






"""simple docstring"""

import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 	_lowercase       (    unittest.TestCase		):




	@property
	def       _UpperCamelCase   (			self )		->							str:
			torch.manual_seed(0 )
			lowerCamelCase					:      Optional[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')		,			)
			return model




	@property
	def       _UpperCamelCase   (			self )		->							List[Any]:
			torch.manual_seed(0 )
			lowerCamelCase					:      Dict          =   VQModel(
			    block_out_channels=[32, 64]		,			in_channels=3		,			out_channels=3		,			down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,			up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,			latent_channels=3		,			)
			return model




	@property
	def       _UpperCamelCase   (			self )		->							List[Any]:
			torch.manual_seed(0 )
			lowerCamelCase					:      int          =   CLIPTextConfig(
			    bos_token_id=0		,			eos_token_id=2		,			hidden_size=32		,			intermediate_size=37		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=1000		,			)
			return CLIPTextModel(__A )




	def       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase					:      str          =   self.dummy_uncond_unet
			lowerCamelCase					:      str          =   DDIMScheduler()
			lowerCamelCase					:      Optional[int]          =   self.dummy_vq_model

			lowerCamelCase					:      Tuple          =   LDMPipeline(unet=__A		,			vqvae=__A		,			scheduler=__A )
			ldm.to(__A )
			ldm.set_progress_bar_config(disable=__A )

			lowerCamelCase					:      Optional[int]          =   torch.manual_seed(0 )
			lowerCamelCase					:      Optional[int]          =   ldm(generator=__A		,			num_inference_steps=2		,			output_type='numpy' ).images

			lowerCamelCase					:      Optional[int]          =   torch.manual_seed(0 )
			lowerCamelCase					:      Tuple          =   ldm(generator=__A		,			num_inference_steps=2		,			output_type='numpy'		,			return_dict=__A )[0]

			lowerCamelCase					:      int          =   image[0, -3:, -3:, -1]
			lowerCamelCase					:      int          =   image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			lowerCamelCase					:      Dict          =   np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
			lowerCamelCase					:      Any          =   1E-2 if torch_device != """mps""" else 3E-2

			assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance






@slow
@require_torch
class 	_lowercase       (    unittest.TestCase		):




	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      str          =   LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
			ldm.to(__A )
			ldm.set_progress_bar_config(disable=__A )

			lowerCamelCase					:      Optional[int]          =   torch.manual_seed(0 )
			lowerCamelCase					:      Any          =   ldm(generator=__A		,			num_inference_steps=5		,			output_type='numpy' ).images

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

			assert image.shape == (1, 256, 256, 3)
			lowerCamelCase					:      Dict          =   np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447] )
			lowerCamelCase					:      Optional[int]          =   1E-2 if torch_device != """mps""" else 3E-2

			assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance








"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


_A						=			logging.get_logger(__name__)

_A						=			{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'big_bird'




	def __init__(			self		,			UpperCAmelCase_=50358		,			UpperCAmelCase_=768		,			UpperCAmelCase_=12		,			UpperCAmelCase_=12		,			UpperCAmelCase_=3072		,			UpperCAmelCase_="gelu_new"		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=4096		,			UpperCAmelCase_=2		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=1E-1_2		,			UpperCAmelCase_=True		,			UpperCAmelCase_=0		,			UpperCAmelCase_=1		,			UpperCAmelCase_=2		,			UpperCAmelCase_=66		,			UpperCAmelCase_="block_sparse"		,			UpperCAmelCase_=True		,			UpperCAmelCase_=False		,			UpperCAmelCase_=64		,			UpperCAmelCase_=3		,			UpperCAmelCase_=None		,			**UpperCAmelCase_		,			)		->							Union[str, Any]:
			super().__init__(
			    pad_token_id=UpperCAmelCase_		,			bos_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_		,			sep_token_id=UpperCAmelCase_		,			**UpperCAmelCase_		,			)

			lowerCamelCase					:      Tuple          =   vocab_size
			lowerCamelCase					:      str          =   max_position_embeddings
			lowerCamelCase					:      str          =   hidden_size
			lowerCamelCase					:      Optional[int]          =   num_hidden_layers
			lowerCamelCase					:      List[str]          =   num_attention_heads
			lowerCamelCase					:      List[str]          =   intermediate_size
			lowerCamelCase					:      Optional[int]          =   hidden_act
			lowerCamelCase					:      Optional[int]          =   hidden_dropout_prob
			lowerCamelCase					:      Dict          =   attention_probs_dropout_prob
			lowerCamelCase					:      int          =   initializer_range
			lowerCamelCase					:      Union[str, Any]          =   type_vocab_size
			lowerCamelCase					:      Union[str, Any]          =   layer_norm_eps
			lowerCamelCase					:      int          =   use_cache

			lowerCamelCase					:      Tuple          =   rescale_embeddings
			lowerCamelCase					:      Dict          =   attention_type
			lowerCamelCase					:      Optional[int]          =   use_bias
			lowerCamelCase					:      Optional[int]          =   block_size
			lowerCamelCase					:      Tuple          =   num_random_blocks
			lowerCamelCase					:      List[Any]          =   classifier_dropout






class 	_lowercase       (    __UpperCAmelCase		):




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






