




import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class       a					(       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[int]       		=  IFInpaintingSuperResolutionPipeline
     lowerCAmelCase    :	List[str]       		=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
     lowerCAmelCase    :	Optional[Any]       		=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}      )
     lowerCAmelCase    :	str       		=  PipelineTesterMixin.required_optional_params - {'latents'}
     def   lowerCamelCase_       (     self       :      Tuple    ):
         return self._get_superresolution_dummy_components()
     def   lowerCamelCase_       (     self       :      Tuple ,   __snake_case       :      Optional[int] ,   __snake_case       :      str=0    ):
         if str(__snake_case    ).startswith('''mps'''    ):
             UpperCAmelCase_										=					torch.manual_seed(__snake_case    )
         else:
             UpperCAmelCase_										=					torch.Generator(device=__snake_case    ).manual_seed(__snake_case    )

         UpperCAmelCase_										=					floats_tensor((1, 3, 16, 16) ,   rng=random.Random(__snake_case    )    ).to(__snake_case    )
         UpperCAmelCase_										=					floats_tensor((1, 3, 32, 32) ,   rng=random.Random(__snake_case    )    ).to(__snake_case    )
         UpperCAmelCase_										=					floats_tensor((1, 3, 32, 32) ,   rng=random.Random(__snake_case    )    ).to(__snake_case    )

         UpperCAmelCase_										=					{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''original_image''': original_image,
             '''mask_image''': mask_image,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }

         return inputs
     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available() ,   reason='''XFormers attention is only available with CUDA and `xformers` installed''' ,   )
     def   lowerCamelCase_       (     self       :      int    ):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3    )
     def   lowerCamelCase_       (     self       :      List[str]    ):
         self._test_save_load_optional_components()
     @unittest.skipIf(torch_device != '''cuda''' ,   reason='''float16 requires CUDA'''    )
     def   lowerCamelCase_       (     self       :      Dict    ):
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1E-1    )
     def   lowerCamelCase_       (     self       :      int    ):
         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2    )
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         self._test_save_load_local()




     def   lowerCamelCase_       (     self       :      List[Any]    ):
         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2 ,   )











import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase      			=						logging.getLogger(__name__)

if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser(
     description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
 )
 parser.add_argument(
     '--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
 )
 parser.add_argument(
     '--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
 )
 parser.add_argument('--vocab_size', default=3_05_22, type=int)
 _lowerCamelCase      			=						parser.parse_args()

 logger.info(F"Loading data from {args.data_file}")
 with open(args.data_file, 'rb') as fp:
  _lowerCamelCase      			=						pickle.load(fp)

 logger.info('Counting occurrences for MLM.')
 _lowerCamelCase      			=						Counter()
 for tk_ids in data:
  counter.update(tk_ids)
 _lowerCamelCase      			=						[0] * args.vocab_size
 for k, v in counter.items():
  _lowerCamelCase      			=						v

 logger.info(F"Dump to {args.token_counts_dump}")
 with open(args.token_counts_dump, 'wb') as handle:
  pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)





