




import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE				,      unittest.TestCase ):

      """simple docstring"""

      lowerCAmelCase__      :			Tuple     =       IFSuperResolutionPipeline
      lowerCAmelCase__      :			str     =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
      lowerCAmelCase__      :			Optional[int]     =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      lowerCAmelCase__      :			Union[str, Any]     =       PipelineTesterMixin.required_optional_params - {"latents"}







      def    snake_case						(  self       :			Dict					):
          return self._get_superresolution_dummy_components()







      def    snake_case						(  self       :			Any			,							snake_case       :			Dict			,							snake_case       :			Any=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, 32, 32)			,							rng=random.Random(snake_case					)					).to(snake_case					)

          __UpperCamelCase			=    {
              '''prompt''': '''A painting of a squirrel eating a burger''',
              '''image''': 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    snake_case						(  self       :			Dict					):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)







      def    snake_case						(  self       :			int					):
          self._test_save_load_optional_components()







      @unittest.skipIf(torch_device != '''cuda'''			,							reason='''float16 requires CUDA'''					)
      def    snake_case						(  self       :			List[str]					):
          # 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    snake_case						(  self       :			str					):
          self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)







      def    snake_case						(  self       :			Tuple					):
          self._test_save_load_local()







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












'''simple docstring'''






def      __lowerCAmelCase   (       snake_case__		):
			__UpperCamelCase :     int										=  word.upper()
			__UpperCamelCase :     List[str]										=  0
			__UpperCamelCase :     int										=  {}
			__UpperCamelCase :     Tuple										=  []

			for letter in word:
						if letter not in letter_nums:
									__UpperCamelCase :     Tuple										=  str(snake_case__		)
									next_num += 1
						word_pattern.append(letter_nums[letter]		)
			return ".".join(snake_case__		)


if __name__ == "__main__":
				import pprint
				import time

				_lowerCAmelCase       =   time.time()
				with open('''dictionary.txt''') as in_file:
								_lowerCAmelCase       =   in_file.read().splitlines()

				_lowerCAmelCase       =   {}
				for word in word_list:
								_lowerCAmelCase       =   get_word_pattern(word)
								if pattern in all_patterns:
												all_patterns[pattern].append(word)
								else:
												_lowerCAmelCase       =   [word]

				with open('''word_patterns.txt''', '''w''') as out_file:
								out_file.write(pprint.pformat(all_patterns))

				_lowerCAmelCase       =   round(time.time() - start_time, 2)
				print(f'Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.')
				# Done!  9,581 word patterns found in 0.58 seconds.



