"""
    #################################################################################################################################################################################################
    # 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 math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling
from ...modeling_tf_utils import TFPreTrainedModel, shape_list
from ...tf_utils import flatten
from ...utils import ModelOutput, logging
from .configuration_idefics import IdeficsVisionConfig
logger = logging.get_logger(__name__)
@dataclass
class TFIdeficsVisionModelOutput(ModelOutput):
    """Args:"""
    image_embeds: Optional[tf.Tensor] = None
    last_hidden_state: tf.Tensor = None
    hidden_states: Optional[Tuple[tf.Tensor]] = None
    attentions: Optional[Tuple[tf.Tensor]] = None
class TFIdeficsVisionEmbeddings(tf.keras.layers.Layer):
    def __init__(self, config: IdeficsVisionConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_size
        self.patch_size = config.patch_size
        self.patch_embedding = tf.keras.layers.Conv2D(filters=self.embed_dim, kernel_size=self.patch_size, strides=self.patch_size, use_bias=False, padding="valid", data_format="channels_last", name="patch_embedding")
        self.num_patches = (self.image_size // self.patch_size) ** 2
        self.num_positions = self.num_patches + 1
        self.position_embedding = tf.keras.layers.Embedding(self.num_positions, self.embed_dim, name="position_embedding")
    def interpolate_pos_encoding(self, embeddings: tf.Tensor, height: int, width: int) -> tf.Tensor:
        num_patches = shape_list(embeddings)[1] - 1
        pos_embed = self.position_embedding(self.position_ids)
        num_positions = shape_list(pos_embed)[1] - 1
        if num_patches == num_positions and height == width: return pos_embed
        class_pos_embed = pos_embed[:, 0]
        patch_pos_embed = pos_embed[:, 1:]
        embed_dim = shape_list(embeddings)[-1]
        num_h_patches = height // self.config.patch_size
        num_w_patches = width // self.config.patch_size
        num_h_patches, num_w_patches = num_h_patches + 0.1, num_w_patches + 0.1
        sqrt_num_positions = math.sqrt(float(num_positions))
        patch_pos_embed = tf.reshape(patch_pos_embed, (1, int(sqrt_num_positions), int(sqrt_num_positions), embed_dim))
        scale_height = num_h_patches / sqrt_num_positions
        scale_width = num_w_patches / sqrt_num_positions
        original_height = tf.cast(tf.shape(patch_pos_embed)[1], tf.float32)
        original_width = tf.cast(tf.shape(patch_pos_embed)[2], tf.float32)
        new_height = tf.cast(original_height * scale_height, tf.int32)
        new_width = tf.cast(original_width * scale_width, tf.int32)
        patch_pos_embed = tf.image.resize(patch_pos_embed, size=[new_height, new_width], method=tf.image.ResizeMethod.BICUBIC)
        if (int(num_h_patches) != shape_list(patch_pos_embed)[-3] or int(num_w_patches) != shape_list(patch_pos_embed)[-2]): raise ValueError(f"Number of patches for images ({int(num_h_patches), int(num_w_patches)}) don't match the shape of position embedding ({shape_list(patch_pos_embed)[-2], shape_list(patch_pos_embed)[-1]})")
        patch_pos_embed = tf.reshape(patch_pos_embed, (1, -1, embed_dim))
        return tf.concat((class_pos_embed[tf.newaxis, :], patch_pos_embed), axis=1)
    def call(self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False) -> tf.Tensor:
        if isinstance(pixel_values, dict): pixel_values = pixel_values["pixel_values"]
        pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
        batch_size, height, width, num_channels = shape_list(pixel_values)
        if not interpolate_pos_encoding:
            if height != self.image_size or width != self.image_size: raise ValueError(f"Input image size ({height}*{width}) doesn't match model ({self.image_size}*{self.image_size}). You should try to set `interpolate_pos_encoding=True`")
        patch_embeds = self.patch_embedding(pixel_values)
        patch_embeds = flatten(patch_embeds, 1, 2)
        class_embeds = tf.broadcast_to(self.class_embedding[tf.newaxis, tf.newaxis, :], [batch_size, 1, self.embed_dim])
        embeddings = tf.concat([class_embeds, patch_embeds], axis=1)
        if interpolate_pos_encoding: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
        else: embeddings = embeddings + self.position_embedding(self.position_ids)
        return embeddings
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        self.position_ids = tf.range(self.num_positions, name="self.position_ids")[tf.newaxis, :]
        self.class_embedding = self.add_weight(shape=(self.embed_dim,), name="class_embedding")
        if getattr(self, "patch_embedding", None) is not None:
            with tf.name_scope(self.patch_embedding.name): self.patch_embedding.build([None, None, None, self.config.num_channels])
        if getattr(self, "position_embedding", None) is not None:
            with tf.name_scope(self.position_embedding.name): self.position_embedding.build(None)
class TFIdeficsVisionAttention(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_heads
        if self.head_dim * self.num_heads != self.embed_dim: raise ValueError(f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads}).")
        self.scale = self.head_dim**-0.5
        self.dropout = config.attention_dropout
        self.k_proj = tf.keras.layers.Dense(self.embed_dim, name="k_proj")
        self.v_proj = tf.keras.layers.Dense(self.embed_dim, name="v_proj")
        self.q_proj = tf.keras.layers.Dense(self.embed_dim, name="q_proj")
        self.out_proj = tf.keras.layers.Dense(self.embed_dim, name="out_proj")
    def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int): return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), perm=[0, 2, 1, 3])
    def call(self, hidden_states: tf.Tensor, attention_mask: Optional[tf.Tensor] = None, causal_attention_mask: Optional[tf.Tensor] = None,
    output_attentions: Optional[bool] = False) -> Tuple[tf.Tensor, Optional[tf.Tensor], Optional[Tuple[tf.Tensor]]]:
        bsz, tgt_len, embed_dim = shape_list(hidden_states)
        query_states = self.q_proj(hidden_states) * self.scale
        key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
        value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
        query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape)
        key_states = tf.reshape(key_states, proj_shape)
        value_states = tf.reshape(value_states, proj_shape)
        src_len = shape_list(key_states)[1]
        attn_weights = tf.linalg.matmul(query_states, key_states, transpose_b=True)
        tf.debugging.assert_equal(tf.shape(attn_weights), [bsz * self.num_heads, tgt_len, src_len], message=f"Attention weights should be of size {[bsz * self.num_heads, tgt_len, src_len]}, but is {tf.shape(attn_weights)}")
        if causal_attention_mask is not None:
            if shape_list(causal_attention_mask) != [bsz, 1, tgt_len, src_len]: raise ValueError(f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {shape_list(causal_attention_mask)}")
            attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + causal_attention_mask
            attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
        if attention_mask is not None:
            if shape_list(attention_mask) != [bsz, 1, tgt_len, src_len]: raise ValueError(f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {shape_list(attention_mask)}")
            attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask
            attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len))
        attn_weights = tf.nn.softmax(attn_weights, axis=-1)
        if output_attentions:
            attn_weights_reshaped = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len))
            attn_weights = tf.reshape(attn_weights_reshaped, (bsz * self.num_heads, tgt_len, src_len))
        else: attn_weights_reshaped = None
        attn_probs = tf.nn.dropout(attn_weights, rate=self.dropout)
        attn_output = tf.linalg.matmul(attn_probs, value_states)
        tf.debugging.assert_equal(tf.shape(attn_output), [bsz * self.num_heads, tgt_len, self.head_dim], message=f"Attention weights should be of size {[bsz * self.num_heads, tgt_len, self.head_dim]}, but is {tf.shape(attn_output)}")
        attn_output = tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim))
        attn_output = tf.transpose(attn_output, perm=[0, 2, 1, 3])
        attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim))
        attn_output = self.out_proj(attn_output)
        return attn_output, attn_weights_reshaped
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "k_proj", None) is not None:
            with tf.name_scope(self.k_proj.name): self.k_proj.build((self.embed_dim, self.embed_dim))
        if getattr(self, "v_proj", None) is not None:
            with tf.name_scope(self.v_proj.name): self.v_proj.build((self.embed_dim, self.embed_dim))
        if getattr(self, "q_proj", None) is not None:
            with tf.name_scope(self.q_proj.name): self.q_proj.build((self.embed_dim, self.embed_dim))
        if getattr(self, "out_proj", None) is not None:
            with tf.name_scope(self.out_proj.name): self.out_proj.build((self.embed_dim, self.embed_dim))
class TFIdeficsVisionMLP(tf.keras.layers.Layer):
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.activation_fn = get_tf_activation(config.hidden_act)
        self.fc1 = tf.keras.layers.Dense(config.intermediate_size, name="fc1")
        self.fc2 = tf.keras.layers.Dense(config.hidden_size, name="fc2")
    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)
        hidden_states = self.fc2(hidden_states)
        return hidden_states
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "fc1", None) is not None:
            with tf.name_scope(self.fc1.name): self.fc1.build(self.config.hidden_size)
        if getattr(self, "fc2", None) is not None:
            with tf.name_scope(self.fc2.name): self.fc2.build(self.config.intermediate_size)
class TFIdeficsVisionEncoderLayer(tf.keras.layers.Layer):
    def __init__(self, config: IdeficsVisionConfig, **kwargs):
        super().__init__(**kwargs)
        self.embed_dim = config.hidden_size
        self.self_attn = TFIdeficsVisionAttention(config, name="self_attn")
        self.layer_norm1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm1")
        self.mlp = TFIdeficsVisionMLP(config, name="mlp")
        self.layer_norm2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm2")
    def call(self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, causal_attention_mask: tf.Tensor, output_attentions: Optional[bool] = False) -> Tuple[tf.Tensor]:
        residual = hidden_states
        hidden_states = self.layer_norm1(hidden_states)
        hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask, causal_attention_mask=causal_attention_mask, output_attentions=output_attentions)
        hidden_states = residual + hidden_states
        residual = hidden_states
        hidden_states = self.layer_norm2(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states
        outputs = (hidden_states,)
        if output_attentions: outputs += (attn_weights,)
        return outputs
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "layer_norm1", None) is not None:
            with tf.name_scope(self.layer_norm1.name): self.layer_norm1.build([None, None, self.embed_dim])
        if getattr(self, "layer_norm2", None) is not None:
            with tf.name_scope(self.layer_norm2.name): self.layer_norm2.build([None, None, self.embed_dim])
class TFIdeficsVisionEncoder(tf.keras.layers.Layer):
    def __init__(self, config: IdeficsVisionConfig, **kwargs):
        super().__init__(**kwargs)
        self.config = config
        self.layers = [TFIdeficsVisionEncoderLayer(config, name=f"layers.{i}") for i in range(config.num_hidden_layers)]
        self.gradient_checkpointing = False
    def call(self, inputs_embeds, attention_mask: Optional[tf.Tensor] = None, causal_attention_mask: Optional[tf.Tensor] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: Optional[bool] = None) -> Union[Tuple, TFBaseModelOutput]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        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
        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        hidden_states = inputs_embeds
        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states: encoder_states = encoder_states + (hidden_states,)
            if self.gradient_checkpointing and training:
                def create_custom_forward(module):
                    def custom_forward(*inputs): return module(*inputs, output_attentions)
                    return custom_forward
                layer_outputs = tf.recompute_grad(create_custom_forward(encoder_layer), hidden_states, attention_mask, causal_attention_mask)
            else: layer_outputs = encoder_layer(hidden_states, attention_mask, causal_attention_mask, output_attentions=output_attentions)
            hidden_states = layer_outputs[0]
            if output_attentions: all_attentions = all_attentions + (layer_outputs[1],)
        if output_hidden_states: encoder_states = encoder_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
        return TFBaseModelOutput(last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions)
    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)
class TFIdeficsVisionTransformer(TFPreTrainedModel):
    def __init__(self, config: IdeficsVisionConfig, **kwargs):
        super().__init__(config, **kwargs)
        self.config = config
        self.embed_dim = config.hidden_size
        self.embeddings = TFIdeficsVisionEmbeddings(config, name="embeddings")
        self.pre_layrnorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="pre_layrnorm")
        self.encoder = TFIdeficsVisionEncoder(config, name="encoder")
        self.post_layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="post_layernorm")
    def call(self, pixel_values: Optional[tf.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = False,
    return_dict: Optional[bool] = None, training: Optional[bool] = False) -> Union[Tuple, TFBaseModelOutputWithPooling]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        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")
        hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
        hidden_states = self.pre_layrnorm(hidden_states)
        encoder_outputs = self.encoder(inputs_embeds=hidden_states, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training)
        last_hidden_state = encoder_outputs[0]
        pooled_output = last_hidden_state[:, 0, :]
        pooled_output = self.post_layernorm(pooled_output)
        if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:]
        return TFBaseModelOutputWithPooling(last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions)
    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, "pre_layrnorm", None) is not None:
            with tf.name_scope(self.pre_layrnorm.name): self.pre_layrnorm.build([None, None, self.embed_dim])
        if getattr(self, "encoder", None) is not None:
            with tf.name_scope(self.encoder.name): self.encoder.build(None)
        if getattr(self, "post_layernorm", None) is not None:
            with tf.name_scope(self.post_layernorm.name): self.post_layernorm.build([None, self.embed_dim])
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
