import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

					if is_vision_available():
										from transformers import MaskaFormerImageProcessor

if is_vision_available():
					from PIL import Image
class     lowerCAmelCase_     :







							def __init__(   self      :     Union[str, Any] ,			_A      :     List[Any] ,			_A      :     List[Any]=2 ,			_A      :     Tuple=True ,			_A      :     Any=False ,			_A      :     Dict=10 ,			_A      :     Optional[int]=3 ,			_A      :     Union[str, Any]=32 * 8 ,			_A      :     Optional[Any]=32 * 8 ,			_A      :     List[str]=4 ,			_A      :     Dict=64 ,			):
												_UpperCamelCase			     =     parent
												_UpperCamelCase			     =     batch_size
												_UpperCamelCase			     =     is_training
												_UpperCamelCase			     =     use_auxiliary_loss
												_UpperCamelCase			     =     num_queries
												_UpperCamelCase			     =     num_channels
												_UpperCamelCase			     =     min_size
												_UpperCamelCase			     =     max_size
												_UpperCamelCase			     =     num_labels
												_UpperCamelCase			     =     hidden_dim
												_UpperCamelCase			     =     hidden_dim







							def  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
												    _A		)

												_UpperCamelCase			     =     torch.ones([self.batch_size, self.min_size, self.max_size] ,			device=_A		)

												_UpperCamelCase			     =     (
												    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] ,			device=_A		) > 0.5
												).float()
												_UpperCamelCase			     =     (torch.rand((self.batch_size, self.num_labels) ,			device=_A		) > 0.5).long()

												_UpperCamelCase			     =     self.get_config()
												return config, pixel_values, pixel_mask, mask_labels, class_labels







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     MaskaFormerConfig(
												    hidden_size=self.hidden_dim ,			)
												_UpperCamelCase			     =     self.num_queries
												_UpperCamelCase			     =     self.num_labels

												_UpperCamelCase			     =     [1, 1, 1, 1]
												_UpperCamelCase			     =     self.num_channels

												_UpperCamelCase			     =     64
												_UpperCamelCase			     =     128
												_UpperCamelCase			     =     self.hidden_dim
												_UpperCamelCase			     =     self.hidden_dim
												_UpperCamelCase			     =     self.hidden_dim
												return config







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     self.prepare_config_and_inputs()
												_UpperCamelCase			     =     {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
												return config, inputs_dict







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Optional[int] ,			_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     output.encoder_hidden_states
												_UpperCamelCase			     =     output.pixel_decoder_hidden_states
												_UpperCamelCase			     =     output.transformer_decoder_hidden_states

												self.parent.assertTrue(len(_A		) ,			len(config.backbone_config.depths		)		)
												self.parent.assertTrue(len(_A		) ,			len(config.backbone_config.depths		)		)
												self.parent.assertTrue(len(_A		) ,			config.decoder_layers		)







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     List[Any] ,			_A      :     Dict ,			_A      :     int ,			_A      :     List[Any]=False		):
												with torch.no_grad():
																	_UpperCamelCase			     =     MaskaFormerModel(config=_A		)
																	model.to(_A		)
																	model.eval()

																	_UpperCamelCase			     =     model(pixel_values=_A ,			pixel_mask=_A		)
																	_UpperCamelCase			     =     model(_A ,			output_hidden_states=_A		)

												self.parent.assertEqual(
												    output.transformer_decoder_last_hidden_state.shape ,			(self.batch_size, self.num_queries, self.hidden_dim) ,			)
												# let's ensure the other two hidden state exists
												self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None		)
												self.parent.assertTrue(output.encoder_last_hidden_state is not None		)

												if output_hidden_states:
																	self.check_output_hidden_state(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Optional[Any] ,			_A      :     Any ,			_A      :     Any ,			_A      :     Any ,			_A      :     Tuple		):
												_UpperCamelCase			     =     MaskaFormerForUniversalSegmentation(config=_A		)
												model.to(_A		)
												model.eval()

												def comm_check_on_output(_A      :     List[Any]		):
																	# let's still check that all the required stuff is there
																	self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None		)
																	self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None		)
																	self.parent.assertTrue(result.encoder_last_hidden_state is not None		)
																	# okay, now we need to check the logits shape
																	# due to the encoder compression, masks have a //4 spatial size
																	self.parent.assertEqual(
																	    result.masks_queries_logits.shape ,			(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) ,			)
																	# + 1 for null class
																	self.parent.assertEqual(
																	    result.class_queries_logits.shape ,			(self.batch_size, self.num_queries, self.num_labels + 1)		)

												with torch.no_grad():
																	_UpperCamelCase			     =     model(pixel_values=_A ,			pixel_mask=_A		)
																	_UpperCamelCase			     =     model(_A		)

																	comm_check_on_output(_A		)

																	_UpperCamelCase			     =     model(
																	    pixel_values=_A ,			pixel_mask=_A ,			mask_labels=_A ,			class_labels=_A		)

												comm_check_on_output(_A		)

												self.parent.assertTrue(result.loss is not None		)
												self.parent.assertEqual(result.loss.shape ,			torch.Size([1]		)		)




@require_torch
class     lowerCAmelCase_     (       __lowercase,  __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							UpperCAmelCase				   =     {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     MaskaFormerModelTester(self		)
												_UpperCamelCase			     =     ConfigTester(self ,			config_class=_A ,			has_text_modality=_A		)







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												self.config_tester.run_common_tests()







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase     ,    _UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.create_and_check_maskaformer_model(_A ,			**_A ,			output_hidden_states=_A		)







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_A		)







							@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''		)
							def  UpperCamelCase_						(   self      :     Dict		):
												pass







							@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''		)
							def  UpperCamelCase_						(   self      :     str		):
												pass







							@unittest.skip(reason='''Mask2Former is not a generative model'''		)
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												pass







							@unittest.skip(reason='''Mask2Former does not use token embeddings'''		)
							def  UpperCamelCase_						(   self      :     List[str]		):
												pass







							@require_torch_multi_gpu
							@unittest.skip(
							    reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''		)
							def  UpperCamelCase_						(   self      :     Tuple		):
												pass







							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												pass







							def  UpperCamelCase_						(   self      :     Optional[int]		):
												_UpperCamelCase     ,    _UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs_for_common()

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

																	_UpperCamelCase			     =     ['''pixel_values''']
																	self.assertListEqual(arg_names[:1] ,			_A		)







							@slow
							def  UpperCamelCase_						(   self      :     int		):
												for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																	_UpperCamelCase			     =     MaskaFormerModel.from_pretrained(_A		)
																	self.assertIsNotNone(_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     (self.model_tester.min_size,) * 2
												_UpperCamelCase			     =     {
												    '''pixel_values''': torch.randn((2, 3, *size) ,			device=_A		),
												    '''mask_labels''': torch.randn((2, 10, *size) ,			device=_A		),
												    '''class_labels''': torch.zeros(2 ,			10 ,			device=_A		).long(),
												}
												_UpperCamelCase			     =     self.model_tester.get_config()

												_UpperCamelCase			     =     MaskaFormerForUniversalSegmentation(_A		).to(_A		)
												_UpperCamelCase			     =     model(**_A		)
												self.assertTrue(outputs.loss is not None		)







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase     ,    _UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.create_and_check_maskaformer_model(_A ,			**_A ,			output_hidden_states=_A		)







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase     ,    _UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_UpperCamelCase			     =     model_class(_A		).to(_A		)
																	_UpperCamelCase			     =     model(**_A ,			output_attentions=_A		)
																	self.assertTrue(outputs.attentions is not None		)







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												if not self.model_tester.is_training:
																	return

												_UpperCamelCase			     =     self.all_model_classes[1]
												_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs()

												_UpperCamelCase			     =     model_class(_A		)
												model.to(_A		)
												model.train()

												_UpperCamelCase			     =     model(_A ,			mask_labels=_A ,			class_labels=_A		).loss
												loss.backward()







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     self.all_model_classes[1]
												_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs()
												_UpperCamelCase			     =     True
												_UpperCamelCase			     =     True

												_UpperCamelCase			     =     model_class(_A		).to(_A		)
												model.train()

												_UpperCamelCase			     =     model(_A ,			mask_labels=_A ,			class_labels=_A		)

												_UpperCamelCase			     =     outputs.encoder_hidden_states[0]
												encoder_hidden_states.retain_grad()

												_UpperCamelCase			     =     outputs.pixel_decoder_hidden_states[0]
												pixel_decoder_hidden_states.retain_grad()

												_UpperCamelCase			     =     outputs.transformer_decoder_hidden_states[0]
												transformer_decoder_hidden_states.retain_grad()

												_UpperCamelCase			     =     outputs.attentions[0]
												attentions.retain_grad()

												outputs.loss.backward(retain_graph=_A		)

												self.assertIsNotNone(encoder_hidden_states.grad		)
												self.assertIsNotNone(pixel_decoder_hidden_states.grad		)
												self.assertIsNotNone(transformer_decoder_hidden_states.grad		)
												self.assertIsNotNone(attentions.grad		)




_lowerCAmelCase       =		1E-4
def 					_snake_case	(  ):
					_UpperCamelCase			     =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
					return image







@require_vision
@slow
class     lowerCAmelCase_     (       unittest.TestCase ):







							@cached_property
							def  UpperCamelCase_						(   self      :     Any		):
												return "facebook/mask2former-swin-small-coco-instance"







							@cached_property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints		) if is_vision_available() else None







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												_UpperCamelCase			     =     MaskaFormerModel.from_pretrained(self.model_checkpoints		).to(_A		)
												_UpperCamelCase			     =     self.default_image_processor
												_UpperCamelCase			     =     prepare_img()
												_UpperCamelCase			     =     image_processor(_A ,			return_tensors='''pt'''		).to(_A		)
												_UpperCamelCase			     =     inputs['''pixel_values'''].shape
												# check size is divisible by 32
												self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
												# check size
												self.assertEqual(_A ,			(1, 3, 384, 384)		)

												with torch.no_grad():
																	_UpperCamelCase			     =     model(**_A		)

												_UpperCamelCase			     =     torch.tensor(
												    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]		).to(_A		)
												self.assertTrue(
												    torch.allclose(
												        outputs.encoder_last_hidden_state[0, 0, :3, :3] ,			_A ,			atol=_A		)		)

												_UpperCamelCase			     =     torch.tensor(
												    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]		).to(_A		)
												self.assertTrue(
												    torch.allclose(
												        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] ,			_A ,			atol=_A		)		)

												_UpperCamelCase			     =     torch.tensor(
												    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]		).to(_A		)
												self.assertTrue(
												    torch.allclose(
												        outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,			_A ,			atol=_A		)		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(_A		).eval()
												_UpperCamelCase			     =     self.default_image_processor
												_UpperCamelCase			     =     prepare_img()
												_UpperCamelCase			     =     image_processor(_A ,			return_tensors='''pt'''		).to(_A		)
												_UpperCamelCase			     =     inputs['''pixel_values'''].shape
												# check size is divisible by 32
												self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
												# check size
												self.assertEqual(_A ,			(1, 3, 384, 384)		)

												with torch.no_grad():
																	_UpperCamelCase			     =     model(**_A		)
												# masks_queries_logits
												_UpperCamelCase			     =     outputs.masks_queries_logits
												self.assertEqual(
												    masks_queries_logits.shape ,			(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		)
												_UpperCamelCase			     =     [
												    [-8.7839, -9.0056, -8.8121],
												    [-7.4104, -7.0313, -6.5401],
												    [-6.6105, -6.3427, -6.4675],
												]
												_UpperCamelCase			     =     torch.tensor(_A		).to(_A		)
												self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,			_A ,			atol=_A		)		)
												# class_queries_logits
												_UpperCamelCase			     =     outputs.class_queries_logits
												self.assertEqual(class_queries_logits.shape ,			(1, model.config.num_queries, model.config.num_labels + 1)		)
												_UpperCamelCase			     =     torch.tensor(
												    [
												        [1.8324, -8.0835, -4.1922],
												        [0.8450, -9.0050, -3.6053],
												        [0.3045, -7.7293, -3.0275],
												    ]		).to(_A		)
												self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,			_A ,			atol=_A		)		)







							def  UpperCamelCase_						(   self      :     str		):
												_UpperCamelCase			     =     MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(_A		).eval()
												_UpperCamelCase			     =     self.default_image_processor

												_UpperCamelCase			     =     image_processor(
												    [np.zeros((3, 800, 1333)		), np.zeros((3, 800, 1333)		)] ,			segmentation_maps=[np.zeros((384, 384)		).astype(np.floataa		), np.zeros((384, 384)		).astype(np.floataa		)] ,			return_tensors='''pt''' ,			)

												_UpperCamelCase			     =     inputs['''pixel_values'''].to(_A		)
												_UpperCamelCase			     =     [el.to(_A		) for el in inputs['''mask_labels''']]
												_UpperCamelCase			     =     [el.to(_A		) for el in inputs['''class_labels''']]

												with torch.no_grad():
																	_UpperCamelCase			     =     model(**_A		)

												self.assertTrue(outputs.loss is not None		)

'''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()

UpperCamelCase__  :						Optional[Any]					=      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
UpperCamelCase__  :						str					=      {
    # 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
UpperCamelCase__  :						Tuple					=      {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
  UpperCamelCase__  :						Tuple					=      '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",
]:
  UpperCamelCase__  :						Tuple					=      'allenai'







def __UpperCamelCase( _A       :					int     ):
   '''simple docstring'''
   # (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}
   UpperCAmelCase__				:					int 					=	dict((re.sub(R'''@@$'''	,      ''''''	,      _A     ), v) if k.endswith('''@@'''     ) else (re.sub(R'''$'''	,      '''</w>'''	,      _A     ), v) for k, v in d.items()     )
   UpperCAmelCase__				:					List[str] 					=	'''<s> <pad> </s> <unk>'''.split()
   # restore the special tokens
   for k in keep_keys:
      del da[F'''{k}</w>''']
      UpperCAmelCase__				:					Optional[Any] 					=	d[k]  # restore
   return da







def __UpperCamelCase( _A       :					Tuple	,      _A       :					List[Any]     ):
   '''simple docstring'''
   # prep
   assert os.path.exists(_A     )
   os.makedirs(_A	,      exist_ok=_A     )
   print(F'''Writing results to {pytorch_dump_folder_path}'''     )

   # handle various types of models

   UpperCAmelCase__				:					Dict 					=	basename(_A     )
   UpperCAmelCase__				:					List[str] 					=	dirname(_A     )

   UpperCAmelCase__				:					Union[str, Any] 					=	fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
   UpperCAmelCase__				:					Dict 					=	cls.hub_models()
   UpperCAmelCase__				:					Dict 					=	{'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
   UpperCAmelCase__				:					int 					=	'''.'''
   # 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}'''     )
   UpperCAmelCase__				:					Union[str, Any] 					=	hub_utils.from_pretrained(
       _A	,      _A	,      _A	,      archive_map=_A	,      **_A     )

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

   UpperCAmelCase__				:					str 					=	args['''source_lang''']
   UpperCAmelCase__				:					Tuple 					=	args['''target_lang''']

   UpperCAmelCase__				:					Optional[int] 					=	dirname(_A     )
   UpperCAmelCase__				:					int 					=	basename(_A     )

   # dicts
   UpperCAmelCase__				:					List[Any] 					=	os.path.join(_A	,      F'''dict.{src_lang}.txt'''     )
   UpperCAmelCase__				:					Tuple 					=	os.path.join(_A	,      F'''dict.{tgt_lang}.txt'''     )

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

   # 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
   UpperCAmelCase__				:					Tuple 					=	True
   for k in src_vocab.keys():
      if not k.islower():
         UpperCAmelCase__				:					int 					=	False
         break

   UpperCAmelCase__				:					str 					=	Dictionary.load(_A     )
   UpperCAmelCase__				:					Tuple 					=	rewrite_dict_keys(tgt_dict.indices     )
   UpperCAmelCase__				:					List[str] 					=	len(_A     )
   UpperCAmelCase__				:					Dict 					=	os.path.join(_A	,      '''vocab-tgt.json'''     )
   print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'''     )
   with open(_A	,      '''w'''	,      encoding='''utf-8'''     ) as f:
      f.write(json.dumps(_A	,      ensure_ascii=_A	,      indent=_A     )     )

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

   # model config
   UpperCAmelCase__				:					Optional[int] 					=	os.path.join(_A	,      '''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']}'''

   UpperCAmelCase__				:					Optional[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
   UpperCAmelCase__				:					Optional[int] 					=	5
   UpperCAmelCase__				:					Optional[Any] 					=	False
   if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
      UpperCAmelCase__				:					str 					=	best_score_hparams[model_dir]['''length_penalty''']
   else:
      UpperCAmelCase__				:					Dict 					=	1.0

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

   # tokenizer config
   UpperCAmelCase__				:					List[str] 					=	os.path.join(_A	,      _A     )

   UpperCAmelCase__				:					Any 					=	{
       '''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(_A	,      '''w'''	,      encoding='''utf-8'''     ) as f:
      f.write(json.dumps(_A	,      ensure_ascii=_A	,      indent=_A     )     )

   # model
   UpperCAmelCase__				:					Dict 					=	chkpt['''models'''][0]
   UpperCAmelCase__				:					Any 					=	model.state_dict()

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

   # remove unneeded keys
   UpperCAmelCase__				:					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(_A	,      _A     )

   UpperCAmelCase__				:					List[str] 					=	FSMTConfig.from_pretrained(_A     )
   UpperCAmelCase__				:					int 					=	FSMTForConditionalGeneration(_A     )

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

   # save
   UpperCAmelCase__				:					List[str] 					=	os.path.join(_A	,      _A     )
   print(F'''Generating {pytorch_weights_dump_path}'''     )
   torch.save(_A	,      _A     )

   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__":
  UpperCamelCase__  :						int					=      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.'
  )
  UpperCamelCase__  :						List[str]					=      parser.parse_args()
  convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




