"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__lowerCAmelCase    :							Optional[int]													=						logging.get_logger(__name__)

__lowerCAmelCase    :							Dict													=						{
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}

class       a_      (     a__ ,		a__			):
					UpperCamelCase_ :	str			=    "resnet"
					UpperCamelCase_ :	str			=    ["basic", "bottleneck"]




					def __init__(	self						:     int  ,						snake_case__						:     Union[str, Any]=3  ,						snake_case__						:     Optional[Any]=64  ,						snake_case__						:     int=[256, 512, 1024, 2048]  ,						snake_case__						:     List[Any]=[3, 4, 6, 3]  ,						snake_case__						:     Any="bottleneck"  ,						snake_case__						:     Optional[int]="relu"  ,						snake_case__						:     Any=False  ,						snake_case__						:     Tuple=None  ,						snake_case__						:     Union[str, Any]=None  ,						**snake_case__						:     Any  ,						):
							super().__init__(**_A     )
							if layer_type not in self.layer_types:
									raise ValueError(F"""layer_type={layer_type} is not one of {','.join(self.layer_types     )}"""     )
							lowerCAmelCase__        =   num_channels
							lowerCAmelCase__        =   embedding_size
							lowerCAmelCase__        =   hidden_sizes
							lowerCAmelCase__        =   depths
							lowerCAmelCase__        =   layer_type
							lowerCAmelCase__        =   hidden_act
							lowerCAmelCase__        =   downsample_in_first_stage
							lowerCAmelCase__        =   ['stem'] + [F"""stage{idx}""" for idx in range(1  ,						len(_A     ) + 1     )]
							lowerCAmelCase__        =   get_aligned_output_features_output_indices(
							    out_features=_A  ,						out_indices=_A  ,						stage_names=self.stage_names     )

class       a_      (     a__			):
					UpperCamelCase_ :	Tuple			=    version.parse("1.11"			)




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							return OrderedDict(
							    [
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]     )




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							return 1E-3





"""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


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

@require_torch
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Dict     ):
							return FSMTTokenizer.from_pretrained(snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     Any     ):
							lowerCAmelCase__        =   FSMTForConditionalGeneration.from_pretrained(snake_case__     ).to(snake_case__     )
							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 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Any  ,						snake_case__						:     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
							lowerCAmelCase__        =   F"""facebook/wmt19-{pair}"""
							lowerCAmelCase__        =   self.get_tokenizer(snake_case__     )
							lowerCAmelCase__        =   self.get_model(snake_case__     )

							lowerCAmelCase__        =   bleu_data[pair]["""src"""]
							lowerCAmelCase__        =   bleu_data[pair]["""tgt"""]

							lowerCAmelCase__        =   tokenizer(snake_case__  ,						return_tensors="""pt"""  ,						truncation=snake_case__  ,						padding="""longest"""     ).to(snake_case__     )
							lowerCAmelCase__        =   model.generate(
							    input_ids=batch.input_ids  ,						num_beams=8  ,						)
							lowerCAmelCase__        =   tokenizer.batch_decode(
							    snake_case__  ,						skip_special_tokens=snake_case__  ,						clean_up_tokenization_spaces=snake_case__     )
							lowerCAmelCase__        =   calculate_bleu(snake_case__  ,						snake_case__     )
							print(snake_case__     )
							self.assertGreaterEqual(scores["""bleu"""]  ,						snake_case__     )




