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149 lines
7.6 KiB
149 lines
7.6 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/pixio/modular_pixio.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_pixio.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Copyright 2025 Meta AI and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ...backbone_utils import BackboneConfigMixin
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from ...configuration_utils import PreTrainedConfig
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class PixioConfig(BackboneConfigMixin, PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`PixioModel`]. It is used to instantiate a
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Pixio model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the ViT
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[facebook/pixio-huge](https://huggingface.co/facebook/pixio-huge) architecture.
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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hidden_size (`int`, *optional*, defaults to 1280):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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mlp_ratio (`int`, *optional*, defaults to 4):
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Ratio of the hidden size of the MLPs relative to the `hidden_size`.
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n_cls_tokens (`int`, *optional*, defaults to 8):
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Number of class tokens in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the layer normalization layers.
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image_size (`int`, *optional*, defaults to 256):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 16):
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The size (resolution) of each patch.
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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether to add a bias to the queries, keys and values.
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drop_path_rate (`float`, *optional*, defaults to 0.0):
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Stochastic depth rate per sample (when applied in the main path of residual layers).
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out_features (`list[str]`, *optional*):
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If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
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(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
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corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
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same order as defined in the `stage_names` attribute.
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out_indices (`list[int]`, *optional*):
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If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
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many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
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If unset and `out_features` is unset, will default to the last stage. Must be in the
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same order as defined in the `stage_names` attribute.
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apply_layernorm (`bool`, *optional*, defaults to `True`):
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Whether to apply layer normalization to the feature maps in case the model is used as backbone.
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reshape_hidden_states (`bool`, *optional*, defaults to `True`):
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Whether to reshape the feature maps to 4D tensors of shape `(batch_size, hidden_size, height, width)` in
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case the model is used as backbone. If `False`, the feature maps will be 3D tensors of shape `(batch_size,
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seq_len, hidden_size)`.
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Example:
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```python
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>>> from transformers import PixioConfig, PixioModel
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>>> # Initializing a Pixio pixio-huge style configuration
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>>> configuration = PixioConfig()
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>>> # Initializing a model (with random weights) from the pixio-huge style configuration
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>>> model = PixioModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "pixio"
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def __init__(
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self,
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hidden_size=1280,
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num_hidden_layers=32,
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num_attention_heads=16,
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mlp_ratio=4,
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n_cls_tokens=8,
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hidden_act="gelu",
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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initializer_range=0.02,
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layer_norm_eps=1e-6,
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image_size=256,
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patch_size=16,
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num_channels=3,
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qkv_bias=True,
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drop_path_rate=0.0,
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out_features=None,
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out_indices=None,
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apply_layernorm=True,
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reshape_hidden_states=True,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.mlp_ratio = mlp_ratio
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.qkv_bias = qkv_bias
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self.drop_path_rate = drop_path_rate
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self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, num_hidden_layers + 1)]
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self.set_output_features_output_indices(out_indices=out_indices, out_features=out_features)
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self.apply_layernorm = apply_layernorm
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self.reshape_hidden_states = reshape_hidden_states
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self.n_cls_tokens = n_cls_tokens
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__all__ = ["PixioConfig"]
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