
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


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







@dataclass
class 				lowercase		:
							lowercase_       :       str       					=field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()		)}		)
							lowercase_       :       str       					=field(
							    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the 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_       :       bool       					=field(
							    default=SCREAMING_SNAKE_CASE__       ,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)




							def  A__      (				self):
												lowercase							=						self.task_name.lower()







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       List[Any]       					='''train'''
							lowercase_       :       List[str]       					='''dev'''
							lowercase_       :       Dict       					='''test'''







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       GlueDataTrainingArguments
							lowercase_       :       str
							lowercase_       :       List[InputFeatures]




							def __init__(				self   ,A__   ,A__   ,A__ = None   ,A__ = Split.train   ,A__ = None   ,):
												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):
												return len(self.features)




							def __getitem__(				self   ,A__):
												return self.features[i]





							def  A__      (				self):
												return self.label_list






import unittest

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

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Any       					=KandinskyVaaPriorPipeline
							lowercase_       :       str       					=['''prompt''']
							lowercase_       :       Union[str, Any]       					=['''prompt''', '''negative_prompt''']
							lowercase_       :       Dict       					=[
							    '''num_images_per_prompt''',
							    '''generator''',
							    '''num_inference_steps''',
							    '''latents''',
							    '''negative_prompt''',
							    '''guidance_scale''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_       :       List[str]       					=False




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return self.time_input_dim




							@property
							def  A__      (				self):
												return self.time_input_dim * 4




							@property
							def  A__      (				self):
												return 1_0_0




							@property
							def  A__      (				self):
												lowercase							=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
												return tokenizer




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						CLIPTextConfig(
												    bos_token_id=0   ,eos_token_id=2   ,hidden_size=self.text_embedder_hidden_size   ,projection_dim=self.text_embedder_hidden_size   ,intermediate_size=3_7   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_0_0_0   ,)
												return CLIPTextModelWithProjection(A__)




							@property
							def  A__      (				self):
												torch.manual_seed(0)

												lowercase							=						{
												    '''num_attention_heads''': 2,
												    '''attention_head_dim''': 1_2,
												    '''embedding_dim''': self.text_embedder_hidden_size,
												    '''num_layers''': 1,
												}

												lowercase							=						PriorTransformer(**A__)
												# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
												lowercase							=						nn.Parameter(torch.ones(model.clip_std.shape))
												return model




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						CLIPVisionConfig(
												    hidden_size=self.text_embedder_hidden_size   ,image_size=2_2_4   ,projection_dim=self.text_embedder_hidden_size   ,intermediate_size=3_7   ,num_attention_heads=4   ,num_channels=3   ,num_hidden_layers=5   ,patch_size=1_4   ,)

												lowercase							=						CLIPVisionModelWithProjection(A__)
												return model




							@property
							def  A__      (				self):
												lowercase							=						CLIPImageProcessor(
												    crop_size=2_2_4   ,do_center_crop=A__   ,do_normalize=A__   ,do_resize=A__   ,image_mean=[0.48145466, 0.4578275, 0.40821073]   ,image_std=[0.26862954, 0.26130258, 0.27577711]   ,resample=3   ,size=2_2_4   ,)

												return image_processor




							def  A__      (				self):
												lowercase							=						self.dummy_prior
												lowercase							=						self.dummy_image_encoder
												lowercase							=						self.dummy_text_encoder
												lowercase							=						self.dummy_tokenizer
												lowercase							=						self.dummy_image_processor

												lowercase							=						UnCLIPScheduler(
												    variance_type='''fixed_small_log'''   ,prediction_type='''sample'''   ,num_train_timesteps=1_0_0_0   ,clip_sample=A__   ,clip_sample_range=10.0   ,)

												lowercase							=						{
												    '''prior''': prior,
												    '''image_encoder''': image_encoder,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''scheduler''': scheduler,
												    '''image_processor''': image_processor,
												}

												return components




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)
												lowercase							=						{
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''guidance_scale''': 4.0,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''np''',
												}
												return inputs




							def  A__      (				self):
												lowercase							=						'''cpu'''

												lowercase							=						self.get_dummy_components()

												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)

												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						pipe(**self.get_dummy_inputs(A__))
												lowercase							=						output.image_embeds

												lowercase							=						pipe(
												    **self.get_dummy_inputs(A__)   ,return_dict=A__   ,)[0]

												lowercase							=						image[0, -1_0:]
												lowercase							=						image_from_tuple[0, -1_0:]

												assert image.shape == (1, 3_2)

												lowercase							=						np.array(
												    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156])

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




							@skip_mps
							def  A__      (				self):
												lowercase							=						torch_device == '''cpu'''
												lowercase							=						True
												lowercase							=						False

												self._test_inference_batch_single_identical(
												    test_max_difference=A__   ,relax_max_difference=A__   ,test_mean_pixel_difference=A__   ,)





							@skip_mps
							def  A__      (				self):
												lowercase							=						torch_device == '''cpu'''
												lowercase							=						False

												self._test_attention_slicing_forward_pass(
												    test_max_difference=A__   ,test_mean_pixel_difference=A__   ,)




