



import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__snake_case       :	Optional[Any]            =logging.get_logger(__name__)



@dataclass
class 		lowerCamelCase__    :




		'''simple docstring'''



		snake_case_			=field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys())})
		snake_case_			=field(
		    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""})
		snake_case_			=field(
		    default=128   ,       metadata={
		        """help""": (
		            """The maximum total input sequence length after tokenization. Sequences longer """
		            """than this will be truncated, sequences shorter will be padded."""
		        )
		    }   ,       )
		snake_case_			=field(
		    default=__lowerCAmelCase   ,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""})



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.task_name.lower()



class 		lowerCamelCase__    (							__lowerCAmelCase):




		'''simple docstring'''



		snake_case_			='''train'''
		snake_case_			='''dev'''
		snake_case_			='''test'''



class 		lowerCamelCase__    (							__lowerCAmelCase):




		'''simple docstring'''



		snake_case_			=42
		snake_case_			=42
		snake_case_			=42



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = Split.train	,__lowerCamelCase = None	,)  ->    List[Any]:

						"""simple docstring"""






						warnings.warn(
						    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
						    '''library. You can have a look at this example script for pointers: '''
						    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''	,lowerCAmelCase_	,)
						lowerCAmelCase__  :       Dict				=  args
						lowerCAmelCase__  :       int				=  glue_processors[args.task_name]()
						lowerCAmelCase__  :       Optional[Any]				=  glue_output_modes[args.task_name]
						if isinstance(lowerCAmelCase_	,lowerCAmelCase_				):
										try:
														lowerCAmelCase__  :       Optional[int]				=  Split[mode]
										except KeyError:
														raise KeyError('''mode is not a valid split name'''				)
        # Load data features from cache or dataset file
						lowerCAmelCase__  :       List[Any]				=  os.path.join(
						    cache_dir if cache_dir is not None else args.data_dir	,f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""	,)
						lowerCAmelCase__  :       Dict				=  self.processor.get_labels()
						if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
						    "RobertaTokenizer",
						    "RobertaTokenizerFast",
						    "XLMRobertaTokenizer",
						    "BartTokenizer",
						    "BartTokenizerFast",
						):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[Any]				=  label_list[2], label_list[1]
						lowerCAmelCase__  :       str				=  label_list

						# Make sure only the first process in distributed training processes the dataset,
						# and the others will use the cache.
						lowerCAmelCase__  :       Optional[int]				=  cached_features_file + '''.lock'''
						with FileLock(lowerCAmelCase_				):
										if os.path.exists(lowerCAmelCase_				) and not args.overwrite_cache:
														lowerCAmelCase__  :       Dict				=  time.time()
														lowerCAmelCase__  :       Dict				=  torch.load(lowerCAmelCase_				)
														logger.info(
														    f"""Loading features from cached file {cached_features_file} [took %.3f s]"""	,time.time() - start				)
										else:
														logger.info(f"""Creating features from dataset file at {args.data_dir}"""				)

														if mode == Split.dev:
																		lowerCAmelCase__  :       int				=  self.processor.get_dev_examples(args.data_dir				)
														elif mode == Split.test:
																		lowerCAmelCase__  :       str				=  self.processor.get_test_examples(args.data_dir				)
														else:
																		lowerCAmelCase__  :       Optional[Any]				=  self.processor.get_train_examples(args.data_dir				)
														if limit_length is not None:
																		lowerCAmelCase__  :       Tuple				=  examples[:limit_length]
														lowerCAmelCase__  :       int				=  glue_convert_examples_to_features(
														    lowerCAmelCase_	,lowerCAmelCase_	,max_length=args.max_seq_length	,label_list=lowerCAmelCase_	,output_mode=self.output_mode	,)
														lowerCAmelCase__  :       str				=  time.time()
														torch.save(self.features	,lowerCAmelCase_				)
														# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
														logger.info(
														    f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""				)



		def __len__(self				)  ->    Tuple:

						"""simple docstring"""






						return len(self.features				)



		def __getitem__(self	,__lowerCamelCase				)  ->    InputFeatures:

						"""simple docstring"""






						return self.features[i]



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						return self.label_list






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  lowerCamelCase__    (							lowerCamelCase__   ,       lowerCamelCase__   ,       lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=AltDiffusionPipeline
		snake_case_			=TEXT_TO_IMAGE_PARAMS
		snake_case_			=TEXT_TO_IMAGE_BATCH_PARAMS
		snake_case_			=TEXT_TO_IMAGE_IMAGE_PARAMS
		snake_case_			=TEXT_TO_IMAGE_IMAGE_PARAMS



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						torch.manual_seed(0				)
						lowerCAmelCase__  :       int				=  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	,)
						lowerCAmelCase__  :       List[str]				=  DDIMScheduler(
						    beta_start=0.0_0085	,beta_end=0.012	,beta_schedule='''scaled_linear'''	,clip_sample=__lowerCamelCase	,set_alpha_to_one=__lowerCamelCase	,)
						torch.manual_seed(0				)
						lowerCAmelCase__  :       Optional[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				)
						lowerCAmelCase__  :       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=50_02	,)
						lowerCAmelCase__  :       int				=  CLIPTextModel(__lowerCamelCase				)

						lowerCAmelCase__  :       str				=  XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''				)
						lowerCAmelCase__  :       Union[str, Any]				=  77

						lowerCAmelCase__  :       str				=  {
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''vae''': vae,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''safety_checker''': None,
						    '''feature_extractor''': None,
						}
						return components



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase=0				)  ->    Optional[Any]:

						"""simple docstring"""






						if str(__lowerCamelCase				).startswith('''mps'''				):
										lowerCAmelCase__  :       Tuple				=  torch.manual_seed(__lowerCamelCase				)
						else:
										lowerCAmelCase__  :       Any				=  torch.Generator(device=__lowerCamelCase				).manual_seed(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[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				)  ->    str:

						"""simple docstring"""






						super().test_attention_slicing_forward_pass(expected_max_diff=3e-3				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						super().test_inference_batch_single_identical(expected_max_diff=3e-3				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator

						lowerCAmelCase__  :       int				=  self.get_dummy_components()
						torch.manual_seed(0				)
						lowerCAmelCase__  :       str				=  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=50_02	,)
						# TODO: remove after fixing the non-deterministic text encoder
						lowerCAmelCase__  :       Any				=  RobertaSeriesModelWithTransformation(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  text_encoder

						lowerCAmelCase__  :       List[Any]				=  AltDiffusionPipeline(**__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  alt_pipe.to(__lowerCamelCase				)
						alt_pipe.set_progress_bar_config(disable=__lowerCamelCase				)

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

						assert image.shape == (1, 64, 64, 3)
						lowerCAmelCase__  :       Optional[Any]				=  np.array(
						    [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093]				)

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



		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator

						lowerCAmelCase__  :       str				=  self.get_dummy_components()
						lowerCAmelCase__  :       Optional[Any]				=  PNDMScheduler(skip_prk_steps=__lowerCamelCase				)
						torch.manual_seed(0				)
						lowerCAmelCase__  :       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=50_02	,)
						# TODO: remove after fixing the non-deterministic text encoder
						lowerCAmelCase__  :       Tuple				=  RobertaSeriesModelWithTransformation(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  text_encoder
						lowerCAmelCase__  :       List[Any]				=  AltDiffusionPipeline(**__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  alt_pipe.to(__lowerCamelCase				)
						alt_pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       Union[str, Any]				=  self.get_dummy_inputs(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  alt_pipe(**__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  output.images
						lowerCAmelCase__  :       str				=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						lowerCAmelCase__  :       Union[str, Any]				=  np.array(
						    [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237]				)

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



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




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''	,safety_checker=__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  alt_pipe.to(__lowerCamelCase				)
						alt_pipe.set_progress_bar_config(disable=__lowerCamelCase				)

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

						lowerCAmelCase__  :       str				=  output.images

						lowerCAmelCase__  :       int				=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						lowerCAmelCase__  :       Dict				=  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:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''	,subfolder='''scheduler'''				)

						lowerCAmelCase__  :       List[str]				=  AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''	,scheduler=__lowerCamelCase	,safety_checker=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  alt_pipe.to(__lowerCamelCase				)
						alt_pipe.set_progress_bar_config(disable=__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  '''A painting of a squirrel eating a burger'''
						lowerCAmelCase__  :       Optional[Any]				=  torch.manual_seed(0				)

						lowerCAmelCase__  :       List[str]				=  alt_pipe([prompt]	,generator=__lowerCamelCase	,num_inference_steps=2	,output_type='''numpy'''				)
						lowerCAmelCase__  :       List[str]				=  output.images

						lowerCAmelCase__  :       Optional[int]				=  image[0, -3:, -3:, -1]

						assert image.shape == (1, 5_12, 5_12, 3)
						lowerCAmelCase__  :       List[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

