






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 							SCREAMING_SNAKE_CASE__						(    _UpperCAmelCase			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		DDIMPipeline
					a_     =		UNCONDITIONAL_IMAGE_GENERATION_PARAMS
					a_     =		PipelineTesterMixin.required_optional_params - {
					    "num_images_per_prompt",
					    "latents",
					    "callback",
					    "callback_steps",
					}
					a_     =		UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
					a_     =		False



					def 	_lowercase					( self					:  Optional[Any]	):
						torch.manual_seed(0	)
						snake_case__			:				str       =  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")							,		)
						snake_case__			:				Optional[Any]       =  DDIMScheduler()
						snake_case__			:				Tuple       =  {"""unet""": unet, """scheduler""": scheduler}
						return components



					def 	_lowercase					( self					:  int							,		__A					:  Optional[int]							,		__A					:  List[Any]=0	):
						if str(lowercase_	).startswith("mps"	):
							snake_case__			:				List[str]       =  torch.manual_seed(lowercase_	)
						else:
							snake_case__			:				str       =  torch.Generator(device=lowercase_	).manual_seed(lowercase_	)
						snake_case__			:				Dict       =  {
						    """batch_size""": 1,
						    """generator""": generator,
						    """num_inference_steps""": 2,
						    """output_type""": """numpy""",
						}
						return inputs



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				Tuple       =  """cpu"""

						snake_case__			:				Union[str, Any]       =  self.get_dummy_components()
						snake_case__			:				str       =  self.pipeline_class(**lowercase_	)
						pipe.to(lowercase_	)
						pipe.set_progress_bar_config(disable=lowercase_	)

						snake_case__			:				List[str]       =  self.get_dummy_inputs(lowercase_	)
						snake_case__			:				str       =  pipe(**lowercase_	).images
						snake_case__			:				Dict       =  image[0, -3:, -3:, -1]

						self.assertEqual(image.shape							,		(1, 3_2, 3_2, 3)	)
						snake_case__			:				str       =  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]	)
						snake_case__			:				List[Any]       =  np.abs(image_slice.flatten() - expected_slice	).max()
						self.assertLessEqual(lowercase_							,		1e-3	)



					def 	_lowercase					( self					:  int	):
						super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3	)



					def 	_lowercase					( self					:  Optional[int]	):
						super().test_save_load_local(expected_max_difference=3e-3	)



					def 	_lowercase					( self					:  Any	):
						super().test_save_load_optional_components(expected_max_difference=3e-3	)






					def 	_lowercase					( self					:  Tuple	):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3	)



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





					"""simple docstring"""






					def 	_lowercase					( self					:  str	):
						snake_case__			:				Optional[Any]       =  """google/ddpm-cifar10-32"""

						snake_case__			:				Any       =  UNetaDModel.from_pretrained(lowercase_	)
						snake_case__			:				Union[str, Any]       =  DDIMScheduler()

						snake_case__			:				str       =  DDIMPipeline(unet=lowercase_							,		scheduler=lowercase_	)
						ddim.to(lowercase_	)
						ddim.set_progress_bar_config(disable=lowercase_	)

						snake_case__			:				Tuple       =  torch.manual_seed(0	)
						snake_case__			:				Dict       =  ddim(generator=lowercase_							,		eta=0.0							,		output_type="numpy"	).images

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

						assert image.shape == (1, 3_2, 3_2, 3)
						snake_case__			:				str       =  np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]	)

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






					def 	_lowercase					( self					:  Any	):
						snake_case__			:				Tuple       =  """google/ddpm-ema-bedroom-256"""

						snake_case__			:				Any       =  UNetaDModel.from_pretrained(lowercase_	)
						snake_case__			:				int       =  DDIMScheduler.from_pretrained(lowercase_	)

						snake_case__			:				Any       =  DDIMPipeline(unet=lowercase_							,		scheduler=lowercase_	)
						ddpm.to(lowercase_	)
						ddpm.set_progress_bar_config(disable=lowercase_	)

						snake_case__			:				Optional[Any]       =  torch.manual_seed(0	)
						snake_case__			:				Tuple       =  ddpm(generator=lowercase_							,		output_type="numpy"	).images

						snake_case__			:				List[Any]       =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						snake_case__			:				Tuple       =  np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]	)

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











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase :      List[str]										=				logging.get_logger(__name__)

__lowerCamelCase :      List[Any]										=				{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"xmod"



					def __init__( self					:  List[str]							,		__A					:  List[str]=3_0_5_2_2							,		__A					:  Tuple=7_6_8							,		__A					:  str=1_2							,		__A					:  List[Any]=1_2							,		__A					:  List[str]=3_0_7_2							,		__A					:  List[str]="gelu"							,		__A					:  List[Any]=0.1							,		__A					:  Tuple=0.1							,		__A					:  str=5_1_2							,		__A					:  Union[str, Any]=2							,		__A					:  List[Any]=0.0_2							,		__A					:  List[str]=1e-1_2							,		__A					:  Tuple=1							,		__A					:  List[Any]=0							,		__A					:  Optional[Any]=2							,		__A					:  Optional[int]="absolute"							,		__A					:  Optional[int]=True							,		__A					:  Dict=None							,		__A					:  Optional[int]=False							,		__A					:  Dict=2							,		__A					:  List[str]=False							,		__A					:  Dict=True							,		__A					:  Union[str, Any]=True							,		__A					:  Tuple=("en_XX",)							,		__A					:  Optional[Any]=None							,		**__A					:  Tuple							,		):
						super().__init__(pad_token_id=__A							,		bos_token_id=__A							,		eos_token_id=__A							,		**__A	)

						snake_case__			:				Tuple       =  vocab_size
						snake_case__			:				Union[str, Any]       =  hidden_size
						snake_case__			:				int       =  num_hidden_layers
						snake_case__			:				str       =  num_attention_heads
						snake_case__			:				Tuple       =  hidden_act
						snake_case__			:				Optional[Any]       =  intermediate_size
						snake_case__			:				List[str]       =  hidden_dropout_prob
						snake_case__			:				Optional[int]       =  attention_probs_dropout_prob
						snake_case__			:				Union[str, Any]       =  max_position_embeddings
						snake_case__			:				str       =  type_vocab_size
						snake_case__			:				List[str]       =  initializer_range
						snake_case__			:				Dict       =  layer_norm_eps
						snake_case__			:				str       =  position_embedding_type
						snake_case__			:				List[str]       =  use_cache
						snake_case__			:				Tuple       =  classifier_dropout
						snake_case__			:				Any       =  pre_norm
						snake_case__			:				List[str]       =  adapter_reduction_factor
						snake_case__			:				List[Any]       =  adapter_layer_norm
						snake_case__			:				str       =  adapter_reuse_layer_norm
						snake_case__			:				Union[str, Any]       =  ln_before_adapter
						snake_case__			:				Tuple       =  list(__A	)
						snake_case__			:				int       =  default_language



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					@property
					def 	_lowercase					( self					:  int	):
						if self.task == "multiple-choice":
							snake_case__			:				Any       =  {0: "batch", 1: "choice", 2: "sequence"}
						else:
							snake_case__			:				Any       =  {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]	)



