

"""simple docstring"""


import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class        snake_case      (							unittest.TestCase    ):

				@property
				def 				lowercase_	(      self				:							List[str])->  List[Any]:



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:			Tuple        =     UNetaDModel(
						    block_out_channels=(3_2, 6_4)	,    layers_per_block=2	,    sample_size=3_2	,    in_channels=3	,    out_channels=3	,    down_block_types=("DownBlock2D", "AttnDownBlock2D")	,    up_block_types=("AttnUpBlock2D", "UpBlock2D")	,    )
						return model




				def 				lowercase_	(      self				:							Tuple)->  int:



						'''simple docstring'''


						__lowerCAmelCase:			Tuple        =     self.dummy_uncond_unet
						__lowerCAmelCase:			Optional[int]        =     KarrasVeScheduler()

						__lowerCAmelCase:			str        =     KarrasVePipeline(unet=UpperCAmelCase__	,    scheduler=UpperCAmelCase__)
						pipe.to(UpperCAmelCase__)
						pipe.set_progress_bar_config(disable=UpperCAmelCase__)

						__lowerCAmelCase:			Union[str, Any]        =     torch.manual_seed(0)
						__lowerCAmelCase:			str        =     pipe(num_inference_steps=2	,    generator=UpperCAmelCase__	,    output_type="numpy").images

						__lowerCAmelCase:			Dict        =     torch.manual_seed(0)
						__lowerCAmelCase:			int        =     pipe(num_inference_steps=2	,    generator=UpperCAmelCase__	,    output_type="numpy"	,    return_dict=UpperCAmelCase__)[0]

						__lowerCAmelCase:			List[str]        =     image[0, -3:, -3:, -1]
						__lowerCAmelCase:			Optional[Any]        =     image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						__lowerCAmelCase:			Union[str, Any]        =     np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2





@slow
@require_torch
class        snake_case      (							unittest.TestCase    ):

				def 				lowercase_	(      self				:							Tuple)->  List[str]:



						'''simple docstring'''


						__lowerCAmelCase:			List[Any]        =     "google/ncsnpp-celebahq-256"
						__lowerCAmelCase:			int        =     UNetaDModel.from_pretrained(UpperCAmelCase__)
						__lowerCAmelCase:			List[Any]        =     KarrasVeScheduler()

						__lowerCAmelCase:			str        =     KarrasVePipeline(unet=UpperCAmelCase__	,    scheduler=UpperCAmelCase__)
						pipe.to(UpperCAmelCase__)
						pipe.set_progress_bar_config(disable=UpperCAmelCase__)

						__lowerCAmelCase:			Optional[Any]        =     torch.manual_seed(0)
						__lowerCAmelCase:			int        =     pipe(num_inference_steps=2_0	,    generator=UpperCAmelCase__	,    output_type="numpy").images

						__lowerCAmelCase:			int        =     image[0, -3:, -3:, -1]
						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						__lowerCAmelCase:			Tuple        =     np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2








from typing import TYPE_CHECKING

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


_lowerCamelCase      :						Any							=  {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						int							=  ["""XLNetTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						int							=  ["""XLNetTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						Union[str, Any]							=  [
      """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XLNetForMultipleChoice""",
      """XLNetForQuestionAnswering""",
      """XLNetForQuestionAnsweringSimple""",
      """XLNetForSequenceClassification""",
      """XLNetForTokenClassification""",
      """XLNetLMHeadModel""",
      """XLNetModel""",
      """XLNetPreTrainedModel""",
      """load_tf_weights_in_xlnet""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						Dict							=  [
      """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFXLNetForMultipleChoice""",
      """TFXLNetForQuestionAnsweringSimple""",
      """TFXLNetForSequenceClassification""",
      """TFXLNetForTokenClassification""",
      """TFXLNetLMHeadModel""",
      """TFXLNetMainLayer""",
      """TFXLNetModel""",
      """TFXLNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase      :						int							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


