






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

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

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


__magic_name__:					Dict							=     logging.get_logger(__name__)

__magic_name__:					str							=     list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__magic_name__:					Dict							=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class 	snake_case__ :
	lowercase__						:					str											=   field(
	    default=_lowerCAmelCase						,  metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(_lowerCAmelCase				)}				)
	lowercase__						:					str											=   field(
	    default=_lowerCAmelCase						,  metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''}				)
	lowercase__						:					int											=   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.'''
	        )
	    }						,  )
	lowercase__						:					int											=   field(
	    default=128						,  metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''}						,  )
	lowercase__						:					int											=   field(
	    default=64						,  metadata={
	        '''help''': (
	            '''The maximum number of tokens for the question. Questions longer than this will '''
	            '''be truncated to this length.'''
	        )
	    }						,  )
	lowercase__						:					int											=   field(
	    default=30						,  metadata={
	        '''help''': (
	            '''The maximum length of an answer that can be generated. This is needed because the start '''
	            '''and end predictions are not conditioned on one another.'''
	        )
	    }						,  )
	lowercase__						:					bool											=   field(
	    default=_lowerCAmelCase						,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}				)
	lowercase__						:					bool											=   field(
	    default=_lowerCAmelCase						,  metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''}				)
	lowercase__						:					float											=   field(
	    default=0.0						,  metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''}				)
	lowercase__						:					int											=   field(
	    default=20						,  metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''}				)
	lowercase__						:					int											=   field(
	    default=0						,  metadata={
	        '''help''': (
	            '''language id of input for language-specific xlm models (see'''
	            ''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
	        )
	    }						,  )
	lowercase__						:					int											=   field(default=1						,  metadata={'''help''': '''multiple threads for converting example to features'''}				)





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					int											=   '''train'''
	lowercase__						:					Optional[int]											=   '''dev'''





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					SquadDataTrainingArguments
	lowercase__						:					List[SquadFeatures]
	lowercase__						:					Split
	lowercase__						:					bool
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = Split.train      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = "pt"      ,       )							->				Dict:
							__magic_name__      :       Optional[int]       						= args
							__magic_name__      :       Dict       						= is_language_sensitive
							__magic_name__      :       Any       						= SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
							if isinstance(lowerCAmelCase__      ,       lowerCAmelCase__			):
													try:
																			__magic_name__      :       int       						= Split[mode]
													except KeyError:
																			raise KeyError("""mode is not a valid split name"""			)
							__magic_name__      :       Tuple       						= mode
							# Load data features from cache or dataset file
							__magic_name__      :       List[str]       						= """v2""" if args.version_2_with_negative else """v1"""
							__magic_name__      :       Optional[int]       						= 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}_{version_tag}'      ,       )

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							__magic_name__      :       str       						= cached_features_file + """.lock"""
							with FileLock(lowerCAmelCase__			):
													if os.path.exists(lowerCAmelCase__			) and not args.overwrite_cache:
																			__magic_name__      :       Optional[Any]       						= time.time()
																			__magic_name__      :       List[Any]       						= torch.load(lowerCAmelCase__			)

																			# Legacy cache files have only features, while new cache files
																			# will have dataset and examples also.
																			__magic_name__      :       Dict       						= self.old_features["""features"""]
																			__magic_name__      :       Optional[Any]       						= self.old_features.get("""dataset"""      ,       lowerCAmelCase__			)
																			__magic_name__      :       str       						= self.old_features.get("""examples"""      ,       lowerCAmelCase__			)
																			logger.info(
																			    F'Loading features from cached file {cached_features_file} [took %.3f s]'      ,       time.time() - start			)

																			if self.dataset is None or self.examples is None:
																									logger.warning(
																									    F'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
																									    """ future run"""			)
													else:
																			if mode == Split.dev:
																									__magic_name__      :       Tuple       						= self.processor.get_dev_examples(args.data_dir			)
																			else:
																									__magic_name__      :       Optional[int]       						= self.processor.get_train_examples(args.data_dir			)

																			__magic_name__      :       str       						= squad_convert_examples_to_features(
																			    examples=self.examples      ,       tokenizer=lowerCAmelCase__      ,       max_seq_length=args.max_seq_length      ,       doc_stride=args.doc_stride      ,       max_query_length=args.max_query_length      ,       is_training=mode == Split.train      ,       threads=args.threads      ,       return_dataset=lowerCAmelCase__      ,       )

																			__magic_name__      :       List[str]       						= time.time()
																			torch.save(
																			    {"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples}      ,       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			)							->				Dict:
							return len(self.features			)







	def __getitem__(    self      ,       lowerCAmelCase__			)							->				Dict[str, torch.Tensor]:
							# Convert to Tensors and build dataset
							__magic_name__      :       str       						= self.features[i]

							__magic_name__      :       Optional[int]       						= torch.tensor(feature.input_ids      ,       dtype=torch.long			)
							__magic_name__      :       List[Any]       						= torch.tensor(feature.attention_mask      ,       dtype=torch.long			)
							__magic_name__      :       Optional[Any]       						= torch.tensor(feature.token_type_ids      ,       dtype=torch.long			)
							__magic_name__      :       List[str]       						= torch.tensor(feature.cls_index      ,       dtype=torch.long			)
							__magic_name__      :       Union[str, Any]       						= torch.tensor(feature.p_mask      ,       dtype=torch.float			)
							__magic_name__      :       Union[str, Any]       						= torch.tensor(feature.is_impossible      ,       dtype=torch.float			)

							__magic_name__      :       Dict       						= {
							    """input_ids""": input_ids,
							    """attention_mask""": attention_mask,
							    """token_type_ids""": token_type_ids,
							}

							if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
													del inputs["token_type_ids"]

							if self.args.model_type in ["xlnet", "xlm"]:
													inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask}			)
													if self.args.version_2_with_negative:
																			inputs.update({"""is_impossible""": is_impossible}			)
													if self.is_language_sensitive:
																			inputs.update({"""langs""": (torch.ones(input_ids.shape      ,       dtype=torch.intaa			) * self.args.lang_id)}			)

							if self.mode == Split.train:
													__magic_name__      :       str       						= torch.tensor(feature.start_position      ,       dtype=torch.long			)
													__magic_name__      :       List[str]       						= torch.tensor(feature.end_position      ,       dtype=torch.long			)
													inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions}			)

							return inputs








import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					str											=   StableUnCLIPImgaImgPipeline
	lowercase__						:					Any											=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
	lowercase__						:					Tuple											=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	lowercase__						:					Union[str, Any]											=   frozenset(
	    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowercase__						:					Union[str, Any]											=   frozenset([]				)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Any       						= 32
							__magic_name__      :       Union[str, Any]       						= embedder_hidden_size

							# image encoding components

							__magic_name__      :       Optional[int]       						= CLIPImageProcessor(crop_size=32      ,       size=32			)

							torch.manual_seed(0			)
							__magic_name__      :       Optional[Any]       						= CLIPVisionModelWithProjection(
							    CLIPVisionConfig(
							        hidden_size=lowerCAmelCase__      ,       projection_dim=lowerCAmelCase__      ,       num_hidden_layers=5      ,       num_attention_heads=4      ,       image_size=32      ,       intermediate_size=37      ,       patch_size=1      ,       )			)

							# regular denoising components

							torch.manual_seed(0			)
							__magic_name__      :       Any       						= StableUnCLIPImageNormalizer(embedding_dim=lowerCAmelCase__			)
							__magic_name__      :       int       						= DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""			)

							torch.manual_seed(0			)
							__magic_name__      :       Tuple       						= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

							torch.manual_seed(0			)
							__magic_name__      :       Optional[Any]       						= CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=lowerCAmelCase__      ,       projection_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1e-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=10_00      ,       )			)

							torch.manual_seed(0			)
							__magic_name__      :       str       						= UNetaDConditionModel(
							    sample_size=32      ,       in_channels=4      ,       out_channels=4      ,       down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""")      ,       up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""")      ,       block_out_channels=(32, 64)      ,       attention_head_dim=(2, 4)      ,       class_embed_type="""projection"""      ,       projection_class_embeddings_input_dim=embedder_projection_dim * 2      ,       cross_attention_dim=lowerCAmelCase__      ,       layers_per_block=1      ,       upcast_attention=lowerCAmelCase__      ,       use_linear_projection=lowerCAmelCase__      ,       )

							torch.manual_seed(0			)
							__magic_name__      :       Optional[Any]       						= DDIMScheduler(
							    beta_schedule="""scaled_linear"""      ,       beta_start=0.0_0_0_8_5      ,       beta_end=0.0_1_2      ,       prediction_type="""v_prediction"""      ,       set_alpha_to_one=lowerCAmelCase__      ,       steps_offset=1      ,       )

							torch.manual_seed(0			)
							__magic_name__      :       List[str]       						= AutoencoderKL()

							__magic_name__      :       List[str]       						= {
							    # image encoding components
							    """feature_extractor""": feature_extractor,
							    """image_encoder""": image_encoder.eval(),
							    # image noising components
							    """image_normalizer""": image_normalizer.eval(),
							    """image_noising_scheduler""": image_noising_scheduler,
							    # regular denoising components
							    """tokenizer""": tokenizer,
							    """text_encoder""": text_encoder.eval(),
							    """unet""": unet.eval(),
							    """scheduler""": scheduler,
							    """vae""": vae.eval(),
							}

							return components
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=0      ,       lowerCAmelCase__=True			)							->				List[Any]:
							if str(lowerCAmelCase__			).startswith("""mps"""			):
													__magic_name__      :       Optional[int]       						= torch.manual_seed(lowerCAmelCase__			)
							else:
													__magic_name__      :       Optional[int]       						= torch.Generator(device=lowerCAmelCase__			).manual_seed(lowerCAmelCase__			)

							__magic_name__      :       Any       						= floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(lowerCAmelCase__			)			).to(lowerCAmelCase__			)

							if pil_image:
													__magic_name__      :       Optional[Any]       						= input_image * 0.5 + 0.5
													__magic_name__      :       int       						= input_image.clamp(0      ,       1			)
													__magic_name__      :       Union[str, Any]       						= input_image.cpu().permute(0      ,       2      ,       3      ,       1			).float().numpy()
													__magic_name__      :       Optional[int]       						= DiffusionPipeline.numpy_to_pil(lowerCAmelCase__			)[0]

							return {
							    "prompt": "An anime racoon running a marathon",
							    "image": input_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "np",
							}
	@skip_mps
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Optional[Any]       						= """cpu"""  # ensure determinism for the device-dependent torch.Generator
							__magic_name__      :       List[str]       						= self.get_dummy_components()
							__magic_name__      :       int       						= StableUnCLIPImgaImgPipeline(**lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= sd_pipe.to(lowerCAmelCase__			)
							sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__			)

							__magic_name__      :       Any       						= self.get_dummy_inputs(lowerCAmelCase__			)
							inputs.update({"""image_embeds""": None}			)
							__magic_name__      :       List[str]       						= sd_pipe(**lowerCAmelCase__			).images
							__magic_name__      :       Dict       						= image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							__magic_name__      :       int       						= np.array([0.3_8_7_2, 0.7_2_2_4, 0.5_6_0_1, 0.4_7_4_1, 0.6_8_7_2, 0.5_8_1_4, 0.4_6_3_6, 0.3_8_6_7, 0.5_0_7_8]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3
	def       __magic_name__    (    self			)							->				Dict:
							__magic_name__      :       int       						= torch_device in ["""cpu""", """mps"""]

							self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Tuple       						= torch_device in ["""cpu""", """mps"""]

							self._test_inference_batch_single_identical(test_max_difference=lowerCAmelCase__			)







	@unittest.skipIf(
	    torch_device != """cuda""" or not is_xformers_available()      ,       reason="""XFormers attention is only available with CUDA and `xformers` installed"""      ,       )
	def       __magic_name__    (    self			)							->				str:
							self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowerCAmelCase__			)





@slow
@require_torch_gpu
class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				int:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Optional[Any]       						= load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""			)

							__magic_name__      :       int       						= load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy"""			)

							__magic_name__      :       List[Any]       						= StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-l-img2img"""      ,       torch_dtype=torch.floataa			)
							pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__magic_name__      :       Union[str, Any]       						= torch.Generator(device="""cpu"""			).manual_seed(0			)
							__magic_name__      :       Union[str, Any]       						= pipe(lowerCAmelCase__      ,       """anime turle"""      ,       generator=lowerCAmelCase__      ,       output_type="""np"""			)

							__magic_name__      :       Optional[Any]       						= output.images[0]

							assert image.shape == (7_68, 7_68, 3)

							assert_mean_pixel_difference(lowerCAmelCase__      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Union[str, Any]       						= load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""			)

							__magic_name__      :       Dict       						= load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy"""			)

							__magic_name__      :       Union[str, Any]       						= StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-h-img2img"""      ,       torch_dtype=torch.floataa			)
							pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__magic_name__      :       List[str]       						= torch.Generator(device="""cpu"""			).manual_seed(0			)
							__magic_name__      :       Any       						= pipe(lowerCAmelCase__      ,       """anime turle"""      ,       generator=lowerCAmelCase__      ,       output_type="""np"""			)

							__magic_name__      :       Any       						= output.images[0]

							assert image.shape == (7_68, 7_68, 3)

							assert_mean_pixel_difference(lowerCAmelCase__      ,       lowerCAmelCase__			)







	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       Any       						= load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"""			)

							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							__magic_name__      :       Union[str, Any]       						= StableUnCLIPImgaImgPipeline.from_pretrained(
							    """fusing/stable-unclip-2-1-h-img2img"""      ,       torch_dtype=torch.floataa			)
							__magic_name__      :       int       						= pipe.to(lowerCAmelCase__			)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__			)
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__magic_name__      :       List[Any]       						= pipe(
							    lowerCAmelCase__      ,       """anime turtle"""      ,       num_inference_steps=2      ,       output_type="""np"""      ,       )

							__magic_name__      :       List[str]       						= torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9
