

import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        A_		(       a__			,							a__			,							a__			,							unittest.TestCase				):
							_UpperCAmelCase    :  Optional[int]						      =      AltDiffusionPipeline
							_UpperCAmelCase    :  Optional[Any]						      =      TEXT_TO_IMAGE_PARAMS
							_UpperCAmelCase    :  Optional[Any]						      =      TEXT_TO_IMAGE_BATCH_PARAMS
							_UpperCAmelCase    :  List[Any]						      =      TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase    :  Optional[int]						      =      TEXT_TO_IMAGE_IMAGE_PARAMS
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								torch.manual_seed(0)
								__lowerCamelCase				:				Union[str, Any]					       = UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)   ,layers_per_block=2   ,sample_size=3_2   ,in_channels=4   ,out_channels=4   ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,cross_attention_dim=3_2   ,)
								__lowerCamelCase				:				Any					       = DDIMScheduler(
								    beta_start=0.00085   ,beta_end=0.012   ,beta_schedule='scaled_linear'   ,clip_sample=SCREAMING_SNAKE_CASE_   ,set_alpha_to_one=SCREAMING_SNAKE_CASE_   ,)
								torch.manual_seed(0)
								__lowerCamelCase				:				Tuple					       = AutoencoderKL(
								    block_out_channels=[3_2, 6_4]   ,in_channels=3   ,out_channels=3   ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,latent_channels=4   ,)

								# TODO: address the non-deterministic text encoder (fails for save-load tests)
								# torch.manual_seed(0)
								# text_encoder_config = RobertaSeriesConfig(
								#     hidden_size=32,
								#     project_dim=32,
								#     intermediate_size=37,
								#     layer_norm_eps=1e-05,
								#     num_attention_heads=4,
								#     num_hidden_layers=5,
								#     vocab_size=5002,
								# )
								# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

								torch.manual_seed(0)
								__lowerCamelCase				:				List[str]					       = CLIPTextConfig(
								    bos_token_id=0   ,eos_token_id=2   ,hidden_size=3_2   ,projection_dim=3_2   ,intermediate_size=3_7   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=5_0_0_2   ,)
								__lowerCamelCase				:				str					       = CLIPTextModel(SCREAMING_SNAKE_CASE_)

								__lowerCamelCase				:				List[Any]					       = XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
								__lowerCamelCase				:				Union[str, Any]					       = 7_7

								__lowerCamelCase				:				Dict					       = {
								    'unet': unet,
								    'scheduler': scheduler,
								    'vae': vae,
								    'text_encoder': text_encoder,
								    'tokenizer': tokenizer,
								    'safety_checker': None,
								    'feature_extractor': None,
								}
								return components
							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			List[str]=0):
								if str(SCREAMING_SNAKE_CASE_).startswith('mps'):
									__lowerCamelCase				:				Union[str, Any]					       = torch.manual_seed(SCREAMING_SNAKE_CASE_)
								else:
									__lowerCamelCase				:				List[Any]					       = torch.Generator(device=SCREAMING_SNAKE_CASE_).manual_seed(SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				Union[str, Any]					       = {
								    'prompt': 'A painting of a squirrel eating a burger',
								    'generator': generator,
								    'num_inference_steps': 2,
								    'guidance_scale': 6.0,
								    'output_type': 'numpy',
								}
								return inputs
							def     lowerCAmelCase     (		self		:			List[Any]):
								super().test_attention_slicing_forward_pass(expected_max_diff=3E-3)
							def     lowerCAmelCase     (		self		:			List[Any]):
								super().test_inference_batch_single_identical(expected_max_diff=3E-3)
							def     lowerCAmelCase     (		self		:			List[str]):
								__lowerCamelCase				:				Any					       = 'cpu'  # ensure determinism for the device-dependent torch.Generator

								__lowerCamelCase				:				List[str]					       = self.get_dummy_components()
								torch.manual_seed(0)
								__lowerCamelCase				:				Optional[Any]					       = RobertaSeriesConfig(
								    hidden_size=3_2   ,project_dim=3_2   ,intermediate_size=3_7   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,vocab_size=5_0_0_2   ,)
								# TODO: remove after fixing the non-deterministic text encoder
								__lowerCamelCase				:				Tuple					       = RobertaSeriesModelWithTransformation(SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				Any					       = text_encoder

								__lowerCamelCase				:				Tuple					       = AltDiffusionPipeline(**SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				Union[str, Any]					       = alt_pipe.to(SCREAMING_SNAKE_CASE_)
								alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

								__lowerCamelCase				:				List[Any]					       = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				Optional[Any]					       = 'A photo of an astronaut'
								__lowerCamelCase				:				int					       = alt_pipe(**SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				int					       = output.images
								__lowerCamelCase				:				Dict					       = image[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)
								__lowerCamelCase				:				int					       = np.array(
								    [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093])

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


							def     lowerCAmelCase     (		self		:			List[Any]):
								__lowerCamelCase				:				Dict					       = 'cpu'  # ensure determinism for the device-dependent torch.Generator

								__lowerCamelCase				:				Dict					       = self.get_dummy_components()
								__lowerCamelCase				:				Tuple					       = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_)
								torch.manual_seed(0)
								__lowerCamelCase				:				Optional[int]					       = RobertaSeriesConfig(
								    hidden_size=3_2   ,project_dim=3_2   ,intermediate_size=3_7   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,vocab_size=5_0_0_2   ,)
								# TODO: remove after fixing the non-deterministic text encoder
								__lowerCamelCase				:				Any					       = RobertaSeriesModelWithTransformation(SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				Union[str, Any]					       = text_encoder
								__lowerCamelCase				:				str					       = AltDiffusionPipeline(**SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				str					       = alt_pipe.to(SCREAMING_SNAKE_CASE_)
								alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

								__lowerCamelCase				:				Optional[int]					       = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				List[str]					       = alt_pipe(**SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				Tuple					       = output.images
								__lowerCamelCase				:				List[str]					       = image[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)
								__lowerCamelCase				:				Optional[int]					       = np.array(
								    [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237])

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






@slow
@require_torch_gpu
class        A_		(       unittest.TestCase				):
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()
							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								# make sure here that pndm scheduler skips prk
								__lowerCamelCase				:				List[str]					       = AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'   ,safety_checker=SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				List[Any]					       = alt_pipe.to(SCREAMING_SNAKE_CASE_)
								alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

								__lowerCamelCase				:				Tuple					       = 'A painting of a squirrel eating a burger'
								__lowerCamelCase				:				Any					       = torch.manual_seed(0)
								__lowerCamelCase				:				Optional[int]					       = alt_pipe([prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=6.0   ,num_inference_steps=2_0   ,output_type='np')

								__lowerCamelCase				:				str					       = output.images

								__lowerCamelCase				:				Dict					       = image[0, -3:, -3:, -1]

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								__lowerCamelCase				:				Union[str, Any]					       = np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586])

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


							def     lowerCAmelCase     (		self		:			Any):
								__lowerCamelCase				:				List[str]					       = DDIMScheduler.from_pretrained('BAAI/AltDiffusion'   ,subfolder='scheduler')

								__lowerCamelCase				:				Tuple					       = AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'   ,scheduler=SCREAMING_SNAKE_CASE_   ,safety_checker=SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				int					       = alt_pipe.to(SCREAMING_SNAKE_CASE_)
								alt_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

								__lowerCamelCase				:				Tuple					       = 'A painting of a squirrel eating a burger'
								__lowerCamelCase				:				int					       = torch.manual_seed(0)

								__lowerCamelCase				:				Optional[Any]					       = alt_pipe([prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,num_inference_steps=2   ,output_type='numpy')
								__lowerCamelCase				:				Optional[int]					       = output.images

								__lowerCamelCase				:				List[str]					       = image[0, -3:, -3:, -1]

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								__lowerCamelCase				:				Any					       = np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323])

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









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


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Any        =  "canine"
       def __init__(			self,			SCREAMING_SNAKE_CASE_=768,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=3072,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=1e-12,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=0XE000,			SCREAMING_SNAKE_CASE_=0XE001,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=8,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=128,			**SCREAMING_SNAKE_CASE_,			)     ->		int:
          super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,			bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )

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

          # Character config:
          UpperCamelCase    :  List[Any]							=	downsampling_rate
          UpperCamelCase    :  Optional[int]							=	upsampling_kernel_size
          UpperCamelCase    :  Tuple							=	num_hash_functions
          UpperCamelCase    :  Union[str, Any]							=	num_hash_buckets
          UpperCamelCase    :  str							=	local_transformer_stride



