




import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       DDIMPipeline
						UpperCamelCase      =       UNCONDITIONAL_IMAGE_GENERATION_PARAMS
						UpperCamelCase      =       PipelineTesterMixin.required_optional_params - {
						    """num_images_per_prompt""",
						    """latents""",
						    """callback""",
						    """callback_steps""",
						}
						UpperCamelCase      =       UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
						UpperCamelCase      =       False

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						Optional[Any]										=UNetaDModel(
										    block_out_channels=(32, 64)     ,			layers_per_block=2     ,			sample_size=32     ,			in_channels=3     ,			out_channels=3     ,			down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")     ,			up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")     ,			)
										__magic_name__						:						str										=DDIMScheduler()
										__magic_name__						:						str										={"""unet""": unet, """scheduler""": scheduler}
										return components

						def      A__    (		self :Any     ,			__snake_case :Union[str, Any]     ,			__snake_case :Any=0			):
										'''simple docstring'''

										if str(__snake_case			).startswith("""mps"""			):
														__magic_name__						:						List[str]										=torch.manual_seed(__snake_case			)
										else:
														__magic_name__						:						List[str]										=torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
										__magic_name__						:						str										={
										    """batch_size""": 1,
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """output_type""": """numpy""",
										}
										return inputs

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Any										="""cpu"""

										__magic_name__						:						Tuple										=self.get_dummy_components()
										__magic_name__						:						Union[str, Any]										=self.pipeline_class(**__snake_case			)
										pipe.to(__snake_case			)
										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						str										=self.get_dummy_inputs(__snake_case			)
										__magic_name__						:						int										=pipe(**__snake_case			).images
										__magic_name__						:						Any										=image[0, -3:, -3:, -1]

										self.assertEqual(image.shape     ,			(1, 32, 32, 3)			)
										__magic_name__						:						List[str]										=np.array(
										    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]			)
										__magic_name__						:						Tuple										=np.abs(image_slice.flatten() - expected_slice			).max()
										self.assertLessEqual(__snake_case     ,			1E-3			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3			)

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										super().test_save_load_local(expected_max_difference=3E-3			)

						def      A__    (		self :str			):
										'''simple docstring'''

										super().test_save_load_optional_components(expected_max_difference=3E-3			)


						def      A__    (		self :List[str]			):
										'''simple docstring'''

										super().test_inference_batch_single_identical(expected_max_diff=3E-3			)




@slow
@require_torch_gpu
class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										="""google/ddpm-cifar10-32"""

										__magic_name__						:						Dict										=UNetaDModel.from_pretrained(__snake_case			)
										__magic_name__						:						Dict										=DDIMScheduler()

										__magic_name__						:						Optional[int]										=DDIMPipeline(unet=__snake_case     ,			scheduler=__snake_case			)
										ddim.to(__snake_case			)
										ddim.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Optional[Any]										=torch.manual_seed(0			)
										__magic_name__						:						int										=ddim(generator=__snake_case     ,			eta=0.0     ,			output_type="""numpy"""			).images

										__magic_name__						:						Tuple										=image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										__magic_name__						:						List[Any]										=np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]			)

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


						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										="""google/ddpm-ema-bedroom-256"""

										__magic_name__						:						str										=UNetaDModel.from_pretrained(__snake_case			)
										__magic_name__						:						Any										=DDIMScheduler.from_pretrained(__snake_case			)

										__magic_name__						:						Any										=DDIMPipeline(unet=__snake_case     ,			scheduler=__snake_case			)
										ddpm.to(__snake_case			)
										ddpm.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Union[str, Any]										=torch.manual_seed(0			)
										__magic_name__						:						List[str]										=ddpm(generator=__snake_case     ,			output_type="""numpy"""			).images

										__magic_name__						:						Dict										=image[0, -3:, -3:, -1]

										assert image.shape == (1, 2_56, 2_56, 3)
										__magic_name__						:						Union[str, Any]										=np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]			)

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Dict     						=  'biogpt'
      def __init__(      self		,						lowerCamelCase=4_23_84		,						lowerCamelCase=10_24		,						lowerCamelCase=24		,						lowerCamelCase=16		,						lowerCamelCase=40_96		,						lowerCamelCase="gelu"		,						lowerCamelCase=0.1		,						lowerCamelCase=0.1		,						lowerCamelCase=10_24		,						lowerCamelCase=0.0_2		,						lowerCamelCase=1e-12		,						lowerCamelCase=True		,						lowerCamelCase=True		,						lowerCamelCase=0.0		,						lowerCamelCase=0.0		,						lowerCamelCase=1		,						lowerCamelCase=0		,						lowerCamelCase=2		,						**lowerCamelCase		,						):
          snake_case__    =					vocab_size
          snake_case__    =					max_position_embeddings
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					initializer_range
          snake_case__    =					layer_norm_eps
          snake_case__    =					scale_embedding
          snake_case__    =					use_cache
          snake_case__    =					layerdrop
          snake_case__    =					activation_dropout
          super().__init__(pad_token_id=lowerCamelCase		,						bos_token_id=lowerCamelCase		,						eos_token_id=lowerCamelCase		,						**lowerCamelCase			)



