





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


__lowercase				    =      logging.get_logger(__name__)

__lowercase				    =      {
    """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class 	__snake_case					(      _a ):






    '''simple docstring'''
    _lowerCAmelCase  =						"""vit-hybrid"""





    def __init__( self    :		Dict      ,    snake_case    :		List[Any]=None      ,    snake_case    :		Dict=768      ,    snake_case    :		int=12      ,    snake_case    :		Tuple=12      ,    snake_case    :		List[Any]=3_072      ,    snake_case    :		int="gelu"      ,    snake_case    :		Dict=0.0      ,    snake_case    :		Dict=0.0      ,    snake_case    :		Tuple=0.02      ,    snake_case    :		List[str]=1e-12      ,    snake_case    :		Tuple=224      ,    snake_case    :		List[str]=1      ,    snake_case    :		Optional[int]=3      ,    snake_case    :		Dict=[1, 1_024, 24, 24]      ,    snake_case    :		List[Any]=True      ,    **snake_case    :		Optional[int]      ,    ):

      """simple docstring"""





      super().__init__(**snake_case				)

      if backbone_config is None:
        logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''				)
        __lowerCAmelCase								=					{
            '''global_padding''': '''same''',
            '''layer_type''': '''bottleneck''',
            '''depths''': [3, 4, 9],
            '''out_features''': ['''stage3'''],
            '''embedding_dynamic_padding''': True,
        }

      if isinstance(snake_case      ,    snake_case				):
        if "model_type" in backbone_config:
          __lowerCAmelCase								=					CONFIG_MAPPING[backbone_config['''model_type''']]
        else:
          logger.info(
              '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''				)
          __lowerCAmelCase								=					BitConfig
        __lowerCAmelCase								=					backbone_config_class(**snake_case				)

      __lowerCAmelCase								=					backbone_featmap_shape
      __lowerCAmelCase								=					backbone_config
      __lowerCAmelCase								=					hidden_size
      __lowerCAmelCase								=					num_hidden_layers
      __lowerCAmelCase								=					num_attention_heads
      __lowerCAmelCase								=					intermediate_size
      __lowerCAmelCase								=					hidden_act
      __lowerCAmelCase								=					hidden_dropout_prob
      __lowerCAmelCase								=					attention_probs_dropout_prob
      __lowerCAmelCase								=					initializer_range
      __lowerCAmelCase								=					layer_norm_eps
      __lowerCAmelCase								=					image_size
      __lowerCAmelCase								=					patch_size
      __lowerCAmelCase								=					num_channels
      __lowerCAmelCase								=					qkv_bias





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      __lowerCAmelCase								=					copy.deepcopy(self.__dict__				)
      __lowerCAmelCase								=					self.backbone_config.to_dict()
      __lowerCAmelCase								=					self.__class__.model_type
      return output







import os
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():
      import torch

      from transformers import GPTaLMHeadModel

@require_torch
@require_sentencepiece
@require_tokenizers
class 	__snake_case					(      unittest.TestCase ):






    '''simple docstring'''





    @slow
    @unittest.skip('''Model is not available.'''				)
    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					'''nvidia/megatron-gpt2-345m/'''
      if "MYDIR" in os.environ:
        __lowerCAmelCase								=					os.path.join(os.environ['''MYDIR''']      ,    snake_case				)
      __lowerCAmelCase								=					GPTaLMHeadModel.from_pretrained(snake_case				)
      model.to(snake_case				)
      model.half()

      __lowerCAmelCase								=					torch.tensor(
          [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]      ,    device=snake_case      ,    dtype=torch.long      ,    )

      with torch.no_grad():
        __lowerCAmelCase								=					model(snake_case				).logits

      __lowerCAmelCase								=					torch.Size((1, 9, 50_257)				)
      self.assertEqual(output.shape      ,    snake_case				)

      __lowerCAmelCase								=					torch.tensor(
          [
              4.9414,
              -0.2920,
              -1.2148,
              -4.0273,
              -0.5161,
              -5.2109,
              -1.2412,
              -1.8301,
              -1.7734,
              -4.7148,
              -0.2317,
              -1.0811,
              -2.1777,
              0.4141,
              -3.7969,
              -4.0586,
              -2.5332,
              -3.3809,
              4.3867,
          ]      ,    device=snake_case      ,    dtype=torch.half      ,    )

      for i in range(19				):
        __lowerCAmelCase						,    __lowerCAmelCase								=					8 * i // 17, 2_792 * i  # along the diagonal
        __lowerCAmelCase						,    __lowerCAmelCase								=					output[0, r, c], expected_diag[i]
        __lowerCAmelCase								=					f'''row={r} col={c} computed={computed} expected={expected}'''
        self.assertAlmostEqual(snake_case      ,    snake_case      ,    delta=1e-4      ,    msg=snake_case				)
