






'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A:      Dict  				=				logging.get_logger(__name__)






class 							__magic_name__     (	UpperCAmelCase_,						UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Dict        =  'maskformer-swin'

	SCREAMING_SNAKE_CASE_					:      Dict        =  {
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}
	def __init__(							self  ,      _lowercase=224  ,      _lowercase=4  ,      _lowercase=3  ,      _lowercase=96  ,      _lowercase=[2, 2, 6, 2]  ,      _lowercase=[3, 6, 12, 24]  ,      _lowercase=7  ,      _lowercase=4.0  ,      _lowercase=True  ,      _lowercase=0.0  ,      _lowercase=0.0  ,      _lowercase=0.1  ,      _lowercase="gelu"  ,      _lowercase=False  ,      _lowercase=0.02  ,      _lowercase=1E-5  ,      _lowercase=None  ,      _lowercase=None  ,      **_lowercase  ,      )						->  str:
							super().__init__(**_lowercase       )

							lowercase_      :   List[str]        =			image_size
							lowercase_      :   Dict        =			patch_size
							lowercase_      :   Union[str, Any]        =			num_channels
							lowercase_      :   Tuple        =			embed_dim
							lowercase_      :   int        =			depths
							lowercase_      :   str        =			len(_lowercase       )
							lowercase_      :   Optional[Any]        =			num_heads
							lowercase_      :   Any        =			window_size
							lowercase_      :   int        =			mlp_ratio
							lowercase_      :   Dict        =			qkv_bias
							lowercase_      :   Optional[int]        =			hidden_dropout_prob
							lowercase_      :   Any        =			attention_probs_dropout_prob
							lowercase_      :   List[str]        =			drop_path_rate
							lowercase_      :   Any        =			hidden_act
							lowercase_      :   Union[str, Any]        =			use_absolute_embeddings
							lowercase_      :   Tuple        =			layer_norm_eps
							lowercase_      :   str        =			initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							lowercase_      :   Optional[Any]        =			int(embed_dim * 2 ** (len(_lowercase       ) - 1)       )
							lowercase_      :   Tuple        =			['stem'] + [f"stage{idx}" for idx in range(1  ,      len(_lowercase       ) + 1       )]
							lowercase_      :   List[str]        =			get_aligned_output_features_output_indices(
							    out_features=_lowercase  ,      out_indices=_lowercase  ,      stage_names=self.stage_names       )








'''simple docstring'''

import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


A:      Any  				=				get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
						A:      List[Any]  				=				json.load(f)






@require_torch
class 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Tuple:
							return FSMTTokenizer.from_pretrained(_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Optional[int]:
							lowercase_      :   str        =			FSMTForConditionalGeneration.from_pretrained(_lowercase       ).to(_lowercase       )
							if torch_device == "cuda":
													model.half()
							return model







	@parameterized.expand(
	    [
	        ['en-ru', 26.0],
	        ['ru-en', 22.0],
	        ['en-de', 22.0],
	        ['de-en', 29.0],
	    ]       )
	@slow
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase       )						->  Optional[int]:
							# note: this test is not testing the best performance since it only evals a small batch
							# but it should be enough to detect a regression in the output quality
							lowercase_      :   Optional[Any]        =			f"facebook/wmt19-{pair}"
							lowercase_      :   str        =			self.get_tokenizer(_lowercase       )
							lowercase_      :   Any        =			self.get_model(_lowercase       )

							lowercase_      :   Any        =			bleu_data[pair]['src']
							lowercase_      :   Any        =			bleu_data[pair]['tgt']

							lowercase_      :   Dict        =			tokenizer(_lowercase  ,      return_tensors='pt'  ,      truncation=_lowercase  ,      padding='longest'       ).to(_lowercase       )
							lowercase_      :   str        =			model.generate(
							    input_ids=batch.input_ids  ,      num_beams=8  ,      )
							lowercase_      :   Any        =			tokenizer.batch_decode(
							    _lowercase  ,      skip_special_tokens=_lowercase  ,      clean_up_tokenization_spaces=_lowercase       )
							lowercase_      :   Union[str, Any]        =			calculate_bleu(_lowercase  ,      _lowercase       )
							print(_lowercase       )
							self.assertGreaterEqual(scores['bleu']  ,      _lowercase       )
