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diff --git a/evalkit_tf433/lib/python3.10/site-packages/transformers/models/poolformer/feature_extraction_poolformer.py b/evalkit_tf433/lib/python3.10/site-packages/transformers/models/poolformer/feature_extraction_poolformer.py
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index 0000000000000000000000000000000000000000..79ffa037eed36a03669a60b43a5997dd7a647f8e
--- /dev/null
+++ b/evalkit_tf433/lib/python3.10/site-packages/transformers/models/poolformer/feature_extraction_poolformer.py
@@ -0,0 +1,33 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Feature extractor class for PoolFormer."""
+
+import warnings
+
+from ...utils import logging
+from .image_processing_poolformer import PoolFormerImageProcessor
+
+
+logger = logging.get_logger(__name__)
+
+
+class PoolFormerFeatureExtractor(PoolFormerImageProcessor):
+    def __init__(self, *args, **kwargs) -> None:
+        warnings.warn(
+            "The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
+            " Please use PoolFormerImageProcessor instead.",
+            FutureWarning,
+        )
+        super().__init__(*args, **kwargs)
diff --git a/evalkit_tf433/lib/python3.10/site-packages/transformers/models/poolformer/modeling_poolformer.py b/evalkit_tf433/lib/python3.10/site-packages/transformers/models/poolformer/modeling_poolformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..6acc8ec98e6939447179fb5f46e66d164e8ff289
--- /dev/null
+++ b/evalkit_tf433/lib/python3.10/site-packages/transformers/models/poolformer/modeling_poolformer.py
@@ -0,0 +1,455 @@
+# coding=utf-8
+# Copyright 2022 Sea AI Lab and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch PoolFormer model."""
+
+
+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__)
+
+# General docstring
+_CONFIG_FOR_DOC = "PoolFormerConfig"
+
+# Base docstring
+_CHECKPOINT_FOR_DOC = "sail/poolformer_s12"
+_EXPECTED_OUTPUT_SHAPE = [1, 512, 7, 7]
+
+# Image classification docstring
+_IMAGE_CLASS_CHECKPOINT = "sail/poolformer_s12"
+_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
+
+POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "sail/poolformer_s12",
+    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
+]
+
+
+# Copied from transformers.models.beit.modeling_beit.drop_path
+def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
+    """
+    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+    Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
+    however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
+    layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
+    argument.
+    """
+    if drop_prob == 0.0 or not training:
+        return input
+    keep_prob = 1 - drop_prob
+    shape = (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
+    random_tensor.floor_()  # binarize
+    output = input.div(keep_prob) * random_tensor
+    return output
+
+
+# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->PoolFormer
+class PoolFormerDropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+    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):
+    """
+    Construct Patch Embeddings.
+    """
+
+    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):
+    """
+    Group Normalization with 1 group. Input: tensor in shape [B, C, H, W]
+    """
+
+    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):
+    """This corresponds to the 'PoolFormerBlock' class in the original implementation."""
+
+    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)
+
+        # Useful for training neural nets
+        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
+            # First residual connection
+            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
+            # Second residual connection
+            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)))
+            # First residual connection
+            hidden_states = pooling_output + hidden_states
+            outputs = ()
+
+            # Second residual connection inside the PoolFormerOutput block
+            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
+        # stochastic depth decay rule
+        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
+
+        # patch embeddings
+        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)
+
+        # Transformer blocks
+        blocks = []
+        cur = 0
+        for i in range(config.num_encoder_blocks):
+            # each block consists of layers
+            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
+            # Get patch embeddings from hidden_states
+            hidden_states = embedding_layer(hidden_states)
+            # Send the embeddings through the blocks
+            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):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = PoolFormerConfig
+    base_model_prefix = "poolformer"
+    main_input_name = "pixel_values"
+    supports_gradient_checkpointing = True
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        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)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, PoolFormerEncoder):
+            module.gradient_checkpointing = value
+
+
+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)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.patch_embeddings
+
+    @add_start_docstrings_to_model_forward(POOLFORMER_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutputWithNoAttention,
+        config_class=_CONFIG_FOR_DOC,
+        modality="vision",
+        expected_output=_EXPECTED_OUTPUT_SHAPE,
+    )
+    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)
+
+        # Final norm
+        self.norm = PoolFormerGroupNorm(config.hidden_sizes[-1])
+        # Classifier head
+        self.classifier = (
+            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(POOLFORMER_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_IMAGE_CLASS_CHECKPOINT,
+        output_type=ImageClassifierOutputWithNoAttention,
+        config_class=_CONFIG_FOR_DOC,
+        expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
+    )
+    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]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        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)