
"""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


_A     :			str  						=			logging.get_logger(__name__)

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

class 						a__		( a_,     a_		):
   __lowerCAmelCase				=							"""resnet"""
   __lowerCAmelCase				=							["""basic""", """bottleneck"""]
   def __init__(     self      ,       _a=3      ,       _a=64      ,       _a=[256, 512, 1_024, 2_048]      ,       _a=[3, 4, 6, 3]      ,       _a="bottleneck"      ,       _a="relu"      ,       _a=False      ,       _a=None      ,       _a=None      ,       **_a      ,       ):
          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							)}"""							)
          lowercase       :							Dict									=				num_channels
          lowercase       :							str									=				embedding_size
          lowercase       :							Optional[Any]									=				hidden_sizes
          lowercase       :							Dict									=				depths
          lowercase       :							Optional[Any]									=				layer_type
          lowercase       :							Optional[Any]									=				hidden_act
          lowercase       :							List[str]									=				downsample_in_first_stage
          lowercase       :							List[str]									=				["stem"] + [f"""stage{idx}""" for idx in range(1      ,       len(_a							) + 1							)]
          lowercase						,       lowercase       :							Dict									=				get_aligned_output_features_output_indices(
              out_features=_a      ,       out_indices=_a      ,       stage_names=self.stage_names							)


class 						a__		( a_		):
   __lowerCAmelCase				=							version.parse("""1.11"""		)
   @property
   def      __magic_name__     (     self							):
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]							)

   @property
   def      __magic_name__     (     self							):
          return 1E-3




"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 						a__		( unittest.TestCase		):
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"      ,       return_dict=_a							).to(_a							)
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("google/mt5-small"							)

          lowercase       :							Optional[Any]									=				tokenizer("Hello there"      ,       return_tensors="pt"							).input_ids
          lowercase       :							Union[str, Any]									=				tokenizer("Hi I am"      ,       return_tensors="pt"							).input_ids

          lowercase       :							int									=				model(input_ids.to(_a							)      ,       labels=labels.to(_a							)							).loss
          lowercase       :							str									=				-(labels.shape[-1] * loss.item())

          lowercase       :							List[str]									=				-8_4.9_1_2_7
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1E-4							)


