






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a:     List[Any]   					=					logging.get_logger(__name__)

__a:     List[str]   					=					{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  '''cvt'''


  def __init__(      self		:			Optional[Any]       ,				lowerCamelCase		:			int=3       ,				lowerCamelCase		:			List[str]=[7, 3, 3]       ,				lowerCamelCase		:			int=[4, 2, 2]       ,				lowerCamelCase		:			Optional[int]=[2, 1, 1]       ,				lowerCamelCase		:			Optional[Any]=[64, 192, 384]       ,				lowerCamelCase		:			Optional[Any]=[1, 3, 6]       ,				lowerCamelCase		:			List[str]=[1, 2, 10]       ,				lowerCamelCase		:			Tuple=[4.0, 4.0, 4.0]       ,				lowerCamelCase		:			int=[0.0, 0.0, 0.0]       ,				lowerCamelCase		:			Dict=[0.0, 0.0, 0.0]       ,				lowerCamelCase		:			Dict=[0.0, 0.0, 0.1]       ,				lowerCamelCase		:			Tuple=[True, True, True]       ,				lowerCamelCase		:			Union[str, Any]=[False, False, True]       ,				lowerCamelCase		:			List[str]=["dw_bn", "dw_bn", "dw_bn"]       ,				lowerCamelCase		:			Optional[int]=[3, 3, 3]       ,				lowerCamelCase		:			Union[str, Any]=[1, 1, 1]       ,				lowerCamelCase		:			int=[2, 2, 2]       ,				lowerCamelCase		:			Union[str, Any]=[1, 1, 1]       ,				lowerCamelCase		:			int=[1, 1, 1]       ,				lowerCamelCase		:			Tuple=0.02       ,				lowerCamelCase		:			str=1E-12       ,				**lowerCamelCase		:			Union[str, Any]       ,				)						->	List[str]:


         """simple docstring"""
         super().__init__(**lowerCamelCase							)
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    patch_sizes
         _UpperCAmelCase							=    patch_stride
         _UpperCAmelCase							=    patch_padding
         _UpperCAmelCase							=    embed_dim
         _UpperCAmelCase							=    num_heads
         _UpperCAmelCase							=    depth
         _UpperCAmelCase							=    mlp_ratio
         _UpperCAmelCase							=    attention_drop_rate
         _UpperCAmelCase							=    drop_rate
         _UpperCAmelCase							=    drop_path_rate
         _UpperCAmelCase							=    qkv_bias
         _UpperCAmelCase							=    cls_token
         _UpperCAmelCase							=    qkv_projection_method
         _UpperCAmelCase							=    kernel_qkv
         _UpperCAmelCase							=    padding_kv
         _UpperCAmelCase							=    stride_kv
         _UpperCAmelCase							=    padding_q
         _UpperCAmelCase							=    stride_q
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    layer_norm_eps







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a:     int   					=					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Dict   					=					['''MvpTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Tuple   					=					[
      '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MvpForCausalLM''',
      '''MvpForConditionalGeneration''',
      '''MvpForQuestionAnswering''',
      '''MvpForSequenceClassification''',
      '''MvpModel''',
      '''MvpPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
  from .tokenization_mvp import MvpTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mvp_fast import MvpTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mvp import (
        MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
        MvpForCausalLM,
        MvpForConditionalGeneration,
        MvpForQuestionAnswering,
        MvpForSequenceClassification,
        MvpModel,
        MvpPreTrainedModel,
    )

else:
  import sys

  __a:     Optional[Any]   					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)