"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
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 ACT2FN
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
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "PoolFormerConfig"
_CHECKPOINT_FOR_DOC = "sail/poolformer_s12"
_EXPECTED_OUTPUT_SHAPE = [1, 512, 7, 7]
_IMAGE_CLASS_CHECKPOINT = "sail/poolformer_s12"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
    if drop_prob == 0.0 or not training: return input
    keep_prob = 1 - drop_prob
    shape = (input.shape[0],) + (1,) * (input.ndim - 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
    random_tensor.floor_()
    output = input.div(keep_prob) * random_tensor
    return output
class PoolFormerDropPath(nn.Module):
    def __init__(self, drop_prob: Optional[float] = None) -> None:
        super().__init__()
        self.drop_prob = drop_prob
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: return drop_path(hidden_states, self.drop_prob, self.training)
    def extra_repr(self) -> str: return "p={}".format(self.drop_prob)
class PoolFormerEmbeddings(nn.Module):
    def __init__(self, hidden_size, num_channels, patch_size, stride, padding, norm_layer=None):
        super().__init__()
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        stride = stride if isinstance(stride, collections.abc.Iterable) else (stride, stride)
        padding = padding if isinstance(padding, collections.abc.Iterable) else (padding, padding)
        self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=stride, padding=padding)
        self.norm = norm_layer(hidden_size) if norm_layer else nn.Identity()
    def forward(self, pixel_values):
        embeddings = self.projection(pixel_values)
        embeddings = self.norm(embeddings)
        return embeddings
class PoolFormerGroupNorm(nn.GroupNorm):
    def __init__(self, num_channels, **kwargs): super().__init__(1, num_channels, **kwargs)
class PoolFormerPooling(nn.Module):
    def __init__(self, pool_size):
        super().__init__()
        self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size // 2, count_include_pad=False)
    def forward(self, hidden_states): return self.pool(hidden_states) - hidden_states
class PoolFormerOutput(nn.Module):
    def __init__(self, config, dropout_prob, hidden_size, intermediate_size):
        super().__init__()
        self.conv1 = nn.Conv2d(hidden_size, intermediate_size, 1)
        self.conv2 = nn.Conv2d(intermediate_size, hidden_size, 1)
        self.drop = PoolFormerDropPath(dropout_prob)
        if isinstance(config.hidden_act, str): self.act_fn = ACT2FN[config.hidden_act]
        else: self.act_fn = config.hidden_act
    def forward(self, hidden_states):
        hidden_states = self.conv1(hidden_states)
        hidden_states = self.act_fn(hidden_states)
        hidden_states = self.drop(hidden_states)
        hidden_states = self.conv2(hidden_states)
        hidden_states = self.drop(hidden_states)
        return hidden_states
class PoolFormerLayer(nn.Module):
    def __init__(self, config, num_channels, pool_size, hidden_size, intermediate_size, drop_path):
        super().__init__()
        self.pooling = PoolFormerPooling(pool_size)
        self.output = PoolFormerOutput(config, drop_path, hidden_size, intermediate_size)
        self.before_norm = PoolFormerGroupNorm(num_channels)
        self.after_norm = PoolFormerGroupNorm(num_channels)
        self.drop_path = PoolFormerDropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.use_layer_scale = config.use_layer_scale
        if config.use_layer_scale:
            self.layer_scale_1 = nn.Parameter(config.layer_scale_init_value * torch.ones((num_channels)), requires_grad=True)
            self.layer_scale_2 = nn.Parameter(config.layer_scale_init_value * torch.ones((num_channels)), requires_grad=True)
    def forward(self, hidden_states):
        if self.use_layer_scale:
            pooling_output = self.pooling(self.before_norm(hidden_states))
            scaled_op = self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) * pooling_output
            hidden_states = hidden_states + self.drop_path(scaled_op)
            outputs = ()
            layer_output = self.output(self.after_norm(hidden_states))
            scaled_op = self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) * layer_output
            output = hidden_states + self.drop_path(scaled_op)
            outputs = (output,) + outputs
            return outputs
        else:
            pooling_output = self.drop_path(self.pooling(self.before_norm(hidden_states)))
            hidden_states = pooling_output + hidden_states
            outputs = ()
            layer_output = self.drop_path(self.output(self.after_norm(hidden_states)))
            output = hidden_states + layer_output
            outputs = (output,) + outputs
            return outputs
class PoolFormerEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
        embeddings = []
        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]))
        self.patch_embeddings = nn.ModuleList(embeddings)
        blocks = []
        cur = 0
        for i in range(config.num_encoder_blocks):
            layers = []
            if i != 0: cur += config.depths[i - 1]
            for j in range(config.depths[i]): layers.append(PoolFormerLayer(config, 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(layers))
        self.block = nn.ModuleList(blocks)
    def forward(self, pixel_values, output_hidden_states=False, return_dict=True):
        all_hidden_states = () if output_hidden_states else None
        hidden_states = pixel_values
        for idx, layers in enumerate(zip(self.patch_embeddings, self.block)):
            embedding_layer, block_layer = layers
            hidden_states = embedding_layer(hidden_states)
            for _, blk in enumerate(block_layer):
                layer_outputs = blk(hidden_states)
                hidden_states = layer_outputs[0]
            if output_hidden_states: all_hidden_states = 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=hidden_states, hidden_states=all_hidden_states)
class PoolFormerPreTrainedModel(PreTrainedModel):
    config_class = PoolFormerConfig
    base_model_prefix = "poolformer"
    main_input_name = "pixel_values"
    _no_split_modules = ["PoolFormerLayer"]
    def _init_weights(self, module):
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
POOLFORMER_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
    Parameters:
        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
POOLFORMER_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PoolFormerImageProcessor.__call__`] for details.
"""
@add_start_docstrings("The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.", POOLFORMER_START_DOCSTRING)
class PoolFormerModel(PoolFormerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.encoder = PoolFormerEncoder(config)
        self.post_init()
    def get_input_embeddings(self): return self.embeddings.patch_embeddings
    @add_start_docstrings_to_model_forward(POOLFORMER_INPUTS_DOCSTRING)
    def forward(self, pixel_values: Optional[torch.FloatTensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, BaseModelOutputWithNoAttention]:
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = 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")
        encoder_outputs = self.encoder(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = encoder_outputs[0]
        if not return_dict: return (sequence_output, None) + encoder_outputs[1:]
        return BaseModelOutputWithNoAttention(last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states)
class PoolFormerFinalPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
    def forward(self, hidden_states):
        output = self.dense(hidden_states)
        return output
@add_start_docstrings("PoolFormer Model transformer with an image classification head on top", POOLFORMER_START_DOCSTRING)
class PoolFormerForImageClassification(PoolFormerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.poolformer = PoolFormerModel(config)
        self.norm = PoolFormerGroupNorm(config.hidden_sizes[-1])
        self.classifier = (nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity())
        self.post_init()
    @add_start_docstrings_to_model_forward(POOLFORMER_INPUTS_DOCSTRING)
    def forward(self, pixel_values: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.poolformer(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        logits = self.classifier(self.norm(sequence_output).mean([-2, -1]))
        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1: self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification"
                else: self.config.problem_type = "multi_label_classification"
            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze())
                else: loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
