from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase      			=     {
    'configuration_prophetnet': ['PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ProphetNetConfig'],
    'tokenization_prophetnet': ['ProphetNetTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase      			=     [
					    'PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'ProphetNetDecoder',
					    'ProphetNetEncoder',
					    'ProphetNetForCausalLM',
					    'ProphetNetForConditionalGeneration',
					    'ProphetNetModel',
					    'ProphetNetPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
					from .tokenization_prophetnet import ProphetNetTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_prophetnet import (
										    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ProphetNetDecoder,
										    ProphetNetEncoder,
										    ProphetNetForCausalLM,
										    ProphetNetForConditionalGeneration,
										    ProphetNetModel,
										    ProphetNetPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase      			=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .image_encoder import PaintByExampleImageEncoder
					from .pipeline_paint_by_example import PaintByExamplePipeline





