"""
    #################################################################################################################################################################################################
    # 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 TYPE_CHECKING
from ...utils import (OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_sentencepiece_available, is_tf_available, is_tokenizers_available, is_torch_available)
_import_structure = {"configuration_xglm": ["XGLMConfig"]}
try:
    if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else: _import_structure["tokenization_xglm"] = ["XGLMTokenizer"]
try:
    if not is_tokenizers_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else: _import_structure["tokenization_xglm_fast"] = ["XGLMTokenizerFast"]
try:
    if not is_torch_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else: _import_structure["modeling_xglm"] = ["XGLMForCausalLM", "XGLMModel", "XGLMPreTrainedModel"]
try:
    if not is_flax_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else: _import_structure["modeling_flax_xglm"] = ["FlaxXGLMForCausalLM", "FlaxXGLMModel", "FlaxXGLMPreTrainedModel"]
try:
    if not is_tf_available(): raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable: pass
else: _import_structure["modeling_tf_xglm"] = ["TFXGLMForCausalLM", "TFXGLMModel", "TFXGLMPreTrainedModel"]
if TYPE_CHECKING:
    from .configuration_xglm import XGLMConfig
    try:
        if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable: pass
    else: from .tokenization_xglm import XGLMTokenizer
    try:
        if not is_tokenizers_available(): raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable: pass
    else: from .tokenization_xglm_fast import XGLMTokenizerFast
    try:
        if not is_torch_available(): raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable: pass
    else: from .modeling_xglm import XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
    try:
        if not is_flax_available(): raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable: pass
    else: from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
    try:
        if not is_tf_available(): raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable: pass
    else: from .modeling_tf_xglm import (TFXGLMForCausalLM, TFXGLMModel, TFXGLMPreTrainedModel)
else:
    import sys
    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
