


import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     int					=					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 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					self.dummy_uncond_unet
										SCREAMING_SNAKE_CASE_		:     Dict					=					ScoreSdeVeScheduler()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					ScoreSdeVePipeline(unet=lowerCAmelCase__			,						scheduler=lowerCAmelCase__       )
										sde_ve.to(lowerCAmelCase__       )
										sde_ve.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					sde_ve(num_inference_steps=2			,						output_type='numpy'			,						generator=lowerCAmelCase__       ).images

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Any					=					sde_ve(num_inference_steps=2			,						output_type='numpy'			,						generator=lowerCAmelCase__			,						return_dict=lowerCAmelCase__       )[
										    0
										]

										SCREAMING_SNAKE_CASE_		:     Tuple					=					image[0, -3:, -3:, -1]
										SCREAMING_SNAKE_CASE_		:     Any					=					image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 3_2, 3_2, 3)
										SCREAMING_SNAKE_CASE_		:     List[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 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					'google/ncsnpp-church-256'
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					UNetaDModel.from_pretrained(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     int					=					ScoreSdeVeScheduler.from_pretrained(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Any					=					ScoreSdeVePipeline(unet=lowerCAmelCase__			,						scheduler=lowerCAmelCase__       )
										sde_ve.to(lowerCAmelCase__       )
										sde_ve.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     int					=					torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					sde_ve(num_inference_steps=1_0			,						output_type='numpy'			,						generator=lowerCAmelCase__       ).images

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

										assert image.shape == (1, 2_5_6, 2_5_6, 3)

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]       )

										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  =logging.get_logger(__name__)

lowerCamelCase  ={
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	'''distilbert'''
     SCREAMING_SNAKE_CASE_		 =	{
         '''hidden_size''': '''dim''',
         '''num_attention_heads''': '''n_heads''',
         '''num_hidden_layers''': '''n_layers''',
     }






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE=3_0_5_2_2    ,    __SCREAMING_SNAKE_CASE=5_1_2    ,    __SCREAMING_SNAKE_CASE=False    ,    __SCREAMING_SNAKE_CASE=6    ,    __SCREAMING_SNAKE_CASE=1_2    ,    __SCREAMING_SNAKE_CASE=7_6_8    ,    __SCREAMING_SNAKE_CASE=4 * 7_6_8    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE="gelu"    ,    __SCREAMING_SNAKE_CASE=0.02    ,    __SCREAMING_SNAKE_CASE=0.1    ,    __SCREAMING_SNAKE_CASE=0.2    ,    __SCREAMING_SNAKE_CASE=0    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					vocab_size
           UpperCamelCase__     : Dict											=					max_position_embeddings
           UpperCamelCase__     : Any											=					sinusoidal_pos_embds
           UpperCamelCase__     : Dict											=					n_layers
           UpperCamelCase__     : List[Any]											=					n_heads
           UpperCamelCase__     : Dict											=					dim
           UpperCamelCase__     : Dict											=					hidden_dim
           UpperCamelCase__     : Optional[int]											=					dropout
           UpperCamelCase__     : Optional[Any]											=					attention_dropout
           UpperCamelCase__     : Tuple											=					activation
           UpperCamelCase__     : Optional[int]											=					initializer_range
           UpperCamelCase__     : Optional[int]											=					qa_dropout
           UpperCamelCase__     : str											=					seq_classif_dropout
           super().__init__(**__SCREAMING_SNAKE_CASE    ,    pad_token_id=__SCREAMING_SNAKE_CASE	)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Mapping[str, Mapping[int, str]]:




           """simple docstring"""







           if self.task == "multiple-choice":
                 UpperCamelCase__     : Union[str, Any]											=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
           else:
                 UpperCamelCase__     : int											=					{0: '''batch''', 1: '''sequence'''}
           return OrderedDict(
               [
                   ('''input_ids''', dynamic_axis),
                   ('''attention_mask''', dynamic_axis),
               ]	)


