

'''simple docstring'''





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()





@skip_mps
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    StableDiffusionPanoramaPipeline
				lowerCamelCase_		       =    TEXT_TO_IMAGE_PARAMS
				lowerCamelCase_		       =    TEXT_TO_IMAGE_BATCH_PARAMS
				lowerCamelCase_		       =    TEXT_TO_IMAGE_IMAGE_PARAMS
				lowerCamelCase_		       =    TEXT_TO_IMAGE_IMAGE_PARAMS







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										torch.manual_seed(0   )
										lowercase :							Dict    		=UNetaDConditionModel(
										    block_out_channels=(32, 64)		,					layers_per_block=1		,					sample_size=32		,					in_channels=4		,					out_channels=4		,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,					cross_attention_dim=32		,					)
										lowercase :							int    		=DDIMScheduler()
										torch.manual_seed(0   )
										lowercase :							Optional[int]    		=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		,					)
										torch.manual_seed(0   )
										lowercase :							Optional[Any]    		=CLIPTextConfig(
										    bos_token_id=0		,					eos_token_id=2		,					hidden_size=32		,					intermediate_size=37		,					layer_norm_eps=1E-05		,					num_attention_heads=4		,					num_hidden_layers=5		,					pad_token_id=1		,					vocab_size=1000		,					)
										lowercase :							Optional[Any]    		=CLIPTextModel(UpperCAmelCase__   )
										lowercase :							List[Any]    		=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

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







				def  lowerCamelCase_	(       self     :		Dict		,					UpperCAmelCase__     :		Union[str, Any]		,					UpperCAmelCase__     :		Any=0   ):
										'''simple docstring'''
										lowercase :							str    		=torch.manual_seed(UpperCAmelCase__   )
										lowercase :							int    		={
										    '''prompt''': '''a photo of the dolomites''',
										    '''generator''': generator,
										    # Setting height and width to None to prevent OOMs on CPU.
										    '''height''': None,
										    '''width''': None,
										    '''num_inference_steps''': 1,
										    '''guidance_scale''': 6.0,
										    '''output_type''': '''numpy''',
										}
										return inputs







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowercase :							Optional[int]    		=self.get_dummy_components()
										lowercase :							Union[str, Any]    		=StableDiffusionPanoramaPipeline(**UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=sd_pipe.to(UpperCAmelCase__   )
										sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							List[str]    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							str    		=sd_pipe(**UpperCAmelCase__   ).images
										lowercase :							str    		=image[0, -3:, -3:, -1]
										assert image.shape == (1, 64, 64, 3)

										lowercase :							int    		=np.array([0.61_86, 0.53_74, 0.49_15, 0.41_35, 0.41_14, 0.45_63, 0.51_28, 0.49_77, 0.47_57]   )

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







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										super().test_inference_batch_consistent(batch_sizes=[1, 2]   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										super().test_inference_batch_single_identical(batch_size=2		,					expected_max_diff=3.25E-3   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowercase :							Any    		=self.get_dummy_components()
										lowercase :							int    		=StableDiffusionPanoramaPipeline(**UpperCAmelCase__   )
										lowercase :							List[Any]    		=sd_pipe.to(UpperCAmelCase__   )
										sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Dict    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							Tuple    		='''french fries'''
										lowercase :							Union[str, Any]    		=sd_pipe(**UpperCAmelCase__		,					negative_prompt=UpperCAmelCase__   )
										lowercase :							Tuple    		=output.images
										lowercase :							Any    		=image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										lowercase :							Union[str, Any]    		=np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]   )

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







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							List[str]    		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowercase :							Tuple    		=self.get_dummy_components()
										lowercase :							Tuple    		=StableDiffusionPanoramaPipeline(**UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=sd_pipe.to(UpperCAmelCase__   )
										sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							List[str]    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							int    		=sd_pipe(**UpperCAmelCase__		,					view_batch_size=2   )
										lowercase :							Optional[int]    		=output.images
										lowercase :							List[str]    		=image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										lowercase :							List[str]    		=np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]   )

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







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							int    		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowercase :							str    		=self.get_dummy_components()
										lowercase :							str    		=EulerAncestralDiscreteScheduler(
										    beta_start=0.0_00_85		,					beta_end=0.0_12		,					beta_schedule='''scaled_linear'''   )
										lowercase :							Optional[int]    		=StableDiffusionPanoramaPipeline(**UpperCAmelCase__   )
										lowercase :							Tuple    		=sd_pipe.to(UpperCAmelCase__   )
										sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							int    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							Tuple    		=sd_pipe(**UpperCAmelCase__   ).images
										lowercase :							Any    		=image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										lowercase :							Optional[int]    		=np.array([0.40_24, 0.65_10, 0.49_01, 0.53_78, 0.58_13, 0.56_22, 0.47_95, 0.44_67, 0.49_52]   )

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







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Tuple    		='''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowercase :							int    		=self.get_dummy_components()
										lowercase :							Optional[int]    		=PNDMScheduler(
										    beta_start=0.0_00_85		,					beta_end=0.0_12		,					beta_schedule='''scaled_linear'''		,					skip_prk_steps=UpperCAmelCase__   )
										lowercase :							Optional[int]    		=StableDiffusionPanoramaPipeline(**UpperCAmelCase__   )
										lowercase :							int    		=sd_pipe.to(UpperCAmelCase__   )
										sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							Dict    		=sd_pipe(**UpperCAmelCase__   ).images
										lowercase :							Optional[int]    		=image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										lowercase :							Optional[int]    		=np.array([0.63_91, 0.62_91, 0.48_61, 0.51_34, 0.55_52, 0.45_78, 0.50_32, 0.50_23, 0.45_39]   )

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





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







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		Tuple=0   ):
										'''simple docstring'''
										lowercase :							Dict    		=torch.manual_seed(UpperCAmelCase__   )
										lowercase :							Optional[int]    		={
										    '''prompt''': '''a photo of the dolomites''',
										    '''generator''': generator,
										    '''num_inference_steps''': 3,
										    '''guidance_scale''': 7.5,
										    '''output_type''': '''numpy''',
										}
										return inputs







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		='''stabilityai/stable-diffusion-2-base'''
										lowercase :							Tuple    		=DDIMScheduler.from_pretrained(UpperCAmelCase__		,					subfolder='''scheduler'''   )
										lowercase :							Optional[Any]    		=StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase__		,					scheduler=UpperCAmelCase__		,					safety_checker=UpperCAmelCase__   )
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing()

										lowercase :							List[Any]    		=self.get_inputs()
										lowercase :							List[Any]    		=pipe(**UpperCAmelCase__   ).images
										lowercase :							Union[str, Any]    		=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 2048, 3)

										lowercase :							str    		=np.array(
										    [
										        0.36_96_83_92,
										        0.27_02_53_72,
										        0.32_44_67_66,
										        0.28_37_93_87,
										        0.36_36_32_74,
										        0.30_73_33_47,
										        0.27_10_00_27,
										        0.27_05_41_25,
										        0.25_53_60_96,
										    ]   )

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







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=StableDiffusionPanoramaPipeline.from_pretrained(
										    '''stabilityai/stable-diffusion-2-base'''		,					safety_checker=UpperCAmelCase__   )
										lowercase :							List[str]    		=LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing()

										lowercase :							Optional[Any]    		=self.get_inputs()
										lowercase :							List[Any]    		=pipe(**UpperCAmelCase__   ).images
										lowercase :							Any    		=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 2048, 3)

										lowercase :							Tuple    		=np.array(
										    [
										        [
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										        ]
										    ]   )

										assert np.abs(expected_slice - image_slice   ).max() < 1E-3







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										lowercase :							Any    		=0

										def callback_fn(UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		torch.FloatTensor   ) -> None:
																lowercase :							Optional[Any]    		=True
																nonlocal number_of_steps
																number_of_steps += 1
																if step == 1:
																						lowercase :							Any    		=latents.detach().cpu().numpy()
																						assert latents.shape == (1, 4, 64, 256)
																						lowercase :							Tuple    		=latents[0, -3:, -3:, -1]

																						lowercase :							List[str]    		=np.array(
																						    [
																						        0.18_68_18_69,
																						        0.33_90_78_16,
																						        0.5_36_12_76,
																						        0.14_43_28_65,
																						        -0.02_85_66_11,
																						        -0.73_94_11_23,
																						        0.23_39_79_87,
																						        0.47_32_26_82,
																						        -0.37_82_31_64,
																						    ]   )
																						assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2
																elif step == 2:
																						lowercase :							Union[str, Any]    		=latents.detach().cpu().numpy()
																						assert latents.shape == (1, 4, 64, 256)
																						lowercase :							Any    		=latents[0, -3:, -3:, -1]

																						lowercase :							int    		=np.array(
																						    [
																						        0.18_53_96_45,
																						        0.33_98_72_48,
																						        0.5_37_85_59,
																						        0.14_43_71_42,
																						        -0.02_45_52_61,
																						        -0.7_33_83_17,
																						        0.23_99_07_55,
																						        0.47_35_62_72,
																						        -0.3_78_65_05,
																						    ]   )

																						assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2

										lowercase :							Union[str, Any]    		=False

										lowercase :							Any    		='''stabilityai/stable-diffusion-2-base'''
										lowercase :							List[Any]    		=DDIMScheduler.from_pretrained(UpperCAmelCase__		,					subfolder='''scheduler'''   )
										lowercase :							Any    		=StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase__		,					scheduler=UpperCAmelCase__		,					safety_checker=UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing()

										lowercase :							Tuple    		=self.get_inputs()
										pipe(**UpperCAmelCase__		,					callback=UpperCAmelCase__		,					callback_steps=1   )
										assert callback_fn.has_been_called
										assert number_of_steps == 3







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										lowercase :							Optional[Any]    		='''stabilityai/stable-diffusion-2-base'''
										lowercase :							str    		=DDIMScheduler.from_pretrained(UpperCAmelCase__		,					subfolder='''scheduler'''   )
										lowercase :							Optional[int]    		=StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase__		,					scheduler=UpperCAmelCase__		,					safety_checker=UpperCAmelCase__   )
										lowercase :							str    		=pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										pipe.enable_attention_slicing(1   )
										pipe.enable_sequential_cpu_offload()

										lowercase :							str    		=self.get_inputs()
										lowercase :							Tuple    		=pipe(**UpperCAmelCase__   )

										lowercase :							Optional[int]    		=torch.cuda.max_memory_allocated()
										# make sure that less than 5.2 GB is allocated
										assert mem_bytes < 5.5 * 10**9









"""simple docstring"""

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


UpperCAmelCase__					=logging.get_logger(__name__)





@dataclass
class  lowerCamelCase__    :
      a			:  str           =		field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()     )}     )
      a			:  str           =		field(
          metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}     )
      a			:  int           =		field(
          default=1_28      , metadata={
              """help""": (
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )
          }      , )
      a			:  bool           =		field(
          default=_a      , metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.task_name.lower()






class  lowerCamelCase__    (		_a     ):
      a			:  List[Any]           =		"""train"""
      a			:  List[str]           =		"""dev"""
      a			:  Optional[int]           =		"""test"""






class  lowerCamelCase__    (		_a     ):
      a			:  GlueDataTrainingArguments
      a			:  str
      a			:  List[InputFeatures]
      def __init__(		self						:						Optional[int]   ,							A_						:						GlueDataTrainingArguments   ,							A_						:						PreTrainedTokenizerBase   ,							A_						:						Optional[int] = None   ,							A_						:						Union[str, Split] = Split.train   ,							A_						:						Optional[str] = None   ,							):






       '''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"""   ,							A_   ,							)
       __lowercase						=						args
       __lowercase						=						glue_processors[args.task_name]()
       __lowercase						=						glue_output_modes[args.task_name]
       if isinstance(A_   ,							A_				):
        try:
         __lowercase						=						Split[mode]
        except KeyError:
         raise KeyError("""mode is not a valid split name"""				)
        # Load data features from cache or dataset file
       __lowercase						=						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}'''   ,							)
       __lowercase						=						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)
        __lowercase				,		__lowercase						=						label_list[2], label_list[1]
       __lowercase						=						label_list

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       __lowercase						=						cached_features_file + """.lock"""
       with FileLock(A_				):
        if os.path.exists(A_				) and not args.overwrite_cache:
         __lowercase						=						time.time()
         __lowercase						=						torch.load(A_				)
         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:
          __lowercase						=						self.processor.get_dev_examples(args.data_dir				)
         elif mode == Split.test:
          __lowercase						=						self.processor.get_test_examples(args.data_dir				)
         else:
          __lowercase						=						self.processor.get_train_examples(args.data_dir				)
         if limit_length is not None:
          __lowercase						=						examples[:limit_length]
         __lowercase						=						glue_convert_examples_to_features(
             A_   ,							A_   ,							max_length=args.max_seq_length   ,							label_list=A_   ,							output_mode=self.output_mode   ,							)
         __lowercase						=						time.time()
         torch.save(self.features   ,							A_				)
         # ^ 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						:						int				):






       '''simple docstring'''


       return len(self.features				)
      def __getitem__(		self						:						Optional[Any]   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       return self.features[i]






      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return self.label_list
