

"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_A							=  logging.get_logger(__name__)

_A							=  {
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}

class       lowerCamelCase	(       lowerCAmelCase__					,					lowerCAmelCase__   ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  'convnextv2'



  def __init__(self							,       _lowerCamelCase=3							,       _lowerCamelCase=4							,       _lowerCamelCase=4							,       _lowerCamelCase=None							,       _lowerCamelCase=None							,       _lowerCamelCase="gelu"							,       _lowerCamelCase=0.02							,       _lowerCamelCase=1e-12							,       _lowerCamelCase=0.0							,       _lowerCamelCase=224							,       _lowerCamelCase=None							,       _lowerCamelCase=None							,       **_lowerCamelCase							,       ):


        """simple docstring"""

        super().__init__(**_lowerCamelCase	)

        UpperCAmelCase__			:							List[Any]							=    num_channels
        UpperCAmelCase__			:							Tuple							=    patch_size
        UpperCAmelCase__			:							str							=    num_stages
        UpperCAmelCase__			:							str							=    [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
        UpperCAmelCase__			:							Tuple							=    [3, 3, 9, 3] if depths is None else depths
        UpperCAmelCase__			:							Optional[Any]							=    hidden_act
        UpperCAmelCase__			:							List[Any]							=    initializer_range
        UpperCAmelCase__			:							int							=    layer_norm_eps
        UpperCAmelCase__			:							Optional[Any]							=    drop_path_rate
        UpperCAmelCase__			:							Optional[Any]							=    image_size
        UpperCAmelCase__			:							Union[str, Any]							=    ["""stem"""] + [F"""stage{idx}""" for idx in range(1							,       len(self.depths	) + 1	)]
        UpperCAmelCase__					,		UpperCAmelCase__			:							str							=    get_aligned_output_features_output_indices(
            out_features=_lowerCamelCase							,       out_indices=_lowerCamelCase							,       stage_names=self.stage_names	)




"""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       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""							,       return_dict=_lowerCamelCase	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    AutoTokenizer.from_pretrained("""google/mt5-small"""	)

        UpperCAmelCase__			:							Any							=    tokenizer("""Hello there"""							,       return_tensors="""pt"""	).input_ids
        UpperCAmelCase__			:							str							=    tokenizer("""Hi I am"""							,       return_tensors="""pt"""	).input_ids

        UpperCAmelCase__			:							Tuple							=    model(input_ids.to(_lowerCamelCase	)							,       labels=labels.to(_lowerCamelCase	)	).loss
        UpperCAmelCase__			:							Union[str, Any]							=    -(labels.shape[-1] * loss.item())

        UpperCAmelCase__			:							List[str]							=    -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE	) < 1e-4	)

