





from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION

class 	__snake_case					(      snake_case__							,					snake_case__ ):






    '''simple docstring'''





    def __init__( self    :		int				):

      """simple docstring"""





      super().__init__()

      self.register_buffer('''watermark_image'''      ,    torch.zeros((62, 62, 4)				)				)
      __lowerCAmelCase								=					None





    def 						A		( self    :		Dict      ,    snake_case    :		Dict      ,    snake_case    :		Dict=None				):

      """simple docstring"""





      __lowerCAmelCase								=					images[0].height
      __lowerCAmelCase								=					images[0].width

      __lowerCAmelCase								=					sample_size or h

      __lowerCAmelCase								=					min(h / sample_size      ,    w / sample_size				)
      __lowerCAmelCase								=					(int(h / coef				), int(w / coef				)) if coef < 1 else (h, w)

      __lowerCAmelCase								=					1_024**2, img_w * img_h
      __lowerCAmelCase								=					(Sa / Sa) ** 0.5
      __lowerCAmelCase								=					int(K * 62				), img_w - int(14 * K				), img_h - int(14 * K				)

      if self.watermark_image_as_pil is None:
        __lowerCAmelCase								=					self.watermark_image.to(torch.uinta				).cpu().numpy()
        __lowerCAmelCase								=					Image.fromarray(lowerCAmelCase_      ,    mode='''RGBA'''				)
        __lowerCAmelCase								=					watermark_image

      __lowerCAmelCase								=					self.watermark_image_as_pil.resize(
          (wm_size, wm_size)      ,    PIL_INTERPOLATION['''bicubic''']      ,    reducing_gap=lowerCAmelCase_				)

      for pil_img in images:
        pil_img.paste(lowerCAmelCase_      ,    box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)      ,    mask=wm_img.split()[-1]				)

      return images

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,
)


UpperCAmelCase				 =   {"""configuration_pegasus""": ["""PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   ["""PegasusTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   ["""PegasusTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """PegasusForCausalLM""",
      """PegasusForConditionalGeneration""",
      """PegasusModel""",
      """PegasusPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """TFPegasusForConditionalGeneration""",
      """TFPegasusModel""",
      """TFPegasusPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				 =   [
      """FlaxPegasusForConditionalGeneration""",
      """FlaxPegasusModel""",
      """FlaxPegasusPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_pegasus import PegasusTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_pegasus_fast import PegasusTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_pegasus import (
        PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
        PegasusForCausalLM,
        PegasusForConditionalGeneration,
        PegasusModel,
        PegasusPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_pegasus import (
        FlaxPegasusForConditionalGeneration,
        FlaxPegasusModel,
        FlaxPegasusPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase				 =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)