





import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


UpperCAmelCase_           =	logging.get_logger(__name__)

# General docstring
UpperCAmelCase_           =	"PoolFormerConfig"

# Base docstring
UpperCAmelCase_           =	"sail/poolformer_s12"
UpperCAmelCase_           =	[1, 5_12, 7, 7]

# Image classification docstring
UpperCAmelCase_           =	"sail/poolformer_s12"
UpperCAmelCase_           =	"tabby, tabby cat"

UpperCAmelCase_           =	[
    "sail/poolformer_s12",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def      A__       (    SCREAMING_SNAKE_CASE_   :      Any				,	SCREAMING_SNAKE_CASE_   :      float = 0.0				,	SCREAMING_SNAKE_CASE_   :      bool = False						)  ->  str:






   """simple docstring"""







   if drop_prob == 0.0 or not training:
      return input
   _UpperCAmelCase           =   1 - drop_prob
   _UpperCAmelCase           =   (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
   _UpperCAmelCase           =   keep_prob + torch.rand(SCREAMING_SNAKE_CASE_				,	dtype=input.dtype				,	device=input.device						)
   random_tensor.floor_()  # binarize
   _UpperCAmelCase           =   input.div(SCREAMING_SNAKE_CASE_						) * random_tensor
   return output




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase = None					):
        super().__init__()
        _UpperCAmelCase           =   drop_prob





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        return drop_path(_UpperCamelCase				,							self.drop_prob				,							self.training					)





     def 		UpperCamelCase(     self					):
        return "p={}".format(self.drop_prob					)




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase=None					):
        super().__init__()
        _UpperCAmelCase           =   patch_size if isinstance(_UpperCamelCase				,							collections.abc.Iterable					) else (patch_size, patch_size)
        _UpperCAmelCase           =   stride if isinstance(_UpperCamelCase				,							collections.abc.Iterable					) else (stride, stride)
        _UpperCAmelCase           =   padding if isinstance(_UpperCamelCase				,							collections.abc.Iterable					) else (padding, padding)

        _UpperCAmelCase           =   nn.Convad(_UpperCamelCase				,							_UpperCamelCase				,							kernel_size=_UpperCamelCase				,							stride=_UpperCamelCase				,							padding=_UpperCamelCase					)
        _UpperCAmelCase           =   norm_layer(_UpperCamelCase					) if norm_layer else nn.Identity()





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   self.projection(_UpperCamelCase					)
        _UpperCAmelCase           =   self.norm(_UpperCamelCase					)
        return embeddings




class 			__UpperCamelCase    (							nn.GroupNorm    ):





     def __init__(     self				,							_UpperCamelCase				,							**_UpperCamelCase					):
        super().__init__(1				,							_UpperCamelCase				,							**_UpperCamelCase					)




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase					):
        super().__init__()
        _UpperCAmelCase           =   nn.AvgPoolad(_UpperCamelCase				,							stride=1				,							padding=pool_size // 2				,							count_include_pad=_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        return self.pool(_UpperCamelCase					) - hidden_states




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__()
        _UpperCAmelCase           =   nn.Convad(_UpperCamelCase				,							_UpperCamelCase				,							1					)
        _UpperCAmelCase           =   nn.Convad(_UpperCamelCase				,							_UpperCamelCase				,							1					)
        _UpperCAmelCase           =   PoolFormerDropPath(_UpperCamelCase					)
        if isinstance(config.hidden_act				,							_UpperCamelCase					):
           _UpperCAmelCase           =   ACTaFN[config.hidden_act]
        else:
           _UpperCAmelCase           =   config.hidden_act





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   self.conva(_UpperCamelCase					)
        _UpperCAmelCase           =   self.act_fn(_UpperCamelCase					)
        _UpperCAmelCase           =   self.drop(_UpperCamelCase					)
        _UpperCAmelCase           =   self.conva(_UpperCamelCase					)
        _UpperCAmelCase           =   self.drop(_UpperCamelCase					)

        return hidden_states




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__()
        _UpperCAmelCase           =   PoolFormerPooling(_UpperCamelCase					)
        _UpperCAmelCase           =   PoolFormerOutput(_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase					)
        _UpperCAmelCase           =   PoolFormerGroupNorm(_UpperCamelCase					)
        _UpperCAmelCase           =   PoolFormerGroupNorm(_UpperCamelCase					)

        # Useful for training neural nets
        _UpperCAmelCase           =   PoolFormerDropPath(_UpperCamelCase					) if drop_path > 0.0 else nn.Identity()
        _UpperCAmelCase           =   config.use_layer_scale
        if config.use_layer_scale:
           _UpperCAmelCase           =   nn.Parameter(
               config.layer_scale_init_value * torch.ones((_UpperCamelCase)					)				,							requires_grad=_UpperCamelCase					)
           _UpperCAmelCase           =   nn.Parameter(
               config.layer_scale_init_value * torch.ones((_UpperCamelCase)					)				,							requires_grad=_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        if self.use_layer_scale:
           _UpperCAmelCase           =   self.pooling(self.before_norm(_UpperCamelCase					)					)
           _UpperCAmelCase           =   self.layer_scale_a.unsqueeze(-1					).unsqueeze(-1					) * pooling_output
           # First residual connection
           _UpperCAmelCase           =   hidden_states + self.drop_path(_UpperCamelCase					)
           _UpperCAmelCase           =   ()

           _UpperCAmelCase           =   self.output(self.after_norm(_UpperCamelCase					)					)
           _UpperCAmelCase           =   self.layer_scale_a.unsqueeze(-1					).unsqueeze(-1					) * layer_output
           # Second residual connection
           _UpperCAmelCase           =   hidden_states + self.drop_path(_UpperCamelCase					)

           _UpperCAmelCase           =   (output,) + outputs
           return outputs

        else:
           _UpperCAmelCase           =   self.drop_path(self.pooling(self.before_norm(_UpperCamelCase					)					)					)
           # First residual connection
           _UpperCAmelCase           =   pooling_output + hidden_states
           _UpperCAmelCase           =   ()

           # Second residual connection inside the PoolFormerOutput block
           _UpperCAmelCase           =   self.drop_path(self.output(self.after_norm(_UpperCamelCase					)					)					)
           _UpperCAmelCase           =   hidden_states + layer_output

           _UpperCAmelCase           =   (output,) + outputs
           return outputs




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase					):
        super().__init__()
        _UpperCAmelCase           =   config
        # stochastic depth decay rule
        _UpperCAmelCase           =   [x.item() for x in torch.linspace(0				,							config.drop_path_rate				,							sum(config.depths					)					)]

        # patch embeddings
        _UpperCAmelCase           =   []
        for i in range(config.num_encoder_blocks					):
           embeddings.append(
               PoolFormerEmbeddings(
                   patch_size=config.patch_sizes[i]				,							stride=config.strides[i]				,							padding=config.padding[i]				,							num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]				,							hidden_size=config.hidden_sizes[i]				,							)					)
        _UpperCAmelCase           =   nn.ModuleList(_UpperCamelCase					)

        # Transformer blocks
        _UpperCAmelCase           =   []
        _UpperCAmelCase           =   0
        for i in range(config.num_encoder_blocks					):
           # each block consists of layers
           _UpperCAmelCase           =   []
           if i != 0:
              cur += config.depths[i - 1]
           for j in range(config.depths[i]					):
              layers.append(
                  PoolFormerLayer(
                      _UpperCamelCase				,							num_channels=config.hidden_sizes[i]				,							pool_size=config.pool_size				,							hidden_size=config.hidden_sizes[i]				,							intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio					)				,							drop_path=dpr[cur + j]				,							)					)
           blocks.append(nn.ModuleList(_UpperCamelCase					)					)

        _UpperCAmelCase           =   nn.ModuleList(_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase=False				,							_UpperCamelCase=True					):
        _UpperCAmelCase           =   () if output_hidden_states else None

        _UpperCAmelCase           =   pixel_values
        for idx, layers in enumerate(zip(self.patch_embeddings				,							self.block					)					):
           _UpperCAmelCase       ,      _UpperCAmelCase           =   layers
           # Get patch embeddings from hidden_states
           _UpperCAmelCase           =   embedding_layer(_UpperCamelCase					)
           # Send the embeddings through the blocks
           for _, blk in enumerate(_UpperCamelCase					):
              _UpperCAmelCase           =   blk(_UpperCamelCase					)
              _UpperCAmelCase           =   layer_outputs[0]

           if output_hidden_states:
              _UpperCAmelCase           =   all_hidden_states + (hidden_states,)

        if not return_dict:
           return tuple(v for v in [hidden_states, all_hidden_states] if v is not None					)

        return BaseModelOutputWithNoAttention(last_hidden_state=_UpperCamelCase				,							hidden_states=_UpperCamelCase					)




class 			__UpperCamelCase    (							A__    ):
     __A	:  Tuple							  =   PoolFormerConfig
     __A	:  int							  =   """poolformer"""
     __A	:  Tuple							  =   """pixel_values"""
     __A	:  Optional[Any]							  =   True





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        if isinstance(_UpperCamelCase				,							(nn.Linear, nn.Convad)					):
           module.weight.data.normal_(mean=0.0				,							std=self.config.initializer_range					)
           if module.bias is not None:
              module.bias.data.zero_()
        elif isinstance(_UpperCamelCase				,							nn.LayerNorm					):
           module.bias.data.zero_()
           module.weight.data.fill_(1.0					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase=False					):
        if isinstance(_UpperCamelCase				,							_UpperCamelCase					):
           _UpperCAmelCase           =   value




UpperCAmelCase_           =	r"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

UpperCAmelCase_           =	r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`PoolFormerImageProcessor.__call__`] for details.\n"




@add_start_docstrings(
    """The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top."""	, A__	, )
class 			__UpperCamelCase    (							A__    ):





     def __init__(     self				,							_UpperCamelCase					):
        super().__init__(_UpperCamelCase					)
        _UpperCAmelCase           =   config

        _UpperCAmelCase           =   PoolFormerEncoder(_UpperCamelCase					)

        # Initialize weights and apply final processing
        self.post_init()





     def 		UpperCamelCase(     self					):
        return self.embeddings.patch_embeddings





     @add_start_docstrings_to_model_forward(_UpperCamelCase					)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC				,							output_type=_UpperCamelCase				,							config_class=_CONFIG_FOR_DOC				,							modality='''vision'''				,							expected_output=_EXPECTED_OUTPUT_SHAPE				,							)
     def 		UpperCamelCase(     self				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							):
        _UpperCAmelCase           =   (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        _UpperCAmelCase           =   return_dict if return_dict is not None else self.config.use_return_dict

        if pixel_values is None:
           raise ValueError('''You have to specify pixel_values'''					)

        _UpperCAmelCase           =   self.encoder(
            _UpperCamelCase				,							output_hidden_states=_UpperCamelCase				,							return_dict=_UpperCamelCase				,							)
        _UpperCAmelCase           =   encoder_outputs[0]

        if not return_dict:
           return (sequence_output, None) + encoder_outputs[1:]

        return BaseModelOutputWithNoAttention(
            last_hidden_state=_UpperCamelCase				,							hidden_states=encoder_outputs.hidden_states				,							)




class 			__UpperCamelCase    (							nn.Module    ):





     def __init__(     self				,							_UpperCamelCase					):
        super().__init__()
        _UpperCAmelCase           =   nn.Linear(config.hidden_size				,							config.hidden_size					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   self.dense(_UpperCamelCase					)
        return output




@add_start_docstrings(
    """
    PoolFormer Model transformer with an image classification head on top
    """	, A__	, )
class 			__UpperCamelCase    (							A__    ):





     def __init__(     self				,							_UpperCamelCase					):
        super().__init__(_UpperCamelCase					)
        _UpperCAmelCase           =   config.num_labels
        _UpperCAmelCase           =   PoolFormerModel(_UpperCamelCase					)

        # Final norm
        _UpperCAmelCase           =   PoolFormerGroupNorm(config.hidden_sizes[-1]					)
        # Classifier head
        _UpperCAmelCase           =   (
            nn.Linear(config.hidden_sizes[-1]				,							config.num_labels					) if config.num_labels > 0 else nn.Identity()
        )

        # Initialize weights and apply final processing
        self.post_init()





     @add_start_docstrings_to_model_forward(_UpperCamelCase					)
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT				,							output_type=_UpperCamelCase				,							config_class=_CONFIG_FOR_DOC				,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,							)
     def 		UpperCamelCase(     self				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							):
        _UpperCAmelCase           =   return_dict if return_dict is not None else self.config.use_return_dict

        _UpperCAmelCase           =   self.poolformer(
            _UpperCamelCase				,							output_hidden_states=_UpperCamelCase				,							return_dict=_UpperCamelCase				,							)

        _UpperCAmelCase           =   outputs[0]

        _UpperCAmelCase           =   self.classifier(self.norm(_UpperCamelCase					).mean([-2, -1]					)					)

        _UpperCAmelCase           =   None
        if labels is not None:
           if self.config.problem_type is None:
              if self.num_labels == 1:
                 _UpperCAmelCase           =   '''regression'''
              elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                 _UpperCAmelCase           =   '''single_label_classification'''
              else:
                 _UpperCAmelCase           =   '''multi_label_classification'''

           if self.config.problem_type == "regression":
              _UpperCAmelCase           =   MSELoss()
              if self.num_labels == 1:
                 _UpperCAmelCase           =   loss_fct(logits.squeeze()				,							labels.squeeze()					)
              else:
                 _UpperCAmelCase           =   loss_fct(_UpperCamelCase				,							_UpperCamelCase					)
           elif self.config.problem_type == "single_label_classification":
              _UpperCAmelCase           =   CrossEntropyLoss()
              _UpperCAmelCase           =   loss_fct(logits.view(-1				,							self.num_labels					)				,							labels.view(-1					)					)
           elif self.config.problem_type == "multi_label_classification":
              _UpperCAmelCase           =   BCEWithLogitsLoss()
              _UpperCAmelCase           =   loss_fct(_UpperCamelCase				,							_UpperCamelCase					)

        if not return_dict:
           _UpperCAmelCase           =   (logits,) + outputs[2:]
           return ((loss,) + output) if loss is not None else output

        return ImageClassifierOutputWithNoAttention(loss=_UpperCamelCase				,							logits=_UpperCamelCase				,							hidden_states=outputs.hidden_states					)






UpperCAmelCase_           =	{
    "A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
    "H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
    "O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
    "V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
    "2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
    "8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
    ":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
    "?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
    "(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase_           =	{value: key for key, value in MORSE_CODE_DICT.items()}
def      A__       (    SCREAMING_SNAKE_CASE_   :      str						)  ->  str:






   """simple docstring"""







   return " ".join(MORSE_CODE_DICT[char] for char in message.upper()						)
def      A__       (    SCREAMING_SNAKE_CASE_   :      str						)  ->  str:






   """simple docstring"""







   return "".join(REVERSE_DICT[char] for char in message.split()						)
def      A__       (    )  ->  None:






   """simple docstring"""







   _UpperCAmelCase           =   '''Morse code here!'''
   print(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   encrypt(SCREAMING_SNAKE_CASE_						)
   print(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   decrypt(SCREAMING_SNAKE_CASE_						)
   print(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
   main()