

"""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


lowercase__ 	=     logging.get_logger(__name__)




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



      def   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					int      							=     self.task_name.lower()







class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		"""train"""
      a__				=		"""dev"""
      a__				=		"""test"""







class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		42
      a__				=		42
      a__				=		42



      def __init__(						self   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      lowercase = Split.train   ,      lowercase = None   ,      )     ->      Dict:





        '''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'   ,      lowercase   ,      )
        a__:					str      							=     args
        a__:					Dict      							=     glue_processors[args.task_name]()
        a__:					List[Any]      							=     glue_output_modes[args.task_name]
        if isinstance(lowercase   ,      lowercase):
          try:
            a__:					int      							=     Split[mode]
          except KeyError:
            raise KeyError('mode is not a valid split name')
        # Load data features from cache or dataset file
        a__:					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}_{args.task_name}'   ,      )
        a__:					str      							=     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)
          a__:					int      							=     label_list[2], label_list[1]
        a__:					Dict      							=     label_list

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





        '''simple docstring'''





        return len(self.features)



      def __getitem__(						self   ,      lowercase)     ->      InputFeatures:





        '''simple docstring'''





        return self.features[i]



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''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        __snake_case	(					__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		PriorTransformer
      a__				=		"""hidden_states"""



      @property
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     4
        a__:					Any      							=     8
        a__:					Optional[Any]      							=     7

        a__:					Tuple      							=     floats_tensor((batch_size, embedding_dim)).to(lowercase)

        a__:					Optional[int]      							=     floats_tensor((batch_size, embedding_dim)).to(lowercase)
        a__:					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   lowerCamelCase_       (						self   ,      lowercase=0)     ->      str:





        '''simple docstring'''





        torch.manual_seed(lowercase)
        a__:					Optional[Any]      							=     4
        a__:					Optional[Any]      							=     8
        a__:					Union[str, Any]      							=     7

        a__:					Optional[Any]      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)

        a__:					List[str]      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)
        a__:					Tuple      							=     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   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        return (4, 8)



      @property
      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        return (4, 8)



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					int      							=     {
            'num_attention_heads': 2,
            'attention_head_dim': 4,
            'num_layers': 2,
            'embedding_dim': 8,
            'num_embeddings': 7,
            'additional_embeddings': 4,
        }
        a__:					Union[str, Any]      							=     self.dummy_input
        return init_dict, inputs_dict



      def   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        a__							,   a__:					Union[str, Any]      							=     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)
        a__:					Any      							=     model(**self.dummy_input)[0]

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



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





        '''simple docstring'''





        a__							,   a__:					Tuple      							=     self.prepare_init_args_and_inputs_for_common()

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

        a__:					List[Any]      							=     ['hidden_states', 'timestep']
        self.assertListEqual(arg_names[:2]   ,      lowercase)



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





        '''simple docstring'''





        a__:					str      							=     PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy')
        a__:					str      							=     model.to(lowercase)

        if hasattr(lowercase   ,      'set_default_attn_processor'):
          model.set_default_attn_processor()

        a__:					Dict      							=     self.get_dummy_seed_input()

        with torch.no_grad():
          a__:					str      							=     model(**lowercase)[0]

        a__:					str      							=     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.
        a__:					Any      							=     torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239])
        self.assertTrue(torch_all_close(lowercase   ,      lowercase   ,      rtol=1e-2))







@slow
class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self   ,      lowercase=1   ,      lowercase=7_68   ,      lowercase=77   ,      lowercase=0)     ->      int:





        '''simple docstring'''





        torch.manual_seed(lowercase)
        a__:					Union[str, Any]      							=     batch_size
        a__:					List[str]      							=     embedding_dim
        a__:					str      							=     num_embeddings

        a__:					Tuple      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)

        a__:					List[str]      							=     torch.randn((batch_size, embedding_dim)).to(lowercase)
        a__:					str      							=     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   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





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



      @parameterized.expand(
          [
              # fmt: off
              [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
              [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
              # fmt: on
          ])
      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      str:





        '''simple docstring'''





        a__:					Tuple      							=     PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'   ,      subfolder='prior')
        model.to(lowercase)
        a__:					Optional[Any]      							=     self.get_dummy_seed_input(seed=lowercase)

        with torch.no_grad():
          a__:					Optional[int]      							=     model(**lowercase)[0]

        assert list(sample.shape) == [1, 7_68]

        a__:					List[str]      							=     sample[0, :8].flatten().cpu()
        print(lowercase)
        a__:					Union[str, Any]      							=     torch.tensor(lowercase)

        assert torch_all_close(lowercase   ,      lowercase   ,      atol=1e-3)


