



'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 				__lowerCAmelCase(							unittest.TestCase      ):






			@property
			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:str										=      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')      ,       )
					return model






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      'cpu'
					SCREAMING_SNAKE_CASE_						:int										=      self.dummy_uncond_unet
					SCREAMING_SNAKE_CASE_						:str										=      DDPMScheduler()

					SCREAMING_SNAKE_CASE_						:Tuple										=      DDPMPipeline(unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE	)
					ddpm.to(SCREAMING_SNAKE_CASE	)
					ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	).images

					SCREAMING_SNAKE_CASE_						:str										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'      ,       return_dict=SCREAMING_SNAKE_CASE	)[0]

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

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE_						:Any										=      np.array(
					    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.0_00, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04]	)

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






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.dummy_uncond_unet
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      DDPMScheduler(prediction_type='sample'	)

					SCREAMING_SNAKE_CASE_						:Any										=      DDPMPipeline(unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE	)
					ddpm.to(SCREAMING_SNAKE_CASE	)
					ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:int										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	).images

					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	)[0]

					SCREAMING_SNAKE_CASE_						:List[Any]										=      image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE_						:Dict										=      image_eps[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE_						:Tuple										=      1E-2 if torch_device != 'mps' else 3E-2
					assert np.abs(image_slice.flatten() - image_eps_slice.flatten()	).max() < tolerance




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






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      'google/ddpm-cifar10-32'

					SCREAMING_SNAKE_CASE_						:Any										=      UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      DDPMScheduler.from_pretrained(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      DDPMPipeline(unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE	)
					ddpm.to(SCREAMING_SNAKE_CASE	)
					ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       output_type='numpy'	).images

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

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE_						:Dict										=      np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85]	)
					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


UpperCAmelCase      		=					logging.get_logger(__name__)

UpperCAmelCase      		=					{
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						List[Any]					     =			'flaubert'
 _lowerCamelCase    :						List[str]					     =			{
     'hidden_size': 'emb_dim',
     'num_attention_heads': 'n_heads',
     'num_hidden_layers': 'n_layers',
     'n_words': 'vocab_size',  # For backward compatibility
 }

 def __init__(      self							:		List[Any]							,      lowerCamelCase							:		Tuple=False							,      lowerCamelCase							:		str=0.0							,      lowerCamelCase							:		int=3_0_1_4_5							,      lowerCamelCase							:		List[str]=2_0_4_8							,      lowerCamelCase							:		List[str]=1_2							,      lowerCamelCase							:		Dict=1_6							,      lowerCamelCase							:		Any=0.1							,      lowerCamelCase							:		List[str]=0.1							,      lowerCamelCase							:		List[str]=True							,      lowerCamelCase							:		Any=False							,      lowerCamelCase							:		Optional[int]=False							,      lowerCamelCase							:		int=False							,      lowerCamelCase							:		Optional[Any]=1							,      lowerCamelCase							:		int=True							,      lowerCamelCase							:		str=5_1_2							,      lowerCamelCase							:		Tuple=2_0_4_8**-0.5							,      lowerCamelCase							:		str=1e-12							,      lowerCamelCase							:		Any=0.0_2							,      lowerCamelCase							:		Dict=0							,      lowerCamelCase							:		Union[str, Any]=1							,      lowerCamelCase							:		List[Any]=2							,      lowerCamelCase							:		Union[str, Any]=3							,      lowerCamelCase							:		List[Any]=5							,      lowerCamelCase							:		int=True							,      lowerCamelCase							:		Dict="first"							,      lowerCamelCase							:		Optional[int]=True							,      lowerCamelCase							:		Any=None							,      lowerCamelCase							:		Optional[int]=True							,      lowerCamelCase							:		List[str]=0.1							,      lowerCamelCase							:		List[Any]=5							,      lowerCamelCase							:		Optional[Any]=5							,      lowerCamelCase							:		str=0							,      lowerCamelCase							:		Optional[int]=0							,      lowerCamelCase							:		List[str]=2							,      lowerCamelCase							:		Any=0							,      **lowerCamelCase							:		Tuple							,      )			->				Any:
        _UpperCamelCase  :							Optional[Any]	    =	pre_norm
        _UpperCamelCase  :							Optional[int]	    =	layerdrop
        _UpperCamelCase  :							Dict	    =	vocab_size
        _UpperCamelCase  :							Dict	    =	emb_dim
        _UpperCamelCase  :							int	    =	n_layers
        _UpperCamelCase  :							Tuple	    =	n_heads
        _UpperCamelCase  :							Tuple	    =	dropout
        _UpperCamelCase  :							Any	    =	attention_dropout
        _UpperCamelCase  :							Union[str, Any]	    =	gelu_activation
        _UpperCamelCase  :							Dict	    =	sinusoidal_embeddings
        _UpperCamelCase  :							str	    =	causal
        _UpperCamelCase  :							str	    =	asm
        _UpperCamelCase  :							Tuple	    =	n_langs
        _UpperCamelCase  :							Optional[int]	    =	use_lang_emb
        _UpperCamelCase  :							Optional[Any]	    =	layer_norm_eps
        _UpperCamelCase  :							Optional[Any]	    =	bos_index
        _UpperCamelCase  :							Dict	    =	eos_index
        _UpperCamelCase  :							Union[str, Any]	    =	pad_index
        _UpperCamelCase  :							Union[str, Any]	    =	unk_index
        _UpperCamelCase  :							Optional[Any]	    =	mask_index
        _UpperCamelCase  :							Any	    =	is_encoder
        _UpperCamelCase  :							List[str]	    =	max_position_embeddings
        _UpperCamelCase  :							Union[str, Any]	    =	embed_init_std
        _UpperCamelCase  :							Optional[int]	    =	init_std
        _UpperCamelCase  :							Any	    =	summary_type
        _UpperCamelCase  :							Any	    =	summary_use_proj
        _UpperCamelCase  :							Optional[int]	    =	summary_activation
        _UpperCamelCase  :							Union[str, Any]	    =	summary_proj_to_labels
        _UpperCamelCase  :							Dict	    =	summary_first_dropout
        _UpperCamelCase  :							Union[str, Any]	    =	start_n_top
        _UpperCamelCase  :							Any	    =	end_n_top
        _UpperCamelCase  :							List[Any]	    =	mask_token_id
        _UpperCamelCase  :							str	    =	lang_id

        if "n_words" in kwargs:
               _UpperCamelCase  :							Tuple	    =	kwargs['n_words']

        super().__init__(pad_token_id=lowerCamelCase							,      bos_token_id=lowerCamelCase							,      **lowerCamelCase	)







class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''

 @property
 def        __snake_case       (      self							:		str	)			->				Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
               _UpperCamelCase  :							int	    =	{0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
               _UpperCamelCase  :							Dict	    =	{0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]	)



