
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





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase       :    Any         =		{
    "configuration_openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig"],
    "tokenization_openai": ["OpenAIGPTTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :    Optional[int]         =		["OpenAIGPTTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :    List[Any]         =		[
      "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "OpenAIGPTDoubleHeadsModel",
      "OpenAIGPTForSequenceClassification",
      "OpenAIGPTLMHeadModel",
      "OpenAIGPTModel",
      "OpenAIGPTPreTrainedModel",
      "load_tf_weights_in_openai_gpt",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCamelCase       :    Tuple         =		[
      "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFOpenAIGPTDoubleHeadsModel",
      "TFOpenAIGPTForSequenceClassification",
      "TFOpenAIGPTLMHeadModel",
      "TFOpenAIGPTMainLayer",
      "TFOpenAIGPTModel",
      "TFOpenAIGPTPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
  from .tokenization_openai import OpenAIGPTTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_openai_fast import OpenAIGPTTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_openai import (
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OpenAIGPTDoubleHeadsModel,
        OpenAIGPTForSequenceClassification,
        OpenAIGPTLMHeadModel,
        OpenAIGPTModel,
        OpenAIGPTPreTrainedModel,
        load_tf_weights_in_openai_gpt,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_openai import (
        TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFOpenAIGPTDoubleHeadsModel,
        TFOpenAIGPTForSequenceClassification,
        TFOpenAIGPTLMHeadModel,
        TFOpenAIGPTMainLayer,
        TFOpenAIGPTModel,
        TFOpenAIGPTPreTrainedModel,
    )

else:
  import sys

  __UpperCamelCase       :    str         =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)