"""
    #################################################################################################################################################################################################
    # 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 functools import partial
from typing import Optional, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from jax.random import PRNGKey
from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxMaskedLMOutput
from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring
from ...utils import add_start_docstrings, logging
from .configuration_opt import OPTConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "facebook/opt-350m"
_CONFIG_FOR_DOC = "OPTConfig"
OPT_START_DOCSTRING = r"""
    This model inherits from [`FlaxPreTrainedModel`]. 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 Flax Linen
    [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
    regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
    Finally, this model supports inherent JAX features such as:
    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
    Parameters:
        config ([`OPTConfig`]): 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 [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
        dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
            The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
            `jax.numpy.bfloat16` (on TPUs).
            This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
            specified all the computation will be performed with the given `dtype`.
            *Note that this only specifies the dtype of the computation and does not influence the dtype of model
            parameters.*
            If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
            [`~FlaxPreTrainedModel.to_bf16`].
"""
OPT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
        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*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
class FlaxOPTAttention(nn.Module):
    config: OPTConfig
    embed_dim: int
    num_heads: int
    dropout: float = 0.0
    causal: bool = False
    bias: bool = True
    dtype: jnp.dtype = jnp.float32
    def setup(self) -> None:
        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}).")
        dense = partial(nn.Dense, self.embed_dim, use_bias=self.bias, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std))
        self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()
        self.out_proj = dense()
        self.dropout_layer = nn.Dropout(rate=self.dropout)
        if self.causal: self.causal_mask = make_causal_mask(jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool")
    def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
    def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
    @nn.compact
    def _concatenate_to_cache(self, key, value, query, attention_mask):
        is_initialized = self.has_variable("cache", "cached_key")
        cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
        cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
        cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
        if is_initialized:
            *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
            cur_index = cache_index.value
            indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
            key = lax.dynamic_update_slice(cached_key.value, key, indices)
            value = lax.dynamic_update_slice(cached_value.value, value, indices)
            cached_key.value = key
            cached_value.value = value
            num_updated_cache_vectors = query.shape[1]
            cache_index.value = cache_index.value + num_updated_cache_vectors
            pad_mask = jnp.broadcast_to(jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length))
            attention_mask = combine_masks(pad_mask, attention_mask)
        return key, value, attention_mask
    def __call__(self, hidden_states: jnp.ndarray, key_value_states: Optional[jnp.ndarray] = None, attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False,
    deterministic: bool = True) -> Tuple[jnp.ndarray]:
        is_cross_attention = key_value_states is not None
        batch_size = hidden_states.shape[0]
        query_states = self.q_proj(hidden_states)
        if is_cross_attention:
            key_states = self.k_proj(key_value_states)
            value_states = self.v_proj(key_value_states)
        else:
            key_states = self.k_proj(hidden_states)
            value_states = self.v_proj(hidden_states)
        query_states = self._split_heads(query_states)
        key_states = self._split_heads(key_states)
        value_states = self._split_heads(value_states)
        if self.causal:
            query_length, key_length = query_states.shape[1], key_states.shape[1]
            if self.has_variable("cache", "cached_key"):
                mask_shift = self.variables["cache"]["cache_index"]
                max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
                causal_mask = lax.dynamic_slice(self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length))
            else: causal_mask = self.causal_mask[:, :, :query_length, :key_length]
            causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
        if attention_mask is not None and self.causal:
            attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
            attention_mask = combine_masks(attention_mask, causal_mask)
        elif self.causal: attention_mask = causal_mask
        elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
        if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache(key_states, value_states, query_states, attention_mask)
        if attention_mask is not None: attention_bias = lax.select(attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype))
        else: attention_bias = None
        dropout_rng = None
        if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout")
        attn_weights = dot_product_attention_weights(query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout,
        broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None)
        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
        attn_output = self._merge_heads(attn_output)
        attn_output = self.out_proj(attn_output)
        return attn_output, attn_weights
class FlaxOPTDecoderLayer(nn.Module):
    config: OPTConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self) -> None:
        self.embed_dim = self.config.hidden_size
        self.self_attn = FlaxOPTAttention(config=self.config, embed_dim=self.embed_dim, num_heads=self.config.num_attention_heads, dropout=self.config.attention_dropout,
        causal=True, dtype=self.dtype)
        self.do_layer_norm_before = self.config.do_layer_norm_before
        self.dropout_layer = nn.Dropout(rate=self.config.dropout)
        self.activation_fn = ACT2FN[self.config.activation_function]
        self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
        self.fc1 = nn.Dense(self.config.ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std))
        self.fc2 = nn.Dense(self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std))
        self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
    def __call__(self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, init_cache: bool = False, output_attentions: bool = True, deterministic: bool = True) -> Tuple[jnp.ndarray]:
        residual = hidden_states
        if self.do_layer_norm_before: hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states, self_attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache, deterministic=deterministic)
        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
        hidden_states = residual + hidden_states
        if not self.do_layer_norm_before: hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states_shape = hidden_states.shape
        hidden_states = hidden_states.reshape(-1, hidden_states.shape[-1])
        residual = hidden_states
        if self.do_layer_norm_before: hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)
        hidden_states = self.fc2(hidden_states)
        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
        hidden_states = (residual + hidden_states).reshape(hidden_states_shape)
        if not self.do_layer_norm_before: hidden_states = self.final_layer_norm(hidden_states)
        outputs = (hidden_states,)
        if output_attentions: outputs += (self_attn_weights,)
        return outputs
class FlaxOPTDecoderLayerCollection(nn.Module):
    config: OPTConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.layers = [FlaxOPTDecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)]
        self.layerdrop = self.config.layerdrop
    def __call__(self, hidden_states, attention_mask, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False):
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        for decoder_layer in self.layers:
            if output_hidden_states: all_hidden_states += (hidden_states,)
            layer_outputs = decoder_layer(hidden_states, attention_mask=attention_mask, init_cache=init_cache, output_attentions=output_attentions, deterministic=deterministic)
            hidden_states = layer_outputs[0]
            if output_attentions: all_self_attns += (layer_outputs[1],)
        outputs = [hidden_states, all_hidden_states, all_self_attns]
        return outputs
class FlaxOPTLearnedPositionalEmbedding(nn.Embed):
    def setup(self):
        self.offset = 2
        self.embedding = self.param("embedding", self.embedding_init, (self.num_embeddings + self.offset, self.features), self.param_dtype)
    def __call__(self, positions): return super().__call__(positions + self.offset)
class FlaxOPTDecoder(nn.Module):
    config: OPTConfig
    dtype: jnp.dtype = jnp.float32
    offset: int = 2
    def setup(self):
        self.dropout_layer = nn.Dropout(rate=self.config.dropout)
        embed_dim = self.config.hidden_size
        self.padding_idx = self.config.pad_token_id
        self.max_target_positions = self.config.max_position_embeddings
        self.embed_tokens = nn.Embed(self.config.vocab_size, self.config.word_embed_proj_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), dtype=self.dtype)
        self.embed_positions = FlaxOPTLearnedPositionalEmbedding(self.config.max_position_embeddings, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), dtype=self.dtype)
        if self.config.word_embed_proj_dim != self.config.hidden_size:
            self.project_in = nn.Dense(self.config.hidden_size, use_bias=False)
            self.project_out = nn.Dense(self.config.word_embed_proj_dim, use_bias=False)
        else:
            self.project_in = None
            self.project_out = None
        if self.config.do_layer_norm_before and not self.config._remove_final_layer_norm: self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
        else: self.final_layer_norm = None
        self.layers = FlaxOPTDecoderLayerCollection(self.config, self.dtype)
    def __call__(self, input_ids, attention_mask, position_ids, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False,
    return_dict: bool = True, deterministic: bool = True):
        input_shape = input_ids.shape
        input_ids = input_ids.reshape(-1, input_shape[-1])
        inputs_embeds = self.embed_tokens(input_ids)
        if self.project_in is not None: inputs_embeds = self.project_in(inputs_embeds)
        positions = self.embed_positions(position_ids)
        hidden_states = inputs_embeds + positions
        hidden_state, all_hidden_states, attentions = self.layers(hidden_states, attention_mask, deterministic=deterministic, init_cache=init_cache,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states)
        if self.final_layer_norm is not None: hidden_state = self.final_layer_norm(hidden_state)
        if self.project_out is not None: hidden_state = self.project_out(hidden_state)
        if output_hidden_states: all_hidden_states += (hidden_state,)
        outputs = [hidden_state, all_hidden_states, attentions]
        if not return_dict: return tuple(v for v in outputs if v is not None)
        return FlaxBaseModelOutput(last_hidden_state=hidden_state, hidden_states=all_hidden_states, attentions=attentions)
class FlaxOPTPreTrainedModel(FlaxPreTrainedModel):
    config_class = OPTConfig
    base_model_prefix: str = "model"
    module_class: nn.Module = None
    def __init__(self, config: OPTConfig, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs):
        module = self.module_class(config=config, dtype=dtype, **kwargs)
        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        input_ids = jnp.zeros(input_shape, dtype="i4")
        attention_mask = jnp.ones_like(input_ids)
        batch_size, sequence_length = input_ids.shape
        position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}
        module_init_outputs = self.module.init(rngs, input_ids, attention_mask, position_ids, return_dict=False)
        random_params = module_init_outputs["params"]
        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else: return random_params
    def init_cache(self, batch_size, max_length):
        input_ids = jnp.ones((batch_size, max_length), dtype="i4")
        attention_mask = jnp.ones_like(input_ids, dtype="i4")
        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
        init_variables = self.module.init(jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True)
        return unfreeze(init_variables["cache"])
    def __call__(self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, params: dict = None,
    past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None,
    dropout_rng: PRNGKey = None, deterministic: bool = True):
        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.return_dict
        if attention_mask is None: attention_mask = jnp.ones_like(input_ids)
        if position_ids is None: position_ids = (attention_mask.cumsum(axis=1) * attention_mask) - 1
        rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
        inputs = {"params": params or self.params}
        if past_key_values:
            inputs["cache"] = past_key_values
            mutable = ["cache"]
        else: mutable = False
        outputs = self.module.apply(inputs, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"),
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, rngs=rngs, mutable=mutable)
        if past_key_values is not None and return_dict:
            outputs, past_key_values = outputs
            outputs["past_key_values"] = unfreeze(past_key_values["cache"])
            return outputs
        elif past_key_values is not None and not return_dict:
            outputs, past_key_values = outputs
            outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]
        return outputs
class FlaxOPTModule(nn.Module):
    config: OPTConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self): self.decoder = FlaxOPTDecoder(self.config, dtype=self.dtype)
    def _get_decoder_module(self): return self.decoder
    def __call__(self, input_ids, attention_mask, position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True,
    deterministic: bool = True, init_cache=False):
        decoder_outputs = self.decoder(input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, init_cache=init_cache)
        if not return_dict: return decoder_outputs
        return FlaxBaseModelOutput(last_hidden_state=decoder_outputs.last_hidden_state, hidden_states=decoder_outputs.hidden_states, attentions=decoder_outputs.attentions)
class FlaxOPTModel(FlaxOPTPreTrainedModel):
    config: OPTConfig
    dtype: jnp.dtype = jnp.float32
    module_class = FlaxOPTModule
append_call_sample_docstring(FlaxOPTModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC)
@add_start_docstrings("The bare OPT Model transformer outputting raw hidden-states without any specific head on top.", OPT_START_DOCSTRING)
class FlaxOPTForCausalLMModule(nn.Module):
    config: OPTConfig
    dtype: jnp.dtype = jnp.float32
    def setup(self):
        self.model = FlaxOPTModule(config=self.config, dtype=self.dtype)
        self.lm_head = nn.Dense(self.config.vocab_size, use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std))
    def __call__(self, input_ids, attention_mask, position_ids, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False,
    return_dict: bool = True, deterministic: bool = True):
        outputs = self.model(input_ids, attention_mask, position_ids, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states,
        return_dict=return_dict, deterministic=deterministic)
        hidden_states = outputs[0]
        if self.config.tie_word_embeddings:
            shared_embedding = self.model.variables["params"]["decoder"]["embed_tokens"]["embedding"]
            lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
        else: lm_logits = self.lm_head(hidden_states)
        if not return_dict: return (lm_logits,) + outputs[1:]
        return FlaxMaskedLMOutput(logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("OPT Model with a language modeling head on top (linear layer with weights tied to the input embeddings) e.g for autoregressive tasks.", OPT_START_DOCSTRING)
class FlaxOPTForCausalLM(FlaxOPTPreTrainedModel):
    module_class = FlaxOPTForCausalLMModule
    def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
        batch_size, seq_length = input_ids.shape
        past_key_values = self.init_cache(batch_size, max_length)
        extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
        if attention_mask is not None:
            position_ids = attention_mask.cumsum(axis=1) - 1
            extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
        else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
        return {"past_key_values": past_key_values, "attention_mask": extended_attention_mask, "position_ids": position_ids}
    def update_inputs_for_generation(self, model_outputs, model_kwargs):
        model_kwargs["past_key_values"] = model_outputs.past_key_values
        model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
        return model_kwargs
append_call_sample_docstring(FlaxOPTForCausalLM, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
