to(lowerCAmelCase)
    self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]				,		lowerCAmelCase				,		atol=1E-4))


   def      UpperCamelCase_  (					self							:			List[str])				->				str:

    """simple docstring"""







    _snake_case    :  Any					=					AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny""")
    _snake_case    :  Tuple					=					UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny""").to(lowerCAmelCase)

    _snake_case    :  List[str]					=					prepare_img()
    _snake_case    :  Any					=					processor(images=lowerCAmelCase				,		return_tensors="""pt""").to(lowerCAmelCase)

    with torch.no_grad():
     _snake_case    :  Tuple					=					model(**lowerCAmelCase)

    _snake_case    :  Dict					=					torch.Size((1, model.config.num_labels, 512, 512))
    self.assertEqual(outputs.logits.shape				,		lowerCAmelCase)

    _snake_case    :  int					=					torch.tensor(
        [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]).to(lowerCAmelCase)
    self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]				,		lowerCAmelCase				,		atol=1E-4))





# 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       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				)  ->     Union[str, Any]:
 # (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}
 _snake_case    :  Union[str, Any]					=					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    :  int					=					"""<s> <pad> </s> <unk>""".split()
 # restore the special tokens
 for k in keep_keys:
  del da[F'''{k}</w>''']
  _snake_case    :  Tuple					=					d[k]  # restore
 return da



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	str				)  ->     str:
 # prep
 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    :  Optional[Any]					=					basename(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					dirname(SCREAMING_SNAKE_CASE__				)

 _snake_case    :  int					=					fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
 _snake_case    :  List[str]					=					cls.hub_models()
 _snake_case    :  Tuple					=					{"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
 _snake_case    :  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}'''				)
 _snake_case    :  List[Any]					=					hub_utils.from_pretrained(
     SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       archive_map=SCREAMING_SNAKE_CASE__				,       **SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Dict					=					vars(chkpt["""args"""]["""model"""]				)

 _snake_case    :  Union[str, Any]					=					args["""source_lang"""]
 _snake_case    :  Tuple					=					args["""target_lang"""]

 _snake_case    :  Any					=					dirname(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[int]					=					basename(SCREAMING_SNAKE_CASE__				)

 # dicts
 _snake_case    :  List[str]					=					os.path.join(SCREAMING_SNAKE_CASE__				,       F'''dict.{src_lang}.txt'''				)
 _snake_case    :  Any					=					os.path.join(SCREAMING_SNAKE_CASE__				,       F'''dict.{tgt_lang}.txt'''				)

 _snake_case    :  List[Any]					=					Dictionary.load(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[Any]					=					rewrite_dict_keys(src_dict.indices				)
 _snake_case    :  Dict					=					len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  List[Any]					=					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    :  str					=					True
 for k in src_vocab.keys():
  if not k.islower():
   _snake_case    :  Any					=					False
   break

 _snake_case    :  Union[str, Any]					=					Dictionary.load(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Union[str, Any]					=					rewrite_dict_keys(tgt_dict.indices				)
 _snake_case    :  Union[str, Any]					=					len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Tuple					=					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    :  str					=					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    :  Union[str, Any]					=					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    :  Dict					=					fin.read()
 _snake_case    :  Optional[Any]					=					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    :  List[str]					=					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    :  Optional[int]					=					{
     """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
 _snake_case    :  Tuple					=					5
 _snake_case    :  int					=					False
 if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
  _snake_case    :  List[str]					=					best_score_hparams[model_dir]["""length_penalty"""]
 else:
  _snake_case    :  Optional[Any]					=					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    :  List[Any]					=					os.path.join(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 _snake_case    :  str					=					{
     """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
 _snake_case    :  Optional[Any]					=					chkpt["""models"""][0]
 _snake_case    :  List[str]					=					model.state_dict()

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

 # remove unneeded keys
 _snake_case    :  Union[str, Any]					=					[
     """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    :  Optional[int]					=					FSMTConfig.from_pretrained(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  List[Any]					=					FSMTForConditionalGeneration(SCREAMING_SNAKE_CASE__				)

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

 # save
 _snake_case    :  int					=					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__":
  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)
