from __future__ import annotations
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
from typing import List, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling, TFSequenceClassifierOutput
from ...modeling_tf_utils import (TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras, keras_serializable, unpack_inputs)
from ...tf_utils import shape_list
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_convnext import ConvNextConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "ConvNextConfig"
_CHECKPOINT_FOR_DOC = "facebook/convnext-tiny-224"
class TFConvNextDropPath(keras.layers.Layer):
    def __init__(self, drop_path: float, **kwargs):
        super().__init__(**kwargs)
        self.drop_path = drop_path
    def call(self, x: tf.Tensor, training=None):
        if training:
            keep_prob = 1 - self.drop_path
            shape = (tf.shape(x)[0],) + (1,) * (len(tf.shape(x)) - 1)
            random_tensor = keep_prob + tf.random.uniform(shape, 0, 1)
            random_tensor = tf.floor(random_tensor)
            return (x / keep_prob) * random_tensor
        return x
class TFConvNextEmbeddings(keras.layers.Layer):
    def __init__(self, config: ConvNextConfig, **kwargs):
        super().__init__(**kwargs)
        self.patch_embeddings = keras.layers.Conv2D(filters=config.hidden_sizes[0], kernel_size=config.patch_size, strides=config.patch_size, name="patch_embeddings",
        kernel_initializer=get_initializer(config.initializer_range), bias_initializer=keras.initializers.Zeros())
        self.layernorm = keras.layers.LayerNormalization(epsilon=1e-6, name="layernorm")
        self.num_channels = config.num_channels
        self.config = config
    def call(self, pixel_values):
        if isinstance(pixel_values, dict): pixel_values = pixel_values["pixel_values"]
        tf.debugging.assert_equal(shape_list(pixel_values)[1], self.num_channels, message="Make sure that the channel dimension of the pixel values match with the one set in the configuration.")
        pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
        embeddings = self.patch_embeddings(pixel_values)
        embeddings = self.layernorm(embeddings)
        return embeddings
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "patch_embeddings", None) is not None:
            with tf.name_scope(self.patch_embeddings.name): self.patch_embeddings.build([None, None, None, self.config.num_channels])
        if getattr(self, "layernorm", None) is not None:
            with tf.name_scope(self.layernorm.name): self.layernorm.build([None, None, None, self.config.hidden_sizes[0]])
class TFConvNextLayer(keras.layers.Layer):
    def __init__(self, config, dim, drop_path=0.0, **kwargs):
        super().__init__(**kwargs)
        self.dim = dim
        self.config = config
        self.dwconv = keras.layers.Conv2D(filters=dim, kernel_size=7, padding="same", groups=dim, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="dwconv")
        self.layernorm = keras.layers.LayerNormalization(epsilon=1e-6, name="layernorm")
        self.pwconv1 = keras.layers.Dense(units=4 * dim, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="pwconv1")
        self.act = get_tf_activation(config.hidden_act)
        self.pwconv2 = keras.layers.Dense(units=dim, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="pwconv2")
        self.drop_path = (TFConvNextDropPath(drop_path, name="drop_path") if drop_path > 0.0 else keras.layers.Activation("linear", name="drop_path"))
    def build(self, input_shape: tf.TensorShape = None):
        self.layer_scale_parameter = (self.add_weight(shape=(self.dim,), initializer=keras.initializers.Constant(value=self.config.layer_scale_init_value), trainable=True, name="layer_scale_parameter") if self.config.layer_scale_init_value > 0 else None)
        if self.built: return
        self.built = True
        if getattr(self, "dwconv", None) is not None:
            with tf.name_scope(self.dwconv.name): self.dwconv.build([None, None, None, self.dim])
        if getattr(self, "layernorm", None) is not None:
            with tf.name_scope(self.layernorm.name): self.layernorm.build([None, None, None, self.dim])
        if getattr(self, "pwconv1", None) is not None:
            with tf.name_scope(self.pwconv1.name): self.pwconv1.build([None, None, self.dim])
        if getattr(self, "pwconv2", None) is not None:
            with tf.name_scope(self.pwconv2.name): self.pwconv2.build([None, None, 4 * self.dim])
        if getattr(self, "drop_path", None) is not None:
            with tf.name_scope(self.drop_path.name): self.drop_path.build(None)
    def call(self, hidden_states, training=False):
        input = hidden_states
        x = self.dwconv(hidden_states)
        x = self.layernorm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.pwconv2(x)
        if self.layer_scale_parameter is not None: x = self.layer_scale_parameter * x
        x = input + self.drop_path(x, training=training)
        return x
class TFConvNextStage(keras.layers.Layer):
    def __init__(self, config: ConvNextConfig, in_channels: int, out_channels: int, kernel_size: int = 2, stride: int = 2, depth: int = 2, drop_path_rates: Optional[List[float]] = None, **kwargs):
        super().__init__(**kwargs)
        if in_channels != out_channels or stride > 1: self.downsampling_layer = [keras.layers.LayerNormalization(epsilon=1e-6, name="downsampling_layer.0"), keras.layers.Conv2D(filters=out_channels, kernel_size=kernel_size, strides=stride, kernel_initializer=get_initializer(config.initializer_range), bias_initializer=keras.initializers.Zeros(), name="downsampling_layer.1")]
        else: self.downsampling_layer = [tf.identity]
        drop_path_rates = drop_path_rates or [0.0] * depth
        self.layers = [TFConvNextLayer(config, dim=out_channels, drop_path=drop_path_rates[j], name=f"layers.{j}") for j in range(depth)]
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.stride = stride
    def call(self, hidden_states):
        for layer in self.downsampling_layer: hidden_states = layer(hidden_states)
        for layer in self.layers: hidden_states = layer(hidden_states)
        return hidden_states
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "layers", None) is not None:
            for layer in self.layers:
                with tf.name_scope(layer.name): layer.build(None)
        if self.in_channels != self.out_channels or self.stride > 1:
            with tf.name_scope(self.downsampling_layer[0].name): self.downsampling_layer[0].build([None, None, None, self.in_channels])
            with tf.name_scope(self.downsampling_layer[1].name): self.downsampling_layer[1].build([None, None, None, self.in_channels])
class TFConvNextEncoder(keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.stages = []
        drop_path_rates = tf.linspace(0.0, config.drop_path_rate, sum(config.depths))
        drop_path_rates = tf.split(drop_path_rates, config.depths)
        drop_path_rates = [x.numpy().tolist() for x in drop_path_rates]
        prev_chs = config.hidden_sizes[0]
        for i in range(config.num_stages):
            out_chs = config.hidden_sizes[i]
            stage = TFConvNextStage(config, in_channels=prev_chs, out_channels=out_chs, stride=2 if i > 0 else 1, depth=config.depths[i], drop_path_rates=drop_path_rates[i], name=f"stages.{i}")
            self.stages.append(stage)
            prev_chs = out_chs
    def call(self, hidden_states, output_hidden_states=False, return_dict=True):
        all_hidden_states = () if output_hidden_states else None
        for i, layer_module in enumerate(self.stages):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            hidden_states = layer_module(hidden_states)
        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 TFBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
    def build(self, input_shape=None):
        for stage in self.stages:
            with tf.name_scope(stage.name): stage.build(None)
@keras_serializable
class TFConvNextMainLayer(keras.layers.Layer):
    config_class = ConvNextConfig
    def __init__(self, config: ConvNextConfig, add_pooling_layer: bool = True, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.embeddings = TFConvNextEmbeddings(config, name="embeddings")
        self.encoder = TFConvNextEncoder(config, name="encoder")
        self.layernorm = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
        self.pooler = keras.layers.GlobalAvgPool2D(data_format="channels_first") if add_pooling_layer else None
    @unpack_inputs
    def call(self, pixel_values: TFModelInputType | None = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
        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")
        embedding_output = self.embeddings(pixel_values, training=training)
        encoder_outputs = self.encoder(embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        last_hidden_state = encoder_outputs[0]
        last_hidden_state = tf.transpose(last_hidden_state, perm=(0, 3, 1, 2))
        pooled_output = self.layernorm(self.pooler(last_hidden_state))
        if output_hidden_states: hidden_states = tuple([tf.transpose(h, perm=(0, 3, 1, 2)) for h in encoder_outputs[1]])
        if not return_dict:
            hidden_states = hidden_states if output_hidden_states else ()
            return (last_hidden_state, pooled_output) + hidden_states
        return TFBaseModelOutputWithPooling(last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "embeddings", None) is not None:
            with tf.name_scope(self.embeddings.name): self.embeddings.build(None)
        if getattr(self, "encoder", None) is not None:
            with tf.name_scope(self.encoder.name): self.encoder.build(None)
        if getattr(self, "layernorm", None) is not None:
            with tf.name_scope(self.layernorm.name): self.layernorm.build([None, self.config.hidden_sizes[-1]])
class TFConvNextPreTrainedModel(TFPreTrainedModel):
    config_class = ConvNextConfig
    base_model_prefix = "convnext"
    main_input_name = "pixel_values"
CONVNEXT_START_DOCSTRING = r"""
    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)
    This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
    behavior.
    <Tip>
    TensorFlow models and layers in `transformers` accept two formats as input:
    - having all inputs as keyword arguments (like PyTorch models), or
    - having all inputs as a list, tuple or dict in the first positional argument.
    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
    the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
    positional argument:
    - a single Tensor with `pixel_values` only and nothing else: `model(pixel_values)`
    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
    `model([pixel_values, attention_mask])` or `model([pixel_values, attention_mask, token_type_ids])`
    - a dictionary with one or several input Tensors associated to the input names given in the docstring:
    `model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})`
    Note that when creating models and layers with
    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
    about any of this, as you can just pass inputs like you would to any other Python function!
    </Tip>
    Parameters:
        config ([`ConvNextConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""
CONVNEXT_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
            used instead.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in
            eager mode, in graph mode the value will always be set to True.
"""
@add_start_docstrings("The bare ConvNext model outputting raw features without any specific head on top.", CONVNEXT_START_DOCSTRING)
class TFConvNextModel(TFConvNextPreTrainedModel):
    def __init__(self, config, *inputs, add_pooling_layer=True, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.convnext = TFConvNextMainLayer(config, add_pooling_layer=add_pooling_layer, name="convnext")
    @unpack_inputs
    @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
    def call(self, pixel_values: TFModelInputType | None = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
        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")
        outputs = self.convnext(pixel_values=pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        if not return_dict: return (outputs[0],) + outputs[1:]
        return TFBaseModelOutputWithPooling(last_hidden_state=outputs.last_hidden_state, pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "convnext", None) is not None:
            with tf.name_scope(self.convnext.name): self.convnext.build(None)
@add_start_docstrings("ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet.", CONVNEXT_START_DOCSTRING)
class TFConvNextForImageClassification(TFConvNextPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config: ConvNextConfig, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
        self.num_labels = config.num_labels
        self.convnext = TFConvNextMainLayer(config, name="convnext")
        self.classifier = keras.layers.Dense(units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), bias_initializer="zeros", name="classifier")
        self.config = config
    @unpack_inputs
    @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
    def call(self, pixel_values: TFModelInputType | None = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
        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")
        outputs = self.convnext(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        pooled_output = outputs.pooler_output if return_dict else outputs[1]
        logits = self.classifier(pooled_output)
        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TFSequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "convnext", None) is not None:
            with tf.name_scope(self.convnext.name): self.convnext.build(None)
        if getattr(self, "classifier", None) is not None:
            if hasattr(self.classifier, "name"):
                with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_sizes[-1]])
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
