



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		=  logging.get_logger(__name__)



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







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    self.task_name.lower()



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'train'
							lowerCamelCase__   			=		'dev'
							lowerCamelCase__   			=		'test'



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		42
							lowerCamelCase__   			=		42
							lowerCamelCase__   			=		42







							def __init__(   self,			__a,			__a,			__a = None,			__a = Split.train,			__a = 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,			)
														_lowerCAmelCase     :							List[Any]    =    args
														_lowerCAmelCase     :							List[str]    =    glue_processors[args.task_name]()
														_lowerCAmelCase     :							List[str]    =    glue_output_modes[args.task_name]
														if isinstance(__a,			__a):
																					try:
																												_lowerCAmelCase     :							List[Any]    =    Split[mode]
																					except KeyError:
																												raise KeyError("mode is not a valid split name")
        # Load data features from cache or dataset file
														_lowerCAmelCase     :							Dict    =    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     :							Optional[int]    =    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     :							Union[str, Any]    =    label_list[2], label_list[1]
														_lowerCAmelCase     :							Optional[int]    =    label_list

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

														'''simple docstring'''

														return len(self.features)







							def __getitem__(   self,			__a):

														'''simple docstring'''

														return self.features[i]







							def    snake_case__      (   self):

														'''simple docstring'''

														return self.label_list

'''simple docstring'''




import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class     __magic_name__						(    _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       PriorTransformer
			UpperCamelCase__				=       '''hidden_states'''







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Optional[Any]										=       4
										lowercase_					:			int										=       8
										lowercase_					:			Union[str, Any]										=       7

										lowercase_					:			List[str]										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[str]										=       floats_tensor((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[Any]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			int										=       4
										lowercase_					:			Any										=       8
										lowercase_					:			Tuple										=       7

										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										return (4, 8)







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return (4, 8)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			Optional[Any]										=       {
										    """num_attention_heads""": 2,
										    """attention_head_dim""": 4,
										    """num_layers""": 2,
										    """embedding_dim""": 8,
										    """num_embeddings""": 7,
										    """additional_embeddings""": 4,
										}
										lowercase_					:			List[Any]										=       self.dummy_input
										return init_dict, inputs_dict







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_						,						lowercase_					:			Tuple										=       PriorTransformer.from_pretrained(
										    """hf-internal-testing/prior-dummy"""				,			output_loading_info=lowercase_    )
										self.assertIsNotNone(lowercase_    )
										self.assertEqual(len(loading_info["""missing_keys"""]    )				,			0    )

										model.to(lowercase_    )
										lowercase_					:			Optional[int]										=       model(**self.dummy_input    )[0]

										assert hidden_states is not None, "Make sure output is not None"







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_						,						lowercase_					:			str										=       self.prepare_init_args_and_inputs_for_common()

										lowercase_					:			List[Any]										=       self.model_class(**lowercase_    )
										lowercase_					:			Any										=       inspect.signature(model.forward    )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowercase_					:			Any										=       [*signature.parameters.keys()]

										lowercase_					:			Optional[Any]										=       ["""hidden_states""", """timestep"""]
										self.assertListEqual(arg_names[:2]				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_					:			Optional[Any]										=       PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""    )
										lowercase_					:			Any										=       model.to(lowercase_    )

										if hasattr(lowercase_				,			"""set_default_attn_processor"""    ):
																	model.set_default_attn_processor()

										lowercase_					:			Any										=       self.get_dummy_seed_input()

										with torch.no_grad():
																	lowercase_					:			List[str]										=       model(**lowercase_    )[0]

										lowercase_					:			Tuple										=       output[0, :5].flatten().cpu()
										print(lowercase_    )

										# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
										# the expected output slices are not the same for CPU and GPU.
										lowercase_					:			Dict										=       torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]    )
										self.assertTrue(torch_all_close(lowercase_				,			lowercase_				,			rtol=1E-2    )    )





@slow
class     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Union[str, Any]=1				,			lowercase_				:  Optional[Any]=768				,			lowercase_				:  Tuple=77				,			lowercase_				:  Optional[int]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			Optional[Any]										=       batch_size
										lowercase_					:			int										=       embedding_dim
										lowercase_					:			int										=       num_embeddings

										lowercase_					:			Dict										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Any										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







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







			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
			        [37, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
			        # fmt: on
			    ]    )
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Any				,			lowercase_				:  List[str]    ):
										lowercase_					:			List[Any]										=       PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""				,			subfolder="""prior"""    )
										model.to(lowercase_    )
										lowercase_					:			Optional[Any]										=       self.get_dummy_seed_input(seed=lowercase_    )

										with torch.no_grad():
																	lowercase_					:			Tuple										=       model(**lowercase_    )[0]

										assert list(sample.shape    ) == [1, 768]

										lowercase_					:			Union[str, Any]										=       sample[0, :8].flatten().cpu()
										print(lowercase_    )
										lowercase_					:			Optional[Any]										=       torch.tensor(lowercase_    )

										assert torch_all_close(lowercase_				,			lowercase_				,			atol=1E-3    )





