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.                                                                 #
    #################################################################################################################################################################################################
"""
import re
import warnings
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...configuration_utils import PretrainedConfig
from ...modeling_tf_outputs import TFBaseModelOutput, TFSeq2SeqLMOutput
from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFPreTrainedModel, get_initializer, keras, unpack_inputs
from ...tf_utils import shape_list
from ...utils import (ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from ..auto.configuration_auto import AutoConfig
from ..auto.modeling_tf_auto import TFAutoModel, TFAutoModelForCausalLM
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "VisionEncoderDecoderConfig"
DEPRECATION_WARNING = ("Version v4.17.0 introduces a better way to train encoder-decoder models by computing the loss inside the"
" encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if"
" fine-tuning a model trained with versions anterior to 4.17.0. The decoder_input_ids are now created based on the"
" labels, no need to pass them yourself anymore.")
VISION_ENCODER_DECODER_START_DOCSTRING = r"""
    This class can be used to initialize an image-to-text-sequence model with any pretrained vision autoencoding model
    as the encoder and any pretrained text autoregressive model as the decoder. The encoder is loaded via
    [`~TFAutoModel.from_pretrained`] function and the decoder is loaded via [`~TFAutoModelForCausalLM.from_pretrained`]
    function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream
    generative task, like image captioning.
    The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation
    tasks was shown in [Leveraging Pre-trained Checkpoints for Sequence Generation
    Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi
    Zhou, Wei Li, Peter J. Liu.
    Additionally, in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained
    Models](https://arxiv.org/abs/2109.10282) it is shown how leveraging large pretrained vision models for optical
    character recognition (OCR) yields a significant performance improvement.
    After such a Vision-Encoder-Text-Decoder model has been trained/fine-tuned, it can be saved/loaded just like any
    other models (see the examples for more information).
    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.
    Parameters:
        config ([`VisionEncoderDecoderConfig`]): 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.
"""
VISION_ENCODER_DECODER_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 the vision's model's image processor. For example, using
            [`AutoImageProcessor`]. See [`ViTImageProcessor.__call__`] for details.
        decoder_input_ids (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary.
            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).
            Provide for sequence to sequence training to the decoder. Indices can be obtained using
            [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for
            details.
        decoder_attention_mask (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.
        encoder_outputs (`tuple(tuple(tf.Tensor)`, *optional*):
            This tuple must consist of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
            `last_hidden_state` (`tf.Tensor` of shape `(0, hidden_size)`) is a tensor of hidden-states at the output
            of the last layer of the encoder. Used in the cross-attention of the decoder.
        past_key_values (`tuple(tuple(tf.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(0)`.
        decoder_inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
            representation. This is useful if you want more control over how to convert `decoder_input_ids` indices
            into associated vectors than the model's internal embedding lookup matrix.
        labels (`np.ndarray` or `tf.Tensor` of shape `(0)`, *optional*):
            Labels for computing the masked language modeling loss for the decoder. Indices should be in `[-100, 0,
            ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        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.
        return_dict (`bool`, *optional*):
            If set to `True`, the model will return a [`~utils.Seq2SeqLMOutput`] instead of a plain tuple.
        training (`bool`, *optional*, defaults to `False`):
            Whether or not to use the model in training mode (some modules like dropout modules have different
            behaviors between training and evaluation).
        kwargs (optional): Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:
            - Without a prefix which will be input as `encoder_kwargs` for the encoder forward function.
            - With a *decoder_* prefix which will be input as `decoder_kwargs` for the decoder forward function.
"""
def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int):
    if pad_token_id is None: raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.")
    pad_token_id = tf.cast(pad_token_id, input_ids.dtype)
    if decoder_start_token_id is None: raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.")
    decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype)
    start_tokens = tf.fill((shape_list(input_ids)[0], 1), decoder_start_token_id)
    shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1)
    shifted_input_ids = tf.where(shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids)
    assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=input_ids.dtype))
    with tf.control_dependencies([assert_gte0]): shifted_input_ids = tf.identity(shifted_input_ids)
    return shifted_input_ids
@add_start_docstrings(VISION_ENCODER_DECODER_START_DOCSTRING)
class TFVisionEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLoss):
    config_class = VisionEncoderDecoderConfig
    base_model_prefix = "vision_encoder_decoder"
    load_weight_prefix = "tf_vision_encoder_decoder_model"
    main_input_name = "pixel_values"
    def __init__(self, config: Optional[PretrainedConfig] = None, encoder: Optional[TFPreTrainedModel] = None, decoder: Optional[TFPreTrainedModel] = None):
        if config is None and (encoder is None or decoder is None): raise ValueError("Either a configuration or an encoder and a decoder has to be provided.")
        if config is None: config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config)
        else:
            if not isinstance(config, self.config_class): raise ValueError(f"config: {config} has to be of type {self.config_class}")
        if config.decoder.cross_attention_hidden_size is not None:
            if config.decoder.cross_attention_hidden_size != config.encoder.hidden_size: raise ValueError(f"If `cross_attention_hidden_size` is specified in the decoder's configuration, it has to be equal to the encoder's `hidden_size`. Got {config.decoder.cross_attention_hidden_size} for `config.decoder.cross_attention_hidden_size` and {config.encoder.hidden_size} for `config.encoder.hidden_size`.")
        super().__init__(config)
        if encoder is None: encoder = TFAutoModel.from_config(config.encoder, name="encoder")
        if decoder is None: decoder = TFAutoModelForCausalLM.from_config(config.decoder, name="decoder")
        self.encoder = encoder
        self.decoder = decoder
        if self.encoder.config.to_dict() != self.config.encoder.to_dict(): logger.warning(f"Config of the encoder: {self.encoder.__class__} is overwritten by shared encoder config: {self.config.encoder}")
        if self.decoder.config.to_dict() != self.config.decoder.to_dict(): logger.warning(f"Config of the decoder: {self.decoder.__class__} is overwritten by shared decoder config: {self.config.decoder}")
        self.encoder.config = self.config.encoder
        self.decoder.config = self.config.decoder
        if (self.encoder.config.hidden_size != self.decoder.config.hidden_size and self.decoder.config.cross_attention_hidden_size is None): self.enc_to_dec_proj = keras.layers.Dense(units=self.decoder.config.hidden_size, kernel_initializer=get_initializer(config.encoder.initializer_range), name="enc_to_dec_proj")
        if self.encoder.get_output_embeddings() is not None: raise ValueError(f"The encoder {self.encoder} should not have a LM Head. Please use a model without LM Head")
    @property
    def input_signature(self):
        vision_config = self.config.encoder
        if hasattr(vision_config, "vision_config"): vision_config = vision_config.vision_config
        if hasattr(vision_config, "image_size"): image_size = vision_config.image_size
        else: image_size = vision_config.input_size
        return {"pixel_values": tf.TensorSpec(shape=(None, vision_config.num_channels, image_size, image_size), dtype=tf.float32), "decoder_input_ids": tf.TensorSpec(shape=(None, None), dtype=tf.int32, name="decoder_input_ids")}
    def get_encoder(self): return self.encoder
    def get_decoder(self): return self.decoder
    def get_input_embeddings(self): return self.encoder.get_input_embeddings()
    def get_output_embeddings(self): return self.decoder.get_output_embeddings()
    def set_output_embeddings(self, new_embeddings): return self.decoder.set_output_embeddings(new_embeddings)
    def tf_to_pt_weight_rename(self, tf_weight):
        encoder_model_type = self.config.encoder.model_type
        if "encoder" in tf_weight and "decoder" not in tf_weight: return (re.sub(rf"encoder\.{encoder_model_type}\.", "encoder.", tf_weight),)
        else: return (tf_weight,)
    @classmethod
    def from_encoder_decoder_pretrained(cls, encoder_pretrained_model_name_or_path: str = None, decoder_pretrained_model_name_or_path: str = None, *model_args, **kwargs) -> TFPreTrainedModel:
        kwargs_encoder = {argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")}
        kwargs_decoder = {argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")}
        for key in kwargs_encoder.keys(): del kwargs["encoder_" + key]
        for key in kwargs_decoder.keys(): del kwargs["decoder_" + key]
        encoder = kwargs_encoder.pop("model", None)
        if encoder is None:
            if encoder_pretrained_model_name_or_path is None: raise ValueError("If `encoder_model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has to be defined.")
            if "config" not in kwargs_encoder:
                encoder_config = AutoConfig.from_pretrained(encoder_pretrained_model_name_or_path)
                if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
                    logger.info(f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model from a decoder model. Cross-attention and casual mask are disabled.")
                    encoder_config.is_decoder = False
                    encoder_config.add_cross_attention = False
                kwargs_encoder["config"] = encoder_config
            kwargs_encoder["name"] = "encoder"
            kwargs_encoder["load_weight_prefix"] = cls.load_weight_prefix
            encoder = TFAutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder)
        decoder = kwargs_decoder.pop("model", None)
        if decoder is None:
            if decoder_pretrained_model_name_or_path is None: raise ValueError("If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has to be defined.")
            if "config" not in kwargs_decoder:
                decoder_config = AutoConfig.from_pretrained(decoder_pretrained_model_name_or_path)
                if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
                    logger.info(f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers.")
                    decoder_config.is_decoder = True
                    decoder_config.add_cross_attention = True
                kwargs_decoder["config"] = decoder_config
            if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False: logger.warning(f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` passed to `.from_encoder_decoder_pretrained(...)` are set to `True` or do not pass a `decoder_config` to `.from_encoder_decoder_pretrained(...)`")
            kwargs_decoder["name"] = "decoder"
            kwargs_decoder["load_weight_prefix"] = cls.load_weight_prefix
            decoder = TFAutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
        if encoder.name != "encoder": raise ValueError("encoder model must be created with the name `encoder`.")
        if decoder.name != "decoder": raise ValueError("decoder model must be created with the name `decoder`.")
        config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)
        return cls(encoder=encoder, decoder=decoder, config=config)
    @unpack_inputs
    @add_start_docstrings_to_model_forward(VISION_ENCODER_DECODER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def call(self, pixel_values: np.ndarray | tf.Tensor | None = None, decoder_input_ids: np.ndarray | tf.Tensor | None = None, decoder_attention_mask: np.ndarray | tf.Tensor | None = None,
    encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, decoder_inputs_embeds: np.ndarray | tf.Tensor | None = None,
    labels: np.ndarray | tf.Tensor | None = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None, training: bool = False, **kwargs) -> Union[TFSeq2SeqLMOutput, Tuple[tf.Tensor]]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}
        kwargs_decoder = {argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")}
        if encoder_outputs is not None:
            if return_dict and not isinstance(encoder_outputs, ModelOutput): raise ValueError(f"If `return_dict=True` and `encoder_outputs` is provided, it should be an instance of `ModelOutput`. Got an instance {type(encoder_outputs)} for `encoder_outputs`.")
        if encoder_outputs is None:
            encoder_inputs = {"input_ids": pixel_values, "output_attentions": output_attentions, "output_hidden_states": output_hidden_states, "return_dict": return_dict, "training": training}
            encoder_inputs.update(kwargs_encoder)
            if "input_ids" in encoder_inputs: encoder_inputs["pixel_values"] = encoder_inputs.pop("input_ids")
            if encoder_inputs["pixel_values"] is None: raise ValueError("You have to specify pixel_values")
            if "labels" in encoder_inputs: labels = encoder_inputs.pop("labels")
            if "decoder_input_ids" in encoder_inputs: decoder_input_ids = encoder_inputs.pop("decoder_input_ids")
            if "decoder_attention_mask" in encoder_inputs: decoder_attention_mask = encoder_inputs.pop("decoder_attention_mask")
            encoder_outputs = self.encoder(**encoder_inputs)
        encoder_hidden_states = encoder_outputs[0]
        if (self.encoder.config.hidden_size != self.decoder.config.hidden_size and self.decoder.config.cross_attention_hidden_size is None): encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states)
        if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None): decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
        batch_size, sequence_length = shape_list(encoder_hidden_states)[:2]
        encoder_attention_mask = tf.ones(shape=(batch_size, sequence_length), dtype=tf.int32)
        decoder_inputs = {"input_ids": decoder_input_ids, "attention_mask": decoder_attention_mask, "encoder_hidden_states": encoder_hidden_states, "encoder_attention_mask": encoder_attention_mask,
        "inputs_embeds": decoder_inputs_embeds, "output_attentions": output_attentions, "output_hidden_states": output_hidden_states, "use_cache": use_cache,
        "past_key_values": past_key_values, "return_dict": return_dict, "training": training}
        decoder_inputs.update(kwargs_decoder)
        decoder_outputs = self.decoder(**decoder_inputs)
        logits = decoder_outputs[0]
        loss = None
        if labels is not None:
            warnings.warn(DEPRECATION_WARNING, FutureWarning)
            loss = self.hf_compute_loss(labels, logits)
        if not return_dict:
            past_key_values = None
            if use_cache: past_key_values = decoder_outputs[1]
            start_index = sum([1 if x is not None else 0 for x in (loss, logits, past_key_values)])
            if not isinstance(encoder_outputs, tuple): encoder_outputs = encoder_outputs.to_tuple()
            output = (loss, logits, past_key_values) + decoder_outputs[start_index:] + encoder_outputs
            output = tuple([x for x in output if x is not None])
            return output
        return TFSeq2SeqLMOutput(loss=loss, logits=decoder_outputs.logits, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states,
        decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state,
        encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions)
    def serving_output(self, output):
        pkv = tf.tuple(output.past_key_values)[1] if self.config.decoder.use_cache else None
        dec_hs = (tf.convert_to_tensor(output.decoder_hidden_states) if self.config.decoder.output_hidden_states else None)
        dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.decoder.output_attentions else None
        enc_hs = (tf.convert_to_tensor(output.encoder_hidden_states) if self.config.encoder.output_hidden_states else None)
        enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.encoder.output_attentions else None
        cross_attns = (tf.convert_to_tensor(output.cross_attentions) if self.config.decoder.output_attentions and output.cross_attentions is not None else None)
        return TFSeq2SeqLMOutput(logits=output.logits, past_key_values=pkv, decoder_hidden_states=dec_hs, decoder_attentions=dec_attns, encoder_last_hidden_state=output.encoder_last_hidden_state,
        encoder_hidden_states=enc_hs, encoder_attentions=enc_attns, cross_attentions=cross_attns)
    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs):
        decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids, past_key_values=past_key_values)
        decoder_attention_mask = decoder_inputs["attention_mask"] if "attention_mask" in decoder_inputs else None
        past_key_values = decoder_inputs.get("past_key_values")
        input_dict = {"pixel_values": None, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "decoder_input_ids": decoder_inputs["input_ids"],
        "encoder_outputs": TFBaseModelOutput(last_hidden_state=encoder_outputs[0]), "past_key_values": past_key_values, "use_cache": use_cache}
        return input_dict
    def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor): return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
    def resize_token_embeddings(self, *args, **kwargs): raise NotImplementedError("Resizing the embedding layers via the TFVisionEncoderDecoderModel directly is not supported. Please use the respective methods of the wrapped objects (model.decoder.resize_token_embeddings(...))")
    def build(self, input_shape=None):
        if self.built: return
        self.built = True
        if getattr(self, "enc_to_dec_proj", None) is not None:
            with tf.name_scope(self.enc_to_dec_proj.name): self.enc_to_dec_proj.build([None, None, self.encoder.config.hidden_size])
        if getattr(self, "encoder", None) is not None:
            with tf.name_scope(self.encoder.name): self.encoder.build(None)
        if getattr(self, "decoder", None) is not None:
            with tf.name_scope(self.decoder.name): self.decoder.build(None)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
