



import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger

class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     List[str]):


           '''simple docstring'''

           __a						       =				params

           __a						       =				np.array(__SCREAMING_SNAKE_CASE)
           __a						       =				np.array([len(__SCREAMING_SNAKE_CASE) for t in data])

           self.check()
           self.remove_long_sequences()
           self.remove_empty_sequences()
           self.remove_unknown_sequences()
           self.check()
           self.print_statistics()



       def __getitem__(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           return (self.token_ids[index], self.lengths[index])



       def __len__(		self							:     Union[str, Any]):


           '''simple docstring'''

           return len(self.lengths)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           assert len(self.token_ids) == len(self.lengths)
           assert all(self.lengths[i] == len(self.token_ids[i]) for i in range(len(self.lengths)))



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				self.params.max_model_input_size
           __a						       =				self.lengths > max_len
           logger.info(F'Splitting {sum(__SCREAMING_SNAKE_CASE)} too long sequences.')

           def divide_chunks(__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Any):
               return [l[i : i + n] for i in range(0     ,		len(__SCREAMING_SNAKE_CASE)     ,		__SCREAMING_SNAKE_CASE)]

           __a						       =				[]
           __a						       =				[]
           if self.params.mlm:
               __a       ,   __a						       =				self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
           else:
               __a       ,   __a						       =				self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

           for seq_, len_ in zip(self.token_ids     ,		self.lengths):
               assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
               if len_ <= max_len:
                   new_tok_ids.append(seq_)
                   new_lengths.append(len_)
               else:
                   __a						       =				[]
                   for sub_s in divide_chunks(seq_     ,		max_len - 2):
                       if sub_s[0] != cls_id:
                           __a						       =				np.insert(__SCREAMING_SNAKE_CASE     ,		0     ,		__SCREAMING_SNAKE_CASE)
                       if sub_s[-1] != sep_id:
                           __a						       =				np.insert(__SCREAMING_SNAKE_CASE     ,		len(__SCREAMING_SNAKE_CASE)     ,		__SCREAMING_SNAKE_CASE)
                       assert len(__SCREAMING_SNAKE_CASE) <= max_len
                       assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                       sub_seqs.append(__SCREAMING_SNAKE_CASE)

                   new_tok_ids.extend(__SCREAMING_SNAKE_CASE)
                   new_lengths.extend([len(__SCREAMING_SNAKE_CASE) for l in sub_seqs])

           __a						       =				np.array(__SCREAMING_SNAKE_CASE)
           __a						       =				np.array(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				len(self)
           __a						       =				self.lengths > 11
           __a						       =				self.token_ids[indices]
           __a						       =				self.lengths[indices]
           __a						       =				len(self)
           logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.')



       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           if "unk_token" not in self.params.special_tok_ids:
               return
           else:
               __a						       =				self.params.special_tok_ids['''unk_token''']
           __a						       =				len(self)
           __a						       =				np.array([np.count_nonzero(a == unk_token_id) for a in self.token_ids])
           __a						       =				(unk_occs / self.lengths) < 0.5
           __a						       =				self.token_ids[indices]
           __a						       =				self.lengths[indices]
           __a						       =				len(self)
           logger.info(F'Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).')



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           if not self.params.is_master:
               return
           logger.info(F'{len(self)} sequences')
           # data_len = sum(self.lengths)
           # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
           # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

           # unk_idx = self.params.special_tok_ids['unk_token']
           # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
           # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')





       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				[t[0] for t in batch]
           __a						       =				[t[1] for t in batch]
           assert len(__SCREAMING_SNAKE_CASE) == len(__SCREAMING_SNAKE_CASE)

           # Max for paddings
           __a						       =				max(__SCREAMING_SNAKE_CASE)

           # Pad token ids
           if self.params.mlm:
               __a						       =				self.params.special_tok_ids['''pad_token''']
           else:
               __a						       =				self.params.special_tok_ids['''unk_token''']
           __a						       =				[list(t.astype(__SCREAMING_SNAKE_CASE)) + [pad_idx] * (max_seq_len_ - len(__SCREAMING_SNAKE_CASE)) for t in token_ids]
           assert len(tk_) == len(__SCREAMING_SNAKE_CASE)
           assert all(len(__SCREAMING_SNAKE_CASE) == max_seq_len_ for t in tk_)

           __a						       =				torch.tensor(tk_)  # (bs, max_seq_len_)
           __a						       =				torch.tensor(__SCREAMING_SNAKE_CASE)  # (bs)
           return tk_t, lg_t










'''simple docstring'''






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 				SCREAMING_SNAKE_CASE(							A__							,		A__							,		A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       AltDiffusionPipeline
	lowerCamelCase__			 =       TEXT_TO_IMAGE_PARAMS
	lowerCamelCase__			 =       TEXT_TO_IMAGE_BATCH_PARAMS
	lowerCamelCase__			 =       TEXT_TO_IMAGE_IMAGE_PARAMS
	lowerCamelCase__			 =       TEXT_TO_IMAGE_IMAGE_PARAMS







	def      A	(						self			:     Dict		)			->		int:
								torch.manual_seed(0		)
								UpperCAmelCase :       str 							=		UNetaDConditionModel(
								    block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=4		,			out_channels=4		,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,			cross_attention_dim=32		,			)
								UpperCAmelCase :       Dict 							=		DDIMScheduler(
								    beta_start=0.0_00_85		,			beta_end=0.0_12		,			beta_schedule='''scaled_linear'''		,			clip_sample=__snake_case		,			set_alpha_to_one=__snake_case		,			)
								torch.manual_seed(0		)
								UpperCAmelCase :       Union[str, Any] 							=		AutoencoderKL(
								    block_out_channels=[32, 64]		,			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		)
								UpperCAmelCase :       Dict 							=		CLIPTextConfig(
								    bos_token_id=0		,			eos_token_id=2		,			hidden_size=32		,			projection_dim=32		,			intermediate_size=37		,			layer_norm_eps=1E-05		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=5002		,			)
								UpperCAmelCase :       List[Any] 							=		CLIPTextModel(__snake_case		)

								UpperCAmelCase :       Optional[Any] 							=		XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''		)
								UpperCAmelCase :       Optional[int] 							=		77

								UpperCAmelCase :       Optional[int] 							=		{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     Dict		,			__snake_case			:     List[str]=0		)			->		Union[str, Any]:
								if str(__snake_case		).startswith('''mps'''		):
															UpperCAmelCase :       str 							=		torch.manual_seed(__snake_case		)
								else:
															UpperCAmelCase :       Tuple 							=		torch.Generator(device=__snake_case		).manual_seed(__snake_case		)
								UpperCAmelCase :       Dict 							=		{
								    '''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      A	(						self			:     Union[str, Any]		)			->		List[str]:
								super().test_attention_slicing_forward_pass(expected_max_diff=3E-3		)







	def      A	(						self			:     Tuple		)			->		List[str]:
								super().test_inference_batch_single_identical(expected_max_diff=3E-3		)







	def      A	(						self			:     Tuple		)			->		Optional[int]:
								UpperCAmelCase :       List[str] 							=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator

								UpperCAmelCase :       Any 							=		self.get_dummy_components()
								torch.manual_seed(0		)
								UpperCAmelCase :       Optional[int] 							=		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		,			)
								# TODO: remove after fixing the non-deterministic text encoder
								UpperCAmelCase :       List[str] 							=		RobertaSeriesModelWithTransformation(__snake_case		)
								UpperCAmelCase :       str 							=		text_encoder

								UpperCAmelCase :       Optional[int] 							=		AltDiffusionPipeline(**__snake_case		)
								UpperCAmelCase :       str 							=		alt_pipe.to(__snake_case		)
								alt_pipe.set_progress_bar_config(disable=__snake_case		)

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

								assert image.shape == (1, 64, 64, 3)
								UpperCAmelCase :       List[str] 							=		np.array(
								    [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93]		)

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







	def      A	(						self			:     int		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[int] 							=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator

								UpperCAmelCase :       int 							=		self.get_dummy_components()
								UpperCAmelCase :       int 							=		PNDMScheduler(skip_prk_steps=__snake_case		)
								torch.manual_seed(0		)
								UpperCAmelCase :       int 							=		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		,			)
								# TODO: remove after fixing the non-deterministic text encoder
								UpperCAmelCase :       Union[str, Any] 							=		RobertaSeriesModelWithTransformation(__snake_case		)
								UpperCAmelCase :       Union[str, Any] 							=		text_encoder
								UpperCAmelCase :       Optional[int] 							=		AltDiffusionPipeline(**__snake_case		)
								UpperCAmelCase :       Dict 							=		alt_pipe.to(__snake_case		)
								alt_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase :       int 							=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		alt_pipe(**__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		output.images
								UpperCAmelCase :       int 							=		image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								UpperCAmelCase :       Optional[int] 							=		np.array(
								    [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37]		)

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

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







	"""simple docstring"""






	def      A	(						self			:     str		)			->		Any:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	def      A	(						self			:     List[Any]		)			->		Any:
								# make sure here that pndm scheduler skips prk
								UpperCAmelCase :       List[Any] 							=		AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''		,			safety_checker=__snake_case		)
								UpperCAmelCase :       Tuple 							=		alt_pipe.to(__snake_case		)
								alt_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase :       List[Any] 							=		'''A painting of a squirrel eating a burger'''
								UpperCAmelCase :       Any 							=		torch.manual_seed(0		)
								UpperCAmelCase :       Optional[int] 							=		alt_pipe([prompt]		,			generator=__snake_case		,			guidance_scale=6.0		,			num_inference_steps=20		,			output_type='''np'''		)

								UpperCAmelCase :       Dict 							=		output.images

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

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase :       List[str] 							=		np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86]		)

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







	def      A	(						self			:     Tuple		)			->		int:
								UpperCAmelCase :       int 							=		DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''		,			subfolder='''scheduler'''		)

								UpperCAmelCase :       Tuple 							=		AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''		,			scheduler=__snake_case		,			safety_checker=__snake_case		)
								UpperCAmelCase :       Dict 							=		alt_pipe.to(__snake_case		)
								alt_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase :       Tuple 							=		'''A painting of a squirrel eating a burger'''
								UpperCAmelCase :       Optional[int] 							=		torch.manual_seed(0		)

								UpperCAmelCase :       List[Any] 							=		alt_pipe([prompt]		,			generator=__snake_case		,			num_inference_steps=2		,			output_type='''numpy'''		)
								UpperCAmelCase :       Dict 							=		output.images

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

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase :       Union[str, Any] 							=		np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23]		)

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





