






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    ( lowercase__							,       unittest.TestCase   ):



      """simple docstring"""


      a  : List[Any]							       =	DDIMPipeline
      a  : int							       =	UNCONDITIONAL_IMAGE_GENERATION_PARAMS
      a  : Dict							       =	PipelineTesterMixin.required_optional_params - {
          'num_images_per_prompt',
          'latents',
          'callback',
          'callback_steps',
      }
      a  : Tuple							       =	UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
      a  : Optional[int]							       =	False
      def 	UpperCAmelCase      (      self					:			Dict					)							->      List[str]:
        torch.manual_seed(0					)
        __UpperCAmelCase							:	int           =  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""")		,      )
        __UpperCAmelCase							:	Tuple           =  DDIMScheduler()
        __UpperCAmelCase							:	int           =  {"""unet""": unet, """scheduler""": scheduler}
        return components
      def 	UpperCAmelCase      (      self					:			str		,      __lowercase					:			Tuple		,      __lowercase					:			str=0					)							->      int:
        if str(__lowercase					).startswith("""mps"""					):
          __UpperCAmelCase							:	Tuple           =  torch.manual_seed(__lowercase					)
        else:
          __UpperCAmelCase							:	Optional[Any]           =  torch.Generator(device=__lowercase					).manual_seed(__lowercase					)
        __UpperCAmelCase							:	Tuple           =  {
            """batch_size""": 1,
            """generator""": generator,
            """num_inference_steps""": 2,
            """output_type""": """numpy""",
        }
        return inputs
      def 	UpperCAmelCase      (      self					:			Tuple					)							->      Any:
        __UpperCAmelCase							:	Dict           =  """cpu"""

        __UpperCAmelCase							:	Any           =  self.get_dummy_components()
        __UpperCAmelCase							:	int           =  self.pipeline_class(**__lowercase					)
        pipe.to(__lowercase					)
        pipe.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	Dict           =  self.get_dummy_inputs(__lowercase					)
        __UpperCAmelCase							:	Tuple           =  pipe(**__lowercase					).images
        __UpperCAmelCase							:	Optional[int]           =  image[0, -3:, -3:, -1]

        self.assertEqual(image.shape		,      (1, 32, 32, 3)					)
        __UpperCAmelCase							:	Optional[Any]           =  np.array(
            [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4]					)
        __UpperCAmelCase							:	int           =  np.abs(image_slice.flatten() - expected_slice					).max()
        self.assertLessEqual(__lowercase		,      1e-3					)
      def 	UpperCAmelCase      (      self					:			str					)							->      Tuple:
        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      Dict:
        super().test_save_load_local(expected_max_difference=3e-3					)
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Dict:
        super().test_save_load_optional_components(expected_max_difference=3e-3					)




      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Optional[int]:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3					)


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



      """simple docstring"""


      def 	UpperCAmelCase      (      self					:			Tuple					)							->      List[str]:
        __UpperCAmelCase							:	List[str]           =  """google/ddpm-cifar10-32"""

        __UpperCAmelCase							:	List[Any]           =  UNetaDModel.from_pretrained(__lowercase					)
        __UpperCAmelCase							:	str           =  DDIMScheduler()

        __UpperCAmelCase							:	int           =  DDIMPipeline(unet=__lowercase		,      scheduler=__lowercase					)
        ddim.to(__lowercase					)
        ddim.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	List[str]           =  torch.manual_seed(0					)
        __UpperCAmelCase							:	str           =  ddim(generator=__lowercase		,      eta=0.0		,      output_type="""numpy"""					).images

        __UpperCAmelCase							:	Optional[int]           =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __UpperCAmelCase							:	Dict           =  np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]					)

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




      def 	UpperCAmelCase      (      self					:			Any					)							->      Dict:
        __UpperCAmelCase							:	List[str]           =  """google/ddpm-ema-bedroom-256"""

        __UpperCAmelCase							:	Optional[Any]           =  UNetaDModel.from_pretrained(__lowercase					)
        __UpperCAmelCase							:	List[str]           =  DDIMScheduler.from_pretrained(__lowercase					)

        __UpperCAmelCase							:	Optional[Any]           =  DDIMPipeline(unet=__lowercase		,      scheduler=__lowercase					)
        ddpm.to(__lowercase					)
        ddpm.set_progress_bar_config(disable=__lowercase					)

        __UpperCAmelCase							:	Optional[int]           =  torch.manual_seed(0					)
        __UpperCAmelCase							:	List[str]           =  ddpm(generator=__lowercase		,      output_type="""numpy"""					).images

        __UpperCAmelCase							:	Union[str, Any]           =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        __UpperCAmelCase							:	Optional[Any]           =  np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]					)

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










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


a		:       Optional[int]			     = logging.get_logger(__name__)

a		:       List[Any]			     = {
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[Any]							       =	'xglm'
      a  : str							       =	['past_key_values']

      a  : Any							       =	{
          'num_attention_heads': 'attention_heads',
          'hidden_size': 'd_model',
          'num_hidden_layers': 'num_layers',
      }
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int=256008		,      __lowercase					:			Tuple=2048		,      __lowercase					:			List[Any]=1024		,      __lowercase					:			str=4096		,      __lowercase					:			Optional[Any]=24		,      __lowercase					:			Optional[int]=16		,      __lowercase					:			List[Any]="gelu"		,      __lowercase					:			str=0.1		,      __lowercase					:			Dict=0.1		,      __lowercase					:			Tuple=0.0		,      __lowercase					:			Optional[int]=0.0		,      __lowercase					:			Dict=0.02		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			Any=True		,      __lowercase					:			Dict=2		,      __lowercase					:			Optional[Any]=1		,      __lowercase					:			List[Any]=0		,      __lowercase					:			Optional[Any]=2		,      **__lowercase					:			List[str]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	List[str]           =  vocab_size
        __UpperCAmelCase							:	Optional[Any]           =  max_position_embeddings
        __UpperCAmelCase							:	Optional[Any]           =  d_model
        __UpperCAmelCase							:	str           =  ffn_dim
        __UpperCAmelCase							:	List[str]           =  num_layers
        __UpperCAmelCase							:	Dict           =  attention_heads
        __UpperCAmelCase							:	str           =  activation_function
        __UpperCAmelCase							:	Optional[Any]           =  dropout
        __UpperCAmelCase							:	Any           =  attention_dropout
        __UpperCAmelCase							:	int           =  activation_dropout
        __UpperCAmelCase							:	Tuple           =  layerdrop
        __UpperCAmelCase							:	Tuple           =  init_std
        __UpperCAmelCase							:	List[str]           =  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:	Union[str, Any]           =  use_cache

        super().__init__(
            pad_token_id=__lowercase		,      bos_token_id=__lowercase		,      eos_token_id=__lowercase		,      decoder_start_token_id=__lowercase		,      **__lowercase		,      )


