




import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 			a__    (					unittest.TestCase      ):






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											torch.manual_seed(0			)
											__lowerCAmelCase				   =       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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.dummy_uncond_unet
											__lowerCAmelCase				   =       PNDMScheduler()

											__lowerCAmelCase				   =       PNDMPipeline(unet=A_		,			scheduler=A_			)
											pndm.to(A_			)
											pndm.set_progress_bar_config(disable=A_			)

											__lowerCAmelCase				   =       torch.manual_seed(0			)
											__lowerCAmelCase				   =       pndm(generator=A_		,			num_inference_steps=2_0		,			output_type="numpy"			).images

											__lowerCAmelCase				   =       torch.manual_seed(0			)
											__lowerCAmelCase				   =       pndm(generator=A_		,			num_inference_steps=2_0		,			output_type="numpy"		,			return_dict=A_			)[0]

											__lowerCAmelCase				   =       image[0, -3:, -3:, -1]
											__lowerCAmelCase				   =       image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 3_2, 3_2, 3)
											__lowerCAmelCase				   =       np.array([1.0, 1.0, 0.0, 1.0, 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 			a__    (					unittest.TestCase      ):






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "google/ddpm-cifar10-32"

											__lowerCAmelCase				   =       UNetaDModel.from_pretrained(A_			)
											__lowerCAmelCase				   =       PNDMScheduler()

											__lowerCAmelCase				   =       PNDMPipeline(unet=A_		,			scheduler=A_			)
											pndm.to(A_			)
											pndm.set_progress_bar_config(disable=A_			)
											__lowerCAmelCase				   =       torch.manual_seed(0			)
											__lowerCAmelCase				   =       pndm(generator=A_		,			output_type="numpy"			).images

											__lowerCAmelCase				   =       image[0, -3:, -3:, -1]

											assert image.shape == (1, 3_2, 3_2, 3)
											__lowerCAmelCase				   =       np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]			)

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










from typing import TYPE_CHECKING

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


lowerCamelCase				:		Union[str, Any]     =   {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Optional[Any]     =   ["ConvBertTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Dict     =   [
     "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConvBertForMaskedLM",
     "ConvBertForMultipleChoice",
     "ConvBertForQuestionAnswering",
     "ConvBertForSequenceClassification",
     "ConvBertForTokenClassification",
     "ConvBertLayer",
     "ConvBertModel",
     "ConvBertPreTrainedModel",
     "load_tf_weights_in_convbert",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Tuple     =   [
     "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFConvBertForMaskedLM",
     "TFConvBertForMultipleChoice",
     "TFConvBertForQuestionAnswering",
     "TFConvBertForSequenceClassification",
     "TFConvBertForTokenClassification",
     "TFConvBertLayer",
     "TFConvBertModel",
     "TFConvBertPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
 from .tokenization_convbert import ConvBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_convbert_fast import ConvBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convbert import (
      CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvBertForMaskedLM,
      ConvBertForMultipleChoice,
      ConvBertForQuestionAnswering,
      ConvBertForSequenceClassification,
      ConvBertForTokenClassification,
      ConvBertLayer,
      ConvBertModel,
      ConvBertPreTrainedModel,
      load_tf_weights_in_convbert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_convbert import (
      TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFConvBertForMaskedLM,
      TFConvBertForMultipleChoice,
      TFConvBertForQuestionAnswering,
      TFConvBertForSequenceClassification,
      TFConvBertForTokenClassification,
      TFConvBertLayer,
      TFConvBertModel,
      TFConvBertPreTrainedModel,
  )


else:
 import sys

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



