





import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class      lowercase__    (							__A     ,					__A     ,					__A  ):







  @register_to_config
  def __init__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = False    ,						):
       super().__init__()

       lowerCAmelCase_					:       Any    =		nn.Embedding(_lowercase    ,						_lowercase						)

       lowerCAmelCase_					:       Optional[int]    =		nn.Embedding(_lowercase    ,						_lowercase						)
       lowerCAmelCase_					:       List[str]    =		False

       lowerCAmelCase_					:       List[str]    =		nn.Dropout(p=_lowercase						)

       lowerCAmelCase_					:       Tuple    =		TaConfig(
           vocab_size=_lowercase    ,						d_model=_lowercase    ,						num_heads=_lowercase    ,						d_kv=_lowercase    ,						d_ff=_lowercase    ,						dropout_rate=_lowercase    ,						feed_forward_proj=_lowercase    ,						is_decoder=_lowercase    ,						is_encoder_decoder=_lowercase    ,						)

       lowerCAmelCase_					:       int    =		nn.ModuleList()
       for lyr_num in range(_lowercase						):
            lowerCAmelCase_					:       str    =		TaBlock(_lowercase						)
            self.encoders.append(_lowercase						)

       lowerCAmelCase_					:       List[str]    =		TaLayerNorm(_lowercase						)
       lowerCAmelCase_					:       int    =		nn.Dropout(p=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       List[Any]    =		self.token_embedder(_lowercase						)

       lowerCAmelCase_					:       Dict    =		encoder_input_tokens.shape[1]
       lowerCAmelCase_					:       List[str]    =		torch.arange(_lowercase    ,						device=encoder_input_tokens.device						)
       x += self.position_encoding(_lowercase						)

       lowerCAmelCase_					:       List[Any]    =		self.dropout_pre(_lowercase						)

       # inverted the attention mask
       lowerCAmelCase_					:       int    =		encoder_input_tokens.size()
       lowerCAmelCase_					:       List[Any]    =		self.get_extended_attention_mask(_lowercase    ,						_lowercase						)

       for lyr in self.encoders:
            lowerCAmelCase_					:       List[str]    =		lyr(_lowercase    ,						_lowercase						)[0]
       lowerCAmelCase_					:       List[str]    =		self.layer_norm(_lowercase						)

       return self.dropout_post(_lowercase						), encoder_inputs_mask







from typing import TYPE_CHECKING

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


UpperCAmelCase_					:   Dict 							=						{
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Union[str, Any] 							=						["""PoolFormerFeatureExtractor"""]
      UpperCAmelCase_					:   str 							=						["""PoolFormerImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   List[str] 							=						[
          """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PoolFormerForImageClassification""",
          """PoolFormerModel""",
          """PoolFormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_poolformer import (
          POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          PoolFormerConfig,
          PoolFormerOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_poolformer import PoolFormerFeatureExtractor
            from .image_processing_poolformer import PoolFormerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_poolformer import (
                POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                PoolFormerForImageClassification,
                PoolFormerModel,
                PoolFormerPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   Optional[Any] 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)
