



import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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 (			__UpperCAmelCase   ,__UpperCAmelCase   ,unittest.TestCase					):
       UpperCamelCase__						:					List[Any]		      =		IFInpaintingPipeline
       UpperCamelCase__						:					Dict		      =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
       UpperCamelCase__						:					Dict		      =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       UpperCamelCase__						:					str		      =		PipelineTesterMixin.required_optional_params - {'''latents'''}



       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           return self._get_dummy_components()



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  Tuple=0):


           '''simple docstring'''

           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               __a		 =       torch.manual_seed(__SCREAMING_SNAKE_CASE)
           else:
               __a		 =       torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)

           __a		 =       floats_tensor((1, 3, 32, 32)  ,	rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
           __a		 =       floats_tensor((1, 3, 32, 32)  ,	rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)

           __a		 =       {
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''image''': 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							:  Tuple):


           '''simple docstring'''

           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           self._test_save_load_optional_components()



       @unittest.skipIf(torch_device != '''cuda'''  ,	reason='''float16 requires CUDA''')
       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           super().test_save_load_floataa(expected_max_diff=1E-1)



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           self._test_save_load_local()





       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           self._test_inference_batch_single_identical(
               expected_max_diff=1E-2  ,	)










def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       ''''''
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       [chr(i + 65       ) for i in range(26       )]
    # Remove duplicate characters from key
    __a		 =       remove_duplicates(key.upper()       )
    __a		 =       len(_UpperCAmelCase       )
    # First fill cipher with key characters
    __a		 =       {alphabet[i]: char for i, char in enumerate(_UpperCAmelCase       )}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(_UpperCAmelCase       )     ,				26       ):
        __a		 =       alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            __a		 =       alphabet[i - offset]
        __a		 =       char
    return cipher_alphabet





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    return "".join(cipher_map.get(_UpperCAmelCase     ,				_UpperCAmelCase       ) for ch in message.upper()       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       {v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(_UpperCAmelCase     ,				_UpperCAmelCase       ) for ch in message.upper()       )





def   __snake_case			(    ):
    __a		 =       input('''Enter message to encode or decode: '''       ).strip()
    __a		 =       input('''Enter keyword: '''       ).strip()
    __a		 =       input('''Encipher or decipher? E/D:'''       ).strip()[0].lower()
    try:
        __a		 =       {'''e''': encipher, '''d''': decipher}[option]
    except KeyError:
        raise KeyError('''invalid input option'''       )
    __a		 =       create_cipher_map(_UpperCAmelCase       )
    print(func(_UpperCAmelCase     ,				_UpperCAmelCase       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()





